Regional total carbon emission spatialization method in combination with multiple constraint factors

Abstract

The invention relates to a regional total carbon emission spatialization method in combination with multiple constraint factors, which comprises the following steps: step 1, acquiring statistical data related to carbon emission in a to-be-detected region, and accounting different types of carbon emission in the to-be-detected region according to the statistical data; step 2, spatializing different types of carbon emissions in the to-be-detected area to obtain spatialization results of different types of carbon emissions; 3, according to the different types of carbon emission accounting results, the different types of carbon emission spatialization results are corrected, and the corrected results are the final different types of carbon emission spatialization results; and step 4, summing all different types of final carbon emission spatialization results in each grid to obtain a total carbon emission spatialization result of each grid in the to-be-detected area. According to the method, the error accumulation of the carbon emission in the distribution process is reduced, and meanwhile, the accuracy of carbon emission accounting and the reasonability of spatial distribution are also improved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban research, and particularly relates to a method for spatializing the total regional carbon emissions by combining multiple constraint factors. Background Art

[0002] In the existing methods for spatializing carbon emissions in urban spaces, most are based on statistical data to spatialize the carbon emissions of a single source, or use a single factor or multiple factors to spatialize the regional carbon emissions.

[0003] In the prior art, when spatializing the carbon emissions of a single source calculated based on statistical data, the provincial and municipal-level statistical data are used to calculate the total carbon emissions of various types of land uses such as agriculture, residential life, and transportation road networks within the region. Through data such as land use types, road network vectors, or population density, the carbon emissions are distributed on each pixel or administrative division vector. Although the data acquisition difficulty is small and the calculation method is simple in this method, the accuracy of carbon emission spatialization depends on the accuracy of the constraint data. Since this method only considers the carbon emissions of a single source and does not make a detailed classification for different types (such as residential land, industrial land, commercial service land, transportation land, etc.), it will cause the distortion of the carbon emission spatialization results, and there is no unified measurement standard between different carbon sources, making it difficult to explore the overall spatial distribution of carbon emissions within the region.

[0004] In the prior art, when spatializing the regional carbon emissions using a single factor or multiple factors, it means that after calculating the total carbon emissions of multiple sources at the municipal level, the total carbon emissions are distributed on each pixel or patch through one or more of many factors such as night light data, GDP, population density, and normalized difference vegetation index. Although this method comprehensively considers the influence of various constraint factors on regional carbon emissions, since all types of carbon emissions are considered the same constraint factors during the process of carbon emission spatialization, this will greatly affect the rationality of the carbon emission spatial distribution. Therefore, it is necessary to further improve the prior art. Summary of the Invention

[0005] The technical problem to be solved by the present invention is directed to the above-mentioned prior art, and provides a method for spatializing the total regional carbon emissions by combining multiple constraint factors, which selects corresponding constraint factors according to the carbon emission differences of different types, thereby improving the rationality of the carbon emission spatial distribution.

[0006] The technical solution adopted by the present invention to solve the above technical problem is as follows: A method for spatializing the total regional carbon emissions by combining multiple constraint factors, characterized by including the following steps:

[0007] Step 1: Obtain the statistical data related to carbon emissions within the area to be measured, and calculate the carbon emissions of different types within the area to be measured according to the statistical data;

[0008] Step 2: Spatialize the carbon emissions of different types within the area to be measured to obtain the spatialization results of the carbon emissions of each different type;

[0009] Among them, the carbon emissions of different types within the area to be measured at least include the carbon emissions of industrial land and the carbon emissions of commercial land; taking one of the industrial land and commercial land as the preset type of land, the specific steps for spatializing the carbon emissions of the preset type of land are as follows:

[0010] Step 2-1: Divide the area to be measured into grids, and then screen out the grids within the range of the preset type of land within the area to be measured;

[0011] Step 2-2: Divide the area of the preset type of land into different sub-areas, calculate the average value or total of each constraint factor within each sub-area, construct a non-linear multiple regression model representing the mapping relationship between carbon emissions and each constraint factor, and substitute the average value or total of each constraint factor within each sub-area into the non-linear multiple regression model to obtain each coefficient in the non-linear multiple regression model;

[0012] Step 2-3: Calculate the average value or total of each constraint factor within the coverage range of each pixel in each grid screened out in Step 2-1, and then substitute the average value or total of each constraint factor within the coverage range of each pixel in each grid into the non-linear multiple regression model to obtain the carbon emission values of the preset type of land in each grid;

[0013] Step 3: According to the accounting results of the carbon emissions of different types in Step 1, correct the spatialization results of the carbon emissions of each different type, and the corrected results are the final spatialization results of the carbon emissions of each different type;

[0014] Step 4: Sum up the final spatialization results of the carbon emissions of all different types in each grid to obtain the spatialization results of the total carbon emissions of each grid within the area to be measured.

[0015] Preferably, in Step 1, it at least further includes obtaining remote sensing data, statistical yearbook data, and geographic national conditions within the area to be measured, where the remote sensing data at least includes night light data, normalized difference vegetation index, and land surface temperature data, and the geographic national conditions at least include industrial land area, population density, POI points of interest, and commercial land area.

[0016] Preferably, when the industrial land is used as the preset type of land, select night light data, normalized difference vegetation index, land surface temperature data, and POI points of interest as constraint factors; the POI points of interest include heavy industry POI points of interest and light industry POI points of interest;

[0017] In step 2-2, calculate the average or sum of the night lights, normalized difference vegetation index (NDVI), land surface temperature (LST), industrial land area, nuclear density of heavy industry points of interest (POIs), and nuclear density of light industry POIs for each district or county.

[0018] The calculation formula of the non-linear multiple regression model in step 2-2 is:

[0019]

[0020] where C i，2 is the carbon emissions of industrial land in the i-th area; a1 is a constant coefficient; NTL i，1 , NDVI i，1 , LST i,1 , Area i，1 , HPOI i，1 and LPOI i，1 are the averages of the night lights, NDVI, LST, industrial land area, nuclear density of heavy industry POIs, and nuclear density of light industry POIs in the i-th area, respectively. Or, NTL i，1 , NDVI i，1 , LST i,1 , Area i，1 , HPOI i，1 and LPOI i，1 are the sums of the night lights, NDVI, LST, industrial land area, nuclear density of heavy industry POIs, and nuclear density of light industry POIs in the i-th area, respectively. b1, c1, d1, e1, f1, and g1 are the constant coefficients corresponding to NTL i，1 , NDVI i，1 , LST i，1 , Area i，1 , HPOI i，1 and LPOI i，1 respectively.

[0021] Preferably, when the commercial land is of a preset type, select the night light data, NDVI, and population density as constraint factors.

[0022] In step 2-2, calculate the average or sum of the night lights, NDVI, population density, and commercial land area for each district or county.

[0023] The calculation formula of the non-linear multiple regression model in the step 2-2 is as follows:

[0024]

[0025] Wherein, C i，3 is the carbon emission of commercial land in the i-th area; a2 is a constant coefficient; NTL i，2 , NDVI i，2 , Pop i，2 and Area i，2 are the average values of night-time lights, normalized difference vegetation index, population density, and commercial land area in the i-th area, or, NTL i，2 , NDVI i，2 , Pop i，2 and Area i，2 are the sum of night-time lights, normalized difference vegetation index, population density, and commercial land area in the i-th area; b2, c2, d2, and e2 are the constant coefficients corresponding to NTL i，2 , NDVI i，2 , Pop i,2 and Area i，2 respectively.

[0026] Preferably, the carbon emissions of different types in the area to be measured in the step 2 further include the carbon emissions of residents' living. The specific process of spatializing the carbon emissions of residents' living is as follows:

[0027] Divide the area to be measured into grids, count the total population in each grid, and allocate the total carbon emissions of residents' living to each grid according to the following calculation formula;

[0028]

[0029] Wherein, C i，1 is the carbon emission of the i-th grid; Pop s.i is the population number of the i-th grid; ∑Pop s.i is the sum of the population numbers of all grids, that is, the population number in the area to be measured; C e，ir is the total carbon emissions of residents' living in the area to be measured;

[0030] If there are three-dimensional buildings in the i-th grid, the population number of the three-dimensional buildings is counted according to the following method. The specific steps are as follows:

[0031] According to the statistical yearbook data in the area to be measured, obtain the year-end resident population Pop c，mThe household registered population Pop at the end of the year in the m-th area h，m and obtain the floating population Pop in the m-th area within the area to be measured according to the following calculation formula l，m ;

[0032] Pop l，m = Pop c，m - Pop h，m

[0033] Then, based on the occupancy information in the three-dimensional building model data of the m-th area within the area to be measured, filter out the vacant rooms on each floor in the three-dimensional building model, and distribute the floating population to the vacant rooms according to the following calculation formula to obtain the total number Pop of the floating population evenly distributed in the vacant rooms in the m-th area within the area to be measured M，m ;

[0034]

[0035] wherein, Build Null，m is the total number of vacant rooms in the m-th area within the area to be measured;

[0036] Then reduce the three-dimensional building model to a two-dimensional plane, that is: count the total population of all floors in the three-dimensional building model.

[0037] Preferably, the different types of carbon emissions in the area to be measured in step 2 further include transportation carbon emissions. The specific process of spatializing the transportation carbon emissions is as follows:

[0038] Select the road network density, economic indicators related to transportation carbon emissions, environmental indicators related to transportation carbon emissions, and social indicators related to transportation carbon emissions as constraint factors, and obtain the transportation carbon emissions in each grid according to the same method in steps 2-2 to 2-3.

[0039] Preferably, the different types of carbon emissions in the area to be measured in step 2 further include agricultural carbon emissions. The specific process of spatializing the agricultural carbon emissions is as follows:

[0040] Select the cultivated land area, cultivated land planting type and planting attributes as constraint factors, use the ratio of the cultivated land area in each grid to the total cultivated land area in the area to be measured as the weight, and combine the cultivated land planting type and planting attributes to distribute the total agricultural carbon emissions to each grid to obtain the agricultural carbon emissions in each grid.

[0041] Compared with the prior art, the advantages of the present invention are as follows: This method takes into account the differences in carbon emissions of different types within the area to be measured, and spatially distributes the carbon emissions of different types within the area to be measured by combining multiple constraint factors. Therefore, this method comprehensively considers different types of carbon emissions and the influence of multiple constraint factors on carbon emissions, reduces the error accumulation in the distribution process of carbon emissions, and improves the accuracy of carbon emission accounting and the rationality of spatial distribution at the same time, making the simulation results truly reflect the spatial distribution of regional carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flowchart of the method for spatializing the total regional carbon emissions combined with multiple constraint factors in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The present invention will be further described in detail below with reference to the embodiments of the drawings.

[0044] As Figure 1 shown, the method for spatializing the total regional carbon emissions combined with multiple constraint factors in this embodiment includes the following steps:

[0045] Step 1: Obtain the statistical data related to carbon emissions within the area to be measured, and calculate the carbon emissions of different types within the area to be measured according to the statistical data;

[0046] In this embodiment, it at least further includes obtaining remote sensing data, statistical yearbook data and geographical conditions within the area to be measured. The remote sensing data at least includes night light data, normalized difference vegetation index and land surface temperature data. The geographical conditions at least include industrial land area, population density, POI interest points and commercial land area; the above statistical data includes data such as population, economy, industry, agriculture, animal husbandry, energy, transportation, etc.;

[0047] Step 2: Spatialize the carbon emissions of different types within the area to be measured to obtain the spatialization results of the carbon emissions of each different type;

[0048] In this embodiment, when spatializing the carbon emissions of different types within the area to be measured, the selected constraint factors conform to the carbon emission law in the prior art, follow the natural law, and are widely suitable for the spatialization of carbon emissions in different regions;

[0049] Among them, the carbon emissions of different types within the area to be measured at least include carbon emissions from industrial land and carbon emissions from commercial land; taking one of industrial land and commercial land as the preset type of land, the specific steps for spatializing the carbon emissions of the preset type of land are as follows:

[0050] Step 2-1: Divide the area to be measured into grids, and then screen out the grids within the range of the preset type of land use in the area to be measured; in this embodiment, ArcGIS software is used to create grids of 1000m.

[0051] Step 2-2: Divide the area of the preset type of land use into different sub-areas, calculate the average value or total sum of each constraint factor in each sub-area, construct a non-linear multiple regression model representing the mapping relationship between carbon emissions and each constraint factor, and substitute the average value or total sum of each constraint factor in each sub-area into the non-linear multiple regression model to obtain each coefficient in the non-linear multiple regression model.

[0052] Step 2-3: Calculate the average value or total sum of each constraint factor within the coverage of each pixel in each grid screened out in Step 2-1, and then substitute the average value or total sum of each constraint factor within the coverage of each pixel in each grid into the non-linear multiple regression model to obtain the carbon emission value of the preset type of land use in each grid.

[0053] In addition, the carbon emissions of different types within the above-mentioned area to be measured also include carbon emissions from residents' living, transportation, and agriculture.

[0054] The specific process of spatializing the carbon emissions from residents' living is as follows:

[0055] Divide the area to be measured into grids, count the total population in each grid, and allocate the total carbon emissions from residents' living to each grid according to the following calculation formula;

[0056]

[0057] Among them, C i，1 is the carbon emissions of the i-th grid; Pop s.i is the population quantity of the i-th grid; ∑Pop s.i is the sum of the population quantities of all grids, that is, the population quantity within the area to be measured; C e，ir is the total carbon emissions from residents' living within the area to be measured.

[0058] If there are three-dimensional buildings in the i-th grid, then the population quantity of the three-dimensional buildings is counted according to the following method. The specific steps are: According to the statistical yearbook data within the area to be measured, obtain the permanent population Po pc，m at the end of the year in the m-th area within the area to be measured and the household registered population Po ph,m at the end of the year in the m-th area, and obtain the floating population Pop l，m in the m-th area within the area to be measured according to the following calculation formula;

[0059] Pop l，m = Pop c，m-Pop h，m

[0060] According to the occupancy information in the 3D building model data of the m-th area within the area to be measured, filter out the vacant rooms on each floor in the 3D building model, and allocate the floating population to the vacant rooms according to the following calculation formula to obtain the total number of floating population Pop evenly distributed in the vacant rooms of the m-th area within the area to be measured M,m ;

[0061]

[0062] where Build Null，m is the total number of vacant rooms in the m-th area within the area to be measured;

[0063] Then reduce the 3D building model to a 2D plane, that is: count the total population of all floors in the 3D building model; this total population is the sum of the household registered population and the floating population of all floors in the 3D building model;

[0064] The specific process of spatializing traffic carbon emissions is as follows:

[0065] Select the road network density, economic indicators related to traffic carbon emissions, environmental indicators related to traffic carbon emissions, and social indicators related to traffic carbon emissions as constraint factors, and obtain the traffic carbon emissions in each grid according to the same method in steps 2-2 to 2-3;

[0066] The specific process of spatializing agricultural carbon emissions is as follows:

[0067] Select the cultivated land area, cultivated land planting type and planting attributes as constraint factors, use the ratio of the cultivated land area in each grid to the total cultivated land area in the area to be measured as the weight, and combine the cultivated land planting type and planting attributes to allocate the total agricultural carbon emissions to each grid to obtain the agricultural carbon emissions in each grid;

[0068] Step 3: According to the accounting results of different types of carbon emissions in step 1, correct the spatialization results of different types of carbon emissions, and the corrected results are the final spatialization results of different types of carbon emissions;

[0069] The correction method in this embodiment is the prior art and will not be elaborated here;

[0070] Step 4: Sum up the final spatialization results of all different types of carbon emissions in each grid to obtain the spatialization result of the total carbon emissions in each grid within the area to be measured.

[0071] When industrial land is used as the preset type of land, select night-time light data, normalized difference vegetation index, land surface temperature data, and POI (Point of Interest) as constraint factors; POI includes heavy industry POI and light industry POI; in this embodiment, the night-time light data uses NPP / VIRS night-time light data (annual average), with a spatial resolution of 500 m; the normalized difference vegetation index uses MOD13Q1 data; the land surface temperature data uses MYD11A2 data, and the land surface temperature data uses the annual average land surface temperature; use the ArcGIS search tool to screen out all POIs within the vector range of industrial land, and eliminate POIs that do not belong to industrial land through keywords. Finally, classify the POIs of industrial land into heavy industry POIs and light industry POIs according to the classification criteria between the light industrial area and the heavy industrial area (i.e., the nature of the products produced by each).

[0072] In step 2-2, calculate the average value or sum of the night-time light of each district or county, the normalized difference vegetation index of each district or county, the land surface temperature of each district or county, the industrial land area of each district or county, the kernel density of heavy industry POIs of each district or county, and the kernel density of light industry POIs of each district or county.

[0073] The calculation formula of the non-linear multiple regression model in step 2-2 is as follows:

[0074]

[0075] Among them, C i，2 is the carbon emission of industrial land in the i-th region; a1 is a constant coefficient; NTL i，1 , NDVI i，1 , LST i，1 , Area i，1 , HPOI i，1 and LPOI i，1 are the average values of the night-time light, the normalized difference vegetation index, the land surface temperature, the industrial land area, the kernel density of heavy industry POIs, and the kernel density of light industry POIs in the i-th region, respectively. Or, NTL i，1 , NDVI i，1 , LST i，1 , Area i，1 , HPOI i，1 and LPOI i，1The sum of the night-time light in the \(i\)-th area, the normalized difference vegetation index in the \(i\)-th area, the land surface temperature in the \(i\)-th area, the industrial land area in the \(i\)-th area, the kernel density of heavy industry POIs in the \(i\)-th area, and the kernel density of light industry POIs in the \(i\)-th area; \(b_1\), \(c_1\), \(d_1\), \(e_1\), \(f_1\), and \(g_1\) are the constant coefficients corresponding to NTL i，1 , NDVI i，1 , LST i，1 , Area i，1 , HPOI i，1 and LPOI i，1 respectively.

[0076] In addition, when the commercial land is of a preset type, the night-time light data, the normalized difference vegetation index, and the population density are selected as constraint factors;

[0077] In step 2-2, the average value or sum of the night-time light in each district or county, the normalized difference vegetation index in each district or county, the population density in each district or county, and the commercial land area in each district or county is statistically calculated;

[0078] The calculation formula of the non-linear multiple regression model in step 2-2 is:

[0079]

[0080] where \(C\) i， is the carbon emission of commercial land in the \(i\)-th area; \(a_2\) is a constant coefficient; NTL i，2 , NDVI i，2 , Pop i,2 and Area i,2 are the average values of the night-time light in the \(i\)-th area, the normalized difference vegetation index in the \(i\)-th area, the population density in the \(i\)-th area, and the commercial land area in the \(i\)-th area, or, NTL i，2 , NDVI i，2 , Pop i，2 and Area i，2 are the sums of the night-time light in the \(i\)-th area, the normalized difference vegetation index in the \(i\)-th area, the population density in the \(i\)-th area, and the commercial land area in the \(i\)-th area; \(b_2\), \(c_2\), \(d_2\), and \(e_2\) are the constant coefficients corresponding to NTL i，2 , NDVI i，2 , Pop i，2 and Area i,2 respectively.

[0081] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and refinements can still be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for spatializing the total regional carbon emissions combined with multiple constraint factors, characterized in that It includes the following steps: Step 1: Obtain the statistical data related to carbon emissions in the area to be measured, and calculate the carbon emissions of different types in the area to be measured according to the statistical data; Step 2: Spatialize the carbon emissions of different types in the area to be measured to obtain the spatialization results of the carbon emissions of each different type; Among them, the carbon emissions of different types in the area to be measured at least include the carbon emissions of industrial land and commercial land; taking one of industrial land and commercial land as the preset type of land, the specific steps for spatializing the carbon emissions of the preset type of land are as follows: Step 2-1: Divide the area to be measured into grids, and then screen out the grids within the scope of the preset type of land in the area to be measured; Step 2-2: Divide the area of the preset type of land into different sub-areas, statistically calculate the average value or total of each constraint factor in each sub-area, construct a non-linear multiple regression model representing the mapping relationship between carbon emissions and each constraint factor, and substitute the average value or total of each constraint factor in each sub-area into the non-linear multiple regression model to obtain each coefficient in the non-linear multiple regression model; Step 2-3: Statistically calculate the average value or total of each constraint factor within the coverage of each pixel in each grid screened out in Step 2-1, and then substitute the average value or total of each constraint factor within the coverage of each pixel in each grid into the non-linear multiple regression model to obtain the carbon emission values of the preset type of land in each grid; Step 3: According to the calculation results of the carbon emissions of different types in Step 1, correct the spatialization results of the carbon emissions of each different type, and the corrected results are the final spatialization results of the carbon emissions of each different type; Step 4: Sum up the final spatialization results of the carbon emissions of all different types in each grid to obtain the spatialization results of the total carbon emissions of each grid in the area to be measured.

2. The method according to claim 1, wherein: In Step 1, it at least further includes obtaining remote sensing data, statistical yearbook data and geographical conditions in the area to be measured, where the remote sensing data at least includes night light data, normalized difference vegetation index and land surface temperature data, and the geographical conditions at least include industrial land area, population density, POI points of interest and commercial land area.

3. The method according to claim 2, wherein: When industrial land is used as the preset type of land, select night light data, normalized difference vegetation index, land surface temperature data and POI points of interest as constraint factors; the POI points of interest include heavy industry POI points of interest and light industry POI points of interest; In Step 2-2, statistically calculate the average value or total of the night lights of each district and county, the normalized difference vegetation index of each district and county, the land surface temperature of each district and county, the industrial land area of each district and county, the kernel density of heavy industry POI points of interest of each district and county, and the kernel density of light industry POI points of interest of each district and county; The calculation formula of the non-linear multiple regression model in Step 2-2 is: Among them, C i,2 is the carbon emissions of industrial land in the i-th area; a1 is a constant coefficient; NTL i,1 , NDVI i,1 , LST i,1 , Area i,1 , HPOI i,1 and LPOI i,1 are the averages of the night lights, the normalized difference vegetation index, the land surface temperature, the area of industrial land, the nuclear density of heavy industry POI interest points, and the nuclear density of light industry POI interest points in the i-th area, respectively. Or, NTL i,1 , NDVI i,1 , LST i,1 , Area i,1 , HPOI i,1 and LPOI i,1 are the sums of the night lights, the normalized difference vegetation index, the land surface temperature, the area of industrial land, the nuclear density of heavy industry POI interest points, and the nuclear density of light industry POI interest points in the i-th area, respectively; b1, c1, d1, e1, f1, and g1 are the constant coefficients corresponding to NTL i,1 , NDVI i,1 , LST i,1 , Area i,1 , HPOI i,1 and LPOI i,1 respectively.

4. The method according to claim 2, wherein: When commercial land is used as the preset type of land, select night light data, normalized difference vegetation index and population density as constraint factors; In step 2-2, calculate the average value or sum of the night lights, normalized difference vegetation index (NDVI), population density, and commercial land area of each district or county. The calculation formula of the non-linear multiple regression model in step 2-2 is as follows: Among them, C i,3 is the carbon emission of commercial land in the i-th area; a2 is a constant coefficient; NTL i,2 , NDVI i,2 , Pop i,2 and Area i,2 are the average values of night-time light, normalized difference vegetation index, population density, and commercial land area in the i-th area, or, NTL i,2 , NDVI i,2 , Pop i,2 and Area i,2 are the sums of night-time light, normalized difference vegetation index, population density, and commercial land area in the i-th area; b2, c2, d2, and e2 are the constant coefficients corresponding to NTL i,2 , NDVI i,2 , Pop i,2 and Area i,2 respectively.

5. The method according to any one of claims 2 to 4, characterized in that: In step 2, the different types of carbon emissions in the area to be measured also include residential carbon emissions. The specific process of spatializing residential carbon emissions is as follows: divide the area to be measured into grids, count the total population in each grid, and allocate the total residential carbon emissions to each grid according to the following calculation formula. Among them, C i,1 is the carbon emission of the i-th grid; Pop s.i is the population of the i-th grid; ∑Pop s.i is the sum of the populations of all grids, that is, the population in the area to be measured; C e,ir is the total carbon emission of residents' living in the area to be measured; If there is a three-dimensional building in the i-th grid, the population of the three-dimensional building is counted according to the following method. The specific steps are as follows: According to the statistical yearbook data in the area to be measured, obtain the permanent population Pop at the end of the year in the m-th area in the area to be measured c,m and the household registered population Pop at the end of the year in the m-th area h,m , and obtain the floating population Pop in the m-th area in the area to be measured according to the following calculation formula l,m ; Pop l,m = Pop c,m - Pop h,m Then, according to the occupancy information in the 3D building model data of the m-th area in the area to be measured, the vacant rooms on each floor in the 3D building model are screened out, and the floating population is allocated to the vacant rooms according to the following calculation formula, so as to obtain the total number of floating population Pop evenly distributed in the vacant rooms of the m-th area in the area to be measured M,m ; Among them, Build Null,m is the total number of vacant rooms in the m-th area within the area to be measured; Then, reduce the three-dimensional building model to a two-dimensional plane, that is, count the total population of all floors in the three-dimensional building model.

6. The method according to claim 5, characterized in that: In step 2, the different types of carbon emissions in the area to be measured also include transportation carbon emissions. The specific process of spatializing transportation carbon emissions is as follows: Select the road network density, economic indicators related to transportation carbon emissions, environmental indicators related to transportation carbon emissions, and social indicators related to transportation carbon emissions as constraint factors, and obtain the transportation carbon emissions in each grid by the same method as in steps 2-2 to 2-3.

7. The method according to claim 6, characterized in that: In step 2, the different types of carbon emissions in the area to be measured also include agricultural carbon emissions. The specific process of spatializing agricultural carbon emissions is as follows: Select the cultivated land area, cultivated land planting type, and planting attributes as constraint factors. Using the ratio of the cultivated land area in each grid to the total cultivated land area in the area to be measured as the weight, and combining the cultivated land planting type and planting attributes, allocate the total agricultural carbon emissions to each grid to obtain the agricultural carbon emissions in each grid.

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