A method for monitoring carbon emissions of urban living space

Abstract

The application relates to the technical field of carbon emission monitoring, and particularly discloses a kind of urban residential space carbon emission monitoring methods, including first accounting carbon emissions in urban residents' energy consumption;Urban residential building type and geographical space identification;According to the grouping of residence, different groups are given differentiated carbon emission allocation weight;Introduce building features as a supplementary factor for carbon emission weight allocation;Then superimpose each factor weight, distribute the total carbon emissions of urban residents' energy consumption to each residential land;Finally, the carbon emission detection and simulation results of each residential land under the condition of considering photovoltaic emission reduction are obtained.The application adopts the above-mentioned urban residential space carbon emission monitoring method, which can monitor the carbon emission of residents at a high resolution on a city scale, and provides a scientific and feasible technical path for the precise control of urban residential space carbon emission, the development of energy-saving strategies and the excavation of photovoltaic emission reduction potential.

Description

Technical Field

[0001] This invention relates to the field of carbon emission monitoring technology, and in particular to a method for monitoring carbon emissions in urban residential spaces. Background Technology

[0002] Currently, spatial monitoring of urban carbon emissions primarily employs two methods: top-down and bottom-up approaches. In model building for urban residential carbon emission monitoring, early studies often used nighttime lighting images, population density, and economic variables as data sources. To improve the accuracy of residential carbon emission monitoring and advance micro-scale research, scholars have gradually refined their approach to considering street blocks and individual buildings, tending to use geospatial information such as points of interest (POI) data, residential building footprint, and building height as supporting data.

[0003] With technological advancements, remote sensing data from platforms like Baidu Maps and Gaode Maps, along with datasets such as Open Street Map (OSM), have provided new pathways for large-scale, detailed building type detection by identifying various parameters, including geometric features and spatial attributes. Meanwhile, domestic and international research on building footprint and height has yielded various datasets with different spatial resolutions in recent years. Since the construction year of a building significantly impacts its energy consumption, building age is a crucial indicator for carbon emission monitoring, and current research often employs urban morphology methods for prediction. Housing occupancy rate is another important indicator of household energy consumption and carbon emissions, and its estimation methods include direct surveys and indirect estimations. Previous research has shown that using nighttime lighting images as a substitute indicator is the most convenient and feasible method for large-scale measurement. Furthermore, artificial intelligence technologies have played a significant role in practical applications, with models such as random forests, machine learning algorithms, and deep learning, as well as techniques like natural language processing and XGBoost models, all widely used.

[0004] Furthermore, the use of clean energy reduces the consumption of traditional energy sources, and its impact on urban carbon emission reduction cannot be ignored. Regarding residential rooftop photovoltaic systems, on the one hand, their installation is less common compared to industrial and commercial rooftops; on the other hand, residential rooftop photovoltaic units are smaller in scale, requiring higher resolution images for accurate detection. However, a comprehensive assessment of their potential has become crucial for effectively promoting the realization of residential rooftop photovoltaic power generation.

[0005] Overall, the increased diversity of data sources and technological advancements have provided strong support for large-scale, high-scale urban carbon emission simulations. However, obtaining detailed data (especially non-geometric data) for each building remains a significant challenge. Therefore, a new research framework is urgently needed for top-down, micro-scale urban carbon accounting to effectively promote the application of large-scale, refined residential space carbon emission monitoring. Furthermore, despite the current vigorous promotion of photovoltaic power generation, few studies have systematically incorporated rooftop photovoltaic systems into residential carbon accounting systems and comprehensively calculated their carbon reduction potential. Summary of the Invention

[0006] The purpose of this invention is to provide a method for monitoring carbon emissions in urban residential spaces, which can monitor residents' carbon emissions at a high resolution on a city scale by integrating multi-source data in stages.

[0007] To achieve the above objectives, the present invention provides a method for monitoring carbon emissions in urban residential spaces, comprising the following steps:

[0008] S1. Calculate carbon emissions from urban residents' energy consumption;

[0009] S2. Identify urban residential building types and living spaces;

[0010] S3. Group residential buildings according to their building age and assign different carbon emission allocation weights to different groups;

[0011] S4. Introduce building features as a supplementary factor in carbon emission weight allocation, and assign corresponding weights to them respectively;

[0012] S5, combined with the weights of the factors in S3 and S4, allocates the total carbon emissions from energy consumption of urban residents in the city to each residential land use.

[0013] S6. Based on high-precision remote sensing images, identify residential rooftop photovoltaics, calculate the carbon emission reduction effect of photovoltaic power generation, and obtain the carbon emission detection and simulation results of each residential land under the condition of photovoltaic emission reduction.

[0014] Preferably, in S1, based on the functional attributes and core emission sources of urban residential land, the accounting object is set as the carbon emissions from energy consumption directly related to residents' daily lives carried in urban residential spaces.

[0015] The types of energy used in residential life are identified as direct fossil fuel consumption and indirect fossil fuel consumption. Direct fossil fuel consumption includes coal, oil, and natural gas, while indirect fossil fuel consumption includes electricity and heat. The carbon emission factor method is used to calculate the carbon emissions generated by different energy consumption methods. The calculation formula is as follows:

[0016] ;

[0017] In the formula, This represents the total carbon emissions from urban and town residents' consumption. E j Indicates the first j Energy consumption; Indicates the first j The carbon emission coefficient corresponding to each energy source This indicates the number of energy types selected.

[0018] Preferably, in S2, the types of urban residential buildings include villas, high-rise buildings, and mid-rise buildings. Different building types have different geometric indicators, including physical properties, shape complexity characteristics, and spatial relationship indicators.

[0019] Points of interest (POIs) are obtained using Geographic Information System (GIS), and the XGBoost machine learning model is used for building type classification and urban residential space identification.

[0020] Preferably, in S3, residential buildings are grouped according to building age data. Buildings in the same age group have similar carbon emission levels, and different carbon emission level allocation weights WA are assigned to each group.

[0021] Preferably, in S4, building features include building height and residential occupancy rate. Residential occupancy rate is estimated using nighttime remote sensing imagery, specifically through the following method:

[0022] First, based on the urban residential space identified by S2 as a mask, nighttime light images are extracted. When selecting reference residential areas, the influence of building height on nighttime light values ​​is taken into account.

[0023] Then, based on the building height data, the residential buildings are grouped, and the residential buildings with the same or similar height and the lowest vacancy rate are selected as the reference values ​​for each group.

[0024] Finally, the occupancy rate of each group was estimated based on the nighttime light index. Building characteristics such as building height and occupancy rate were introduced as factors in the carbon emission weight allocation, and corresponding weights WH and WR were assigned respectively. The calculation formula is as follows:

[0025] ;

[0026] ;

[0027] In the formula, , Residential land The corresponding weights for building height and occupancy rate, and Residential land in the area Corresponding building height and occupancy rate, the area has a total ofn Residential land.

[0028] Preferably, in S5, by weighting the various factors, the total carbon emissions from the city's residential consumption are allocated to each residential land use:

[0029] ;

[0030] In the formula, Residential land Carbon emissions from residential consumption The total carbon emissions from urban and town residents' consumption. , and Residential land Carbon emission weights corresponding to each factor.

[0031] Preferably, in S6, a deep learning model is first used to identify the spatial location of residential rooftop photovoltaic systems based on the acquired high-precision remote sensing image data;

[0032] Based on spatial identification, the carbon emission reduction potential of photovoltaic power generation is calculated. The operation phase of photovoltaic power generation includes the carbon emissions generated by the use of electricity and energy during operation and maintenance, as well as the emission reduction effect of photovoltaic power generation offsetting thermal power and coal power.

[0033] Preferably, in S6, the specific method for calculating carbon emission reduction during the photovoltaic power generation operation phase is as follows:

[0034] An estimate of the annual potential power generation is made by combining the potential of solar radiation and the technological potential of photovoltaic systems:

[0035] ;

[0036] In the formula, For the first The annual potential power generation of a residential land plot, For the conversion efficiency of photovoltaic panels, K For the overall efficiency of the photovoltaic system, For the first Solar radiation potential of a residential land plot The specific calculation formula is as follows:

[0037] ;

[0038] In the formula, Indicates the first Available area for rooftop photovoltaic installations on residential land. Indicates the first The annual surface solar radiation received by rooftop photovoltaic panels installed on a residential plot;

[0039] The combined marginal factor CM, representing the carbon emission intensity of the power grid during power generation, is calculated as the carbon emission reduction factor for the photovoltaic system. This factor is derived from the weighted average of the marginal emission factors of electricity (OM) and capacity (BM). The calculation method is as follows:

[0040] ;

[0041] In the formula, Indicates the carbon emission reduction factor; , , These represent the CM, OM, and BM factors, respectively. and These represent the weights of the OM factor and the BM factor, respectively.

[0042] Based on annual power generation and different power grids RPV System carbon emission reduction coefficient And calculate the first one using the following formula. Carbon emission reduction from photovoltaic power generation on residential land :

[0043] ;

[0044] Finally, based on the allocation of total carbon emissions from residential energy consumption in S5, the potential carbon emission reduction from residential photovoltaic power generation is taken into account to obtain the final carbon emissions of residential space for each residential land use and visualize the results. The calculation formula is as follows:

[0045] ;

[0046] In the formula, To consider residential land under the condition of rooftop photovoltaic emission reduction The final carbon emissions, and Residential land Carbon emissions from residential consumption and carbon emission reductions from photovoltaic power generation.

[0047] Therefore, the present invention employs the above-mentioned method for monitoring carbon emissions in urban residential spaces, and the beneficial effects are as follows:

[0048] This invention significantly improves monitoring accuracy by integrating key indicators such as building age, height, floor area, and occupancy rate, and combining the advantages of macroscopic and microscopic detection methods. At the same time, the method framework also incorporates considerations of photovoltaic carbon emission offsetting, which can calculate the emission reduction contribution value based on power generation, and finally generate a high-resolution carbon emission map of urban residential space.

[0049] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0050] Figure 1 This is an overall flowchart of an embodiment of a method for monitoring carbon emissions in urban residential spaces according to the present invention;

[0051] Figure 2 This is a spatial distribution map of carbon emissions from urban residential land for different building age groups according to an embodiment of the method for monitoring carbon emissions from urban residential space of the present invention. Among them, (a) is the spatial distribution simulation of carbon emissions from urban residential land for building age group A: 1984-1990, (b) is the spatial distribution simulation of carbon emissions from urban residential land for building age group B: 1991-2000, (c) is the spatial distribution simulation of carbon emissions from urban residential land for building age group C: 2001-2010, and (d) is the spatial distribution simulation of carbon emissions from urban residential land for building age group D: 2010-2018. Detailed Implementation

[0052] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0053] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0054] This invention takes the main urban area of ​​City A as an example to monitor carbon emissions from urban residential spaces, such as... Figures 1-2 As shown, a method for monitoring carbon emissions from urban residential spaces includes the following steps:

[0055] S1. Calculate the total carbon emissions from urban residents' energy consumption. The specific process is as follows:

[0056] First, based on the functional attributes and core emission sources of urban residential land, the accounting object is set as the carbon emissions from energy consumption directly related to residents' daily lives carried in urban residential spaces.

[0057] Based on the energy consumption structure balance sheets of each province, the specific types of residential energy consumption are determined to include direct fossil fuel consumption and indirect fossil fuel consumption. Direct fossil fuel consumption includes coal, oil, and natural gas, while indirect fossil fuel consumption includes electricity and heat. The carbon emission factor method is used to calculate the carbon emissions generated by different energy consumptions. The calculation formula is as follows:

[0058] ;

[0059] In the formula, This represents the total carbon emissions from urban and town residents' consumption. E j Indicates the first j Energy consumption; Indicates the first jThe carbon emission coefficient corresponding to each energy source This indicates the number of energy types selected.

[0060] S2. Identify urban residential building types and living spaces. Urban residential building types include villas, high-rise buildings, and mid-rise buildings, etc. Different building types have different geometric indicators, including physical properties, shape complexity characteristics, and spatial relationship indicators. Among them, the parameters of physical properties and shape complexity characteristics are obtained or estimated from existing multi-source datasets. In terms of spatial relationship measurement, the Gaussian kernel density estimation KDE value is intersected with the building footprint to calculate the average population density value of each building.

[0061] Supported by Geographic Information System (GIS), Points of Interest (POIs) are obtained from Open Street Map (OSM) or Gaode Map, and the XGBoost machine learning model is used to classify building types and identify urban residential spaces.

[0062] S3. Group residential buildings according to their building age and assign differentiated carbon emission allocation weights to different groups, as follows:

[0063] Based on existing data on the age of residential buildings obtained from a survey conducted by the Ministry of Housing and Urban-Rural Development, residential buildings are grouped according to their age. Buildings in the same age group have similar carbon emission levels, and different carbon emission level allocation weights (WA) are assigned to each group.

[0064] S4. Introduce building features as a supplementary factor in carbon emission weighting, assigning corresponding weights to each. Building features include building height and residential occupancy rate. Large-scale residential occupancy rate estimation is conducted using nighttime remote sensing imagery. The specific method is as follows:

[0065] First, select nighttime light remote sensing images (such as Luojia-1 nighttime light remote sensing public data (LJ1-01 NTL), with a resolution of 130 meters). Use the urban residential space identified in S2 as a mask to extract nighttime light images. When selecting reference residential areas, consider the impact of building height on nighttime light values.

[0066] Then, based on the building height data, the residential buildings are grouped, and the residential buildings with the same or similar height and the lowest vacancy rate are selected as the reference values ​​for each group.

[0067] Finally, the occupancy rate of each group was estimated based on the nighttime light index. Accordingly, building characteristics such as building height and occupancy rate were introduced as factors in the carbon emission weighting, and corresponding weights WH and WR were assigned, respectively. The calculation formula is as follows:

[0068] ;

[0069] ;

[0070] In the formula, , Residential land The corresponding weights for building height and occupancy rate, and Residential land in the area Corresponding building height and occupancy rate, the area has a total of Residential land.

[0071] S5, combined with the weights of factors in S3 and S4, refines the allocation of total carbon emissions from urban residents' energy consumption to each residential land area:

[0072] ;

[0073] In the formula, Residential land Carbon emissions from residential consumption The total carbon emissions from urban and town residents' consumption. , and Residential land Carbon emission weights corresponding to each factor.

[0074] S6. Based on high-precision remote sensing images, identify residential rooftop photovoltaic systems, calculate the carbon emission reduction effect of photovoltaic power generation, and finally obtain the carbon emission detection and simulation results for each residential land area considering photovoltaic emission reduction conditions. The specific process is as follows:

[0075] First, AI-powered deep learning models are used to accurately identify the spatial locations of residential rooftop photovoltaic (PV) systems based on high-precision remote sensing imagery data. Regarding data sources, existing research has constructed a refined PV extraction dataset from domestically produced Gaofen-2 remote sensing satellite images, achieving a resolution as high as 0.8 meters.

[0076] Based on spatial identification, the carbon reduction potential of photovoltaic power generation is calculated by comparing the carbon emissions of traditional fossil fuel power generation. The operation phase of photovoltaic power generation includes the carbon emissions generated by the use of electricity and energy during operation and maintenance, as well as the emission reduction effect of photovoltaic power generation offsetting thermal power and coal power.

[0077] Since the energy consumption and other data related to manual maintenance are difficult to measure and are far less than the carbon emission reduction, they are not included in the calculation. The specific method for calculating the carbon emission reduction during the operation phase of photovoltaic power generation is as follows:

[0078] An estimate of the annual potential power generation is made by combining the potential of solar radiation and the technological potential of photovoltaic systems:

[0079] ;

[0080] In the formula, For the first The annual potential power generation of a residential land plot, For the conversion efficiency of photovoltaic panels, K For the overall efficiency of the photovoltaic system, For the first Solar radiation potential of a residential land plot The specific calculation formula is as follows:

[0081] ;

[0082] In the formula, Indicates the first Available area for rooftop photovoltaic installations on residential land. Indicates the first The annual surface solar radiation received by rooftop photovoltaic panels installed on a residential site.

[0083] The combined marginal factor CM, representing the carbon emission intensity of the power grid during power generation, is calculated as the carbon emission reduction factor for the photovoltaic system. This factor is derived from the weighted average of the marginal emission factors of electricity (OM) and capacity (BM). The calculation method is as follows:

[0084] ;

[0085] In the formula, Indicates the carbon emission reduction factor; , , These represent the CM, OM, and BM factors, respectively. and These represent the weights of the OM factor and the BM factor, respectively.

[0086] Based on annual power generation and different power grids RPV System carbon emission reduction coefficient And calculate the first one using the following formula. Carbon emission reduction from photovoltaic power generation on residential land :

[0087] ;

[0088] Finally, based on the allocation of total carbon emissions from residential energy consumption in S5, the potential carbon emission reduction from residential photovoltaic power generation is taken into account to obtain the final carbon emissions of residential space for each residential land use and visualize the results. The calculation formula is as follows:

[0089] ;

[0090] In the formula, To consider residential land under the condition of rooftop photovoltaic emission reductioni The final carbon emissions, and Residential land Carbon emissions from residential consumption and carbon emission reductions from photovoltaic power generation.

[0091] Therefore, this invention adopts the aforementioned method for monitoring carbon emissions in urban residential spaces. To address the need for refined monitoring, it constructs a multi-dimensional feature-integrated carbon emission allocation and reduction accounting system: first, it calculates the total carbon emissions from residents' energy consumption, assigning basic weights based on housing type, geographical space, and building age, and then incorporating building height and occupancy rate to optimize the weights, achieving a refined allocation of carbon emissions to each residence; simultaneously, it uses high-precision remote sensing to identify rooftop photovoltaics and calculates the emission reduction effect; finally, it generates carbon emission results and visualizations for residential land that consider photovoltaic emission reduction, providing support for accurate monitoring of carbon emissions in urban residential areas.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method of monitoring carbon emissions of urban residential spaces, characterized by, The method comprises the following steps: S1, calculating the total carbon emission of urban residents' energy consumption; S2, identifying the types of urban residential buildings and living space; S3, grouping the residences according to the building age and assigning different carbon emission allocation weights to different groups; S4, introducing building characteristics as a supplementary factor for carbon emission weight allocation, and assigning corresponding weights respectively; S5, superimposing the weight of each factor in S3 and S4 to allocate the total carbon emission of urban residents' energy consumption to each residential land; S6, based on high-precision remote sensing images, identify residential rooftop photovoltaics, calculate the carbon reduction effect of photovoltaic power generation, and obtain the carbon emission detection and simulation results of each residential land considering photovoltaic emission reduction; In S4, the building characteristics include building height and residential occupancy rate, which is estimated by night light remote sensing images. The specific method is as follows: First, based on the urban living space identified in S2, the night light image is extracted as a mask. When selecting the reference residential area, the influence of building height on night light value is considered; Then, based on the building height data, the residential buildings are grouped, and the residential buildings with the same height or similar height and the lowest vacancy rate are selected as the reference value of each group; Finally, the occupancy rate of each group is estimated according to the night light index. The building characteristics of building height and occupancy rate are introduced as factors for carbon emission weight allocation, and corresponding weights WH and WR are assigned respectively. The calculation formula is as follows: ； ； In the formula, , are the residential land corresponding to the building height, occupancy rate of the weight, and are the residential land corresponding to the building height and occupancy rate, the total number of residential land in the region ; In S6, the specific method for calculating the carbon reduction of photovoltaic power generation in the operation stage is as follows: The annual potential power generation is estimated by combining solar radiation potential and photovoltaic system technical potential: ； wherein is the annual potential electricity production of the th residential lot, is the conversion efficiency of the photovoltaic panel, K is the overall efficiency of the photovoltaic system, is the solar radiation potential of the th residential lot, The specific calculation formula is as follows: ； wherein, represents the annual surface solar radiation received by the roof photovoltaic panel installed in the i-th residential land, represents the available area for roof photovoltaic installation in the i-th residential land, represents the annual surface solar radiation received by the roof photovoltaic panel installed in the i-th residential land, represents the available area for roof photovoltaic installation in the i-th residential land, The combination marginal factor CM of the weighted average value OM of the marginal emission factor of electricity and the weighted average value BM of the capacity marginal emission factor is used to represent the carbon emission intensity of the power grid in the power generation process, which is used as the carbon emission reduction factor of the photovoltaic system. The calculation method is as follows: ； wherein, represents the carbon reduction factor of the photovoltaic system; , , respectively represent the CM, OM, BM factors, and respectively represent the weight of the OM factor and the BM factor; According to the first year potential electricity generation of the residential land and the carbon reduction factor of the photovoltaic system , and combining the following formula, the carbon reduction amount of photovoltaic power generation of the first residential land is calculated : ； Finally, based on the distribution of total carbon emission of residents' energy consumption in S5, the potential carbon emission reduction of photovoltaic power generation is considered, the final carbon emission of each residential land is obtained, and the results are visualized. The calculation formula is as follows: ； wherein is the final carbon emission of the residential land under the condition of roof photovoltaic emission reduction, and are the residential carbon emission and the photovoltaic carbon emission reduction of the residential land respectively.

2. The method of claim 1, wherein, In S1, based on the functional attributes and core emission sources of urban residential land, the calculation object is set as the energy consumption carbon emission directly related to residents' daily life in urban living space; The types of residential life energy include direct fossil fuel consumption and indirect fossil fuel consumption. Direct fossil fuel consumption includes coal, oil and natural gas. Indirect fossil fuel consumption includes electricity and heat. Carbon emission coefficient method is used to calculate the carbon emission of different energy consumption. The calculation formula is as follows: ； In the formula, represents the total carbon emissions of urban residents' energy consumption; represents the energy consumption of the th energy type; represents the energy consumption of the th energy type; represents the number of selected energy types.

3. The method of claim 2, wherein, In S2, the types of urban residential buildings include villas, high-rise buildings and small high-rise buildings. Different building types have different geometric indicators, including physical attributes, shape complexity characteristics and spatial relationship indicators; XGBoost machine learning model is used for building type classification and urban living space identification based on geographic information system (GIS).

4. The method of claim 3, wherein, In S3, the residences are grouped according to the building age data. Buildings in the same age group have similar carbon emission levels, and different carbon emission level allocation weights WA are assigned to each group.

5. The method of claim 4, wherein, In S5, the total carbon emissions of the city's residents are allocated to each residential land by superimposing the weights of each factor: ； In the formula, Carbon emissions of residential land Carbon emissions of residential land Carbon emissions of residential land Carbon emissions of residential land Carbon emissions of residential land Carbon emissions of residential land Carbon emissions of residential land 6. The method of claim 1, wherein, In S6, first, a deep learning model is used to identify the spatial location of residential rooftop photovoltaic based on the obtained high-precision remote sensing image data; Based on spatial recognition, the carbon reduction potential of photovoltaic power generation is calculated. The operation stage of photovoltaic power generation includes the carbon emissions generated by the use of electricity and energy during operation and maintenance, as well as the carbon reduction effect of photovoltaic power generation offsetting the power generated by thermal power and coal power.

Images