A carbon emission prediction method based on a regional residential building iterative model

Abstract

This invention provides a carbon emission prediction method based on a regional residential building iterative model, comprising: acquiring historical data on existing residential area, completed area, population and per capita area, and material and energy intensity data; calculating a comprehensive material correction coefficient based on floor segmentation statistics, establishing a dynamic feedback mechanism to iteratively update the operational energy consumption correction coefficient; predicting future population and per capita area trends using a logistic function, estimating total building demand, calculating demolition volume based on building life distribution, and simulating the dynamic evolution of the building system using an iterative model; establishing a full life-cycle carbon emission dynamic accounting model to quantify carbon emissions throughout the entire residential building process; and setting different development paths to analyze regional residential carbon emission trends. This invention integrates population-driven iteration and full life-cycle assessment, supports multi-scenario prediction, and provides a scientific basis for carbon reduction policies and industry decisions.

Description

Technical Field

[0001] This invention relates to the field of regional residential carbon emission prediction technology, and specifically to a carbon emission prediction method based on a regional residential building iterative model. Background Technology

[0002] With the continuous advancement of urbanization and the sustained expansion of urban scale, the area and density of above-ground buildings have increased significantly, with residential buildings accounting for over 50% of the annual completed area. Throughout their entire lifecycle, from raw material production and construction to operation and eventual demolition, buildings emit carbon dioxide directly or indirectly, significantly impacting the environment. Existing regional carbon emission technologies primarily focus on building end-use energy, failing to systematically reflect the carbon emissions at each stage of a building's entire lifecycle. Therefore, quantifying the carbon emissions at each stage of the entire lifecycle for buildings of different structural types and scientifically predicting future emission trends has become a crucial issue that needs to be addressed. Summary of the Invention

[0003] To address the shortcomings of existing methods, the present invention aims to provide a regional carbon emission prediction method based on a building iterative model. This method can analyze the carbon emissions of buildings of various structural types throughout their entire life cycle and predict carbon emission trends by incorporating different future development scenarios, thus providing data support for carbon emission management in the region.

[0004] To achieve the above objectives, this invention provides a regional carbon emission prediction method based on a building iterative model, comprising the following steps: Obtain historical data on the total area of ​​completed residential buildings in the city, the annual completed building area, the urban population and per capita building area over the years, the unit material strength data of residential buildings, and the unit energy consumption intensity data. The material comprehensive correction coefficient is calculated based on the unit area material usage statistics of floor segments; and a dynamic feedback mechanism based on historical prediction deviations is established to iteratively update the operation energy consumption correction coefficient. Based on the logistic growth function, the changing trends of future urban population and per capita building area are predicted; based on the changing trends of future urban population and per capita building area, the total demand for residential building area is estimated; based on the building life distribution function, the annual building demolition volume is calculated; and the dynamic evolution process of the residential building system is predicted through a building iteration model. Based on a dynamic accounting model for carbon emissions throughout the entire life cycle, the carbon emissions of residential building systems are quantified throughout the entire process.

[0005] Different carbon emission pathways were established, and the carbon emission trends of residential buildings in the region were analyzed based on each pathway.

[0006] Preferably, when collecting data on the total area of ​​completed residential buildings, building materials, construction machinery, and building operation energy consumption, the following should be included: Collect information on the construction date, structural type, and floor height of existing residential buildings; The collected building materials include cement, steel bars, concrete, lime, mortar, bricks, glass, wood, and aluminum, while the energy sources for construction machinery include petroleum, diesel, gasoline, and electricity. Collect architectural design drawings of different floor heights and structural types, and use Design-Builder software to build models to simulate building operation energy consumption.

[0007] Preferably, when obtaining the unit usage of building materials, statistics are collected according to different building structure types and floor heights. The structure types include brick-concrete structures and steel-concrete structures, and the floor heights are divided into three segments: 18 floors and below, 19-33 floors, and 34 floors and above. The unit area usage of each material under different structure types and floor height segments is determined by collecting and statistically analyzing the material lists of actual building cases published by the government.

[0008] Preferably, the comprehensive correction factor for building materials is calculated as shown in the following formula: ; in, It is the comprehensive correction factor for the unit area usage of building materials in year t. , and These are the average material usage per unit area for residential buildings of 18 stories or less, 19-33 stories, and 34 stories or more, respectively. , , These represent the percentages of residential buildings with 18 floors or less, 19-33 floors, and 34 floors or more in newly constructed buildings each year, respectively, in year t.

[0009] Preferably, the latest correction factor for building operating energy consumption is calculated as shown in the following formula: ; in, It is the correction factor for the energy consumption of residential building operation in year t. These are the simulated energy consumption data for year t. This is the actual energy consumption data for year t.

[0010] Preferably, the dynamic iterative process of buildings also considers the renovation and refurbishment of buildings during urban renewal, including: By modifying and optimizing the building envelope and replacing doors and windows, the energy demand for summer cooling and winter heating can be reduced. Extend the lifespan of buildings by implementing structural reinforcement and pipeline upgrades; A comprehensive assessment of the impact of extended building lifespan and reduced operational energy consumption on building iteration processes and carbon emissions.

[0011] Preferably, the life distribution function used in calculating the annual demolition probability of residential buildings is as follows: ; in, This represents the lifespan distribution of residential buildings, where t represents the time series and t' represents the year the building was completed and put into use. Indicates the average lifespan of a building. It represents the standard deviation of a normal distribution.

[0012] Preferably, the entire life cycle includes the building material production and transportation stage, the construction stage, the operation stage, and the demolition stage; carbon emissions in the building material production and transportation stage mainly originate from the energy consumed in the mining, transportation, and processing of raw materials; carbon emissions in the construction stage originate from the labor, gasoline, diesel, and electricity consumed in the foundation engineering, main structure construction, and interior and exterior decoration stages; carbon emissions in the operation stage mainly arise from the energy consumption during the operation of systems such as lighting, heating, cooling, ventilation, and hot water supply after the building is put into use; carbon emissions in the demolition stage cover the energy consumption involved in the building demolition operation itself and the disposal of its waste.

[0013] Preferably, there are two scenarios: a baseline scenario and a high-carbon scenario, which have different development settings in terms of factors such as per capita building area, impact of climate change, floor height ratio, average building life, and the adoption rate of energy-saving buildings.

[0014] The present invention has achieved at least the following beneficial effects: By combining a population-driven residential building iterative model with the entire life cycle, a comprehensive building carbon emission management model is provided. The model can scientifically predict the flow of new and demolished buildings through building life distribution, and estimate the carbon emissions of residential buildings throughout the entire process based on the simulated building iterative model. The comprehensive model provides a scientific basis for policymakers by reasonably predicting the carbon emissions of residential buildings under various scenarios (such as per capita building area, average building life, climate change, floor height ratio, and the popularity of energy-saving buildings), and provides strong support for the construction industry to achieve its carbon emission reduction targets. Attached Figure Description

[0015] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 This is a flowchart illustrating the steps of a carbon emission prediction method based on a building iterative model and life cycle assessment in an embodiment of the present invention. Figure 2 This is a schematic diagram of the specific process of step S3 in the carbon emission prediction method based on building iterative model and full life cycle assessment provided by the present invention. Figure 3 This is a schematic diagram of the specific process of step S5 in the carbon emission prediction method based on building iterative model and life cycle assessment provided by the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0017] See Figure 1 The present invention provides a carbon emission prediction method based on a regional residential building iterative model, comprising the following steps: S1. Obtain the total area of ​​completed residential buildings in the city, the annual completed building area, historical data on urban population and per capita building area, data on the unit material strength of residential buildings, and data on the unit energy consumption intensity. S2. Calculate the comprehensive material correction coefficient based on the unit area material usage statistics of floor segmentation; and establish a dynamic feedback mechanism based on historical prediction deviations to iteratively update the operation energy consumption correction coefficient. S3. Based on the logistic growth function, predict the future trend of urban population and per capita building area, and estimate the total demand for residential building area based on the population and area trends; calculate the annual building demolition volume based on the building life distribution function; predict the dynamic evolution process of the residential building system through a building iteration model. S4. Based on the dynamic accounting model of carbon emissions throughout the entire life cycle, quantify the carbon emissions of residential building systems throughout the entire process.

[0018] S5. Set up different carbon emission pathways and analyze the carbon emission trends of residential buildings in the region based on each pathway.

[0019] In fact, in step S1, by statistically analyzing the urban population and per capita building area of ​​the target area, it is helpful to grasp the growth trend of building demand; collecting information on the construction year, structural type and floor height of existing buildings can improve the level of model refinement; at the same time, summarizing the material consumption and energy intensity data per unit area of ​​different structural types provides basic support for determining the carbon emission level of residential buildings.

[0020] In fact, in step S2, the comprehensive correction coefficient for the unit area usage of building materials is not a fixed value, but is dynamically determined based on the proportion of residential buildings with different floor heights and structural types each year, making it a random variable with statistical fluctuation characteristics, thus reflecting the dynamic changes in usage. Simultaneously, the operational energy consumption model is continuously calibrated using actual data to ensure that the deviation between the predicted and actual values ​​gradually converges over time.

[0021] In fact, see Figure 2 During step S3, in the urban residential building system, the building inventory is constantly undergoing a dynamic iteration process. As buildings age, those reaching the end of their service life are typically demolished, and a corresponding number of new buildings are created to meet the continuously growing total building demand. In recent years, the government has actively advocated urban renewal policies, encouraging the renovation and refurbishment of old residential buildings to extend their service life, thereby avoiding complete demolition and achieving resource conservation and environmental reduction. Therefore, the residential building system experiences a dynamic cycle of new construction, renovation, and demolition every year. To quantify this process, a building iteration model on an annual basis is established, including: S3.1 Based on the logistic growth function, by setting the peak growth rates of urban population and per capita building area, the growth trend in the next few years can be scientifically predicted; S3.2. The demand for buildings is represented by the building area, which is estimated by multiplying the urban population and the per capita building area. S3.3 Based on the normal distribution of building lifespan, calculate the probability of demolishing newly built buildings in each previous year in the current year, and sum them up to obtain the building area to be demolished in the current year. S3.4. Based on the renovation ratio, calculate the renovation area within the total area of ​​the building to be demolished in the new building; S3.5 Based on the normal distribution of building lifespan, calculate the probability of demolishing a renovated building in each previous year in the current year, and sum them up to obtain the demolition area of ​​the renovated building in the current year. S3.6. Calculate the required amount of new construction for each year based on the difference in construction demand between previous and subsequent years, the actual amount of new buildings demolished, and the amount of renovated buildings demolished.

[0022] In fact, when performing step S3.1 to predict urban population and per capita building area, based on the collected actual data, growth peaks are set respectively, and the future growth trend is predicted by the logistic growth function to ensure that the goodness of fit is greater than 0.95.

[0023] In fact, when estimating the building demand in step S3.2, it is as follows: (1); in, Indicates the first Year The demand stock of various structural types of buildings, in meters. 2 ; Indicates the first The urban population of the region in that year, in persons; Table No. The per capita building area in this region in [year], in m² 2 / people; Indicates the first Year The proportion of each structural type of building, in units of %.

[0024] In fact, when calculating the proposed demolition area of ​​the new building in step S3.3, the following formula is used: (2); in, This represents the number of years in which the project is to be demolished based on the lifespan distribution. The newly constructed building area for each structural type of building is expressed in square meters. 2 ; This indicates the number of new additions in year t. The area of ​​a building of a certain structural type, in m² 2 ; Represents a simulated time series; Indicates the time when a newly constructed building enters the system; This indicates the average service life of newly constructed buildings, expressed in years. It represents the standard deviation of the normal distribution of building lifespan.

[0025] In fact, when calculating the building area for renovation in step S3.4, the following formula is used: (3); in, The [number]th year was preserved through renovation and refurbishment, thus avoiding demolition. The area of ​​a building of a certain structural type, in m² 2 ; This indicates the percentage of building renovations, expressed in percent.

[0026] In fact, when calculating the demolition area of ​​the renovated building in step S3.5, it is as follows: (4); in, This indicates the tth year after renovation. The area of ​​a building of a certain structural type to be demolished when it reaches the end of its extended service life, in m². 2 ; Represents a simulated time series; Indicates the time when the renovated building enters the system; This indicates the average lifespan of a renovated building, expressed in years. It represents the standard deviation of the normal distribution of building lifespan.

[0027] In fact, when calculating the demand for new buildings in step S3.6, the following formula is used: (5); (6); in, This indicates the number of new additions in year t. The area of ​​a building of a certain structural type, in m² 2 .

[0028] In fact, in step S4, the entire life cycle includes the building material production and transportation stage, the construction stage, the operation stage, and the demolition stage. Carbon emissions from the building material production and transportation stage mainly originate from the energy consumed during the extraction, transportation, and processing of raw materials. Carbon emissions from the construction stage come from the labor, gasoline, diesel, and electricity consumed in foundation engineering, main structure construction, and interior and exterior decoration. Carbon emissions from the operation stage mainly arise from the energy consumption during the operation of systems such as lighting, heating, cooling, ventilation, and hot water supply after the building is put into use. Carbon emissions from the demolition stage cover the energy consumption involved in the building demolition operation itself and its waste disposal. The total carbon emissions are calculated by summing the carbon emissions from each of the four stages of the residential building system, as shown in the following formula: (7); Where C represents the total carbon emissions of a residential building throughout its entire life cycle; Cp, Cc, Co, and Cd represent the carbon emissions of the four stages of building material production and transportation, construction, operation and use, and demolition and disposal, respectively.

[0029] Specifically, the calculation of carbon emissions during the production and transportation of building materials covers three structural types: brick-wood, brick-concrete, and reinforced concrete, and comprehensively considers nine major building materials, including reinforced concrete, cement, mortar, lime, brick, wood, aluminum, and glass. This portion of carbon emissions includes embodied carbon generated during material production and transportation carbon generated from the factory to the construction site. Based on the unit area usage of various building materials, the material carbon emission factor, and the transportation carbon emission factor, the carbon emissions during the production and transportation of building materials are calculated using the following formula: (8); in, Indicates the first The amount of building materials used per unit area, expressed in kg / m² 2 ; Indicates the first A comprehensive correction factor for the unit area usage of various building materials; Indicates the first The carbon emission factor of a type of building material, expressed in kgCO2eq / kg; Indicates the first The transportation distance of various building materials, in meters; Indicates the transportation number The carbon emission factor of the vehicle type used in the transportation of certain building materials, expressed in kgCO2 / (t·km).

[0030] Specifically, the calculation of carbon emissions during the construction phase includes both energy consumption from machinery and labor. The consumption of energy sources such as diesel, gasoline, and electricity, as well as labor, during construction is statistically analyzed. Combined with the energy consumption intensity per unit area and the corresponding energy carbon emission factor, the carbon emissions during the construction phase are calculated using the following formula: (9); in, Indicates the amount consumed during the construction process. The carbon emission factor of this energy source, expressed in kgCO2eq / kg or kgCO2eq / kwh; Indicates the first The energy intensity per unit area, expressed in kg / m² 2 or kwh / m 2 ; This represents the artificial carbon emission factor consumed during the construction process, expressed in kgCO2eq / man-day. This indicates the number of man-days required to construct a unit area of ​​building, expressed in man-days / m². 2 .

[0031] Specifically, when calculating carbon emissions during the operation phase, the total energy consumption of systems such as lighting, heating, cooling, ventilation, and hot water supply is obtained through comprehensive simulation by Design-builder. Combined with the electricity carbon emission factor, the carbon emissions during the operation phase are calculated as shown in the following formula: (10); in, This indicates the energy consumption intensity per unit area of ​​a building during operation, expressed in kWh / m². A correction factor representing the energy consumption intensity per unit area during building operation; The carbon emission factor of electricity is expressed in kgCO2eq / kwh.

[0032] Specifically, when calculating carbon emissions during the demolition phase, carbon emissions from demolition operations and waste transportation are included, and carbon offsetting from material recycling is also considered, as shown in the following formula: (11); In fact, when calculating the carbon emissions from demolition operations... When, as shown in the following formula: (12); in, Indicates the amount consumed in the demolition operation. The carbon emission factor of this energy source, expressed in kgCO2eq / kg or kgCO2eq / kwh; Indicates the first The energy intensity per unit area, expressed in kg / m² 2 or kwh / m 2 ; This indicates the carbon emission factor generated by the demolition work, expressed in kgCO2eq / man-day. This indicates the number of man-days required to demolish a unit area of ​​a building, expressed in man-days / m². 2 .

[0033] In fact, in calculating the carbon emissions from dismantling and transporting... When, as shown in the following formula: (13); in, This indicates the amount of demolition waste generated per unit area of ​​a building, expressed in kg / m². 2 ; This indicates the transportation distance for construction demolition waste, expressed in meters (m). This indicates the carbon emission factor of the vehicle used when transporting construction waste, expressed in kgCO2 / (t·km).

[0034] In fact, when calculating the carbon offset of building demolition waste recycling... When, as shown in the following formula: (14); in, Indicates the demolition of the first The yield per unit area of ​​this material, expressed in kg / m² 2 ; Indicates the first Recovery rate of the materials, in percentages of % Indicates the first The carbon emission factor of the production of this material is expressed in kgCO2eq / kg; No. The carbon emission factor of the material is measured in kgCO2eq / kg.

[0035] In some embodiments, see Figure 3When performing step S5, the following is included: S5.1 The development path of per capita building area is divided into baseline scenario and large-area scenario. By reviewing relevant literature on the peak of per capita building area in urban areas, as well as policy and standard documents, the growth path and peak level of per capita building area are comprehensively determined. S5.2 The climate change development path is divided into a baseline scenario and a high-temperature scenario. The temperature rise level is set with reference to the IPCC Sixth Assessment Report. Based on the above climate targets, parameters are set, and building energy consumption simulation data are collected at set time intervals to assess the impact of climate change on building cooling and heating demand. S5.3 The development path of building height is divided into baseline scenario and high-density scenario. The proportion of buildings of each height is determined by studying relevant literature and standard documents on the spatiotemporal evolution characteristics of residential building height. S5.4 The development path of building life is divided into baseline scenario and long life scenario. Through literature research and standard documents related to residential building life, the building life parameters of three structural types, namely brick-wood, brick-concrete and steel-concrete, are determined in different development periods. S5.5 The development path for the popularization rate of energy-saving buildings is divided into a baseline scenario and a large-scale promotion scenario. By reviewing relevant literature on energy-saving buildings in urban areas, as well as policy and standard documents, the path to achieve the popularization rate of energy-saving buildings is determined comprehensively.

[0036] In fact, when setting the development path of per capita building area in step S5.1, it is divided into a baseline scenario and a large-area scenario. Under the baseline scenario, the per capita residential area of ​​urban residents continues the current development trend, growing steadily and moderately while ensuring the basic comfort of residents. The large-area scenario reflects residents' pursuit of high-quality living spaces. To meet the needs of functional diversity and spaciousness, the per capita building area will grow more rapidly and reach a higher level. Based on the above objectives, the future development trend of per capita building area in each region is predicted.

[0037] In some embodiments, relevant research literature on the peak of per capita building area in urban areas can be referenced to comprehensively determine the growth path and peak level of per capita building area.

[0038] In some embodiments, the peak per capita residential area of ​​urban residents under the baseline scenario and the large-area scenario are shown in the table below.

[0039] Scenario setting Baseline Scenario Large-scale scenarios per capita building area <![CDATA[35m 2 ]]> <![CDATA[45m 2 ]]>

[0040] In practice, step S5.2, when setting the climate change development path, is divided into a baseline scenario and a high-temperature scenario. Referring to the IPCC Sixth Assessment Report, the baseline scenario is set as a global annual average temperature increase of no more than 4°C by 2100 compared to 2020; the high-temperature scenario is set as a temperature increase of no more than 6°C. Based on the above climate target settings, building energy consumption simulation data are collected at set time intervals to assess the impact of climate change on building cooling and heating demand.

[0041] In fact, when setting the development path for building height in step S5.3, it is divided into a baseline scenario and a high-density scenario. The baseline scenario refers to a situation where future urban planning policies, market preferences, and technological conditions do not undergo significant or directional changes, and the current development trend continues inertia. The high-density scenario refers to a situation where the government proactively adopts policies to address land scarcity, protect arable land, and improve infrastructure efficiency, significantly increasing the overall development intensity of the city. Based on the above objectives, the proportion of buildings of different heights in the future is predicted.

[0042] In some embodiments, the development path of buildings with different floor heights can be determined by studying relevant literature on the spatiotemporal evolution of urban residential building heights.

[0043] In fact, when setting the building lifespan development path in step S5.4, it is divided into a baseline scenario and a long-life scenario. Under the baseline scenario, urban renewal still mainly takes demolition and reconstruction as the main method, and the building quality steadily improves but does not undergo fundamental changes; the long-life scenario reflects the transformation of the construction industry from "incremental expansion" to "quality improvement of existing assets," significantly extending the service life of buildings through technological advancements. Building lifespan parameters for different periods are set for the three structural types: brick-wood, brick-concrete, and reinforced concrete.

[0044] In some embodiments, the lifespan development path of a building can be determined by studying relevant literature on the lifespan of residential buildings.

[0045] In some embodiments, the residential building life parameters under the baseline and long-life scenario settings are shown in the table below.

[0046]

[0047] In practice, step S5.5, when setting the development path for the adoption rate of energy-efficient buildings, is divided into a baseline scenario and a large-scale promotion scenario. Energy-efficient buildings refer to buildings where key aspects such as the thermal performance of the building envelope, heating, ventilation, and air conditioning systems are optimized in the design process. Examples include promoting high-efficiency cooling equipment such as air-source heat pumps and replacing coal-fired boilers with heat pump technology for hot water supply. In the baseline scenario, the adoption rate of energy-efficient buildings progresses according to the minimum target of the existing plan; the large-scale promotion scenario accelerates the application and retrofitting of energy-saving technologies, significantly exceeding the planned target.

[0048] In some embodiments, energy-efficient building design methods and pathways to achieve energy-efficient building adoption can be determined through research on relevant literature on energy-efficient buildings.

[0049] In fact, in step S5, the setting and simulation of different carbon emission pathways can systematically identify the trends and differences in future regional residential building carbon emissions under various development possibilities, providing a scientific basis for formulating forward-looking, adaptable strategic decisions that are in line with sustainable development goals.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A carbon emission prediction method based on a regional residential building iterative model, characterized in that, include: Obtain historical data on the total area of ​​completed residential buildings in the city, the annual completed building area, the urban population and per capita building area over the years, the unit material strength data of residential buildings, and the unit energy consumption intensity data. Calculate the comprehensive material correction coefficient based on the unit area material usage statistics of floor segments; A dynamic feedback mechanism based on historical prediction deviations will be established to iteratively update the operational energy consumption correction coefficient. Based on the logistic growth function, the future urban population and per capita building area are predicted to change trends, and the total demand for residential building area is estimated based on the said future urban population and per capita building area change trends. Calculate the annual building demolition volume based on the building life distribution function; Predicting the dynamic evolution of residential building systems using building iteration models; Based on a dynamic accounting model for carbon emissions throughout the entire life cycle, the carbon emissions of residential building systems are quantified throughout the entire process. Different carbon emission pathways were established, and the carbon emission trends of residential areas in the region were analyzed based on each pathway.

2. The carbon emission prediction method based on a regional residential building iterative model according to claim 1, characterized in that, When collecting data on the total area of ​​completed residential buildings, building materials, construction machinery, and building operation energy consumption, the following should be included: Collect information on the construction date, structural type, and floor height of existing residential buildings; The collected building material data covers cement, steel bars, concrete, lime, mortar, bricks, glass, wood, and aluminum. The construction machinery data includes the energy source for the construction machinery, which includes petroleum, diesel, gasoline, and electricity. Collect architectural design drawings of different floor heights and structural types, and use Design-Builder software to build models to simulate building operation energy consumption.

3. The carbon emission prediction method based on a regional residential building iterative model according to claim 2, characterized in that, When obtaining the unit usage of building materials, statistics are compiled according to different building structure types and floor heights. The structure types include brick-concrete structures and steel-concrete structures, and the floor heights are divided into three segments: 18 floors and below, 19-33 floors, and 34 floors and above. By collecting and compiling the material lists of actual building cases published by the government, the unit area usage of each material under different structure types and floor height segments is determined.

4. The carbon emission prediction method based on a regional residential building iterative model according to claim 1, characterized in that, The comprehensive correction factor for building materials is calculated using the following formula: ； in, It is the comprehensive correction factor for the unit area usage of building materials in year t, and These are the average material usage per unit area for residential buildings of 18 stories or less, 19-33 stories, and 34 stories or more, respectively. , , These represent the percentages of residential buildings with 18 floors or less, 19-33 floors, and 34 floors or more in newly constructed buildings each year, respectively, in year t.

5. The carbon emission prediction method based on a regional residential building iterative model according to claim 1, characterized in that, The latest correction factor for building operating energy consumption is calculated using the following formula: ； in, It is the correction factor for the energy consumption of residential building operation in year t. These are the simulated energy consumption data for year t. This is the actual energy consumption data for year t.

6. The carbon emission prediction method based on a regional residential building iterative model according to claim 1, characterized in that, The aforementioned dynamic iterative process for buildings also considers the renovation and refurbishment of buildings during urban renewal, including: By modifying and optimizing the building envelope and replacing doors and windows, the energy demand for summer cooling and winter heating can be reduced. Extend the lifespan of buildings by implementing structural reinforcement and pipeline upgrades; A comprehensive assessment of the impact of extended building lifespan and reduced operational energy consumption on building iteration processes and carbon emissions.

7. The carbon emission prediction method based on a regional residential building iterative model according to claim 1, characterized in that, The lifespan distribution function used to calculate the annual demolition probability of residential buildings is shown in the following formula: ； in This represents the lifespan distribution of residential buildings, where t represents the time series and t' represents the year the building was completed and put into use. Indicates the average building lifespan. It represents the standard deviation of a normal distribution.

8. The carbon emission prediction method based on a regional residential building iterative model according to claim 1, characterized in that, The entire life cycle includes the building material production and transportation stage, the construction stage, the operation stage, and the demolition stage. Carbon emissions in the building material production and transportation stage mainly originate from the energy consumed in the mining, transportation, and processing of raw materials. Carbon emissions in the construction stage come from the labor, gasoline, diesel, and electricity consumed in the foundation engineering, main structure construction, and interior and exterior decoration stages. Carbon emissions in the operation stage mainly arise from the energy consumption during the operation of systems such as lighting, heating, cooling, ventilation, and hot water supply after the building is put into use. Carbon emissions in the demolition stage cover the energy consumption involved in the building demolition operation itself and the disposal of its waste.

9. The carbon emission prediction method based on a regional residential building iterative model according to claim 1, characterized in that, It also includes two scenarios: the baseline scenario and the high-carbon scenario, which have different development settings in terms of per capita building area, climate change impact, average building life, and the proportion of energy-efficient buildings.

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