Building carbon emission spatialization list construction method and system based on behavior and load coupling

Abstract

The invention discloses a construction carbon emission spatialization list construction method and system based on behavior and load coupling, and relates to the field of environmental information technology and carbon emission accounting. According to the method, the core technical problem of constructing a refined and spatialized emission list in a region lacking basic energy consumption activity level data is effectively solved, and the generated list can accurately describe space-time differentiation characteristics of building carbon emission; and a high-precision quantification tool and a reliable decision basis are provided for pollution-reduction and carbon-reduction precise management and control, territorial space planning optimization and regional air quality simulation of urban and rural construction.

Description

Technical Field

[0001] This invention relates to the fields of environmental information technology and carbon emission accounting, specifically to a method and system for constructing a spatialized inventory of building carbon emissions based on behavior and load coupling. Background Technology

[0002] The building operation phase is a key area of ​​urban and rural energy consumption and carbon emissions, and its emissions have a long-term lock-in effect. Therefore, it is crucial to construct a high-resolution, spatialized building carbon emission inventory.

[0003] Currently, the methods for accounting for and constructing carbon emissions from building operations are mainly divided into two categories: The first category is the "bottom-up" physical modeling method. This method relies on detailed information about individual buildings, such as building envelope, equipment energy efficiency, and hourly operating conditions. Energy consumption is calculated using energy simulation software (such as EnergyPlus and DeST), and carbon emissions are then calculated using emission factors. This method is highly accurate, but its data requirements are extremely stringent, requiring a large amount of difficult-to-obtain building physical parameters and operating records. In high-altitude and remote areas such as Tibet, incomplete building records and a lack of monitoring facilities severely limit the applicability of this method and make it difficult to apply on a large scale.

[0004] The second category is the "top-down" statistical allocation method. This method is based on regional energy consumption statistics and allocates them to smaller spatial units according to certain proportions (such as building area, population, and economic indicators). In existing technologies, common allocation proxy indicators include nighttime light data, land use data, and registered population distribution. However, these methods have significant drawbacks: First, they fail to fully consider the actual usage intensity and temporal patterns of buildings. For example, allocating solely based on static building area or population ignores the high-intensity energy consumption characteristics of public buildings during the day and commercial buildings during peak seasons, leading to distorted spatial positioning of emissions. Second, when allocating emissions to micro-spatial units (such as geographic grids or individual buildings), existing methods often rely on simple spatial interpolation or average allocation, lacking a refined allocation model that can reasonably characterize the differences in different building functions and spatial carrying capacity. This results in poor spatial heterogeneity in the generated gridded inventory and an inability to accurately reflect emission hotspots.

[0005] Furthermore, existing inventory compilation efforts largely focus on the accounting of a single greenhouse gas (CO2), independent of the compilation of emission inventories for air pollutants (such as SO2, NOx, and PM2.5). This "separate management of carbon and pollution" model makes it difficult to assess the synergistic benefits of an energy-saving or energy-alternative measure in improving local air quality while reducing carbon emissions.

[0006] Therefore, how to develop a list construction method that can overcome data bottlenecks, accurately reflect the spatiotemporal distribution of building carbon emissions, and support collaborative governance in regions with weak basic activity level data but unique clean energy structures (such as Tibet, which is dominated by hydropower) has become an urgent technical challenge in the field of environmental management and urban sustainable development. Summary of the Invention

[0007] To address the technical problems mentioned above, the present invention provides the following technical solution: A method for constructing a spatialized inventory of building carbon emissions based on behavior and load coupling includes the following steps: S1. Obtain macro-level total carbon emission data of building operation in the target area, a basic geographic information database of buildings containing the geospatial outline and functional attribute codes of all individual buildings, and spatiotemporal behavior characteristic data of residents; S2. Calculate the preliminary carbon emission allocation for each type of building based on residents' spatiotemporal behavior data; S3. Based on the preliminary carbon emission allocation, calculate the refined carbon emission of each individual building; S4. Based on the refined carbon emissions of all individual buildings, generate a gridded spatial distribution list of building carbon emissions covering the entire target area.

[0008] Preferably, S2 includes: Based on residents' spatiotemporal behavior characteristics data, the average daily effective stay time of residents in various types of buildings is extracted, and the contribution weight of the time dimension is calculated. Based on the basic geographic information data of buildings, the total land area of ​​various types of buildings is obtained, and the spatial dimension contribution weight is calculated by combining the corresponding benchmark value of personnel carrying capacity per unit area. Based on the contribution weights of the time dimension and the spatial dimension, a comprehensive category weight coefficient is synthesized. Based on the category-based comprehensive weighting coefficient, the macro-level total data is initially allocated to various types of buildings to obtain the preliminary carbon emission allocation for each category.

[0009] Preferably, S3 includes: For individual buildings under the target category, calculate the static space load benchmark value of the individual building based on the building's floor area and the corresponding personnel carrying density benchmark value per unit area. The utilization coefficient is determined based on the functional type, operating time pattern, and seasonal usage characteristics of the individual building. Calculate the effective space load value based on the static space load benchmark value and utilization factor of the individual building; The refined carbon emissions of a single building are calculated based on its effective spatial load value.

[0010] Preferably, when synthesizing the comprehensive weight coefficient of the category, a weighted average is performed on the contribution weight of the time dimension and the contribution weight of the spatial dimension, wherein a preset adjustment factor is introduced to balance the contributions of time and spatial factors.

[0011] Preferably, the effective space load value is obtained by multiplying the static space load reference value by the utilization factor.

[0012] Preferably, S4 includes: The refined carbon emissions of all individual buildings are linked to their geographical coordinates to form a spatial dataset of point source emissions. The spatial dataset of point source emissions is aggregated into regular geographic grid cells of a preset scale, and the total cumulative carbon emissions within each grid cell are calculated.

[0013] Preferably, the geographic coordinates are the centroid coordinates of the building outline; the preset scale regular geographic grid unit is a geographic grid unit of 1km×1km or 5km×5km.

[0014] This invention also provides a spatialized inventory construction system for building carbon emissions based on behavior and load coupling, the system being used to implement the above method, comprising: The data acquisition module is used to acquire macro-level total carbon emissions data of building operations in the target area, a basic geographic information database of buildings containing the geospatial outlines and functional attribute codes of all individual buildings, and spatiotemporal behavior characteristic data of residents. The first calculation module is used to calculate the preliminary carbon emission allocation of each type of building based on residents' spatiotemporal behavior characteristics data; The second calculation module is used to calculate the refined carbon emissions of each individual building based on the preliminary carbon emission allocation. The generation module is used to generate a gridded spatial distribution list of building carbon emissions covering the entire target area based on the refined carbon emissions of all individual buildings. Compared with the prior art, the beneficial effects of the present invention are as follows: By innovatively coupling resident spatiotemporal behavioral characteristic data, building physical attribute data, and macroscopic total carbon emissions, a multi-layered analytical and allocation model was constructed, encompassing "macroscopic total emissions → building function type → individual building → geographic grid." This method first scientifically allocates the macroscopic total emissions to different functional building categories through spatiotemporal behavioral analysis and the integration of benchmark load parameters. Then, by introducing a utilization coefficient to couple and correct the static spatial load benchmark value, an effective spatial load value representing the actual usage intensity is obtained, thereby achieving refined positioning of emissions at the individual building level. Finally, a high-resolution carbon emission spatial distribution inventory is generated through spatial grid aggregation.

[0015] This invention effectively solves the core technical challenge of constructing a refined and spatialized emission inventory in areas lacking basic energy consumption activity data. The generated inventory can accurately depict the spatiotemporal differentiation characteristics of building carbon emissions, providing a high-precision quantitative tool and reliable decision-making basis for precise control of pollution reduction and carbon reduction in urban and rural construction, optimization of land spatial planning, and regional air quality simulation. Attached Figure Description

[0016] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Example 1: like Figure 1 The diagram shown is a schematic flowchart of a method according to an embodiment of the present invention.

[0021] S1. Obtain macro-level total carbon emission data of building operations in the target area, a basic geographic information database of buildings containing the geospatial outlines and functional attribute codes of all individual buildings, and spatiotemporal behavior characteristic data of residents.

[0022] In this embodiment, the spatiotemporal behavioral characteristic data of residents includes one or more of the following: mobile phone signaling data and building dwelling data collected by IoT sensors.

[0023] S2. Calculate the preliminary carbon emission allocation for each type of building based on residents' spatiotemporal behavior data.

[0024] (1) Calculation of contribution weight of time dimension: Based on the spatiotemporal behavior characteristics data of residents, the average daily effective stay time of residents in the t-th type of building is extracted. Calculate the total effective stay duration, which reflects the overall scale of human activity in the area. ,in n This represents the total number of building function categories. Then, the time-stay weight for each building category is calculated, i.e., the time-dimensional contribution weight, using the following formula: : .

[0025] (2) Calculation of Spatial Dimension Contribution Weight: Based on the basic geographic information data of buildings, the total floor area of ​​various types of buildings is obtained, and combined with the benchmark value of the personnel carrying capacity per unit area corresponding to their functional attributes, the total static spatial load benchmark of various types of buildings is calculated. The proportion of this benchmark total in the total benchmark of all types of buildings is the spatial dimension contribution weight. .

[0026] (3) Synthesis and initial allocation of category comprehensive weight coefficients: weights contributing to the time dimension Contribution weight of spatial dimension A weighted average is used to synthesize the category comprehensive weight coefficients. : , in, The preset adjustment factor ( This is used to balance the contributions of time and space factors. Finally, based on... Macroeconomic aggregates The carbon emission allocations are distributed among various types of buildings to obtain preliminary carbon emission allocations. .

[0027] S3. Based on the preliminary carbon emission allocation, calculate the refined carbon emission of each individual building.

[0028] (1) Calculate the reference value of static space load: For individual buildings under the target category, based on their floor area and the corresponding benchmark value of personnel carrying capacity per unit area Calculate its static spatial load reference value. : .

[0029] (2) Introduce and determine the utilization factor: Based on the functional type, operating time pattern and seasonal usage characteristics of the building, set a dynamic parameter that characterizes its actual operating intensity—the utilization factor. . and Local survey data can be used to determine the value.

[0030] (3) Coupling correction to obtain effective spatial load value: by using coefficients By coupling and correcting the static reference values, the effective spatial load values ​​reflecting the actual usage load of the building are obtained. : .

[0031] (4) Detailed allocation: Calculate the effective spatial load value of the single building. The weight of the total effective load value of all buildings in its category is used to initially allocate carbon emissions for the category. A secondary allocation is performed to obtain the refined carbon emissions of this individual building. : .

[0032] S4. Based on the refined carbon emissions of all individual buildings, generate a gridded spatial distribution list of building carbon emissions covering the entire target area.

[0033] The refined carbon emissions of all individual buildings calculated by S3 are correlated with their corresponding building geographic coordinates (such as the centroid of the building outline) to form a point source emission spatial dataset. Using spatial analysis tools from a Geographic Information System (GIS), this dataset is aggregated into regular geographic grid cells of a preset scale (e.g., 1km×1km, 5km×5km, or other scales set according to management requirements), and the cumulative carbon emissions within each grid cell are calculated. Finally, a high-spatial-resolution gridded spatial distribution list of building carbon emissions covering the entire target area is generated.

[0034] Example 2: This embodiment also provides a spatialized inventory construction system for building carbon emissions based on behavior and load coupling, including: a data acquisition module, a first calculation module, a second calculation module, and a generation module.

[0035] The following will describe in detail, with reference to this embodiment, how the present invention solves the technical problems in practical work.

[0036] First, the data acquisition module is used to obtain macro-level total carbon emissions data of building operations in the target area, a basic geographic information database of buildings containing the geospatial outlines and functional attribute codes of all individual buildings, and spatiotemporal behavioral characteristic data of residents.

[0037] In this embodiment, the spatiotemporal behavioral characteristic data of residents includes one or more of the following: mobile phone signaling data and building dwelling data collected by IoT sensors.

[0038] The first calculation module then calculates the preliminary carbon emission allocation for each type of building based on residents' spatiotemporal behavior data.

[0039] (1) Calculation of contribution weight of time dimension: Based on the spatiotemporal behavior characteristics data of residents, the average daily effective stay time of residents in the t-th type of building is extracted. Calculate the total effective stay duration, which reflects the overall scale of human activity in the area. ,in n This represents the total number of building function categories. Then, the time-stay weight for each building category is calculated, i.e., the time-dimensional contribution weight, using the following formula: : .

[0040] (2) Calculation of Spatial Dimension Contribution Weight: Based on the basic geographic information data of buildings, the total floor area of ​​various types of buildings is obtained, and combined with the benchmark value of the personnel carrying capacity per unit area corresponding to their functional attributes, the total static spatial load benchmark of various types of buildings is calculated. The proportion of this benchmark total in the total benchmark of all types of buildings is the spatial dimension contribution weight. .

[0041] (3) Composition and initial allocation of category comprehensive weight coefficients: weights contributing to the time dimension Contribution weight of spatial dimension A weighted average is then used to synthesize the category comprehensive weight coefficients. : , in, The preset adjustment factor ( This is used to balance the contributions of time and space factors. Finally, based on... Macroeconomic aggregates The carbon emission allocations are distributed among various types of buildings to obtain preliminary carbon emission allocations. .

[0042] Subsequently, the second calculation module calculates the refined carbon emissions of each individual building based on the preliminary carbon emission allocation.

[0043] (1) Calculate the reference value of static space load: For individual buildings under the target category, based on their floor area and the corresponding benchmark value of personnel carrying capacity per unit area Calculate its static spatial load reference value. : .

[0044] (2) Introduce and determine the utilization factor: Based on the functional type, operating time pattern and seasonal usage characteristics of the building, set a dynamic parameter that characterizes its actual operating intensity—the utilization factor. . and Local survey data can be used to determine the value.

[0045] (3) Coupling correction to obtain effective spatial load value: by using coefficients By coupling and correcting the static reference values, the effective spatial load values ​​reflecting the actual usage load of the building are obtained. : .

[0046] (4) Detailed allocation: Calculate the effective spatial load value of the single building. The weight of the total effective load value of all buildings in its category is used to initially allocate carbon emissions for the category. A secondary allocation is performed to obtain the refined carbon emissions of this individual building. : .

[0047] Finally, the generation module generates a gridded spatial distribution list of building carbon emissions covering the entire target area based on the refined carbon emissions of all individual buildings.

[0048] The refined carbon emissions of all individual buildings calculated by the second calculation module are associated with their corresponding building geographic coordinates (such as the centroid of the building outline) to form a point source emission spatial dataset. Using spatial analysis tools from a Geographic Information System (GIS), this dataset is aggregated into regular geographic grid cells of a preset scale (e.g., 1km×1km, 5km×5km, or other scales set according to management needs), and the cumulative carbon emissions within each grid cell are calculated. Finally, a high-spatial-resolution gridded spatial distribution list of building carbon emissions covering the entire target area is generated.

[0049] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for constructing a spatialized inventory of building carbon emissions based on behavior and load coupling, characterized in that, Includes the following steps: S1. Obtain macro-level total carbon emission data of building operations in the target area, a basic geographic information database of buildings containing the geospatial outline and functional attribute codes of all individual buildings, and spatiotemporal behavior characteristic data of residents; S2. Calculate the preliminary carbon emission allocation for each type of building based on residents' spatiotemporal behavior data; S3. Based on the preliminary carbon emission allocation, calculate the refined carbon emission of each individual building; S4. Based on the refined carbon emissions of all individual buildings, generate a gridded spatial distribution list of building carbon emissions covering the entire target area.

2. The method for constructing a spatialized inventory of building carbon emissions based on behavior and load coupling according to claim 1, characterized in that, S2 includes: Based on residents' spatiotemporal behavior characteristics data, the average daily effective stay time of residents in various types of buildings is extracted, and the contribution weight of the time dimension is calculated. Based on the basic geographic information data of buildings, the total land area of ​​various types of buildings is obtained, and the spatial dimension contribution weight is calculated by combining the corresponding benchmark value of personnel carrying capacity per unit area. Based on the contribution weights of the time dimension and the spatial dimension, a comprehensive category weight coefficient is synthesized. Based on the category-based comprehensive weighting coefficient, the macro-level total data is initially allocated to various types of buildings to obtain the preliminary carbon emission allocation for each category.

3. The method for constructing a spatialized inventory of building carbon emissions based on behavior and load coupling according to claim 2, characterized in that, S3 includes: For individual buildings under the target category, calculate the static space load benchmark value of the individual building based on the building's floor area and the corresponding personnel carrying density benchmark value per unit area. The utilization coefficient is determined based on the functional type, operating time pattern, and seasonal usage characteristics of the individual building. Calculate the effective space load value based on the static space load benchmark value and utilization factor of the individual building; The refined carbon emissions of a single building are calculated based on its effective spatial load value.

4. The method for constructing a spatialized inventory of building carbon emissions based on behavior and load coupling according to claim 2, characterized in that, When synthesizing the overall weight coefficient of the category, a weighted average is calculated for the contribution weights of the time dimension and the spatial dimension, with a preset adjustment factor introduced to balance the contributions of time and spatial factors.

5. The method for constructing a spatialized inventory of building carbon emissions based on behavior and load coupling according to claim 3, characterized in that, The effective space load value is obtained by multiplying the static space load reference value by the utilization factor.

6. The method for constructing a spatialized inventory of building carbon emissions based on behavior and load coupling according to claim 1, characterized in that, S4 includes: The refined carbon emissions of all individual buildings are linked to their geographical coordinates to form a spatial dataset of point source emissions. The spatial dataset of point source emissions is aggregated into regular geographic grid cells of a preset scale, and the total cumulative carbon emissions within each grid cell are calculated.

7. The method for constructing a spatialized inventory of building carbon emissions based on behavior and load coupling according to claim 6, characterized in that, The geographic coordinates are the centroid coordinates of the building outline; the preset scale regular geographic grid unit is a geographic grid unit of 1km×1km or 5km×5km.

8. A spatialized inventory construction system for building carbon emissions based on behavior and load coupling, the system being used to implement the method described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to acquire macro-level total carbon emissions data of building operations in the target area, a basic geographic information database of buildings containing the geospatial outlines and functional attribute codes of all individual buildings, and spatiotemporal behavior characteristic data of residents. The first calculation module is used to calculate the preliminary carbon emission allocation of each type of building based on residents' spatiotemporal behavior characteristics data; The second calculation module is used to calculate the refined carbon emissions of each individual building based on the preliminary carbon emission allocation. The generation module is used to generate a gridded spatial distribution list of building carbon emissions covering the entire target area based on the refined carbon emissions of all individual buildings.

Images