Building field carbon emission dynamic evolution analysis method based on multiple driving factors

Abstract

The invention provides a building field carbon emission dynamic evolution analysis method based on multiple driving factors, and belongs to the technical field of carbon emission trend prediction. The method comprises the following steps: firstly, identifying dominant factors influencing the carbon emission development trend in the building field, and constructing a macroscopic carbon emission trend prediction model containing the dominant factors; meanwhile, the stealth factors in the driving factors influencing the carbon emission in the building field are combed, quantitative analysis is conducted on the carbon reduction effect of the stealth factors, and then the trend prediction model is corrected by fitting the quantified stealth factors; and finally, inputting a set multi-driving factor combination and respective parameters into the model to obtain a prediction result and a change trend. According to the method, on the basis of identifying the key driving factors, different carbon reduction effects of dominant factors and invisible factors are clearly quantified, the influence of different technical measures in future development situation prediction can be effectively analyzed, and the problems that influence factors of a previous related method are not comprehensive in coverage, the carbon reduction effects of the technical measures are not easy to quantify and the like are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission trend prediction, and specifically is a method for analyzing the dynamic evolution of carbon emissions in the construction field based on multiple driving factors. Background Art

[0002] At present, the prediction of carbon emission trends in the construction sector at home and abroad can usually be divided into two types: top-down and bottom-up. The top-down method starts with the energy consumption at the macro level in the construction sector, constructs an input-output economic model, and then conducts a downscaling analysis in time and space. Its characteristic is that it emphasizes the influence of macro factors. The bottom-up method starts with the hourly energy consumption at the micro level of a single building, establishes an average carbon emission baseline model, and then enlarges the calculation scale for overall estimation. Its characteristic is that it pays more attention to the impact of carbon reduction technologies.

[0003] The top-down approach mainly characterizes the relationship between carbon emissions and construction operations based on variables such as building area, energy consumption and population, rather than the impact of physical factors on building energy consumption, and often lacks technical details. In addition, historical data trends cannot highlight other influencing factors such as technological progress and changes in energy structure.

[0004] The bottom-up approach takes into account details such as construction methods, building performance, and terminal equipment, and extrapolates trends based on the energy consumption of typical buildings at each stage. However, this method requires the collection of a large amount of basic data, so many studies simplify the system and ignore hidden links and indirect emissions, resulting in limited ability to evaluate the effectiveness of carbon reduction measures. Summary of the invention

[0005] The technical problem to be solved by the present invention is that the carbon emission trend prediction method currently used in the construction field does not fully cover the influencing factors and is not easy to quantify the carbon reduction effects of technical measures.

[0006] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0007] A dynamic evolution analysis method for carbon emissions in the construction sector based on multiple driving factors, including:

[0008] S1: Identify the dominant factors that influence the development trend of carbon emissions in the construction sector;

[0009] S2: Combined with the KAYA identity, the changing characteristics of carbon emissions in the construction sector are decomposed and combined to construct a macro carbon emission trend prediction model containing explicit factors;

[0010] S3: Combine national and regional policies and industry technical measures to sort out the invisible factors that affect carbon emissions in the construction sector, and conduct quantitative analysis on the carbon reduction effect of each factor;

[0011] S4: Modify the constructed trend prediction model by fitting the quantified invisible factors;

[0012] S5: Input the set multi-driving factor combination and their respective parameters into the model to obtain the carbon emissions and changing trends in the construction sector under different stages and scenarios.

[0013] Preferably, in step S1, the driving factors affecting carbon emissions in the building sector are analyzed by formula 1:

[0014]

[0015] In formula 1:

[0016] HCEM is carbon emissions in the construction sector;

[0017] The subscript r indicates the subsectors of the housing and construction sector: 1 construction phase, 2 residential housing operation phase, 3 public building operation phase;

[0018] SEMr is the carbon emissions of each sub-sector.

[0019] Preferably, in step S2, the carbon emissions of each sub-sector are analyzed by formula 2:

[0020]

[0021] In formula 2:

[0022] SEMr is the carbon emissions of each sub-sector;

[0023] The subscript i indicates the energy category, of which there are n categories;

[0024] Eir is the final consumption of energy of type i consumed by subsector r;

[0025] fi represents the carbon emission factor;

[0026] The carbon emissions during the construction phase are analyzed using Formula 3:

[0027]

[0028] In formula 3:

[0029] SEMr=1 is the carbon emission during the construction phase;

[0030] The subscript i indicates the energy category, of which there are n categories;

[0031] CV is the total output value of the construction industry;

[0032] TEr = 1 is the total energy consumption of the construction process, and TEr = 1 / CV represents the energy consumption per unit output value of the construction industry;

[0033] Ei,r=1 is the terminal consumption of the i-th type of energy consumed during the construction process, and Ei,r=1 / TEr=1 represents the energy structure;

[0034] fi represents the carbon emission factor;

[0035] The carbon emissions from the operation phase of residential buildings are analyzed by formula 4:

[0036]

[0037] In formula 4:

[0038] SEMr=2 is residential operation emission;

[0039] The subscript i indicates the energy category, of which there are n categories;

[0040] POP is the total population;

[0041] FS is the total living area, FS / POP is the per capita living area;

[0042] TEr=2 is the total energy consumption of residential operation, and TEr=2 / FS represents the energy consumption per unit area;

[0043] Ei,r=2 is the terminal consumption of the i-th type of energy consumed during the operation of the residence, and Ei,r=2 / TEr=2 represents the energy structure;

[0044] fi represents the carbon emission factor;

[0045] The carbon emissions of public buildings during operation are analyzed by formula 5:

[0046]

[0047] In formula 5:

[0048] SEMr=3 is the emission from the operation of public buildings;

[0049] The subscript i indicates the energy category, of which there are n categories;

[0050] POP is the total population;

[0051] PB is the public building area, PB / POP is the public building area per capita;

[0052] TEr=3 is the total energy consumption of public building operation, and TEr=3 / PB represents the energy consumption per unit area of ​​public building operation;

[0053] Ei,r=3 is the terminal consumption of the i-th type of energy consumed in the operation process of public buildings, and Ei,r=3 / TEr=3 represents the energy structure;

[0054] fi represents the carbon emission factor.

[0055] Preferably, in step S2, a prediction model is established by formula 6:

[0056]

[0057] In formula 6:

[0058] HCEMt is the carbon emissions in the building sector at the target time;

[0059] The subscript r indicates the subsectors of the housing and construction sector: 1 construction phase, 2 residential housing operation phase, 3 public building operation phase;

[0060] SEMt,r is the carbon emissions of each sub-sector at the target time.

[0061] As a preference, when the prediction object is carbon emissions during the construction phase, a prediction model is established using Formula 7:

[0062]

[0063] In formula 7:

[0064] SEMr=1 is the carbon emission during the construction phase;

[0065] The subscript i indicates the energy category, of which there are n categories;

[0066] CV is the total output value of the construction industry;

[0067] TEr = 1 is the total energy consumption of the construction process, and TEr = 1 / CV represents the energy consumption per unit output value of the construction industry;

[0068] Ei,r=1 is the terminal consumption of the i-th type of energy consumed during the construction process, and Ei,r=1 / TEr=1 represents the energy structure;

[0069] g is the target time growth coefficient, and the subscript t represents the category of dominant factors, with a total of t categories;

[0070] fi represents the carbon emission factor.

[0071] Preferably, the method for calculating the growth coefficient of the target time of explicit factors includes: obtaining historical data, calculating the average annual growth rate of explicit factors such as the total output value of the construction industry, energy consumption per unit of output value of the construction industry, total population, per capita building area, operating energy consumption per unit area, and energy structure; at the same time, according to the macro-economic, social and technological trends, adjusting the terminal structure of the target time as the target time growth coefficient.

[0072] Preferably, in step S3, the carbon reduction effect of the implicit factor is quantified by formula 8:

[0073]

[0074] In formula 8:

[0075] REM is the total carbon reduction of implicit factors;

[0076] SEMk is the carbon reduction of each implicit factor;

[0077] The subscript k represents the categories of implicit factors, including 6 categories: prefabricated building technology, sponge city technology, application of building energy-saving standards, application of renewable energy, energy-saving transformation of existing projects, and contract energy management.

[0078] As a preferred method, the carbon reduction effect of prefabricated building technology is quantified by formula 9:

[0079] REM r＝1 =∑Ap×Ap a ×f i

[0080] Formula 9

[0081] In formula 9:

[0082] Ap is the application area of ​​prefabricated buildings;

[0083] Apa is the energy saving per square meter of prefabricated buildings;

[0084] fi represents the carbon emission factor;

[0085] The carbon reduction effect of sponge city technology is quantified by formula 10:

[0086] REM r＝2 =∑As×F×H×As a ×f i

[0087] Formula 10

[0088] In formula 10:

[0089] As is the area occupied by the sponge city technology project;

[0090] F is the project rainfall runoff coefficient;

[0091] H is the average rainfall in the city;

[0092] Asa is the energy saving per unit volume of rainwater resource utilization, which is equal to the energy saving per unit volume of tap water;

[0093] fi represents the carbon emission factor;

[0094] The carbon reduction effect of building energy-saving standards is quantified by formula 11:

[0095] REM r＝3 =∑(E0-E t)×A t ×f i

[0096] Formula 11

[0097] In formula 11:

[0098] E0 is the energy consumption per square meter of existing buildings;

[0099] Et is the energy consumption per square meter of new buildings that apply higher energy-saving standards; At is the construction area of ​​new buildings that apply higher energy-saving standards;

[0100] fi represents the carbon emission factor;

[0101] The carbon reduction effect of renewable energy application is quantified by formula 12:

[0102] REM r＝4 =∑R m ×R f ×f i

[0103] Formula 12

[0104] In formula 12:

[0105] Rm is the installed capacity of renewable energy systems;

[0106] Rf is the average annual power generation per unit installed capacity of renewable energy systems;

[0107] fi represents the carbon emission factor;

[0108] The carbon reduction effect of energy-saving transformation of existing projects is quantified by formula 13:

[0109] REM r＝5 =∑E0×E g ×A g ×f i

[0110] Formula 13

[0111] In formula 13:

[0112] E0 is the energy consumption per square meter of existing buildings;

[0113] Eg is the target energy saving rate for energy-saving transformation of existing projects;

[0114] Ag is the energy-saving renovation area of ​​existing projects;

[0115] fi represents the carbon emission factor;

[0116] The carbon reduction effect of contract energy management is quantified by formula 14:

[0117] REMr＝6 =∑E0×E h ×A h ×f i

[0118] Formula 14

[0119] In formula 14:

[0120] E0 is the energy consumption per square meter of existing buildings;

[0121] Eh is the target energy saving rate of the contract energy management;

[0122] Ah is the area of ​​energy management contract applied in the project;

[0123] fi represents the carbon emission factor.

[0124] Preferably, in step S4, the prediction model is corrected by formula 15:

[0125]

[0126] In formula 15:

[0127] HCEMt is the carbon emissions in the building sector at the target time;

[0128] SEMt,r is the carbon emissions of each sub-sector at the target time;

[0129] REMt,k is the carbon reduction of implicit factors in each sub-sector at the target time.

[0130] Preferably, the construction phase SEM1 includes prefabricated building technology REM1; the residential operation phase SEM2 includes sponge city technology REM2, application of building energy-saving standards REM3, application of renewable energy REM4, and energy-saving transformation of existing projects REM5; the public building operation phase SEM3 includes sponge city technology REM2, application of building energy-saving standards REM3, application of renewable energy REM4, energy-saving transformation of existing projects REM5, and contract energy management REM6.

[0131] In the present invention, the explicit factors are factors that directly affect carbon emissions during the construction phase, residential housing operation phase, and public building operation phase. Implicit factors are factors that indirectly affect carbon emissions during the construction phase, residential housing operation phase, and public building operation phase. This is hereby explained.

[0132] The method of the invention is scientific and reasonable. On the basis of identifying key driving factors, it clearly quantifies the different carbon reduction effects of explicit factors and implicit factors, and can effectively analyze the impact of different technical measures in the prediction of future development scenarios. It provides a theoretical basis for the scientific judgment of carbon emission trends in the construction field, and provides data support for the formulation of carbon peak action plans and implementation paths in the construction field. It is of great value to the green and low-carbon development of the whole society.

[0133] Based on the identification of key driving factors, the method of the present invention clearly quantifies the different carbon reduction effects of explicit factors and implicit factors, can effectively analyze the impact of different technical measures in the prediction of future development scenarios, and solves the problems of incomplete coverage of influencing factors in previous related methods and difficulty in quantifying the carbon reduction effects of technical measures. It can improve the credibility and scientificity of the prediction results, and is a major innovation in the carbon emission trend prediction method in the construction field, with significant social and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0134] Figure 1 It is a flow chart of the present invention.

[0135] Figure 2 This is a flowchart of dominant factor identification in the present invention.

[0136] Figure 3 This is the invisible factor correction flow chart of the present invention. DETAILED DESCRIPTION

[0137] The specific embodiments of the present invention will be described in detail below. In order to avoid too much unnecessary details, the well-known structures or functions will not be described in detail in the following examples. The approximate language used in the following examples can be used for quantitative expression, indicating that the quantity can be allowed to have a certain change without changing the basic function. Unless otherwise defined, the technical and scientific terms used in the following examples have the same meaning as those generally understood by those skilled in the art to which the present invention belongs.

[0138] A dynamic evolution analysis method for carbon emissions in the construction sector based on multiple driving factors includes the following steps:

[0139] 1. Analyze the driving factors that affect carbon emissions in the construction sector, and identify and clarify the dominant factors;

[0140] 2. Combined with the KAYA identity, the changing characteristics of carbon emissions in the construction sector are disassembled and combined to construct a top-down macro carbon emission trend prediction model containing explicit factors;

[0141] 3. Combining national and regional policies and industry technical measures, sort out the invisible factors that affect carbon emissions in the construction sector, and conduct a quantitative analysis of the carbon reduction effect of each factor through a bottom-up approach;

[0142] 4. Modify the constructed trend prediction model by fitting the quantified invisible factors;

[0143] 5. Input the set combination of multiple driving factors and their respective parameters into the model to obtain the carbon emissions and changing trends in the construction sector under different stages and scenarios.

[0144] Among them, the key technical links are as follows:

[0145] 1. Analyze the driving factors affecting carbon emissions in the construction sector (accounting scope and content)

[0146] HCEM (Housing and Construction Emission) analysis of carbon emissions in the construction sector:

[0147]

[0148] ——HCEM is carbon emissions in the construction sector;

[0149] ——The subscript r indicates the subsectors of the housing and construction sector: 1 construction phase, 2 residential housing operation phase, 3 public building operation phase;

[0150] ——SEMr is the carbon emissions of each sub-sector.

[0151] 2. Decompose and combine the KAYA identity to identify the dominant factors

[0152] Analysis of carbon emissions by sector:

[0153]

[0154] ——SEMr is the carbon emissions of each sub-sector;

[0155] ——The subscript i indicates the energy category, there are n categories in total;

[0156] ——Eir is the final consumption of energy of type i consumed by subsector r;

[0157] ——fi represents the carbon emission factor.

[0158] Carbon emission analysis during the construction phase:

[0159]

[0160] ——SEMr=1 is the carbon emission during the construction phase;

[0161] ——The subscript i indicates the energy category, there are n categories in total;

[0162] ——CV is the total output value of the construction industry (dominant factor);

[0163] ——TEr=1 is the total energy consumption of the construction process, and TEr=1 / CV represents the energy consumption per unit output value of the construction industry (explicit factor);

[0164] ——Ei,r=1 is the final consumption of the i-th type of energy consumed during the construction process, and Ei,r=1 / TEr=1 represents the energy structure (dominant factor);

[0165] ——fi represents the carbon emission factor.

[0166] Carbon emission analysis during the operation phase of residential buildings:

[0167]

[0168] ——SEMr=2 is residential operation emission;

[0169] ——The subscript i indicates the energy category, there are n categories in total;

[0170] ——POP is the total population (dominant factor);

[0171] ——FS is the total living area, FS / POP is the per capita living area (dominant factor);

[0172] ——TEr=2 is the total energy consumption of residential operation, and TEr=2 / FS represents the energy consumption per unit area (dominant factor);

[0173] ——Ei,r=2 is the final consumption of the i-th type of energy consumed in the residential operation process, and Ei,r=2 / TEr=2 represents the energy structure (dominant factor);

[0174] ——fi represents the carbon emission factor.

[0175] Carbon emission analysis of public buildings during operation:

[0176]

[0177] ——SEMr=3 is the emission from the operation of public buildings;

[0178] ——The subscript i indicates the energy category, there are n categories in total;

[0179] ——POP is the total population (dominant factor);

[0180] ——PB is the public building area, PB / POP is the per capita public building area (explicit factor);

[0181] ——TEr=3 is the total energy consumption of public building operation, and TEr=3 / PB represents the energy consumption per unit area of ​​public building (explicit factor);

[0182] ——Ei,r=3 is the final consumption of the i-th type of energy consumed in the operation process of public buildings, and Ei,r=3 / TEr=3 represents the energy structure (dominant factor);

[0183] ——fi represents the carbon emission factor.

[0184] 3. Establish a macro forecasting model

[0185] Establishment of HCEM (Housing and Construction Emission) prediction model for carbon emissions in the construction sector:

[0186]

[0187] ——HCEMt is the carbon emissions in the building sector at the target time;

[0188] ——The subscript r indicates the subsectors of the housing and construction sector: 1 construction phase, 2 residential housing operation phase, 3 public building operation phase;

[0189] ——SEMt,r is the carbon emissions of each sub-sector at the target time.

[0190] Take the carbon emission forecast in the construction phase as an example:

[0191]

[0192] ——SEMr=1 is the carbon emission during the construction phase;

[0193] ——The subscript i indicates the energy category, there are n categories in total;

[0194] ——CV is the total output value of the construction industry;

[0195] ——TEr=1 is the total energy consumption of the construction process, and TEr=1 / CV represents the energy consumption per unit output value of the construction industry;

[0196] ——Ei,r=1 is the terminal consumption of the i-th type of energy consumed during the construction process, and Ei,r=1 / TEr=1 represents the energy structure;

[0197] ——g is the target time growth coefficient, the subscript t represents the dominant factor category, there are t categories in total;

[0198] ——fi represents the carbon emission factor.

[0199] Calculation method for the growth coefficient of explicit factors at target time: Obtain historical data and calculate the average annual growth rate of explicit factors such as total output value of the construction industry, energy consumption per unit of output value of the construction industry, total population, per capita building area, operating energy consumption per unit area, and energy structure according to the relevant formulas in step 2; at the same time, adjust the final structure of the target time according to the macro-economic, social and technological trends (taking per capita living area as an example, there is an upper limit and it will not grow indefinitely) as the growth coefficient of the target time.

[0200] 4. Quantification of carbon reduction effects of hidden factors

[0201] Quantification method of carbon reduction effect of hidden factors:

[0202]

[0203] ——REM is the total carbon reduction of implicit factors;

[0204] ——SEMk is the carbon reduction of each implicit factor;

[0205] ——The subscript k represents the categories of latent factors, which are 6 in total: prefabricated building technology, sponge city technology, application of building energy-saving standards, application of renewable energy, energy-saving transformation of existing projects, and contract energy management.

[0206] Quantification method of carbon reduction effect of prefabricated building technology:

[0207] REM r＝1 =∑Ap×Ap a ×f i

[0208] ——Ap is the application area of ​​prefabricated buildings;

[0209] ——Apa is the energy saving per square meter of prefabricated buildings;

[0210] ——fi represents the carbon emission factor;

[0211] Based on actual projects, statistics were collected on the reduction of construction energy consumption at different assembly rates for different types of buildings, and the energy saving per unit building area at different assembly rates was obtained.

[0212]

[0213] ——Ap0 is the applied area of ​​the project building;

[0214] ——Ep0 is the construction energy consumption of non-assembled building technology;

[0215] ——Ep is the construction energy consumption using prefabricated building technology.

[0216] Quantification method of carbon reduction effect of sponge city technology:

[0217] Statistics were collected on the sponge city area and rainwater runoff coefficient of the project, and the average annual rainwater resource utilization was calculated in combination with the average rainfall in the city. The amount of energy saved by saving tap water per unit volume was investigated, and the amount of energy saved by rainwater resource utilization was converted to obtain the carbon reduction amount of the project using sponge city technology.

[0218] REM r＝2 =∑As×F×H×As a ×f i

[0219] ——As is the area occupied by the sponge city technology project;

[0220] ——F is the rainfall runoff coefficient of the project;

[0221] ——H is the average rainfall in the city;

[0222] ——Asa is the energy saving per unit volume of rainwater resource utilization, which is equal to the energy saving per unit volume of tap water.

[0223] ——fi represents the carbon emission factor.

[0224] Quantification method of carbon reduction effect of building energy conservation standards:

[0225] REM r＝3 =∑(E0-E t )×A t ×f i

[0226] ——E0 is the energy consumption per square meter of existing buildings;

[0227] ——Et is the energy consumption per square meter of new buildings that apply higher energy-saving standards;

[0228] ——At is the construction area of ​​new buildings that apply higher energy-saving standards;

[0229] ——fi represents the carbon emission factor.

[0230] Quantification method of carbon reduction effect of renewable energy application:

[0231] REM r＝4 =∑R m ×R f ×f i

[0232] ——Rm is the installed capacity of renewable energy system;

[0233] ——Rf is the average annual power generation per unit installed capacity of renewable energy systems;

[0234] ——fi represents the carbon emission factor.

[0235] Quantification method for energy-saving and carbon-reduction effects of existing projects:

[0236] REM r＝5 =ΣE0×E g ×A g ×f i

[0237] ——E0 is the energy consumption per square meter of existing buildings;

[0238] ——Eg is the target energy saving rate for energy saving transformation of existing projects;

[0239] ——Ag is the energy-saving renovation area of ​​existing projects;

[0240] ——fi represents the carbon emission factor.

[0241] Quantification method of carbon reduction effect of contract energy management:

[0242] REM r＝6 =∑E0×E h ×A h ×f i

[0243] ——E0 is the energy consumption per square meter of existing buildings;

[0244] ——Eh is the target energy saving rate of contract energy management;

[0245] ——Ah is the area of ​​energy management contract applied in the project;

[0246] ——fi represents the carbon emission factor.

[0247] 5. Fitting latent factors and correcting the prediction model

[0248] Modification of the HCEM (Housing and Construction Emission) prediction model in the construction sector:

[0249]

[0250] ——HCEMt is the carbon emissions in the building sector at the target time;

[0251] ——SEMt,r is the carbon emissions of each sub-sector at the target time;

[0252] ——REMt,k is the carbon reduction of implicit factors in each sub-sector at the target time.

[0253] The construction phase (SEM1) includes prefabricated building technology (REM1);

[0254] The residential operation phase (SEM2) includes sponge city technology (REM2), application of building energy efficiency standards (REM3), application of renewable energy (REM4), and energy-saving renovation of existing projects (REM5);

[0255] The public building operation stage (SEM3) includes sponge city technology (REM2), application of building energy-saving standards (REM3), application of renewable energy (REM4), energy-saving transformation of existing projects (REM5), and contract energy management (REM6).

[0256] 6. Input the respective parameters into the model to obtain the carbon emissions and changing trends in the construction sector under different stages and scenarios.

[0257] The embodiments of the present invention are described in detail above, but the contents are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the scope of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dynamic evolution analysis method of carbon emissions in the construction sector based on multiple driving factors, It is characterized in that include: S1: Identify the dominant factors that influence the development trend of carbon emissions in the construction sector; S2: Combined with the KAYA identity, the changing characteristics of carbon emissions in the construction sector are decomposed and combined to construct a macro carbon emission trend prediction model containing explicit factors; S3: Combine national and regional policies and industry technical measures to sort out the invisible factors that affect carbon emissions in the construction sector, and conduct quantitative analysis on the carbon reduction effect of each factor; S4: Modify the constructed trend prediction model by fitting the quantified invisible factors; S5: Input the set multi-driving factor combination and their respective parameters into the model to obtain the carbon emissions and changing trends in the construction sector under different stages and scenarios.

2. According to claim 1, a dynamic evolution analysis method of carbon emissions in the construction field based on multiple driving factors, It is characterized in that In step S1, the driving factors affecting carbon emissions in the building sector are analyzed by formula 1: In formula 1: HCEM is carbon emissions in the construction sector; The subscript r indicates the subsectors of the housing and construction sector: 1 construction phase, 2 residential housing operation phase, 3 public building operation phase; SEMr is the carbon emissions of each sub-sector.

3. According to the method for dynamic evolution analysis of carbon emissions in the construction field based on multiple driving factors as described in claim 1, It is characterized in that In step S2, the carbon emissions of each sub-sector are analyzed using formula 2: In formula 2: SEMr is the carbon emissions of each sub-sector; The subscript i indicates the energy category, of which there are n categories; Eir is the final consumption of energy of type i consumed by subsector r; fi represents the carbon emission factor; The carbon emissions during the construction phase are analyzed using Formula 3: In formula 3: SEMr=1 is the carbon emission during the construction phase; The subscript i indicates the energy category, of which there are n categories; CV is the total output value of the construction industry; TEr = 1 is the total energy consumption of the construction process, and TEr = 1 / CV represents the energy consumption per unit output value of the construction industry; Ei,r=1 is the terminal consumption of the i-th type of energy consumed during the construction process, and Ei,r=1 / TEr=1 represents the energy structure; fi represents the carbon emission factor; The carbon emissions from the operation phase of residential buildings are analyzed by formula 4: In formula 4: SEMr=2 is residential operation emission; The subscript i indicates the energy category, of which there are n categories; POP is the total population; FS is the total living area, FS / POP is the per capita living area; TEr=2 is the total energy consumption of residential operation, and TEr=2 / FS represents the energy consumption per unit area; Ei,r=2 is the terminal consumption of the i-th type of energy consumed during the operation of the residence, and Ei,r=2 / TEr=2 represents the energy structure; fi represents the carbon emission factor; The carbon emissions of public buildings during operation are analyzed by formula 5: In formula 5: SEMr=3 is the emission from the operation of public buildings; The subscript i indicates the energy category, of which there are n categories; POP is the total population; PB is the public building area, PB / POP is the public building area per capita; TEr=3 is the total energy consumption of public building operation, and TEr=3 / PB represents the energy consumption per unit area of ​​public building operation; Ei,r=3 is the terminal consumption of the i-th type of energy consumed in the operation process of public buildings, and Ei,r=3 / TEr=3 represents the energy structure; fi represents the carbon emission factor.

4. According to claim 1, a dynamic evolution analysis method of carbon emissions in the construction field based on multiple driving factors, It is characterized in that In step S2, a prediction model is established by formula 6: In formula 6: HCEMt is the carbon emissions in the building sector at the target time; The subscript r indicates the subsectors of the housing and construction sector: 1 construction phase, 2 residential housing operation phase, 3 public building operation phase; SEMt,r is the carbon emissions of each sub-sector at the target time.

5. According to claim 4, a dynamic evolution analysis method of carbon emissions in the construction field based on multiple driving factors, It is characterized in that When the prediction object is carbon emissions during the construction phase, the prediction model is established through Formula 7: In formula 7: SEMr=1 is the carbon emission during the construction phase; The subscript i indicates the energy category, of which there are n categories; CV is the total output value of the construction industry; TEr = 1 is the total energy consumption of the construction process, and TEr = 1 / CV represents the energy consumption per unit output value of the construction industry; Ei,r=1 is the terminal consumption of the i-th type of energy consumed during the construction process, and Ei,r=1 / TEr=1 represents the energy structure; g is the target time growth coefficient, and the subscript t represents the category of dominant factors, with a total of t categories; fi represents the carbon emission factor.

6. According to claim 5, a dynamic evolution analysis method of carbon emissions in the construction field based on multiple driving factors, It is characterized in that The calculation method of the growth coefficient of explicit factors at target time includes: obtaining historical data, calculating the average annual growth rate of explicit factors such as the total output value of the construction industry, energy consumption per unit of output value of the construction industry, total population, per capita building area, operating energy consumption per unit area, and energy structure; at the same time, adjusting the terminal structure of the target time according to the macro-economic, social and technological trends as the target time growth coefficient.

7. According to claim 1, a dynamic evolution analysis method of carbon emissions in the construction field based on multiple driving factors, It is characterized in that In step S3, the carbon reduction effect of the implicit factor is quantified by formula 8: In formula 8: REM is the total carbon reduction of implicit factors; SEMk is the carbon reduction of each implicit factor; The subscript k represents the categories of implicit factors, including 6 categories: prefabricated building technology, sponge city technology, application of building energy-saving standards, application of renewable energy, energy-saving transformation of existing projects, and contract energy management.

8. According to claim 7, a dynamic evolution analysis method of carbon emissions in the construction field based on multiple driving factors, It is characterized in that The carbon reduction effect of prefabricated building technology is quantified by formula 9: REM r＝1 =ΣAp×Ap a ×f i Formula 9 In formula 9: Ap is the application area of ​​prefabricated buildings; Apa is the energy saving per square meter of prefabricated buildings; fi represents the carbon emission factor; The carbon reduction effect of sponge city technology is quantified by formula 10: REM r＝2 =ΣAs×F×H×As a ×f i Formula 10 In formula 10: As is the area occupied by the sponge city technology project; F is the project rainfall runoff coefficient; H is the average rainfall in the city; Asa is the energy saving per unit volume of rainwater resource utilization, which is equal to the energy saving per unit volume of tap water; fi represents the carbon emission factor; The carbon reduction effect of building energy-saving standards is quantified by formula 11: REM r＝3 =∑(E 0 -AND t )×A t ×f i Formula 11 In formula 11: E0 is the energy consumption per square meter of existing buildings; Et is the energy consumption per square meter of new buildings that apply higher energy-saving standards; At is the construction area of ​​new buildings that apply higher energy-saving standards; fi represents the carbon emission factor; The carbon reduction effect of renewable energy application is quantified by formula 12: REM r＝4 =∑R m ×R f ×f i Formula 12 In formula 12: Rm is the installed capacity of renewable energy systems; Rf is the average annual power generation per unit installed capacity of renewable energy systems; fi represents the carbon emission factor; The carbon reduction effect of energy-saving transformation of existing projects is quantified by formula 13: REM r＝5 =∑E 0 ×E g ×A g ×f i Formula 13 In formula 13: E0 is the energy consumption per square meter of existing buildings; Eg is the target energy saving rate for energy-saving transformation of existing projects; Ag is the energy-saving renovation area of ​​existing projects; fi represents the carbon emission factor; The carbon reduction effect of contract energy management is quantified by formula 14: REM r＝6 =∑E 0 ×E h ×A h ×f i Formula 14 In formula 14: E0 is the energy consumption per square meter of existing buildings; Eh is the target energy saving rate of the contract energy management; Ah is the area of ​​energy management contract applied in the project; fi represents the carbon emission factor.

9. According to claim 1, a dynamic evolution analysis method of carbon emissions in the construction field based on multiple driving factors, It is characterized in that In step S4, the prediction model is corrected by equation 15: In formula 15: HCEMt is the carbon emissions in the building sector at the target time; SEMt,r is the carbon emissions of each sub-sector at the target time; REMt,k is the carbon reduction of implicit factors in each sub-sector at the target time.

10. According to claim 9, a dynamic evolution analysis method of carbon emissions in the construction field based on multiple driving factors, It is characterized in that The construction phase SEM1 includes prefabricated building technology REM1; the residential operation phase SEM2 includes sponge city technology REM2, application of building energy-saving standards REM3, application of renewable energy REM4, and energy-saving transformation of existing projects REM5; the public building operation phase SEM3 includes sponge city technology REM2, application of building energy-saving standards REM3, application of renewable energy REM4, energy-saving transformation of existing projects REM5, and contract energy management REM6.

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