A city-scale multi-source heterogeneous data fusion carbon emission spatial distribution simulation method

Abstract

The application discloses a kind of city scale-oriented multi-source heterogeneous data fusion carbon emission space distribution simulation method, solve the problem of low spatial resolution, insufficient accuracy of city carbon emission space distribution simulation estimation. Including defining city carbon emission accounting range;Collect multi-source heterogeneous data;Calculate the consumption of main energy types of seven big industry departments under city scale;The energy consumption of seven big industry departments in city is corrected;Seven big industry departments are matched with corresponding city building type, and the corresponding relationship between city industry department energy consumption and building function purpose category is established;Accounting carbon emission;Construct fine energy consumption carbon emission space distribution model, and distribute carbon emission space to each kind of single building.The present application comprehensively utilizes multi-source data, realizes the high resolution, dynamic estimation of city scale carbon emission space distribution, and overcomes the limitation that the existing method is coarse in spatial resolution and difficult to quantify the carbon emission of different energy consumption departments.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of urban fine carbon emission accounting, and particularly relates to a multi-source heterogeneous data fusion carbon emission spatial distribution simulation method for urban scale. BACKGROUND

[0002] Global climate change has become a serious challenge for mankind. Fossil fuel combustion is the main reason for exacerbating climate change, accounting for more than 75% of global greenhouse gas emissions and nearly 90% of all carbon dioxide emissions. Cities, as an important carrier of human production and life, have much higher energy consumption and carbon emission intensity than other areas, and are an important source of global carbon emissions, accounting for more than 70% of global carbon emissions. The International Energy Agency (IEA) report proposes that the global urban population will increase by at least 20% by 2050, and urban energy demand will also grow significantly. Therefore, accurate quantitative analysis of carbon emissions generated by urban energy consumption and in-depth understanding of its spatial and temporal distribution characteristics are of great significance for understanding the evolution law of urban carbon emissions, formulating effective control policies, and achieving the emission reduction target of the country and government, and promoting global response to climate change.

[0003] Due to the limitation of data acquisition, the current urban carbon emission spatial and temporal distribution simulation method in China is mainly based on carbon emission monitoring satellite data, population distribution and night light data for estimation. These methods have limitations in spatial resolution, such as night light data with a spatial resolution of only 1km; in addition, existing methods are also difficult to finely quantify and analyze the carbon emissions of different energy consumption departments (residential, industrial, commercial buildings, etc.) within the city.

[0004] In recent years, with the progress of remote sensing technology, the simulation method of urban carbon emission spatial distribution mainly relies on the total amount of urban carbon emission, combined with carbon emission monitoring satellite data, population distribution and night light data, etc., to spatialize the urban carbon emission. The method firstly calculates the total amount of carbon emission based on the urban energy consumption data, and then according to the night light intensity, the number of population and the proportion of carbon dioxide concentration in the city corresponding to the index in the city grid, the total carbon emission in the city is distributed in the spatial dimension through the weight distribution. The technology uses the characteristics of high temporal and spatial resolution of remote sensing data, and can realize the preliminary estimation of the spatial and temporal distribution of urban carbon emission. However, in terms of depicting the fine scale and the difference of carbon emission intensity of different departments in the city, the technology still has limitations. For example, the industrial enterprises such as urban power supply and heating plants are usually strong carbon emission point sources, but when the technology is applied to estimate the spatial and temporal distribution of urban carbon emission, the carbon emission of these areas may be seriously underestimated, and the carbon emission of urban residential buildings may be overestimated. The estimation deviation is caused by the lack of fine differentiation and quantitative ability of different carbon emission sources in the city in the existing method, which leads to a certain difference between the estimation result and the actual spatial distribution of carbon emission.

[0005] In view of the deficiencies of the prior art, the present application aims to provide a high temporal and spatial resolution urban carbon emission spatial and temporal distribution estimation method based on multi-source heterogeneous data fusion. SUMMARY

[0006] The present application aims to provide a multi-source heterogeneous data fusion carbon emission spatial distribution simulation method for urban scale, to solve the technical problems of low spatial resolution and insufficient accuracy in the simulation and estimation of urban carbon emission spatial distribution.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0008] The multi-source heterogeneous data fusion carbon emission spatial distribution simulation method for urban scale provided by the present application comprises the following steps:

[0009] Step 1: defining the urban carbon emission accounting range;

[0010] Step 2: collecting multi-source heterogeneous data;

[0011] Step 3: based on the provincial energy balance table, combining the social and economic indicators of different industry departments, calculating the consumption of main energy types of seven industry departments at urban scale;

[0012] Step 4: based on the building energy consumption splitting method of energy balance table, correcting the energy consumption of seven industry departments in the city;

[0013] Step 5: Match the seven major industry sectors with the corresponding city building types to establish the correspondence between city industry sector energy consumption and building function use category;

[0014] Step 6: Calculate carbon emissions based on energy consumption of different building types and energy types and carbon emission factors;

[0015] Step 7: Integrate multi-source heterogeneous data to build a refined energy consumption carbon emission spatial distribution model, distribute carbon emissions in space to various types of single buildings, and realize refined visualization of urban carbon emissions.

[0016] Further, in step 1, urban carbon emissions are divided into direct carbon emissions and indirect carbon emissions. Direct carbon emissions include physical emissions generated within the city boundary, mainly from fossil fuel combustion activities and industrial production processes. Indirect carbon emissions include carbon emissions contained in electricity and heat imported by buildings, where the heat part includes heat from cogeneration and regional boilers imported into buildings.

[0017] Further, in step 2, the multi-source heterogeneous data includes spatial data sets and non-spatial data sets. The spatial data sets include single building distribution data, community-level population census data, and POI data. The non-spatial data sets include provincial energy balance table statistical data, provincial and municipal social economic indicator data, city industrial enterprise industry directory, key pollution enterprise information, and carbon content, low heat value, and oxidation rate corresponding to energy types.

[0018] Further, in step 3, the seven major industry sectors include industry; transportation, warehousing and postal services; wholesale and retail trade, accommodation and catering; others; urban resident life; rural resident life; thermal power generation; and the calculation formula for energy type consumption is as follows:

[0019] (1);

[0020] In the formula, AD represents energy consumption, i represents different industry types, and A represents different industry selected statistical indicators.

[0021] Further, in step 4, the method for correcting the energy consumption of the seven major industry sectors in the city is as follows:

[0022] Determine the building energy consumption base;

[0023] Remove non-building energy consumption from the building energy consumption base;

[0024] Supplement the building energy consumption contained in the transportation sector.

[0025] Further, in step 6, the carbon emissions of different building types and energy types are calculated as follows:

[0026] (1) Calculate the carbon emission factor based on the low calorific value of the selected energy type, the carbon content per unit energy value and the oxidation rate, and the specific calculation formula is as follows:

[0027] (2);

[0028] Wherein, I is the carbon emission factor, j is the energy type, L is the low calorific value of energy, P is the carbon content per unit energy value (kJ / m 3 or kJ / kg), O is the oxidation rate in the energy combustion process (%), and 44 / 12 is the conversion coefficient of carbon to carbon dioxide;

[0029] (2) Calculate the carbon emission of each building type based on the energy consumption of different building types and the carbon emission factor of energy type, and the specific calculation formula is as follows:

[0030] (3);

[0031] Wherein, E is the total carbon emission of human activity energy consumption, m is different building types, j is energy type, C is energy consumption physical quantity, and I is carbon emission factor.

[0032] Further, in step 7, the specific steps include:

[0033] According to the correspondence between the seven major industry departments of the city and the building types, the calculated carbon emissions are divided into three categories: residential building carbon emissions, third industry building carbon emissions and industrial enterprise carbon emissions;

[0034] Considering different factors affecting the three carbon emission fields, select spatial distribution index for each type of carbon emission, and build a refined energy consumption carbon emission spatial distribution model;

[0035] Apply the refined energy consumption carbon emission spatial distribution model to accurately calculate and spatially distribute the carbon emissions of residential buildings, third industry buildings and industrial enterprises.

[0036] Further, the calculation steps of the residential building carbon emission are as follows:

[0037] Step 711: Screen the plots whose building types belong to urban residential buildings and rural residential buildings in the city single building distribution data, and identify the population census blocks containing these plots;

[0038] Step 712: According to the weight of the number of population in each population census block in the total number of population in the city, distribute the corrected annual carbon emissions of urban and rural residents to the corresponding population census block, and obtain the community-level residential carbon emissions, and the specific calculation formula is as follows:

[0039] (4);

[0040] Ei = ∑j∑kEijk i Ei represents the total carbon emissions of the i-th industry sector; E com(i,j) Eijk represents the carbon emissions in the j-th community within the i-th industry sector; P com(i,j) Pij represents the population in the j-th community within the i-th industry sector; P i Pi represents the total population of the i-th industry sector;

[0041] Step 713: In each population census block, through spatial overlay analysis, further identify the urban residential buildings and rural residential buildings in the block, and calculate the building volume weight of each building in the total building volume of the block;

[0042] Step 714: Based on the calculated weight, further distribute the block-level residential carbon emissions to each single building in the block, realize the fine spatial distribution simulation of residential carbon emissions at the single building level, and the specific calculation formula is as follows:

[0043] (5);

[0044] Eijk = EijkVijk / Vij build(i,j,k) Eijk represents the carbon emissions of the k-th residential building in the j-th community within the i-th industry sector; V build(i,j,k) Vijk represents the volume of the k-th residential building in the j-th community within the i-th industry sector; V com(i,j) Vij represents the total volume of residential buildings in the j-th community within the i-th industry sector.

[0045] Further, the calculation steps of the third industry building carbon emissions are as follows:

[0046] Step 721: Determine the carbon emission sources of the third industry;

[0047] Step 722: Select the corresponding carbon emissions;

[0048] Step 723: Establish the corresponding relationship between the energy consumption department and the building category;

[0049] Step 724: Calculate the carbon emissions of single buildings, in each building category, calculate the weight value of single building volume in the total building volume of the category, and according to the weight value, distribute the carbon emissions of the building category to each single building, to obtain the carbon emissions of single building, generate the spatial distribution result of single building carbon emissions of the third industry, and the specific calculation formula is as follows:

[0050] (6);

[0051] E build(m,k) represents the carbon emissions of the kth building in the mth industry sector; V build(m,k) represents the volume of the kth building in the mth industry sector; E m represents the total carbon emissions of the mth industry sector; V m represents the total building volume of the mth industry sector.

[0052] Further, the calculation steps of the carbon emissions of the industrial enterprises are as follows:

[0053] Step 731: Carbon emission accounting of key emission industrial enterprises; based on the spatial distribution map of single buildings and the geographic information of thermal power plants, identify the building plots in the spatial distribution map of single buildings that belong to thermal power plants; based on the calculated carbon emissions of thermal power plants, combined with the installed capacity information of thermal power plants in the city; calculate the weight of the installed capacity of each thermal power plant in the total installed capacity of the city, and distribute the carbon emissions of thermal power plants to individual thermal power plants according to the weight, the specific calculation formula is as follows:

[0054] (7);

[0055] E n represents the carbon emissions of the nth thermal power plant; E powerplant represents the carbon emissions of the city's thermal power sector; C n represents the installed capacity of the nth thermal power plant; C total represents the sum of the total installed capacity of the city's thermal power plants;

[0056] Step 732: Spatial distribution of carbon emissions of other industrial enterprises;

[0057] Using POI data and industry directory of industrial enterprises, according to the enterprise name matching, get the name, geographic information and industry information of industrial enterprises;

[0058] According to the geographic information of industrial enterprises, determine the building plots of industrial and mining buildings, and according to the industry information, determine the industry of the building plots of industrial and mining buildings;

[0059] Calculate the ratio of energy consumption intensity of each industry to the total energy consumption intensity of industry, and calculate the carbon emissions of each industry of industrial enterprises according to the ratio;

[0060] Assuming that the building area of the building plot where the industrial enterprise is located can reflect the size of the enterprise, calculate the weight of the building volume of each industrial enterprise in the total building volume of the industry;

[0061] According to the industry carbon emissions and building volume weight, distribute the carbon emissions of industrial enterprises to individual industrial enterprises to obtain the carbon emissions of individual industrial enterprises, the specific calculation formula is as follows:

[0062] ;

[0063] ;

[0064] E = ∑ (R a * E a) (1) ind,a represents the total carbon emissions of the a-th industry in the industrial enterprise; R ind,a represents the energy consumption intensity of the a-th industry in the industrial enterprise; E ind represents the total carbon emissions of the urban industrial sector; R ind represents the total energy consumption intensity of the urban industrial sector; E ind(a,b) represents the total carbon emissions of the b-th industrial enterprise in the a-th industry in the industrial enterprise; V ind(a,b) represents the building volume of the b-th industrial enterprise in the a-th industry in the industrial enterprise; V ind,a represents the total building volume of the a-th industry in the industrial enterprise.

[0065] Based on the above technical scheme, the embodiments of the present application can at least produce the following technical effects:

[0066] (1) High degree of refinement. The present application uses multi-source data to calculate the carbon emissions of different industry sectors in the city in detail, and constructs a refined energy consumption carbon emission spatial distribution model for different industry sectors, realizing high-precision simulation of the spatial distribution of urban carbon emissions, and making up for the lack of internal fine scale of existing methods. It provides data support for the next step of urban refined carbon source and sink accounting and dynamic spatio-temporal simulation of atmospheric CO2.

[0067] (2) Consider the difference between industries. The present application fully considers the difference in carbon emission intensity of different industry sectors, and constructs a targeted spatial distribution model for the carbon emission influencing factors of each industry sector, improving the accuracy and reliability of the carbon emission spatial distribution simulation, overcoming the shortcomings of existing methods that do not consider the difference between industries.

[0068] (3) Wide applicability. The method provided by the present application can be applied to different types and scales of cities, and by adjusting the data sources and model parameters, the refined simulation of the spatial distribution of carbon emissions in different cities can be realized, and it has wide applicability and promotional value. BRIEF DESCRIPTION OF DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from the structures shown in these drawings without creative labor.

[0070] Figure 1 is a technical roadmap of the embodiment of the present application;

[0071] Figure 2 is a city fine total carbon emission space distribution simulation result map of the present application;

[0072] Figure 3 is a city resident life, industrial and tertiary industry fine carbon emission space distribution simulation result map of the present application. DETAILED DESCRIPTION

[0073] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the protection scope of the present application. In addition, the technical solutions of various embodiments can be combined with each other, but it must be based on that the combination of technical solutions can be realized by those skilled in the art. When the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, and is not within the protection scope of the present application.

[0074] As shown in Figure 1 , a city-scale multi-source heterogeneous data fusion carbon emission space distribution simulation method comprises the following steps:

[0075] Step 1: defining the city carbon emission accounting range;

[0076] The city carbon emission can be divided into two categories: direct carbon emission and indirect carbon emission. Direct carbon emission refers to the physical emission generated within the city boundary, mainly from fossil energy fuel combustion activities and industrial production processes, specifically including greenhouse gas emissions caused by direct heating, cooking, domestic hot water, hospital or hotel steam, etc. Indirect carbon emission refers to the carbon emission contained in the electricity and heat input to the building from outside, of which the heat part includes the heat from cogeneration and regional boiler input to the building.

[0077] Considering that the use of clean energy such as city wind power and hydropower may cause large errors in carbon emission accounting results, the present application adopts the consumption place principle to calculate only the direct carbon emission generated by city energy consumption, so as to ensure the accuracy and reliability of the accounting results.

[0078] Step 2: collect multi-source heterogeneous data, including provincial energy consumption, provincial and municipal social economic indicators, city single building attributes, population census and key pollution enterprise information, etc.

[0079] Specifically, the data used by the present application can be divided into two categories: spatial data sets and non-spatial data sets. Non-spatial data mainly includes: ① provincial energy balance table statistical data; ② provincial and municipal social economic index data, including main energy consumption of large-scale industrial enterprises, thermal power generation, passenger turnover, freight turnover, total retail sales of social consumer goods, operating income of large-scale service enterprises, urban resident population and rural resident population; ③ city industrial enterprise industry directory; ④ key pollution enterprise information; ⑤ carbon content, low heat value and oxidation rate corresponding to energy type. Spatial data sets mainly include: ① single building distribution data, which needs to include building volume and building type information; ② community-level population census data; ③ POI (point of interest) data.

[0080] Step 3: Based on the provincial energy balance table, combined with the social economic index of different industry departments, the main energy type consumption of the seven big industry departments at the city scale is calculated;

[0081] Step 3 specifically includes the following steps:

[0082] Step 31: Based on the provincial scale energy balance table data, select the following seven big industry department energy consumption statistical information: (1) industry; (2) transportation, warehousing and postal industry; (3) wholesale and retail trade, accommodation and catering industry; (4) other; (5) urban resident life; (6) rural resident life; (7) thermal power generation;

[0083] Step 32: Screen the energy types in the energy consumption statistical information. Exclude non-fuel products such as paraffin, lubricating oil and naphtha, and finally select 21 main energy types for accounting, including raw coal, washed fine coal, other washed coal, coal products, coke, coke oven gas, blast furnace gas, converter gas, other coal gas, other coking products, crude oil, gasoline, kerosene, diesel, fuel oil, liquefied petroleum gas, refinery dry gas, other petroleum products, natural gas and liquefied natural gas;

[0084] Step 33: Calculate the energy consumption of different industry departments and energy types at the city scale. Based on the provincial energy consumption statistical data, combined with the characteristics of different industries, select appropriate social economic index as the distribution factor, and according to the corresponding weight, distribute the provincial energy consumption of the corresponding industry department to the municipal scale, to obtain the energy consumption of different industry departments and energy types at the city scale. The distribution factors selected by different industries are shown in Table 1.

[0085] Table 1 Carbon emission distribution factors of different energy consumption departments in the calculation process of city energy consumption

[0086]

[0087] The energy consumption calculation formula of different industry departments at the city scale is as follows:

[0088] (1);

[0089] In the formula, AD represents the energy consumption, i represents different industry types, and A represents different industry-selected statistical indicators.

[0090] Step 4: Based on the building energy consumption splitting method of the energy balance table, the energy consumption of the seven major industry departments in the city is corrected;

[0091] Since the energy consumption is calculated according to the industry to which it belongs, and there is no separate building energy consumption data, it is necessary to correct the energy consumption data.

[0092] Step 4 specifically includes the following steps:

[0093] Step 41: Determine the building energy consumption base.

[0094] The "wholesale and retail trade and accommodation and catering industry", "other", "urban resident life" and "rural resident life" in the city's energy consumption are mainly building energy consumption, because these four energy consumptions are mainly building energy consumption;

[0095] Step 42: Remove non-building energy consumption from the building energy consumption base.

[0096] The building energy consumption base includes energy consumption of related industry enterprises or private vehicles, and this part of energy consumption needs to be removed. Specifically, in the "wholesale and retail trade, accommodation and catering industry", "other" two energy consumptions, 95% of gasoline consumption and 35% of diesel consumption are deducted as transportation energy consumption; In "urban resident life" and "rural resident life", 100% of gasoline consumption and 95% of diesel consumption are deducted as transportation energy consumption;

[0097] Step 43: Supplement the building energy consumption included in the transportation department.

[0098] The energy consumption of "transportation, warehousing and postal services" includes part of the building energy consumption, such as train station, bus station, terminal building, and post office energy consumption. According to the results of relevant literature research, 100% of coal consumption and 40% of electricity consumption in this industry are used as transportation, warehousing and postal services building department energy, and are supplemented to the building energy consumption.

[0099] Step 5: Match the above seven major industry departments with the corresponding city building types, (five) establish the corresponding relationship between the energy consumption of the city industry department and the building function use category;

[0100] According to the characteristics of different industries, the seven energy consumption departments are matched with the spatial distribution data of single buildings, so that the energy consumption departments correspond to specific building function categories. Table 2 lists the corresponding relationship in detail.

[0101] Table 2 Corresponding relationship between urban energy consumption departments and building function categories

[0102]

[0103] Step 6: Based on the energy consumption of different building types and energy types and the carbon emission factor, calculate the carbon emission amount;

[0104] (1) Calculate the carbon emission factor based on the low calorific value of the selected energy type, the carbon content per unit energy value, and the oxidation rate. The specific calculation formula is as follows:

[0105] (2);

[0106] Where I is the carbon emission factor, j is the energy type, L is the low calorific value of energy, P is the carbon content per unit energy value (kJ / m 3 or kJ / kg), O is the oxidation rate during energy combustion process (%), and 44 / 12 is the conversion factor of carbon to carbon dioxide.

[0107] (2) Calculate the carbon emission amount of each building type based on the energy consumption of different building types and the carbon emission factor of energy type. The specific calculation formula is as follows:

[0108] (3);

[0109] Where E is the total carbon emission amount generated by human activity energy consumption, m is different building types, j is energy type, C is energy consumption physical quantity, and I is carbon emission factor.

[0110] Step 7: Integrate building volume, POI (Point of Interest) data, industrial enterprise industry directory, industrial main industry energy intensity, community-level population census, and city key pollution enterprise information, etc. Multi-source heterogeneous data to build a refined energy consumption carbon emission spatial distribution model, and distribute carbon emission amount to various single buildings to realize the refined visualization of urban carbon emission.

[0111] According to the corresponding relationship between the seven industry sectors of the city and the building types, the calculated carbon emissions are divided into three categories: residential building carbon emissions, third industry building carbon emissions, and industrial enterprise carbon emissions. Considering the different factors affecting the three carbon emission fields, appropriate spatial distribution indicators are selected for each type of carbon emission to build a refined energy consumption carbon emission spatial distribution model. The refined energy consumption carbon emission spatial distribution model is applied to accurately calculate and spatially distribute the carbon emissions of residential buildings, third industry buildings, and industrial enterprises.

[0112] First, for residential building carbon emissions, based on the corrected urban and rural residential carbon emissions, combined with community-level population census data and urban residential building volume, the single building-level spatial distribution simulation of residential carbon emissions is completed, and the specific steps are as follows:

[0113] Step 711: Filter the land parcels in the city single building distribution data whose building type belongs to urban residential buildings and rural residential buildings, and identify the population census blocks containing these land parcels;

[0114] Step 712: According to the weight of the number of people in each population census block in the total number of people in the city, the corrected urban and rural residential annual carbon emissions are allocated to the corresponding population census block to obtain the community-level residential carbon emissions, and the specific calculation formula is as follows:

[0115] (4);

[0116] In the formula, E i represents the total carbon emissions of the i-th industry sector (urban residential or rural residential); E com(i,j) represents the carbon emissions in the j-th community of the i-th industry sector, P com(i,j) represents the number of people in the j-th community of the i-th industry sector; P i represents the total number of people in the i-th industry sector;

[0117] Step 713: In each population census block, through spatial overlay analysis, further identify the urban residential buildings and rural residential buildings in the block, and calculate the weight of the building volume of each building in the total building volume of the block;

[0118] Step 714: Based on the calculated weight, further allocate the block-level residential carbon emissions to each single building in the block to achieve the refined spatial distribution simulation of residential carbon emissions at the single building level, and the specific calculation formula is as follows:

[0119] (5);

[0120] In the formula, Ebuild(i,j,k) represents the carbon emission of the kth residential building in the jth community of the ith industry sector; V build(i,j,k) represents the volume of the kth residential building in the jth community of the ith industry sector; V com(i,j) represents the total volume of residential buildings in the jth community of the ith industry sector.

[0121] Secondly, for the carbon emissions of the third industry buildings, the specific steps are as follows:

[0122] Step 721: Determine the carbon emission sources of the third industry. The carbon emissions of the third industry mainly come from the energy consumption emissions of the third industry enterprises and the service commercial places in the commercial operation activities.

[0123] Step 722: Select the corresponding carbon emission amount. According to the characteristics of the carbon emission sources of the third industry, the carbon emission amounts of the “transportation, storage and postal industry building part”, “wholesale and retail industry, accommodation and catering industry” and “other” three industry sectors are selected as the carbon emission amounts of the third industry.

[0124] Step 723: Establish the correspondence between the energy consumption department and the building category. According to the correspondence between the energy consumption department and the building category in Table 2, the selected carbon emission amount is distributed to the corresponding building category, including transportation housing, commercial building, public management and public service building, business and financial building, entertainment and sports building, storage housing, public facility housing, special building, rural community service facility housing and urban community service facility building.

[0125] Step 724: Calculate the carbon emission amount of a single building. In each building category, the weight value of the volume of a single building to the total volume of the category is calculated, and the carbon emission amount of the building category is distributed to each single building according to the weight value to obtain the carbon emission amount of a single building, and the spatial distribution result of the carbon emission amount of a single building of the third industry is generated. The specific calculation formula is as follows:

[0126] (6);

[0127] In the formula, E build(m,k) represents the carbon emission of the kth building in the mth industry sector (“transportation, storage and postal industry building part”, “wholesale and retail industry, accommodation and catering industry”, “other”); V build(m,k) represents the volume of the kth building in the mth industry sector; E m represents the total carbon emission of the mth industry sector; V m represents the total building volume of the mth industry sector.

[0128] Finally, for the carbon emissions of industrial enterprises, the specific steps are as follows:

[0129] Step 731: Carbon emission accounting of key emission industrial enterprises. First, based on the spatial distribution map of single buildings and the geographic information of thermal power plants, identify the building plots in the spatial distribution map of single buildings that belong to thermal power plants; second, based on the calculated carbon emissions of thermal power plants, combined with the installed capacity information of thermal power plants in the city; calculate the weight of the installed capacity of each thermal power plant in the total installed capacity of the city, and distribute the carbon emissions of thermal power plants to individual thermal power plants according to the weight, the specific calculation formula is as follows:

[0130] (7);

[0131] In the formula, E n represents the carbon emissions of the nth thermal power plant; E powerplant represents the carbon emissions of the city's thermal power sector; C n represents the installed capacity of the nth thermal power plant; C total represents the sum of the total installed capacity of the city's thermal power plants.

[0132] Step 732: Carbon emission space allocation of other industrial enterprises.

[0133] Using POI data and industry directory of industrial enterprises, matching according to enterprise name, obtaining the name, geographic information and industry information of industrial enterprises;

[0134] According to the geographic information of industrial enterprises, determine the building plots of industrial and mining buildings they belong to, and according to the industry information, determine the industry of the building plots of industrial and mining buildings;

[0135] Calculate the ratio of energy intensity of each industry to the total energy intensity of industry, and calculate the carbon emissions of each industry of industrial enterprises according to the ratio;

[0136] Assuming that the building area of the building plot where the industrial enterprise is located can reflect the size of the enterprise, calculate the weight of the building volume of each industrial enterprise in the total building volume of the industry it belongs to;

[0137] According to the industry carbon emissions and building volume weight, distribute the carbon emissions of industrial enterprises to individual industrial enterprises to obtain the carbon emissions of individual industrial enterprises, the specific calculation formula is as follows:

[0138] ;

[0139] ;

[0140] In the formula, E ind,a represents the total carbon emissions of the a th industry in industrial enterprises; R ind,a represents the energy intensity of the a th industry in industrial enterprises; E ind represents the total carbon emissions of the city's industrial sector; R indrepresents the total energy consumption intensity of the urban industrial sector; E ind(a,b) represents the total carbon emission of the bth industrial enterprise in the ath industry in the industrial enterprise; V ind(a,b) represents the plot building volume of the bth industrial enterprise in the ath industry in the industrial enterprise; V ind,a represents the total plot building volume of the ath industry in the industrial enterprise.

[0141] Taking Wuhan City in 2022 as an example, the simulation method of the multi-source heterogeneous data fusion carbon emission spatial distribution simulation method for the city scale is applied to simulate the simulation results of the spatial distribution of the carbon emission of the city as shown in Figure 2 the simulation results of the spatial distribution of the carbon emission of the city residents, the industry and the third industry as shown in Figure 3 .

[0142] The above shows and describes the basic principles and main features of the present application and the advantages of the present application. It should be understood by those skilled in the art that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only to illustrate the principles of the present application, and various changes and improvements can be made without departing from the spirit and scope of the present application. These changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A city-scale multi-source heterogeneous data fusion carbon emission spatial distribution simulation method, characterized in that, The method comprises the following steps: Step 1: defining the accounting range of urban carbon emissions; Step 2: collecting multi-source heterogeneous data; Step 3: based on the provincial energy balance table, combined with the social and economic indicators of different industry sectors, calculating the consumption of main energy types of seven industry sectors at the city scale; Step 4: based on the building energy consumption splitting method of the energy balance table, correcting the energy consumption of the seven industry sectors in the city; In step 4, the method for correcting the energy consumption of the seven industry sectors in the city is as follows: Determine the basic amount of building energy consumption; Remove the non-building energy consumption in the basic amount of building energy consumption; Supplement the building energy consumption contained in the transportation department; Step 5: match the seven industry sectors with the corresponding city building types to establish the corresponding relationship between the energy consumption of the city industry sectors and the building function use categories; Step 6: based on the energy consumption of different building types and energy types and the carbon emission factor, accounting for carbon emissions; Step 7: integrating multi-source heterogeneous data, constructing a refined energy consumption carbon emission spatial distribution model, spatially distributing carbon emissions to various types of single buildings, and realizing the refined visualization of urban carbon emissions; In step 7, the specific steps include: According to the corresponding relationship between the seven industry sectors in the city and the building types, the calculated carbon emissions are divided into three categories: residential building carbon emissions, third industry building carbon emissions and industrial enterprise carbon emissions; Comprehensively consider different factors affecting the three carbon emission fields, select a spatial distribution index for each type of carbon emission, and construct a refined energy consumption carbon emission spatial distribution model; Step 1: divide the urban carbon emissions into two categories: direct carbon emissions and indirect carbon emissions, direct carbon emissions include physical emissions generated within the city boundary, mainly from fossil fuel combustion activities and industrial production processes, indirect carbon emissions include carbon emissions contained in external input buildings, and the heat part includes heat from cogeneration and regional boilers into buildings.

2. The urban-scale multi-source heterogeneous data fusion carbon emission spatial distribution simulation method according to claim 1, characterized in that, In step 2, the multi-source heterogeneous data includes spatial data sets and non-spatial data sets, the spatial data sets include single building distribution data, community-level population census data and POI data; the non-spatial data sets include provincial energy balance table statistical data, provincial and municipal social and economic indicator data, city industrial enterprise industry directory, key pollution enterprise information and carbon content, low heat value and oxidation rate corresponding to energy types.

3. The urban-scale multi-source heterogeneous data fusion carbon emission spatial distribution simulation method according to claim 1, characterized in that, In step 3, the seven industry sectors include industry; transportation, storage and postal services; wholesale and retail trade, accommodation and catering; others; urban resident life; rural resident life; thermal power generation; the calculation formula of energy consumption is as follows:

4. The urban-scale multi-source heterogeneous data fusion carbon emission spatial distribution simulation method according to claim 1, characterized in that, In the formula, AD represents energy consumption, i represents different industry types, and A represents the statistical indicators selected by different industries. （1）； In step 6, the carbon emissions of different building types and energy types are calculated as follows:

5. The city-scale oriented multi-source heterogeneous data fusion carbon emission spatial distribution simulation method according to claim 1, characterized in that, ​ (1) The carbon emission factor is calculated based on the low calorific value of the selected energy type, the carbon content per unit energy value and the oxidation rate, and the specific calculation formula is as follows: （2）； Wherein, I is the carbon emission factor, j is the energy type, L is the low heat value of energy, P is the carbon content per unit energy heat value, kJ / m 3 or kJ / kg, O is the oxidation rate in the energy combustion process, %, 44 / 12 is the conversion coefficient of carbon converted into carbon dioxide; (2) The carbon emission of each building type is calculated based on the energy consumption of different building types and the carbon emission factor of the energy type, and the specific calculation formula is as follows: （3）； Wherein, E is the total carbon emission of human activity energy consumption, m is different building types, j is energy type, C is the physical quantity of energy consumption, and I is the carbon emission factor.

6. The city-scale oriented multi-source heterogeneous data fusion carbon emission spatial distribution simulation method according to claim 1, characterized in that, The calculation steps of the carbon emission of the residential building are as follows: Step 711: Screening the building type of the city single building distribution data belongs to the town residential building and the rural residential building, and identifying the population census block containing these land blocks; Step 712: According to the weight of the population quantity of each population census block in the total population quantity of the city, the corrected carbon emission of the town and rural residents is allocated to the corresponding population census block, and the carbon emission of the community level residential life is obtained, and the specific calculation formula is as follows: （4）； where E i represents the total carbon emissions of the i-th industry sector; E com(i,j) represents the carbon emissions within the j-th community of the i-th industry sector, P com(i,j) represents the population of the j-th community of the i-th industry sector; P i represents the total population of the i-th industry sector; Step 713: In each population census block, the town residential building and the rural residential building are further identified through spatial overlay analysis, and the weight of the building volume of each building in the total building volume of the block is calculated; Step 714: Based on the calculated weight, the block level residential life carbon emission is further allocated to each single building in the block, and the carbon emission of the residential life is realized in the single building level fine space distribution simulation, and the specific calculation formula is as follows: （5）； In the formula, E build(i,j,k) represents the carbon emissions of the kth residential building in the jth community of the ith industry sector; V build(i,j,k) represents the volume of the kth residential building in the jth community of the ith industry sector; V com(i,j) represents the total volume of residential buildings in the jth community of the ith industry sector.

7. The city-scale oriented multi-source heterogeneous data fusion carbon emission spatial distribution simulation method according to claim 1, characterized in that, The calculation steps of the carbon emission of the third industry building are as follows: Step 721: Determine the carbon emission source of the third industry; Step 722: Select the corresponding carbon emission; Step 723: Establish the corresponding relationship between the energy consumption department and the building category; Step 724: Calculate the carbon emission of single building, calculate the weight value of single building volume in the total building volume of each building category, and allocate the carbon emission of building category to each single building according to the weight value, to obtain the carbon emission of single building, and generate the spatial distribution result of the carbon emission of single building of the third industry, and the specific calculation formula is as follows: （6）； In the formula, E build(m,k) represents the carbon emissions of the kth building in the mth industry sector; V build(m,k) represents the volume of the kth building in the mth industry sector; E m represents the total carbon emissions of the mth industry sector; V m represents the total building volume of the mth industry sector.

8. The city-scale oriented multi-source heterogeneous data fusion carbon emission spatial distribution simulation method according to claim 1, characterized in that, The calculation steps of the carbon emission of the industrial enterprise are as follows: Step 731: Carbon emission accounting of key emission industrial enterprises; based on the single building space distribution map and the geographic information of thermal power plant, the building land blocks belonging to the thermal power plant in the single building space distribution map are identified; based on the calculated carbon emission of thermal power plant, combined with the installed capacity information of thermal power plant in the city; the weight of the installed capacity of each thermal power plant in the total installed capacity of the city is calculated, and the carbon emission of thermal power plant is allocated to single thermal power plant according to the weight, and the specific calculation formula is as follows: （7）； In the formula, E n represents the carbon emissions of the nth thermal power plant; E powerplant represents the carbon emissions of the urban thermal power sector; C n represents the installed capacity of the nth thermal power plant; C total represents the sum of the total installed capacity of the urban thermal power plants; Step 732: Carbon emission space distribution of other industrial enterprises; The name, geographic information and industry information of the industrial enterprise are obtained by matching according to the enterprise name by using the POI data and the industry directory of the industrial enterprise; According to the geographic information of the industrial enterprise, the building land block of the industrial enterprise is determined, and according to the industry information, the industry of the building land block of the industrial enterprise is determined. The ratio of energy consumption intensity of each industry to the total energy consumption intensity of the industry is calculated, and the carbon emissions of the industrial enterprises in each industry are calculated accordingly; Assuming that the building area of the industrial enterprise building plot reflects the size of the enterprise, the building volume weight of each industrial enterprise in the total building volume of the industry is calculated; According to the carbon emissions of the industry and the building volume weight, the carbon emissions of the industrial enterprises are allocated to individual industrial enterprises to obtain the carbon emissions of individual industrial enterprises. The specific calculation formula is as follows: ； ； In the formula, E ind,a represents the total carbon emissions of the a-th industry in the industrial enterprise; R ind,a represents the energy intensity of the a-th industry in the industrial enterprise; E ind represents the total carbon emissions of the urban industrial sector; R ind represents the total energy intensity of the urban industrial sector; E ind(a,b) represents the total carbon emissions of the b-th industrial enterprise in the a-th industry in the industrial enterprise; V ind(a,b) represents the building volume of the b-th industrial enterprise in the a-th industry in the industrial enterprise; V ind,a represents the total building volume of the a-th industry in the industrial enterprise.

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