Urban carbon emission regulation and control method and system based on land coverage spatial form optimization

Abstract

The invention discloses an urban carbon emission regulation and control method and system based on land coverage spatial form optimization, and the method comprises the steps: collecting CO2 emission data and land coverage type data of a plurality of cities, and judging a high CO2 emission city according to the collected CO2 emission data; calculating a land coverage spatial form index by using the collected land coverage type data of the CO2 high-emission city; preprocessing all land cover spatial form indexes obtained by calculation, and screening out land cover spatial form indexes having significant influence on carbon emission; by constructing a multi-scale geographic weighted regression model, the space-time driving effect of each screened land coverage spatial form index on carbon emission is analyzed, so that a land coverage spatial form optimization scheme is formulated, and the emission reduction of urban CO2 is realized. According to the method, accurate regulation and control of urban CO2 emission can be realized, meanwhile, the calculation cost is reduced, the universality is high, and the development goal of a low-carbon city can be assisted to be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban carbon emission regulation, and specifically relates to an urban carbon emission regulation method and system based on land cover spatial morphology optimization. Background Art

[0002] Nowadays, global climate change is becoming increasingly severe, bringing with it rising average temperatures, frequent extreme weather events, and biodiversity destruction, which seriously endanger the ecological environment system and human health and safety. The sharp increase in greenhouse gas emissions (such as CO2) is the main driving factor of global climate change. Global CO2 emissions reached 4.4×10 in 2020. 5 Mt, an increase of about 1.5 times compared with 2000. At present, countries around the world are actively formulating policy systems to deal with the rapid increase in carbon emissions, and mainly implement carbon reduction measures based on cities. Urban carbon emissions account for 70% of the world's total carbon emissions. Cities are affected by intensive human activities, with large population flows, dense and complex buildings, and high traffic volume, which further hinders the realization of urban carbon reduction goals.

[0003] The rapid process of urbanization is constantly changing the land cover of cities. For example, in the process of industrial development, part of the cultivated land is converted into artificial surface, and grassland is converted into forest land to increase urban carbon sinks. When the land cover changes, the carbon exchange activities that occur can destroy the dynamic balance of the terrestrial carbon cycle. According to statistics, the global CO2 emissions caused by changes in land use and cover account for about one-third of the total CO2 emissions from human activities, and have become the second largest source of anthropogenic CO2 emissions. Therefore, there is an urgent need for a scientific and accurate method to effectively reduce carbon emissions by optimizing the spatial form of urban land cover.

[0004] Land cover changes are usually accompanied by changes in spatial morphology such as area and patch density. Some scholars have explored the relationship between the spatial morphology of land cover and carbon emissions. Since the cover type of artificial surface is an important carbon source for cities, most studies focus on the spatial morphology of artificial surfaces or use unified indicators for exploration, while the spatial morphology of diversified land covers such as cultivated land and forest land has not been fully studied. Since the spatial morphological characteristics of different land covers have significant differences in their impact on CO2 emissions, a uniform discussion is more likely to form a "one-size-fits-all" carbon reduction measure. Therefore, it is urgent to consider the impact of the differentiated spatial morphological characteristics of land cover types on carbon emissions. Summary of the invention

[0005] In response to the problems existing in the prior art, the present invention provides a method and system for urban carbon emission regulation based on land cover spatial morphology optimization, which aims to analyze the impact of spatial morphological indicators of different land cover types on carbon emissions, propose a land cover spatial morphology optimization plan, and thus reduce urban carbon emissions.

[0006] In order to solve the above technical problems, the present invention is implemented through the following technical solutions: A method for regulating urban carbon emissions based on land cover spatial morphology optimization, comprising: Step 1) Collect CO2 emission data and land cover type data from multiple cities, and conduct spatial autocorrelation analysis based on the collected CO2 emission data to determine the aggregation of CO2, thereby screening out cities with high CO2 emissions. This is the premise for the subsequent use of the multi-scale geographically weighted regression model; Step 2) Using the collected land cover type data of cities with high CO2 emissions, calculate the corresponding land cover spatial morphology indicators; Step 3) Preprocess all calculated land cover spatial morphology indicators to screen out land cover spatial morphology indicators that have a significant impact on carbon emissions; Step 4) By constructing a multi-scale geographically weighted regression model, the spatiotemporal driving effect of each selected land cover spatial morphology indicator on carbon emissions is analyzed, so as to formulate a land cover spatial morphology optimization plan to achieve urban CO2 emission reduction.

[0007] Furthermore, the land cover types include: Cultivated land, wooded land, shrubland, grassland, water area, ice sheet, bare land, artificial surface and wetland.

[0008] Furthermore, the calculation method of the land cover spatial morphology index is: Firstly, ArcGIS platform was used to extract images of land cover types of each city using the city administrative boundaries. Then, Fragstats 4.2 was used to calculate the spatial morphological indicators of land cover using the extracted images of land cover types of each city.

[0009] Furthermore, the land cover spatial morphology indicators include: patch type area (CA), maximum patch index (LPI), average patch area (AREA_MN), average perimeter-to-area ratio (PARA_MN), patch density (PD), interspersed-juxtaposition index (IJI), landscape shape index (LSI), and patch cohesion index (COHESION).

[0010] Furthermore, because the units of various spatial morphological indicators are different and belong to different magnitudes, and some data are highly correlated, resulting in information redundancy, it is necessary to preprocess the land cover spatial morphological indicators. The preprocessing steps include: first performing z-score standardization, then performing multicollinearity test (VIF), and finally performing Pearson correlation analysis.

[0011] Furthermore, the method for formulating the land cover spatial morphology optimization plan is as follows: Firstly, a multi-scale geographically weighted regression model is constructed using the screened land cover spatial morphology indicators. Then, the spatiotemporal driving effects of each screened land cover spatial morphology indicator on carbon emissions are analyzed. Specifically, the regression coefficients of each screened land cover type spatial morphology indicator at different spatial scales are determined, and the specific impacts of each screened land cover type spatial morphology indicator on carbon emissions are quantified. Finally, spatial morphology optimization suggestions for different land cover types are proposed, and a specific land cover spatial morphology optimization plan is formulated, so as to ultimately achieve the goal of reducing urban CO2 emissions.

[0012] Furthermore, the land cover spatial morphology optimization plan adopts one or several optimization methods including reducing the compactness of cultivated land, increasing the uniform adjacency between forested land and surrounding plots, and promoting the construction of grassland corridors.

[0013] An urban carbon emission control system based on land cover spatial morphology optimization at least includes a data collection module, a spatial morphology index calculation module, a preprocessing module, and a multi-scale geographically weighted regression model analysis module; wherein: The data collection module is responsible for collecting CO2 emission data and land cover type data of multiple cities, and judging the cities with high CO2 emission based on the collected CO2 emission data; The spatial morphological index calculation module is responsible for extracting images of land cover types of each city using the city administrative boundaries, and then using Fragstats 4.2 to calculate the spatial morphological index of land cover; The preprocessing module is responsible for preprocessing the land cover spatial morphology indicators, including z-score standardization, multicollinearity test and Pearson correlation analysis, to screen out the land cover spatial morphology indicators that have a significant impact on carbon emissions; The multi-scale geographically weighted regression model analysis module is responsible for constructing a multi-scale geographically weighted regression model using the screened land cover spatial morphology indicators, analyzing the spatiotemporal driving effects of each screened land cover spatial morphology indicator on carbon emissions, determining the regression coefficients of each screened land cover type spatial morphology indicator at different spatial scales, and quantifying the specific impact of each screened land cover type spatial morphology indicator on carbon emissions. Then, spatial morphology optimization suggestions for different land cover types are proposed, and specific land cover spatial morphology optimization plans are formulated, thereby ultimately achieving the goal of reducing urban CO2 emissions.

[0014] A computer device comprises: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus, and the memory is used to store at least one executable instruction, wherein the executable instruction enables the processor to perform operations corresponding to the urban carbon emission control method based on land cover spatial morphology optimization as described in any one of claims 1 to 7.

[0015] A computer-readable storage medium, wherein at least one executable instruction is stored in the computer storage medium, wherein the executable instruction enables a processor to execute operations corresponding to the urban carbon emission control method based on land cover spatial morphology optimization as described in any one of claims 1 to 7.

[0016] The beneficial effects of the present invention are: 1. Precise control: The method and system of the present invention quantitatively analyze the impact of the spatial morphology of different land cover types on carbon emissions, propose targeted optimization solutions, and achieve precise control.

[0017] 2. Reduce computational costs: The method and system of the present invention adopt a multi-scale geographically weighted regression model, which can more accurately reflect spatial heterogeneity and reduce computational costs compared to traditional methods.

[0018] 3. Strong universality: The method and system of the present invention are applicable to different urban areas, and the optimization scheme can be flexibly adjusted according to specific circumstances.

[0019] 4. Decision support: The method and system of the present invention can provide scientific decision-making basis for urban managers and help achieve the goal of low-carbon city development.

[0020] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a logic diagram of the urban carbon emission control method and system based on land cover spatial morphology optimization of the present invention.

[0022] Figure 2 4 is a Pearson correlation coefficient table of four different land cover spatial morphology indicator driving factors in the embodiment of the present invention (*p<= 0.05 **p<= 0.01 ***p<= 0.001). DETAILED DESCRIPTION

[0023] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings so that the purpose, features and advantages of the invention can be more clearly understood. It should be understood that the embodiments shown in the accompanying drawings are not intended to limit the scope of the present invention, but are only intended to illustrate the essential spirit of the technical solution of the present invention.

[0024] In the following description, certain specific details are set forth for the purpose of illustrating various disclosed embodiments to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the relevant art will recognize that the embodiments may be practiced without one or more of these specific details. In other cases, well-known devices, structures, and techniques associated with the present application may not be shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.

[0025] Unless the context requires otherwise, throughout the specification and claims, the word "comprise" and variations such as "include" and "have" should be construed in an open, inclusive sense, ie, should be interpreted as "including, but not limited to."

[0026] References throughout the specification to "one embodiment" or "an embodiment" indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of "in one embodiment" or "in an embodiment" in various places throughout the specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any manner in one or more embodiments.

[0027] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be noted that the term "or" is generally employed in its sense including "and / or" unless the context clearly dictates otherwise.

[0028] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0029] See also Figure 1 As shown, a method for regulating urban carbon emissions based on land cover spatial morphology optimization includes the following steps: Step 1) Collect CO2 emission data and land cover type data of multiple cities, such as cultivated land, forested land, shrub land, grassland, water area, ice sheet, bare land, artificial surface and wetland, and then conduct spatial autocorrelation analysis based on the collected CO2 emission data to determine the aggregation of CO2, so as to screen out cities with high CO2 emissions, which is the prerequisite for the subsequent use of multi-scale geographically weighted regression model.

[0030] Step 2) First, ArcGIS platform was used to extract images of land cover types of each city using the city administrative boundaries. Then, Fragstats 4.2 was used to calculate the spatial morphological indicators of land cover using the extracted images of land cover types of each city.

[0031] The spatial morphological indicators of land cover include patch type area (CA), maximum patch index (LPI), average patch area (AREA_MN), average perimeter area ratio (PARA_MN), patch density (PD), spread and juxtaposition index (IJI), landscape shape index (LSI) and patch cohesion index (COHESION).

[0032] Step 3) Because the units of various spatial morphological indicators are different and belong to different magnitudes, and some data are highly correlated, resulting in information redundancy, it is necessary to preprocess all the calculated land cover spatial morphological indicators. The preprocessing steps include: first z-score standardization, then multicollinearity test (VIF), and finally Pearson correlation analysis, so as to screen out land cover spatial morphological indicators that have a significant impact on carbon emissions.

[0033] (1) z-score standardization: Z-score is a common method of data processing. It can convert data of different magnitudes into z-scores of uniform measurement for comparison to ensure comparability between data. The calculation formula of z-score is as follows: (1); In formula (1), Indicates the z-score value, Indicates a specific value, such as the land cover spatial morphology index of each research city or the specific value of CO2 emissions. represents the average value of land cover spatial morphology index or CO2 emission of all studied cities, Represents the standard deviation of land cover spatial morphology indicators or CO2 emissions.

[0034] (2) Multicollinearity test: Variance inflation factor (VIF) is an indicator used to evaluate multicollinearity. In multivariate regression, multicollinearity between independent variables will lead to a larger confidence interval of the parameter and incorrect sign of the parameter fitting, and the hypothesis test will get an incorrect result. Therefore, it is necessary to detect the VIF value of each CO2 emission driving factor, taking the city CO2 emissions as the dependent variable, to ensure that the VIF value of each morphological indicator is less than 7.5.

[0035] (3) Pearson correlation analysis: The Pearson correlation coefficient is widely used to measure the correlation between two variables, especially in analyzing complex data with the combined influence of multiple factors, and has high reliability.

[0036] The Pearson correlation coefficient is denoted as , The value range of is [-1,1]. r >0, indicating that the two variables are positively correlated; if r <0, indicating that the two variables are negatively correlated. r The larger the absolute value of r =0, indicating that there is no linear correlation between the two. (Note: This explanation can only be made based on a significant test, such as Figure 2 As shown, where * indicates p The value is less than 0.05, ** indicates p The value is less than 0.01, *** means p The value is less than 0.001. p The smaller the value, the more significant it is.) The calculation formula of Pearson correlation coefficient is as follows: (2); In formula (2), represents the Pearson correlation coefficient, represents the observed value of the land cover spatial morphology index, represents the observed value of CO2 emissions, represents the average value of land cover spatial morphology index, Indicates the average value of CO2 emissions.

[0037] Step 4) First, use the screened land cover spatial morphology indicators to construct a multi-scale geographically weighted regression model, and then analyze the spatiotemporal driving effects of each screened land cover spatial morphology indicator on carbon emissions. Specifically, determine the regression coefficients of each screened land cover type spatial morphology indicator at different spatial scales, and quantify the specific impact of each screened land cover type spatial morphology indicator on carbon emissions. Finally, propose spatial morphology optimization suggestions for different land cover types, and formulate specific land cover spatial morphology optimization plans, such as reducing the compactness of cultivated land, increasing the uniform adjacency of forested land and surrounding plots, and promoting the construction of grassland corridors, so as to ultimately achieve the goal of reducing urban CO2 emissions.

[0038] The calculation formula of the multi-scale geographically weighted regression model is as follows: (3); In formula (3), represents the CO2 emissions of city i, represents the coordinates of city i; represents the bandwidth of the j-th driving factor regression coefficient, represents the kth regression parameter of city i, represents the observed value of the j-th driving factor in city i, represents the random error term.

[0039] See also Figure 1 As shown, the present invention also provides a system using the above-mentioned urban carbon emission control method based on land cover spatial morphology optimization, the system at least includes a data collection module, a spatial morphology index calculation module, a preprocessing module, and a multi-scale geographical weighted regression model analysis module. The data collection module is responsible for collecting CO2 emission data and land cover type data of multiple cities, and judging the cities with high CO2 emission based on the collected CO2 emission data; The spatial morphological index calculation module is responsible for extracting images of land cover types of each city using the city administrative boundaries, and then using Fragstats 4.2 to calculate the spatial morphological index of land cover; The preprocessing module is responsible for preprocessing the land cover spatial morphology indicators, including z-score standardization, multicollinearity test and Pearson correlation analysis, to screen out the land cover spatial morphology indicators that have a significant impact on carbon emissions; The multi-scale geographically weighted regression model analysis module is responsible for constructing a multi-scale geographically weighted regression model using the screened land cover spatial morphology indicators, analyzing the spatiotemporal driving effects of each screened land cover spatial morphology indicator on carbon emissions, determining the regression coefficients of each screened land cover type spatial morphology indicator at different spatial scales, and quantifying the specific impact of each screened land cover type spatial morphology indicator on carbon emissions. Then, spatial morphology optimization suggestions for different land cover types are proposed, and specific land cover spatial morphology optimization plans are formulated, thereby ultimately achieving the goal of reducing urban CO2 emissions.

[0040] The following uses 304 cities across the country as examples to further describe the content of the urban carbon emission control method based on land cover spatial morphology optimization of the present invention.

[0041] This example is based on the panel data of main land cover types (cultivated land, forested land, grassland and artificial surface) and differential spatial morphological indicators (patch type area (CA), maximum patch index (LPI), average patch area (AREA_MN), average perimeter area ratio (PARA_MN), patch density (PD), spread and juxtaposition index (IJI), landscape shape index (LSI), patch cohesion index (COHESION)) of 304 cities in China in 2005, 2010, 2015 and 2020. The multi-scale geographically weighted regression model is used to analyze the differential impact of the spatial morphology of these four land cover types on CO2 emissions and their spatiotemporal driving effects. The specific implementation steps are as follows: 1) Obtain the CO2 emission data of the above 304 cities and conduct spatial autocorrelation analysis. The growth rate of total CO2 emissions in the study area dropped from 44.72% to 4.82%. Beijing-Tianjin-Hebei and the Yangtze River Delta are high-carbon emission areas with high spatial aggregation.

[0042] 2) Obtain the land cover type data (cultivated land, forested land, grassland and artificial land) of the above 304 cities. First, use the ArcGIS platform to extract the images of land cover types of each city using the city administrative boundaries. Then use Fragstats4.2 to calculate the spatial morphological indicators of land cover using the extracted images of land cover types of each city. 3) The spatial morphological indicators of the four land cover types (arable land, forested land, grassland and artificial surface) were z-score standardized, and the indicators of different magnitudes were converted into a unified measurement. Then, VIF analysis was performed, and the indicators with VIF values ​​greater than 7.5 were eliminated. Finally, the Pearson correlation coefficient between different morphological driving factors and CO2 emissions was calculated, and the driving factors significantly correlated with CO2 emissions were retained, such as Figure 2 shown.

[0043] 3) Using the filtered data, the software MGWR2.2 was used to construct a multi-scale geographically weighted regression model to obtain the beta value; then, based on the obtained beta value (regression coefficient), a visual analysis was performed on the ArcGIS platform.

[0044] 4) Select the optimal design scheme through the above analysis: The cultivated land area has a two-way effect on CO2 emissions. It is recommended to reduce the compactness of cultivated land and make the cultivated land as unevenly distributed as possible with other surrounding patches; Expanding the area of ​​forested land and increasing its even distribution with other cover types can help reduce CO2 emissions; Grasslands need to increase spatial levels and complexity, promote the construction of grassland corridors, and increase their even distribution with other cover types; Artificial surfaces are the main carbon source, and their expansion rate should be controlled to curb their spread, while their complexity should be adapted to local conditions (e.g., the morphological complexity should be increased in East China and Central China).

[0045] The present invention also provides a computer device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus, and the memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned urban carbon emission control method based on land cover spatial morphology optimization.

[0046] The present invention also provides a computer-readable storage medium, characterized in that at least one executable instruction is stored in the computer storage medium, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned urban carbon emission control method based on land cover spatial morphology optimization.

[0047] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for regulating urban carbon emissions based on land cover spatial morphology optimization, characterized in that: include: Step 1) Collect CO2 emission data and land cover type data from multiple cities, and conduct spatial autocorrelation analysis based on the collected CO2 emission data to determine the aggregation of CO2, thereby screening out cities with high CO2 emissions; Step 2) Using the collected land cover type data of cities with high CO2 emissions, calculate the corresponding land cover spatial morphology indicators; Step 3) Preprocess all calculated land cover spatial morphology indicators to screen out land cover spatial morphology indicators that have a significant impact on carbon emissions; Step 4) By constructing a multi-scale geographically weighted regression model, the spatiotemporal driving effect of each selected land cover spatial morphology indicator on carbon emissions is analyzed, so as to formulate a land cover spatial morphology optimization plan to achieve urban CO2 emission reduction.

2. The urban carbon emission control method based on land cover spatial morphology optimization according to claim 1 is characterized in that: The land cover types include: Cultivated land, wooded land, shrubland, grassland, water area, ice sheet, bare land, artificial surface and wetland.

3. The urban carbon emission control method based on land cover spatial morphology optimization according to claim 1 is characterized in that: The calculation method of the land cover spatial morphology index is: Firstly, ArcGIS platform was used to extract images of land cover types of each city using the city administrative boundaries. Then, Fragstats 4.2 was used to calculate the spatial morphological indicators of land cover using the extracted images of land cover types of each city.

4. The urban carbon emission control method based on land cover spatial morphology optimization according to claim 1 is characterized in that: The land cover spatial morphology indicators include: patch type area, maximum patch index, average patch area, average perimeter-to-area ratio, patch density, dispersion and juxtaposition index, landscape shape index, and patch cohesion index.

5. The urban carbon emission control method based on land cover spatial morphology optimization according to claim 1 is characterized in that: The steps of preprocessing the land cover spatial morphology index include: first performing z-score standardization, then performing multicollinearity test, and finally performing Pearson correlation analysis.

6. The urban carbon emission control method based on land cover spatial morphology optimization according to claim 1 is characterized in that: The method for formulating the land cover spatial morphology optimization plan is as follows: Firstly, a multi-scale geographically weighted regression model is constructed using the screened land cover spatial morphology indicators. Then, the spatiotemporal driving effects of each screened land cover spatial morphology indicator on carbon emissions are analyzed. Specifically, the regression coefficients of each screened land cover type spatial morphology indicator at different spatial scales are determined, and the specific impacts of each screened land cover type spatial morphology indicator on carbon emissions are quantified. Finally, spatial morphology optimization suggestions for different land cover types are proposed, and a specific land cover spatial morphology optimization plan is formulated, so as to ultimately achieve the goal of reducing urban CO2 emissions.

7. The urban carbon emission control method based on land cover spatial morphology optimization according to claim 1 is characterized in that: The land cover spatial morphology optimization plan adopts one or several optimization methods including reducing the compactness of cultivated land, increasing the uniform adjacency between forested land and surrounding plots, and promoting the construction of grassland corridors.

8. A system using the urban carbon emission control method based on land cover spatial morphology optimization as described in any one of claims 1 to 7, characterized in that: It at least includes a data collection module, a spatial morphology index calculation module, a preprocessing module, and a multi-scale geographically weighted regression model analysis module; among which, The data collection module is responsible for collecting CO2 emission data and land cover type data of multiple cities, and judging the cities with high CO2 emission based on the collected CO2 emission data; The spatial morphological index calculation module is responsible for extracting images of land cover types of each city using the city administrative boundaries, and then calculating the spatial morphological index of land cover using Fragstats 4.2; The preprocessing module is responsible for preprocessing the land cover spatial morphology indicators, including z-score standardization, multicollinearity test and Pearson correlation analysis, to screen out the land cover spatial morphology indicators that have a significant impact on carbon emissions; The multi-scale geographically weighted regression model analysis module is responsible for constructing a multi-scale geographically weighted regression model using the screened land cover spatial morphology indicators, analyzing the spatiotemporal driving effects of each screened land cover spatial morphology indicator on carbon emissions, determining the regression coefficients of each screened land cover type spatial morphology indicator at different spatial scales, and quantifying the specific impact of each screened land cover type spatial morphology indicator on carbon emissions. Then, spatial morphology optimization suggestions for different land cover types are proposed, and specific land cover spatial morphology optimization plans are formulated, thereby ultimately achieving the goal of reducing urban CO2 emissions.

9. A computer device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus, and the memory is used to store at least one executable instruction, wherein the executable instruction enables the processor to perform operations corresponding to the urban carbon emission control method based on land cover spatial morphology optimization as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer storage medium stores at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the urban carbon emission control method based on land cover spatial morphology optimization as described in any one of claims 1 to 7.

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