A method for gridding the spatial distribution of urban carbon emissions at the hundred-meter level based on multi-source open data

Abstract

This paper proposes a method for gridding the 100-meter spatial distribution of urban carbon emissions based on multi-source open data. The method includes obtaining multi-source open big data for a target city, constructing a 100-meter grid multidimensional information index, and calculating the city's total carbon emissions; obtaining an initial 100-meter grid carbon emissions value based on the 100-meter grid multidimensional information index and the city's total carbon emissions; and obtaining initial district-level carbon emissions and initial town-scale carbon emissions based on the initial 100-meter grid carbon emissions; obtaining district-level reference carbon emissions and town-scale reference carbon emissions, constructing a fitness function based on the district-level reference carbon emissions, town-scale reference carbon emissions, initial district-level carbon emissions, and initial town-scale carbon emissions; and iteratively solving the fitness function using a multi-agent genetic algorithm to obtain the final 100-meter grid carbon emissions value for the city corresponding to the minimum fitness function. This method can more comprehensively integrate urban information to infer the spatial distribution pattern of carbon emissions, effectively improving the grid accuracy of the carbon emissions spatial distribution pattern.

Description

Technical Field

[0001] The present invention relates to the field of urban planning and carbon emission technology, and more specifically, to a method for gridding the 100-meter-level spatial distribution of urban carbon emissions based on multi-source open data. Background Art

[0002] Urbanization is accompanied by massive greenhouse gas emissions. Ecological challenges such as climate change and extreme weather have become significant obstacles to sustainable development. Spatializing carbon emissions facilitates rapid understanding of urban carbon emission patterns, enabling carbon reduction-oriented optimization of urban spatial layout, and establishing pathways for high-quality urban development, utilization, and conservation, ultimately promoting high-quality urbanization and achieving the "dual carbon" goals.

[0003] Current research on refined spatial simulation of carbon emissions primarily utilizes data on electricity, fossil fuels, and nighttime lights to construct top-down spatial distribution models of carbon emissions. For example, some studies identify urban land use functional zones based on the density of various point of interest (POIs), and spatialize urban carbon emissions by combining the land use types of different zones with the total amount of urban carbon emissions. Other studies have also spatialized carbon emissions by combining nighttime light data with municipal electricity consumption data to determine carbon emission ratios within urban zones. Different research teams have derived carbon emission spatial grid data based on top-down spatial inversion paths, but the accuracy of existing carbon emission grid data is insufficient to reflect the carbon emission pattern within cities. Furthermore, constructing carbon emission allocation models proportionally based on a single data source has limitations in reflecting the spatial pattern of carbon emissions. Summary of the Invention

[0004] In order to overcome the defect of low accuracy of carbon emission grid data in the above-mentioned prior art, the present invention provides a method for gridding the hundred-meter-level spatial distribution of urban carbon emissions based on multi-source open data.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] The present invention proposes a method for gridding the spatial distribution of urban carbon emissions at the hundred-meter level based on multi-source open data, comprising the following steps:

[0007] Obtain multi-source open big data of the target city and construct a 100-meter grid multi-dimensional information index based on the multi-source open big data of the target city;

[0008] Calculate the total carbon emissions of the city based on multi-source open big data of the target city;

[0009] The initial 100-meter grid carbon emissions were obtained based on the multi-dimensional information index of the 100-meter grid and the total urban carbon emissions. The initial district-level carbon emissions and the initial town-level carbon emissions were obtained based on the initial 100-meter grid carbon emissions.

[0010] Obtain the district-level reference total carbon emissions and the town-scale reference total carbon emissions, construct a fitness function based on the district-level reference total carbon emissions, the town-scale reference total carbon emissions, the initial district-level total carbon emissions and the initial town-scale total carbon emissions, and use a multi-agent genetic algorithm to iteratively solve the fitness function to obtain the final urban 100-meter grid carbon emissions corresponding to the minimum fitness function.

[0011] Furthermore, the target city's multi-source open big data includes original data on population distribution, natural environment, architectural environment, economic business model, and historical yearbook data.

[0012] The original data of the population distribution layer include WorldPop population grid data and census data;

[0013] The original data of the natural environment layer includes DEM data and Sentinel-2 image data;

[0014] The raw data of the building environment layer include: building footprint data, Sentinel-2 four-season image data;

[0015] The original data of the economic business layer includes night light data, land parcel vector boundary data, energy consumption data, and POI data.

[0016] Furthermore, the target city multi-dimensional information index includes a 100-meter grid population distribution index. The method for extracting the 100-meter grid population distribution index includes:

[0017] The WorldPop population grid data was corrected according to the census data to obtain the corrected WorldPop population grid data. The corrected WorldPop population grid data was mapped to the grid data using ArcGIS to obtain the 100-meter grid population distribution index.

[0018] Furthermore, the target city multi-dimensional information index also includes a 100-meter grid natural environment index. The method for extracting the 100-meter grid natural environment index includes:

[0019] Based on the DEM data, the average elevation of the city in each grid data is calculated and connected to the grid to obtain the 100-meter grid elevation data;

[0020] Use the spatial analysis software ArcGIS to calculate the DEM data to obtain urban slope data, and use spatial analysis methods to establish a connection between grid data and urban slope data to obtain 100-meter grid slope data;

[0021] The ENVI tool was used to calculate the Sentinel-2 image data to obtain the urban NDVI vegetation index and NDWI vegetation index. The grid data was connected with the urban NDVI vegetation index and NDWI vegetation index to obtain the 100-meter grid NDVI vegetation index and NDWI vegetation index.

[0022] The Shannon entropy weight method was used to construct the 100-meter grid natural environment index based on the 100-meter grid elevation data, 100-meter grid slope data, 100-meter grid NDVI vegetation index and NDWI vegetation index.

[0023] Furthermore, the target city multi-dimensional information also includes a 100-meter grid building environment index. The method for extracting the 100-meter grid building environment index includes:

[0024] Constructing a deep learning-based object detection neural network SEASONet, inputting the Sentinel-2 imagery's four-season composite image and target city building footprint data into the deep learning-based object detection neural network SEASONet to extract the building shadow index;

[0025] Performing spatial analysis on building outline data with high-rise buildings in the building footprint data to obtain building data, wherein the building data includes percentage of building area, average building height, and building volume ratio;

[0026] A 100-meter grid building environment index is constructed based on the building shadow index, building data and grid data.

[0027] Furthermore, the multi-dimensional information of the target city also includes a 100-meter grid economic business index. The method for extracting the 100-meter grid economic business index includes:

[0028] Preprocess the night light data to obtain preprocessed night light data, calculate the average brightness value of the night light data based on the preprocessed night light data, and then connect the grid data to obtain 100-meter grid light data;

[0029] Based on the universality and consistency of urban land classification standards and POI classification, we divided the land parcel vector boundary data into multiple types of land parcels, including public service land parcels, management land parcels, transportation land parcels, residential land parcels, commercial land parcels, recreational land parcels, and industrial land parcels. We then conducted a kernel density cluster analysis on each type of land parcel, and calculated the kernel density proportion of each type of POI within each land parcel to obtain functional land parcel data. This functional land parcel data was then connected to the grid data to obtain 100-meter grid functional land parcel data.

[0030] Based on the 100-meter grid lighting data and the 100-meter grid functional plot data, the 100-meter grid economic business index is obtained.

[0031] Furthermore, the total urban carbon emissions are calculated based on the target city’s multi-source open big data, including:

[0032] Based on historical yearbook data, we can obtain data on actual fossil energy consumption of different industries and types in the target city, urban electricity consumption, and total urban solid waste.

[0033] Carbon emissions from fossil energy consumption are calculated based on the actual fossil energy consumption of different industries and types in the target cities. Specifically:

[0034]

[0035] Among them, FC i represents the consumption of the i-th type of fossil energy, CEF i represents the carbon emission factor of the i-th type of fossil energy;

[0036] The carbon emissions from electricity consumption are calculated based on the city's electricity consumption, specifically:

[0037]

[0038] Among them, EC j represents the electricity consumption of category j; EEF represents the carbon emission factor of electricity, and m represents different categories;

[0039]

[0040] Among them, SW k Expressed as the total amount of solid waste of type k, SEF k represents the carbon emission factor of the kth type of solid waste, and p represents the total number of solid waste categories;

[0041] The total carbon emissions of the city are:

[0042] C=C1+C2+C3

[0043] Where C represents the total carbon emissions of the city.

[0044] Furthermore, the initial 100-meter grid carbon emissions are obtained based on the 100-meter grid multi-dimensional information index and the total urban carbon emissions. The initial district-level carbon emissions and the initial town-level carbon emissions are obtained based on the initial 100-meter grid carbon emissions, including:

[0045] The multidimensional information index of the 100-meter grid is normalized to obtain the normalized multidimensional information index of the 100-meter grid. Based on the normalized multidimensional information index of the 100-meter grid, the entropy weight method is used to determine the initial weight of the multidimensional information index of the 100-meter grid. The normalized multidimensional information index of the 100-meter grid is weighted and summed to obtain the initial carbon emissions of the 100-meter grid. The calculation formula is as follows:

[0046]

[0047] Among them, s j represents the carbon emissions of the jth initial 100-meter grid, D i,norm(j) Denotes the i-th category index in the normalized multi-dimensional information index of the j-th initial 100-meter grid, D 1,norm(j) 、D 2,norm(j) 、D 3,norm(j) and D 4,norm(j) They represent the normalized population distribution index, natural environment index, building environment index, and economic business index of the jth initial 100-meter grid, respectively. i represents the initial weight of the i-th index in the 100-meter grid multidimensional information index, that is, α1, α2, α3 and α4 represent the initial weights of the population distribution index, natural environment index, building environment index and economic industry index in the 100-meter grid multidimensional information index, respectively. total represents the total carbon emissions of the city;

[0048] The initial 100-meter grid carbon emissions within the town and street area are aggregated and calculated to obtain the total carbon emissions at the initial town and street scale;

[0049] The initial 100-meter grid carbon emissions within the district level are aggregated and calculated to obtain the initial district-level carbon emissions. Furthermore, obtaining the district-level reference carbon emissions and town-level reference carbon emissions includes:

[0050] Obtaining district-level reference carbon emissions includes:

[0051] Based on historical yearbook data, we obtained socioeconomic structure data and district-level carbon emissions data for recent years. The socioeconomic structure data included historical population, GDP, total energy consumption of enterprises above a certain scale, and the number of listed companies in different industries in each district. We established a multivariate linear regression model based on the socioeconomic structure data and district-level carbon emissions data, with the socioeconomic structure data as the independent variable and the district-level carbon emissions as the dependent variable. Specifically, the model is as follows:

[0052] Y=β0+β1X1+β2X2+…+β n X n +ε

[0053] Where Y is the total carbon emissions at the district level; X1, X2,…, X n They represent the historical population, GDP, total energy consumption of enterprises above designated size, and the number of listed companies in different industries in each district; β0, β1,…, β n is the parameter to be estimated, ε is the error term;

[0054] Substitute recent socioeconomic structure data and district-level carbon emissions into the multiple linear regression model to calculate the parameters to be estimated;

[0055] The population, GDP, energy consumption of enterprises above designated size, and the number of listed companies in different industries in each district in the reference year were obtained and input into the multivariate linear regression model to obtain the district-level reference carbon emissions.

[0056] Obtaining the total reference carbon emissions at the town and street level includes:

[0057] The EDGAR carbon emission grid dataset of the reference year was obtained and calibrated for the total urban carbon emissions to obtain the corrected EDGAR carbon emission dataset. Based on the corrected EDGAR carbon emission dataset, the total carbon emission data at the town and street scale were statistically analyzed to obtain the town and street scale reference carbon emissions.

[0058] Furthermore, the method for determining the fitness function includes:

[0059]

[0060] Among them, Y0 and Y logical They represent the initial district-level carbon emissions and the district-level reference carbon emissions respectively; Y1 and Y EDGAR They represent the initial total carbon emissions at the town and street scale and the reference total carbon emissions at the town and street scale respectively. D represents the administrative district set, and E represents the town and street set.

[0061] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0062] The present invention proposes a method for gridding the 100-meter spatial distribution of urban carbon emissions based on multi-source open data. The method comprises obtaining multi-source open big data of a target city, constructing a 100-meter grid multidimensional information index based on the target city's multi-source open big data, and calculating the city's total carbon emissions; obtaining an initial 100-meter grid carbon emissions value based on the 100-meter grid multidimensional information index and the city's total carbon emissions; and obtaining initial district-level carbon emissions and initial town-scale carbon emissions based on the initial 100-meter grid carbon emissions; obtaining district-level reference carbon emissions and town-scale reference carbon emissions, constructing a fitness function based on the district-level reference carbon emissions, the town-scale reference carbon emissions, the initial district-level carbon emissions, and the initial town-scale carbon emissions; and iteratively solving the fitness function using a multi-agent genetic algorithm to obtain the final 100-meter grid carbon emissions value for the city corresponding to the minimum fitness function. The present invention can more comprehensively integrate urban information to infer the spatial distribution pattern of carbon emissions, effectively improving the grid accuracy of the carbon emissions spatial distribution pattern. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of the method for gridding the spatial distribution of urban carbon emissions at the hundred-meter level based on multi-source open data described in Example 1;

[0064] Figure 2This is the flow chart of the multi-agent genetic algorithm described in Example 3. DETAILED DESCRIPTION

[0065] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0066] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0067] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0068] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0069] Example 1

[0070] This embodiment provides a method for gridding the spatial distribution of urban carbon emissions at the hundred-meter level using multi-source open data. Figure 1 As shown, the following steps are included:

[0071] Obtain multi-source open big data of the target city and construct a 100-meter grid multi-dimensional information index based on the multi-source open big data of the target city;

[0072] Calculate the total carbon emissions of the city based on multi-source open big data of the target city;

[0073] The initial 100-meter grid carbon emissions were obtained based on the multi-dimensional information index of the 100-meter grid and the total urban carbon emissions. The initial district-level carbon emissions and the initial town-level carbon emissions were obtained based on the initial 100-meter grid carbon emissions.

[0074] Obtain the district-level reference total carbon emissions and the town-scale reference total carbon emissions, construct a fitness function based on the district-level reference total carbon emissions, the town-scale reference total carbon emissions, the initial district-level total carbon emissions and the initial town-scale total carbon emissions, and use a multi-agent genetic algorithm to iteratively solve the fitness function to obtain the final urban 100-meter grid carbon emissions corresponding to the minimum fitness function.

[0075] In the specific implementation process, we first construct a 100-meter grid multi-dimensional information index and calculate the total urban carbon emissions based on the multi-source open big data of the target city. Then, we obtain the initial district-level carbon emissions and the initial town-scale carbon emissions. The district-level reference carbon emissions and the town-scale reference carbon emissions are iteratively solved by a multi-agent genetic algorithm to obtain the final urban 100-meter grid carbon emissions corresponding to the minimum fitness function.

[0076] Example 2

[0077] This embodiment further limits the acquisition of district-level reference carbon emissions and town-level reference carbon emissions based on embodiment 1.

[0078] Obtaining the district-level reference carbon emissions and town-level reference carbon emissions includes:

[0079] Obtaining district-level reference carbon emissions includes:

[0080] Based on historical yearbook data, we obtained socioeconomic structure data and district-level carbon emissions data for recent years. The socioeconomic structure data included historical population, GDP, total energy consumption of enterprises above a certain scale, and the number of listed companies in different industries in each district. We established a multivariate linear regression model based on the socioeconomic structure data and district-level carbon emissions data, with the socioeconomic structure data as the independent variable and the district-level carbon emissions as the dependent variable. Specifically, the model is as follows:

[0081] Y=β0+β1X1+β2X2+…+β n X n +v

[0082] Where Y is the total carbon emissions at the district level; X1, X2,…, X n They represent the historical population, GDP, total energy consumption of enterprises above designated size, and the number of listed companies in different industries in each district; β0, β1,…, β n is the parameter to be estimated, ε is the error term;

[0083] Substitute recent socioeconomic structure data and district-level carbon emissions into the multiple linear regression model to calculate the parameters to be estimated;

[0084] The population, GDP, energy consumption of enterprises above designated size, and the number of listed companies in different industries in each district in the reference year were obtained and input into the multivariate linear regression model to obtain the district-level reference carbon emissions.

[0085] Obtaining the total reference carbon emissions at the town and street level includes:

[0086] The EDGAR carbon emission grid dataset of the reference year was obtained and calibrated for the total urban carbon emissions to obtain the corrected EDGAR carbon emission dataset. Based on the corrected EDGAR carbon emission dataset, the total carbon emission data at the town and street scale were statistically analyzed to obtain the town and street scale reference carbon emissions.

[0087] Example 3

[0088] This embodiment provides a method for gridding the spatial distribution of urban carbon emissions at the hundred-meter level based on multi-source open data, including the following steps:

[0089] Obtain multi-source open big data of the target city and construct a 100-meter grid multi-dimensional information index based on the multi-source open big data of the target city;

[0090] The target city's multi-source open big data includes original data on population distribution, natural environment, architectural environment, economic business model, and historical yearbook data.

[0091] The original data of the population distribution layer include WorldPop population grid data and census data;

[0092] The original data of the natural environment layer includes DEM data and Sentinel-2 image data;

[0093] The raw data of the building environment layer include: building footprint data, Sentinel-2 four-season image data;

[0094] The original data of the economic business layer includes night light data, land parcel vector boundary data, energy consumption data, and POI data.

[0095] The target city's multi-dimensional information index includes a 100-meter grid population distribution index. The method for extracting the 100-meter grid population distribution index is as follows:

[0096] The WorldPop population grid data, combined with census data and calibrated, was used as the population distribution layer for this study. The open-source population grid data was proportionally calibrated based on official census data. Spatial analysis software such as ArcGIS was used to spatially connect the population of the study area and map it onto the grid, completing the mapping of population data and grid. Census data for the study area during the study year was obtained, and the total number of WorldPop population grids within different districts and counties was counted. The population data was then scaled proportionally to obtain the 100-meter grid population distribution index, using the following formula:

[0097]

[0098] Among them, m i Indicates the adjusted grid population, n i represents the population of the i-th grid, M represents the total population of different districts and counties, and p represents the number of grids in a specified district or county.

[0099] The target city multi-dimensional information index also includes a 100-meter grid natural environment index. The method for extracting the 100-meter grid natural environment index is as follows:

[0100] Based on the DEM data, the grid, elevation image, and slope raster data were superimposed, and the average elevation of the city within each grid data was calculated and connected to the grid to obtain the 100-meter grid elevation data;

[0101] Use the spatial analysis software ArcGIS to calculate the DEM data to obtain urban slope data, and use spatial analysis methods to establish a connection between grid data and urban slope data to obtain 100-meter grid slope data;

[0102] The ENVI tool was used to calculate the Sentinel-2 image data to obtain the urban NDVI vegetation index and NDWI vegetation index. The connections between the grid data and the urban NDVI vegetation index and NDWI vegetation index were established respectively to obtain the 100-meter grid NDVI vegetation index and NDWI vegetation index. The calculation formulas for the NDVI vegetation index and NDWI vegetation index are:

[0103]

[0104]

[0105] Among them, B3, B4, and B5 represent the remote sensing data of the 3rd, 4th, and 5th bands respectively.

[0106] Based on the 100-meter grid elevation data, 100-meter grid slope data, 100-meter grid NDVI vegetation index and NDWI vegetation index, the Shannon entropy weight method is used to construct the 100-meter grid natural environment index. The formula is as follows:

[0107] Data standardization:

[0108]

[0109] Where x′ ij is the original value of the index of the jth category of the i-th sample.

[0110] Calculate the ratio value:

[0111]

[0112] Here, m is the total number of samples.

[0113] The entropy value e of the j-th indicator j for:

[0114]

[0115] in, Used to ensure that the entropy value ranges between 0 and 1. If p ij =0, then define p ij ln(p ij )=0.

[0116] The entropy value d of the j-th difference coefficient j for:

[0117] d j =1-e j

[0118] Among them, the coefficient of difference reflects the size of the data. The greater the difference, the greater the entropy and the greater the amount of information.

[0119] Calculate the weight w of the j-th indicator according to the difference coefficient j for:

[0120]

[0121] Where n is the total number of indicators.

[0122] Urban natural environment index layer:

[0123]

[0124] Among them, M i is the grid value of the natural environment index layer of the kth grid.

[0125] The target city's multi-dimensional information also includes a 100-meter grid building environment index. The method for extracting the 100-meter grid building environment index is as follows:

[0126] Constructing a deep learning-based object detection neural network SEASONet, inputting the Sentinel-2 imagery's four-season composite image and target city building footprint data into the deep learning-based object detection neural network SEASONet to extract the building shadow index;

[0127] Performing spatial analysis on the outline data of high-rise buildings in the building footprint data to obtain building data, wherein the building data includes indicators such as percentage of building area, average building height, and building volume ratio to comprehensively describe the urban building environment;

[0128] A 100-meter grid building environment index is constructed based on the building shadow index, building data and grid data.

[0129] The target city's multi-dimensional information also includes a 100-meter grid economic business index. The method for extracting the 100-meter grid economic business index is as follows:

[0130] In ArcGIS, the Raster Calculator tool was used to filter out abnormal light spots in the nighttime light data study area, and radiometric calibration was performed as needed to adjust pixel values. The Zonal Statistics as Table tool was used to obtain the average brightness of the nighttime light data on a grid. The grid data was then concatenated to obtain 100-meter grid light data, which represents the city's economic vitality.

[0131] Based on the universality and consistency of urban land classification standards and POI classification, the land parcel vector boundary data was divided into multiple types of land parcels, including public service land parcels, management land parcels, transportation land parcels, residential land parcels, commercial function land parcels, recreational land parcels, and industrial land parcels. Kernel density cluster analysis was performed on each type of land parcel, and the proportion of each type of POI kernel density within each land parcel was calculated to obtain functional land parcel data. The functional land parcel data was then connected with the grid data to obtain 100-meter grid functional land parcel data. The 100-meter grid functional land parcel data represents economic intensity.

[0132] Based on the 100-meter grid lighting data and the 100-meter grid functional plot data, the 100-meter grid economic business index is obtained.

[0133] Data from historical yearbooks was mined to reveal data on coal, fuel oil, gasoline, diesel, heat, and electricity consumption across various industrial sectors; agricultural fertilizer and pesticide use; diesel use; rural electricity consumption; agricultural film use; the number of poultry, cattle, sheep, and pigs on hand and on market; and total municipal solid waste. Based on the IPCC's carbon accounting formula and multi-source open big data from target cities, the total urban carbon emissions were calculated. Different energy consumption types were multiplied by corresponding carbon emission factors, as were the use of different agricultural factors, to determine the carbon dioxide emissions generated by agricultural production.

[0134] Based on historical yearbook data, we can obtain data on actual fossil energy consumption of different industries and types in the target city, urban electricity consumption, and total urban solid waste.

[0135] Carbon emissions from fossil energy consumption are calculated based on the actual fossil energy consumption of different industries and types in the target cities. Specifically:

[0136]

[0137] Among them, FC i represents the consumption of the i-th type of fossil energy, CEF i represents the carbon emission factor of the i-th type of fossil energy;

[0138] The carbon emissions from electricity consumption are calculated based on the city's electricity consumption, specifically:

[0139]

[0140] Among them, EC j represents the electricity consumption of category j; EEF represents the carbon emission factor of electricity, and m represents different categories;

[0141]

[0142] Among them, SW kExpressed as the total amount of solid waste of type k, SEF k represents the carbon emission factor of the kth type of solid waste, and p represents the total number of solid waste categories;

[0143] The total carbon emissions of the city are:

[0144] C=C1+C2+C3

[0145] Where C represents the total carbon emissions of the city.

[0146] The initial 100-meter grid carbon emissions were obtained based on the multi-dimensional information index of the 100-meter grid and the total urban carbon emissions. The initial district-level carbon emissions and the initial town-level carbon emissions were obtained based on the initial 100-meter grid carbon emissions:

[0147] The multidimensional information index of the 100-meter grid is normalized to obtain the normalized multidimensional information index of the 100-meter grid. Based on the normalized multidimensional information index of the 100-meter grid, the entropy weight method is used to determine the initial weight of the multidimensional information index of the 100-meter grid. The normalized multidimensional information index of the 100-meter grid is weighted and summed to obtain the initial carbon emissions of the 100-meter grid. The calculation formula is as follows:

[0148]

[0149] Among them, s j represents the carbon emissions of the jth initial 100-meter grid, D i,norm(j) Denotes the i-th category index in the normalized multi-dimensional information index of the j-th initial 100-meter grid, D 1,norm(j) 、D 2,norm(j) 、D 3,norm(j) and D 4,norm(j) They represent the normalized population distribution index, natural environment index, building environment index, and economic business index of the jth initial 100-meter grid, respectively. i represents the initial weight of the i-th index in the 100-meter grid multidimensional information index, that is, α1, α2, α3 and α4 represent the initial weights of the population distribution index, natural environment index, building environment index and economic industry index in the 100-meter grid multidimensional information index, respectively. total represents the total carbon emissions of the city;

[0150] The initial 100-meter grid carbon emissions within the town and street area are aggregated and calculated to obtain the total carbon emissions at the initial town and street scale;

[0151] The initial 100-meter grid carbon emissions within the district level were aggregated and calculated to obtain the initial district-level total carbon emissions.

[0152] Obtain the district-level reference total carbon emissions and the town-scale reference total carbon emissions, construct a fitness function based on the district-level reference total carbon emissions, the town-scale reference total carbon emissions, the initial district-level total carbon emissions and the initial town-scale total carbon emissions, and use a multi-agent genetic algorithm to iteratively solve the fitness function to obtain the final urban 100-meter grid carbon emissions corresponding to the minimum fitness function.

[0153] Get the district-level reference carbon emissions:

[0154] Based on historical yearbook data, we obtained socioeconomic structure data and district-level carbon emissions data for the past five years. The socioeconomic structure data included historical population, GDP, total energy consumption of enterprises above a certain scale, and the number of listed companies in different industries in each district. A multivariate linear regression model was established based on the socioeconomic structure data and district-level carbon emissions data, with the socioeconomic structure data as the independent variable and the district-level carbon emissions as the dependent variable. Specifically, the model is as follows:

[0155] Y=β0+β1X1+β2X2+…+β n X n +ε

[0156] Where Y is the total carbon emissions at the district level; X1, X2,…, X n They represent the historical population, GDP, total energy consumption of enterprises above designated size, and the number of listed companies in different industries in each district; β0, β1,…, β n is the parameter to be estimated, ε is the error term;

[0157] Substitute recent socioeconomic structure data and district-level carbon emissions into the multiple linear regression model to calculate the parameters to be estimated;

[0158] The population, GDP, energy consumption of enterprises above designated size, and the number of listed companies in different industries in each district in the reference year were obtained and input into the multivariate linear regression model to obtain the district-level reference carbon emissions.

[0159] Obtain the total reference carbon emissions at the town or street level:

[0160] Obtain the EDGAR carbon emission grid dataset for the reference year and calibrate the total urban carbon emissions to obtain the calibrated EDGAR carbon emission dataset. Based on the calibrated EDGAR carbon emission dataset, calculate the total carbon emission data at the town and street scale to obtain the town and street scale reference carbon emissions. The calculation formula is as follows:

[0161]

[0162] Among them, y i represents the adjusted carbon emissions per kilometer grid, o irepresents the current carbon emissions of the i-th grid, Y′ represents the total carbon emissions of the city, and M1 represents the total carbon emissions of the kilometer grid.

[0163] The method for determining the fitness function is:

[0164]

[0165] Among them, Y0 and Y logical They represent the initial district-level carbon emissions and the district-level reference carbon emissions respectively; Y1 and Y EDGAR They represent the initial total carbon emissions at the town and street scale and the reference total carbon emissions at the town and street scale respectively. D represents the administrative district set, and E represents the town and street set.

[0166] like Figure 2 The figure shows a flowchart for a multi-agent genetic algorithm. In this embodiment, the multi-agent genetic algorithm abstracts the spatial distribution of carbon emissions into individual solutions at different spatial scales. Each agent works independently, optimizing the local carbon emissions distribution through evolutionary strategies. Simultaneously, through the genetic algorithm's crossover and mutation operations, collaboration between different agents is achieved, ultimately optimizing the overall spatial distribution. The multi-agent genetic algorithm iteration terminates when the set number of iterations is reached or when the algorithm outputs the final town-scale carbon emissions summary of 100-meter grid carbon emissions, which is less than 5% of the initial town-scale carbon emissions.

[0167] The same or similar reference numerals correspond to the same or similar components;

[0168] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0169] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for gridding the spatial distribution of urban carbon emissions at the hundred-meter level based on multi-source open data, characterized by: The following steps are involved: Obtain multi-source open big data of the target city and construct a 100-meter grid multi-dimensional information index based on the multi-source open big data of the target city; The target city's multi-source open big data includes original data on population distribution, natural environment, architectural environment, economic business model, and historical yearbook data. The original data of the population distribution layer include WorldPop population grid data and census data; The original data of the natural environment layer includes DEM data and Sentinel-2 image data; The raw data of the building environment layer include: building footprint data, Sentinel-2 four-season image data; The original data of the economic business layer includes night light data, land parcel vector boundary data, energy consumption data, and POI data; The target city's multi-dimensional information index includes a 100-meter grid population distribution index. The method for extracting the 100-meter grid population distribution index includes: The WorldPop population grid data was calibrated according to the census data to obtain the calibrated WorldPop population grid data. The calibrated WorldPop population grid data was mapped to the grid data using ArcGIS to obtain the 100-meter grid population distribution index. The target city multi-dimensional information also includes a 100-meter grid building environment index. The method for extracting the 100-meter grid building environment index includes: Constructing a deep learning-based object detection neural network SEASONet, inputting the Sentinel-2 imagery's four-season composite image and target city building footprint data into the deep learning-based object detection neural network SEASONet to extract the building shadow index; Performing spatial analysis on building outline data with high-rise buildings in the building footprint data to obtain building data, wherein the building data includes percentage of building area, average building height, and building volume ratio; Construct a 100-meter grid building environment index based on building shadow index, building data and grid data; Calculate the total carbon emissions of the city based on multi-source open big data of the target city; The initial 100-meter grid carbon emissions are obtained based on the 100-meter grid multi-dimensional information index and the total urban carbon emissions. The initial district-level carbon emissions and the initial town-level carbon emissions are obtained based on the initial 100-meter grid carbon emissions. The multidimensional information index of the 100-meter grid is normalized to obtain the normalized multidimensional information index of the 100-meter grid. Based on the normalized multidimensional information index of the 100-meter grid, the entropy weight method is used to determine the initial weight of the multidimensional information index of the 100-meter grid. The normalized multidimensional information index of the 100-meter grid is weighted and summed to obtain the initial carbon emissions of the 100-meter grid. The calculation formula is as follows: Among them, s j represents the carbon emissions of the jth initial 100-meter grid, D i,norm(j) Denotes the i-th category index in the normalized multi-dimensional information index of the j-th initial 100-meter grid, D 1,norm(j) 、D 2,norm(j) 、D 3,norm(j) and D 4,norm(j) They represent the normalized population distribution index, natural environment index, building environment index, and economic business index of the jth initial 100-meter grid, respectively. i represents the initial weight of the i-th index in the 100-meter grid multidimensional information index, that is, α1, α2, α3 and α4 represent the initial weights of the population distribution index, natural environment index, building environment index and economic industry index in the 100-meter grid multidimensional information index, respectively. total represents the total carbon emissions of the city; The initial 100-meter grid carbon emissions within the town and street area are aggregated and calculated to obtain the total carbon emissions at the initial town and street scale; The initial 100-meter grid carbon emissions within the district level are aggregated and calculated to obtain the initial district-level total carbon emissions; Obtain district-level reference carbon emissions and town-scale reference carbon emissions, construct a fitness function based on the district-level reference carbon emissions, town-scale reference carbon emissions, initial district-level carbon emissions, and initial town-scale carbon emissions, and use a multi-agent genetic algorithm to iteratively solve the fitness function to obtain the final urban 100-meter grid carbon emissions corresponding to the minimum fitness function; The method for determining the fitness function includes: Among them, Y0 and Y logical They represent the initial district-level carbon emissions and the district-level reference carbon emissions respectively; Y1 and Y EDGAR They represent the initial total carbon emissions at the town and street scale and the reference total carbon emissions at the town and street scale respectively. D represents the administrative district set, and E represents the town and street set.

2. The method for gridding the spatial distribution of urban carbon emissions at the hundred-meter level based on multi-source open data according to claim 1 is characterized in that: The target city multi-dimensional information index also includes a 100-meter grid natural environment index. The method for extracting the 100-meter grid natural environment index includes: Based on the DEM data, the average elevation of the city in each grid data is calculated and connected to the grid to obtain the 100-meter grid elevation data; Use the spatial analysis software ArcGIS to calculate the DEM data to obtain urban slope data, and use spatial analysis methods to establish a connection between grid data and urban slope data to obtain 100-meter grid slope data; The ENVI tool was used to calculate the Sentinel-2 image data to obtain the urban NDVI vegetation index and NDWI vegetation index. The grid data was connected with the urban NDVI vegetation index and NDWI vegetation index to obtain the 100-meter grid NDVI vegetation index and NDWI vegetation index. The Shannon entropy weight method was used to construct the 100-meter grid natural environment index based on the 100-meter grid elevation data, 100-meter grid slope data, 100-meter grid NDVI vegetation index and NDWI vegetation index.

3. The method for gridding the spatial distribution of urban carbon emissions at the hundred-meter level based on multi-source open data according to claim 1 is characterized in that: The target city's multi-dimensional information also includes a 100-meter grid economic business index. The method for extracting the 100-meter grid economic business index includes: Preprocess the night light data to obtain preprocessed night light data, calculate the average brightness value of the night light data based on the preprocessed night light data, and then connect the grid data to obtain 100-meter grid light data; Based on the universality and consistency of urban land classification standards and POI classification, we divided the land parcel vector boundary data into multiple types of land parcels, including public service land parcels, management land parcels, transportation land parcels, residential land parcels, commercial land parcels, recreational land parcels, and industrial land parcels. We then conducted a kernel density cluster analysis on each type of land parcel, and calculated the kernel density proportion of each type of POI within each land parcel to obtain functional land parcel data. This functional land parcel data was then connected to the grid data to obtain 100-meter grid functional land parcel data. Based on the 100-meter grid lighting data and the 100-meter grid functional plot data, the 100-meter grid economic business index is obtained.

4. The method for gridding the spatial distribution of urban carbon emissions at the hundred-meter level based on multi-source open data according to claim 1 is characterized in that: Calculating the total urban carbon emissions based on multi-source open big data of the target city includes: Based on historical yearbook data, we can obtain data on actual fossil energy consumption of different industries and types in the target city, urban electricity consumption, and total urban solid waste. Carbon emissions from fossil energy consumption are calculated based on the actual fossil energy consumption of different industries and types in the target cities. Specifically: Among them, FC i represents the consumption of the i-th type of fossil energy, CEF i represents the carbon emission factor of the i-th type of fossil energy; The carbon emissions from electricity consumption are calculated based on the city's electricity consumption, specifically: Among them, EC j represents the electricity consumption of category j; EEF represents the carbon emission factor of electricity, and m represents different categories; Among them, SW k Expressed as the total amount of solid waste of type k, SEF k represents the carbon emission factor of the kth type of solid waste, and p represents the total number of solid waste categories; The total carbon emissions of the city are: C=C1+C2+C3 Where C represents the total carbon emissions of the city.

5. The method for gridding the spatial distribution of urban carbon emissions at the hundred-meter level based on multi-source open data according to claim 1 is characterized in that: Obtaining the district-level reference carbon emissions and town-level reference carbon emissions includes: Obtaining district-level reference carbon emissions includes: Based on historical yearbook data, we obtained socioeconomic structure data and district-level carbon emissions data for recent years. The socioeconomic structure data included historical population, GDP, total energy consumption of enterprises above a certain scale, and the number of listed companies in different industries in each district. We established a multivariate linear regression model based on the socioeconomic structure data and district-level carbon emissions data, with the socioeconomic structure data as the independent variable and the district-level carbon emissions as the dependent variable. Specifically, the model is as follows: Y=β0+β1X1+β2X2+…+β n X n +e Where X is the total carbon emissions at the district level; X1, X2,…, X n They represent the historical population, GDP, total energy consumption of enterprises above designated size, and the number of listed companies in different industries in each district; β0, β1,…, β n is the parameter to be estimated, ε is the error term; Substitute recent socioeconomic structure data and district-level carbon emissions into the multiple linear regression model to calculate the parameters to be estimated; The population, GDP, energy consumption of enterprises above designated size, and the number of listed companies in different industries in each district in the reference year were obtained and input into the multivariate linear regression model to obtain the district-level reference carbon emissions. Obtaining the total reference carbon emissions at the town and street level includes: The EDGAR carbon emission grid dataset of the reference year was obtained and calibrated for the total urban carbon emissions to obtain the corrected EDGAR carbon emission dataset. Based on the corrected EDGAR carbon emission dataset, the total carbon emission data at the town and street scale were statistically analyzed to obtain the town and street scale reference carbon emissions.

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