High-precision spatial carbon emission accounting and three-dimensional presentation method

Abstract

The present invention discloses a high-precision spatial carbon emission accounting and three-dimensional presentation method, which belongs to the field of carbon emission technology. The method of the present invention comprises: step 1: carbon emission accounting by department; step 2: establishing a corresponding relationship between carbon emissions and land use types; step 3: carbon emission spatial allocation technology development and carbon emission space identification; step 4: surface area technology based on digital elevation and spatial carbon emission intensity algorithm optimization; step 5: three-dimensional spatial carbon emission presentation. The present invention takes land use as the carrying space, and puts the carbon emissions of different departments into different land use spaces, and integrates multi-source big data such as building volume, high-precision night light, and corporate information, and performs spatial secondary distribution of carbon emission intensity, so that the spatial accuracy of carbon emissions has been greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emissions, and specifically relates to a high-precision spatial carbon emissions accounting and three-dimensional presentation method. Background Art

[0002] The existing research on carbon emission space is mainly explored at different scales.

[0003] (1) Global scale

[0004] Globally, the Carbon Dioxide Information Analysis Center (CDIAC) provides spatial grid products with a spatial accuracy of 1°×1° (CDIAC, 2020). The Emissions Database for Global Atmospheric Research (EDGAR) is based on the IEA's emission point source database and global energy statistics, and combines global population distribution, land use and other data to establish an industry-specific annual global greenhouse gas emissions spatial grid dataset with an accuracy of 0.1°×0.1° (EDGAR, 2020). Based on different data sources, some scholars have also carried out simulation studies on the spatial distribution of anthropogenic carbon emissions carried by construction land around the world, such as: based on population density and GDP level (Olivier et al., 2005), based on night light remote sensing data (Oda and Maksyutov, 2011; Rayner et al., 2010).

[0005] (2) Different scales in China

[0006] In recent years, domestic scholars have been continuously conducting spatial research on carbon emissions, and its accuracy is constantly improving. Based on the perspective of land use types, relevant scholars have established the corresponding relationship between energy consumption carbon emissions and different land use types, and attributed various types of energy carbon emissions to specific land use types. Based on DMSP / OLS night light remote sensing image data, Su Yongxian et al. (2013) conducted a spatial simulation study on my country's carbon emissions from 1992 to 2010 by establishing a relationship equation between DMSP / OLS light values ​​and carbon emission statistics; Liu Yongwei and Yan Qingwu (2015) selected NDVI vegetation index, DMSP / OLS night light data, GDP, industrial output value, and population as five indicators to simulate and generate 5km×5km grid carbon emission data products for China in 2000 and 2010; Tsinghua University developed a model for China's anthropogenic source emissions of atmospheric pollutants and greenhouse gases based on a cloud computing platform - the China Multi-resolution Emission Inventory Model (Multi-resolution Emission Inventory Model). The model uses a bottom-up approach to obtain multi-source emission data, and calculates air pollutants or carbon emissions by combining parameters such as energy usage, technology level, and removal efficiency of emission sources (Liu et al., 2015). Then, the point source data is converted into high-precision raster data through a spatial allocation method (Qi et al., 2017; Zheng et al., 2014). The dataset covers the emission data of 10 kinds of air pollutants and greenhouse gases, and the spatial resolution of the data is divided into three types: 0.25°, 0.5°, and 1°. Cai Bofeng and his research team used multi-source data and combined it with the first national pollution source census data to generate the national scale 10km (Wang Jinnan et al., 2014) and 1km greenhouse gas spatial emission dataset High Resolution Emission Database (CHRED) (Cai et al., 2018a). Based on this data and research method, some scholars have also conducted comparative studies on regional differences (Cai et al., 2018b; Hao et al., 2016).

[0007] At present, the research on spatial simulation of carbon emissions lacks data support, the methods are relatively simple, and the research accuracy is relatively rough, which is not enough to provide accurate reference for refined low-carbon space management. For the same land use type, multi-scale big data and information need to be improved, and the accuracy of spatial simulation needs to be further improved.

[0008] Different land types are the basis for conducting spatial verification of carbon emissions, but current research still lacks land use data with high-precision spatial accuracy and detailed land classification support, such as different types of land, such as residential land, industrial land, and commercial land.

[0009] In terms of the spatial presentation of carbon emissions, existing technologies are based on two-dimensional presentations of carbon emission intensity per unit area of ​​land on a two-dimensional plane. There is still a lack of relevant technical support for three-dimensional expressions based on terrain undulations and building heights. Summary of the invention

[0010] Technical problem solved by the present invention: The present invention proposes a high-precision spatial carbon emission accounting and three-dimensional presentation method. Aiming at the multi-source heterogeneity of spatial accounting of anthropogenic carbon emissions, the present invention develops land use carbon emission accounting and multi-scale data fusion technology based on data information heterogeneity and land use function differences. Based on technical support, multi-source data is integrated to carry out high-precision simulation of spatial carbon emissions. On this basis, based on 12.5-meter digital elevation DEM data and vectorized building height data, a three-dimensional spatial presentation technology that integrates terrain and buildings, as well as a technology that integrates with spatial carbon emissions, is designed to carry out technical innovations in spatial bearing strength algorithms and presentation forms.

[0011] Technical solution: In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0012] A high-precision spatial carbon emission accounting and three-dimensional presentation method includes the following steps:

[0013] Step 1: Carbon emissions accounting by sector;

[0014] Step 2: Establish the corresponding relationship between carbon emissions and land use types;

[0015] Step 3: Development of carbon emission space allocation technology and identification of carbon emission space;

[0016] Step 4: Surface area technology based on digital elevation and spatial carbon emission intensity algorithm optimization;

[0017] Step 5: Three-dimensional spatial carbon emissions presentation.

[0018] Preferably, in step 1, the specific contents of the carbon emission accounting by sector are as follows:

[0019] First, we will identify the sectors including agricultural production, industry, urban residents’ life, rural residents’ life, commercial services and transportation;

[0020] Secondly, determine the main sources of carbon emissions in different departments, and use the carbon emission coefficient method to calculate carbon emissions from different sources. The method is as follows:

[0021]

[0022] Among them, CE sector represents the total carbon emissions of the department; T jrepresents the energy consumption, resource consumption, waste treatment or total product volume corresponding to the jth evaluation indicator; σ j Represents the carbon emission coefficient corresponding to the j-th evaluation indicator.

[0023] Preferably, in step 2, the specific contents of establishing the correspondence between carbon emissions and land use types are:

[0024] Carbon emissions from different sectors are allocated to cultivated land, industrial land, urban residential land, rural settlement land, commercial and service land, and transportation land.

[0025] Preferably, in step 3, the specific contents of carbon emission space allocation indicators and allocation technology development are:

[0026] Based on the decomposition results of step 2, secondary carbon emission allocation is carried out spatially for different land use types:

[0027] Identification process of spatial intensity of cultivated land carbon emissions:

[0028] The smallest administrative unit for obtaining agricultural carbon emission data is the basic unit, and its average carbon emission intensity is calculated as follows:

[0029]

[0030] Where: C i is the average spatial carbon emission intensity of the smallest administrative unit i in the study area for which agricultural carbon data can be obtained, t j is the total energy consumption or agricultural resource use of the jth agricultural production; c j is the carbon emission coefficient of the jth agricultural production energy consumption or agricultural resource use; A i is the total area of ​​cultivated land within administrative unit i;

[0031] The process of identifying the spatial intensity of carbon emissions from industrial land is as follows:

[0032] Based on the energy consumption and industrial product statistics of the regional statistical yearbook, the energy consumption data and output value data of the main industries in the national economic industry classification can be obtained. According to the carbon emission coefficient method, the total carbon emissions of different industries are calculated, specifically:

[0033] I i =m i ×c i ,

[0034] Where: I i is the total carbon emissions of industrial sector i; m i and c i are the energy consumption and carbon emission coefficient of industrial sector i respectively.

[0035] As a preferred method, the process of identifying the spatial intensity of carbon emissions from urban residential land is as follows:

[0036] The carbon emissions of urban residential land are allocated to each grid according to the proportion of the building volume of each urban residential land grid in the region to the total building volume of the region:

[0037] U i =EC U ×WS i ,

[0038]

[0039] B i =BH i ×S i ,

[0040] Where: U i is the carbon emission of urban residential land unit i, EC U is the carbon emission of regional urban residential land, WS i is the carbon emission weight of urban residential land unit i in the region, B i is the building volume of land unit i, and there are n unit grids in the area; BH i represents the number of floors in building i, S i Represents the floor area of ​​building i.

[0041] As a preferred method, the spatial intensity identification process of carbon emissions from rural settlements is as follows:

[0042] The carbon emissions of rural residents’ lives are distributed according to the proportion of the population distribution value of each rural residential land use grid in the region:

[0043] R i =EC R ×WC i ,

[0044]

[0045] Where: R i is the carbon emission of rural residential land unit i, EC R is the total carbon emissions of rural residents in the region, WC i is the carbon emission weight of rural residents’ life in rural residential land unit i in the region, P i is the population distribution value of rural residential land unit i, and there are n unit cells in the region.

[0046] As a preferred method, the process of identifying the spatial intensity of carbon emissions in commercial and service areas is as follows:

[0047] Carbon emissions from urban residential land are distributed according to the proportion of the building volume of each commercial and service grid in the region to the total building volume of the region:

[0048] F i =F×WF i ,

[0049]

[0050] f i =fH i ×S i ,

[0051] Where: F i is the carbon emission of commercial and service land unit i, F is the carbon emission of regional commercial and service land, WF i is the carbon emission weight of commercial and service land unit i in the region, f i is the building volume of land unit i, and there are n unit grids in the area; fH i Indicates the height of the floor, S i Represents the floor area of ​​building i.

[0052] As a preferred method, the spatial intensity identification process of carbon emissions from transportation land is as follows:

[0053] Distribute transportation carbon emissions according to the proportion of nighttime light values ​​of each transportation land grid in the region;

[0054] T i =EC T ×WT i ,

[0055]

[0056] Where: T i is the carbon emission of transportation land unit i, EC T is the total regional transportation carbon emissions, WT i is the transportation carbon emission weight of transportation land unit i in the region, DN i is the night light value of transportation land unit i, and there are n unit cells in the area.

[0057] As a preference, in step 4, the specific contents of the surface area technology based on digital elevation and the spatial carbon emission intensity algorithm optimization are as follows:

[0058] First, the DEM data is used as the terrain background data. The slope value of each grid can be obtained from the DEM data. According to the slope value and the surface area calculation model, the surface area of ​​each grid after considering the terrain undulation is obtained:

[0059] Surface Area = C 2 / Cos(S×(π / 180)),

[0060] C=Cell Size,

[0061] S=Slope in Degrees,

[0062] Among them, Surface Area represents the surface area, C represents the land use grid area, and S represents the slope value;

[0063] Secondly, divide the surface area grid layer by the two-dimensional plane area to obtain the surface area and two-dimensional plane layer comparison coefficient layer, specifically:

[0064]

[0065] Among them, a represents the surface area and two-dimensional plane area comparison coefficient layer, and Surface and planimetric represent the surface area and plane area respectively.

[0066] Preferably, based on the spatial carbon emission intensity per unit land use in step 3 and the acquisition of the surface area and area coefficient layer a, the spatial carbon emission intensity per unit land use based on the surface area is calculated, specifically:

[0067]

[0068] Among them, C surface Represents a three-dimensional carbon emission intensity layer based on terrain undulation, C1....C n represents the carbon emission intensity layer of different land use spaces formed in step 3, and Mosaic represents the layer mosaic command in ArcGIS software;

[0069] For key areas with a greater impact from building height, carbon emissions are decomposed in three dimensions based on building height. The specific algorithm is as follows:

[0070]

[0071] in, represents the average carbon emission intensity of each floor of a building i; BH represents the carbon emission intensity per unit land area of ​​a building i based on the terrain surface area; i Indicates the floor height of a building.

[0072] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0073] (1) The main problem with existing technologies for simulating spatial carbon emissions is the relatively low spatial accuracy. Whether it is simulation using remote sensing auxiliary data or inversion research on carbon concentration data, the error is relatively large. At present, there is a lack of high-precision spatial carbon emission monitoring technology in China. The present invention sets up a spatial allocation plan for carbon emissions based on high-precision land use data, building volume data, enterprise data, high-precision night light data, etc., to improve the accuracy of its spatial monitoring.

[0074] (2) The present invention is designed to use land use as the carrier space. First, the carbon emissions of different departments are placed in different land use spaces. On this basis, the multi-source big data such as building volume, high-precision night light, and corporate information are integrated to perform spatial secondary distribution of carbon emission intensity, thereby greatly improving the spatial accuracy of carbon emissions and forming a carbon emission spatial distribution layer with multiple spatial resolutions. In particular, within the urban area, based on the support of building outlines and floor height data, the spatial accuracy of urban carbon emissions can reach the vector level.

[0075] (3) The present invention fully considers the impact of terrain undulation on spatial carbon emission intensity and spatial distribution. Based on the first step of carbon emission spatial distribution simulation, high-resolution DEM data, and vectorized building height data, the intensity of carbon emissions is further corrected in three-dimensional space. Finally, through spatial display technology, the spatial distribution of carbon emissions is vividly displayed in three-dimensional space. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is the overall technical route framework diagram of the high-precision spatial carbon emission accounting and three-dimensional presentation method of the present invention;

[0077] Figure 2 This is the spatial distribution map of carbon emissions in Beijing in 2020. DETAILED DESCRIPTION

[0078] The present invention is further illustrated below in conjunction with specific examples. The examples are implemented based on the technical solutions of the present invention. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0079] The present invention takes Beijing as an example to simulate the spatial distribution of anthropogenic carbon emissions.

[0080] like Figure 1-2 As shown, the high-precision spatial carbon emission accounting and three-dimensional presentation method provided by the present invention specifically includes the following steps:

[0081] Step 1: Carbon emissions accounting by sector;

[0082] Carry out carbon emission accounting by sector. First, determine the main production and living sectors, as shown in Table 1 below, including agricultural production, industry (carbon emissions from energy and industrial production processes), urban residents' lives, rural residents' lives, commercial services, transportation and other sectors; then determine the main carbon emission sources in different sectors, and calculate the carbon emissions in the main production and living activities according to the carbon emission coefficient method.

[0083] Table 1 Carbon emissions accounting by sector

[0084]

[0085] The carbon emission coefficient method is used to calculate carbon emissions from different sources. The method is as follows:

[0086]

[0087] Among them, CE sector represents the total carbon emissions of the department; T j represents the energy consumption, resource consumption, waste treatment or total product volume corresponding to the jth evaluation indicator; σ j Represents the carbon emission coefficient corresponding to the j-th evaluation indicator.

[0088] Step 2: Establish the corresponding relationship between carbon emissions and land use types;

[0089] Carbon emissions from different sectors are allocated to cultivated land, industrial land, urban residential land, rural settlement land, commercial and service land, and transportation land, as shown in Table 2:

[0090] Table 2 Correspondence between carbon emissions by sector and land use type

[0091] Land Type Carbon Emissions Sector arable land Agricultural carbon emissions Industrial land Industrial production process, industrial energy consumption Urban residential land Carbon emissions from urban residents’ daily life Rural settlements Carbon emissions from rural residents’ daily life Commercial and service area Energy consumption of commercial services Traffic land Transport energy consumption

[0092] Step 3: Development of carbon emission space allocation indicators and allocation technology;

[0093] Based on the decomposition results of step 2, secondary carbon emission allocation is carried out spatially for different land use types, as described below:

[0094] Identification process of spatial intensity of cultivated land carbon emissions:

[0095] Since it is very difficult to obtain detailed data on cultivated land, the smallest administrative unit for obtaining agricultural carbon emission-related data is taken as the basic unit to calculate its average carbon emission intensity.

[0096]

[0097] Where: C i is the average spatial carbon emission intensity of the smallest administrative unit i in the study area for which agricultural carbon data can be obtained, tj is the total energy consumption or agricultural resource use of the jth agricultural production; c j is the carbon emission coefficient of the jth agricultural production energy consumption or agricultural resource use; A i is the total area of ​​cultivated land within administrative unit i.

[0098] The process of identifying the spatial intensity of carbon emissions from industrial land is as follows:

[0099] A combination of "top-down" and "bottom-up" methods is adopted. First, based on the energy consumption and industrial product statistics of different industries in the regional statistical yearbook, the energy consumption data and output value data of the main industries in the national economic industry classification can be obtained. According to the carbon emission coefficient method, the total carbon emissions of different industries are calculated, specifically:

[0100] I i =m i ×c i ,

[0101] Where: I i is the total carbon emissions of industrial sector i; m i and c i are the energy consumption (main industrial products) and carbon emission coefficient of industrial sector i;

[0102] Secondly, through the industrial enterprise database, we obtain the point data of major industrial enterprises, including enterprise name, geographic coordinates, enterprise output value, enterprise floor space and other business management information. Assuming that enterprises in the same industry have similar carbon emission levels, we use the ratio of enterprise output value to industry output value to estimate the carbon emissions of the enterprise, specifically:

[0103]

[0104] In the formula, C ij represents the carbon emissions of enterprise j in industry i; P ij represents the output value of enterprise j in industry i.

[0105] Based on the enterprise land area and carbon emissions, the carbon emission intensity per unit area of ​​different enterprises is calculated to form the spatial enterprise point data emission weights; using industrial land as the spatial constraint range, the inverse distance weighted method is used for spatial interpolation to generate an industrial land carbon emission intensity map.

[0106] The process of identifying the spatial intensity of carbon emissions from urban residential land is as follows:

[0107] The carbon emissions of urban residential land are allocated to each grid according to the proportion of the building volume of each urban residential land grid in the region to the total building volume of the region:

[0108] U i =EC U×WS i ,

[0109]

[0110] B i =BH i ×S i ,

[0111] Where: U i is the carbon emission of urban residential land unit i, EC U is the carbon emission of regional urban residential land, WS i is the carbon emission weight of urban residential land unit i in the region, B i is the building volume of land unit i, and there are n unit grids in the area; BH i represents the number of floors in building i, S i Represents the floor area of ​​building i.

[0112] The process of identifying the spatial intensity of carbon emissions from rural settlements is as follows:

[0113] The carbon emissions of rural residents’ lives are distributed according to the proportion of the population distribution value of each rural residential land use grid in the region:

[0114] R i =EC R ×WC i ,

[0115]

[0116] Where: R i is the carbon emission of rural residential land unit i, EC R is the total carbon emissions of rural residents in the region, WC i is the carbon emission weight of rural residents’ life in rural residential land unit i in the region, P i is the population distribution value of rural residential land unit i, and there are n unit cells in the region.

[0117] The process of identifying the spatial intensity of carbon emissions in commercial and service areas is as follows:

[0118] Carbon emissions from urban residential land are distributed according to the proportion of the building volume of each commercial and service grid in the region to the total building volume of the region:

[0119] F i =F×WF i ,

[0120]

[0121] f i =fHi ×S i ,

[0122] Where: F i is the carbon emission of commercial and service land unit i, F is the carbon emission of regional commercial and service land, WF i is the carbon emission weight of commercial and service land unit i in the region, f i is the building volume of land unit i, and there are n unit grids in the area; fH i Indicates the height of the floor, S i Represents the floor area of ​​building i.

[0123] The process of identifying the spatial intensity of carbon emissions from transportation land is as follows:

[0124] Distribute transportation carbon emissions according to the proportion of nighttime light values ​​of each transportation land grid in the region;

[0125] T i =EC T ×WT i ,

[0126]

[0127] Where: T i is the carbon emission of transportation land unit i, EC T is the total regional transportation carbon emissions, WT i is the transportation carbon emission weight of transportation land unit i in the region, DN i is the night light value of transportation land unit i, and there are n unit cells in the area.

[0128] Step 4: The specific contents of the surface area technology based on digital elevation and the optimization of the spatial carbon emission intensity algorithm are as follows;

[0129] Based on the SRTM 12.5-meter DEM data as the terrain background data, the slope value of each grid can be obtained from the DEM data. According to the slope value, the surface area of ​​each grid after considering the terrain undulation is obtained according to the following surface area calculation model (Jenness, 2013):

[0130] Surface Area = C 2 / Cos(S×(π / 180)),

[0131] C = Cell Size (12.5 m),

[0132] S = Slope in Degrees (slope value),

[0133] Among them, Surface Area represents the surface area, C represents the area of ​​the land use grid, and S represents the slope value.

[0134] According to the above calculation process, the surface area calculation of the local study area layer can be carried out through the raster calculator in ArcGIS software to form a surface area raster layer. By dividing the surface area raster layer with the two-dimensional plane area, the surface area and two-dimensional plane layer comparison coefficient layer can be obtained, which is shown as follows:

[0135]

[0136] Among them, a represents the surface area and two-dimensional plane area comparison coefficient layer. The minimum value of a is 1, which means that the surface is flat and has no undulations. Surface and planimetric represent the surface area and plane area respectively.

[0137] Based on the spatial carbon emission intensity per unit land use in step 3 and the acquisition of the surface area and area coefficient layer a in the above calculation, the spatial carbon emission intensity per unit land use based on the surface area is calculated as follows:

[0138]

[0139] Among them, C surface Represents a three-dimensional carbon emission intensity layer based on terrain undulation, C1....C n It represents the carbon emission intensity layer of different land use spaces formed in step 3, including cultivated land, industrial land, urban residential land, rural settlement land, commercial and service land, and transportation land; Mosaic represents the layer mosaic command in ArcGIS software, and a represents the surface area and two-dimensional plane area comparison coefficient layer.

[0140] Secondly, for key areas with a greater impact from building height (mainly urban residential land and commercial and service land), carbon emissions are decomposed in three dimensions based on building height. The specific algorithm is as follows:

[0141]

[0142] in, represents the average carbon emission intensity of each floor of a building i; It represents the carbon emission intensity per unit area of ​​a building i calculated above based on the terrain surface area. The building space is flat, so its intensity value is close to the calculation of the two-dimensional plane. i Indicates the floor height of a building.

[0143] Step 5: Three-dimensional spatial carbon emissions presentation.

[0144] Firstly, the building outline Shapefile data containing floor height information, the carbon emission intensity layer based on terrain undulation and building floor height distribution, and the DEM elevation data of Beijing were imported into the ArcScene work platform of ArcGIS.

[0145] Secondly, set the properties of the DEM layer, set it to "present on the elevation values ​​obtained from the surface", and "adjust the coefficient used to convert the layer elevation values ​​to scene units";

[0146] Then, set the properties of the carbon emission intensity layer based on terrain undulation and building height distribution, set the layer stretch expression element to the layer height Height, and add it to the minimum height of each element to achieve three-dimensional carbon emission presentation.

[0147] Aiming at the multi-source heterogeneity of spatial accounting of anthropogenic carbon emissions, this paper constructs a land use carbon emissions accounting and multi-scale data fusion method system that adapts to multi-level regional space, data information heterogeneity, and land use function differences. It integrates multi-source big data such as land use type, community building volume, and industry energy consumption, designs a technical framework for simulating the spatial distribution of anthropogenic carbon emissions, and develops a carbon emissions spatial allocation plan based on land use spatial intensity.

[0148] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A high-precision spatial carbon emission accounting and three-dimensional presentation method, characterized by: The following steps are involved: Step 1: Carbon emissions accounting by sector; Step 2: Establish the corresponding relationship between carbon emissions and land use types; Step 3: Development of carbon emission space allocation technology and identification of carbon emission space; Allocate carbon emissions from different sectors to cultivated land, industrial land, urban residential land, rural settlement land, commercial and service land, and transportation land; Based on the decomposition results of step 2, secondary carbon emission allocation is carried out spatially for different land use types: Identification process of spatial intensity of cultivated land carbon emissions: Taking the smallest administrative unit for obtaining agricultural carbon emission data as the basic unit, the average carbon emission intensity is calculated as follows: Where: C i is the average spatial carbon emission intensity of the smallest administrative unit i in the study area for which agricultural carbon data can be obtained, t j is the total energy consumption or agricultural resource use of the jth agricultural production; c j is the carbon emission coefficient of the jth agricultural production energy consumption or agricultural resource use; A i is the total area of ​​cultivated land within administrative unit i; The process of identifying the spatial intensity of carbon emissions from industrial land is as follows: Based on the energy consumption and industrial product statistics of different industries in the regional statistical yearbook, the energy consumption data and output value data of the main industries in the national economic industry classification were obtained. According to the carbon emission coefficient method, the total carbon emissions of different industries were calculated, specifically: I i =m i ×c i , Where: I i is the total carbon emissions of industrial sector i; m i and c i are the energy consumption and carbon emission coefficient of industrial sector i respectively; The process of identifying the spatial intensity of carbon emissions from urban residential land is as follows: The carbon emissions of urban residential land are allocated to each grid according to the proportion of the building volume of each urban residential land grid in the region to the total building volume of the region: HER i =EC U ×WS i , B i =BH i ×S i , Where: U i is the carbon emission of urban residential land unit i, EC U is the carbon emission of regional urban residential land, WS i is the carbon emission weight of urban residential land unit i in the region, B i is the building volume of land unit i, and there are n unit grids in the area; BH i represents the number of floors in building i, S i represents the floor area of ​​building i; The process of identifying the spatial intensity of carbon emissions from rural residential land is as follows: The carbon emissions of rural residents’ lives are distributed according to the proportion of the population distribution value of each rural residential land use grid in the region: R i =EC R ×WC i , Where: R i is the carbon emission of rural residential land unit i, EC R is the total carbon emissions of rural residents in the region, WC i is the carbon emission weight of rural residents’ life in rural residential land unit i in the region, P i is the population distribution value of rural residential land unit i, and there are n unit cells in the region; The process of identifying the spatial intensity of carbon emissions in commercial and service areas is as follows: Carbon emissions from urban residential land are distributed according to the proportion of the building volume of each commercial and service grid in the region to the total building volume of the region: F i =F×WF i , f i =fH i ×S i , Where: F i is the carbon emission of commercial and service land unit i, F is the carbon emission of regional commercial and service land, WF i is the carbon emission weight of commercial and service land unit i in the region, f i is the building volume of land unit i, and there are n unit grids in the area; fH i represents the height of floor i, S i represents the floor area of ​​building i; The process of identifying the spatial intensity of carbon emissions from transportation land is as follows: Distribute transportation carbon emissions according to the proportion of nighttime light values ​​of each transportation land grid in the region; T i =EC T ×WT i Where: T i is the carbon emission of transportation land unit i, EC T is the total regional transportation carbon emissions, WT i is the transportation carbon emission weight of transportation land unit i in the region, DN i is the night light value of transportation land unit i, and there are n unit cells in the area; Step 4: Surface area technology based on digital elevation and spatial carbon emission intensity algorithm optimization; Step 5: Three-dimensional spatial carbon emissions presentation.

2. The high-precision spatial carbon emission accounting and three-dimensional presentation method according to claim 1 is characterized by: In step 1, the specific contents of carbon emissions accounting by sector are as follows: First, we will identify the sectors including agricultural production, industry, urban residents’ life, rural residents’ life, commercial services and transportation; Secondly, determine the main sources of carbon emissions in different departments, and use the carbon emission coefficient method to calculate carbon emissions from different sources. The method is as follows: Among them, CE sector represents the total carbon emissions of the department; T j Indicates the energy consumption, resource consumption, waste treatment or total product volume corresponding to the evaluation indicator; δ j Represents the carbon emission coefficient corresponding to the j-th evaluation indicator.

3. The high-precision spatial carbon emission accounting and three-dimensional presentation method according to claim 1 is characterized by: In step 4, the specific contents of the surface area technology based on digital elevation and the spatial carbon emission intensity algorithm optimization are as follows: First, the DEM data is used as the terrain background data. The slope value of each grid is obtained from the DEM data. According to the slope value and the surface area calculation model, the surface area of ​​each grid after considering the terrain undulation is obtained: Surface Area=C2 / Cos(S×(π / 180)), C=Cell Size, S=Slope in Degrees, Among them, Surface Area represents the surface area, C represents the land use grid area, and S represents the slope value; Secondly, divide the surface area grid layer by the two-dimensional plane area to obtain the surface area and two-dimensional plane layer comparison coefficient layer, specifically: Among them, a represents the surface area and two-dimensional plane area comparison coefficient layer, and Surface and planimetric represent the surface area and two-dimensional plane area respectively.

4. The high-precision spatial carbon emission accounting and three-dimensional presentation method according to claim 3 is characterized by: Based on the spatial carbon emission intensity of different departments in step 3 and the acquisition of the surface area and two-dimensional plane area comparison coefficient layer a in step 4, the spatial carbon emission intensity per unit land based on surface area is calculated as follows: Among them, C surface Represents a three-dimensional carbon emission intensity layer based on terrain undulation, C1....C n represents the carbon emission intensity layer of different land use spaces formed in step 3, and Mosaic represents the layer mosaic command in ArcGIS software; For key areas with a greater impact from building height, carbon emissions are decomposed in three dimensions based on building height. The specific algorithm is as follows: in, represents the average carbon emission intensity of each floor of a building i; BH represents the carbon emission intensity per unit land area of ​​a building i based on the terrain surface area; i Indicates the number of floors in building i.

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