Method for monitoring carbon emission of urban living space

Abstract

The invention relates to the technical field of carbon emission monitoring, and particularly discloses a town living space carbon emission monitoring method, which comprises the following steps: firstly, checking carbon emission in energy consumption of town residents; identifying urban residential building types and geographic spaces; the residences are grouped according to the building age, and different groups are endowed with differentiated carbon emission allocation weights; building features are introduced to serve as supplementary factors of carbon emission weight distribution; then, the weight of each factor is superposed, and the total carbon emission of urban resident energy consumption of the whole city is distributed to each residence land; and finally, obtaining a carbon emission detection and simulation result of each residential land under the condition of considering photovoltaic emission reduction. By the adoption of the urban living space carbon emission monitoring method, carbon emission of residents can be monitored with high resolution on the urban scale, and a scientific and feasible technical path is provided for accurate control of urban living space carbon emission, energy-saving strategy making and photovoltaic emission reduction potential mining.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon emission monitoring, in particular to a method for monitoring carbon emissions of urban living space. BACKGROUND

[0002] At present, the spatial monitoring of urban carbon emissions mainly adopts two methods of "top-down" and "bottom-up" for research. In the model construction of urban residential carbon emission monitoring, early researches mostly use night lighting images, population density and economic variables as data sources. In order to improve the accuracy of residential carbon emission monitoring and promote the realization of micro-scale research, scholars gradually refine the consideration of blocks and single buildings, and tend to use geographic spatial information such as community point of interest (POI) data, residential building area and building height data as support.

[0003] With the advancement of technology, remote sensing data provided by Baidu Map, Gaode Map and other platforms and Open Street Map (OSM) data sets provide a new path for large-scale detection of detailed building types by identifying the geometric morphological characteristics, spatial attributes and other parameters of buildings. At the same time, domestic and foreign researches on building area and height have formed various data sets with different spatial resolutions in recent years. Since the construction year of a building has a significant impact on its energy consumption level, building age is one of the important indicators for carbon emission monitoring. Existing researches often use urban morphology methods for prediction. Housing occupancy rate is another important indicator for household energy consumption carbon emission, and its estimation methods include direct investigation and indirect calculation. Previous studies have shown that using night lighting images as a substitute indicator is the most convenient and feasible way for large-scale measurement. At the same time, artificial intelligence technology plays an important role in practical application, and models such as random forest, machine learning algorithm, deep learning, natural language processing and XGBoost model have been widely used.

[0004] In addition, the use of clean energy reduces the consumption of traditional energy, and its impact on urban carbon emission reduction cannot be ignored. For the photovoltaic system on the roof of residential buildings, on the one hand, compared with industrial and commercial roofs, its installation is less common; on the other hand, the scale of residential roof photovoltaic units is small, and higher resolution images are needed to accurately detect. However, a comprehensive assessment of its potential has become a key to effectively promote the implementation of residential roof photovoltaic power generation.

[0005] In general, the improvement of data source diversity and the progress of technology provide strong support for the related research in the fine-scale simulation of large-scale urban carbon emissions, but there are still important challenges in obtaining detailed data of each building, especially non-geometric data. Therefore, in the aspect of "top-down" urban micro-scale carbon accounting, it is urgent to build a new research framework to effectively promote the application of large-scale fine residential space carbon emission monitoring. In addition, under the background of the vigorous promotion of photovoltaic power generation, few studies systematically integrate rooftop photovoltaic systems into the residential carbon accounting system and comprehensively account for the carbon emission reduction potential of their power generation. SUMMARY

[0006] The purpose of the present application is to provide a town residential space carbon emission monitoring method, which can monitor residential carbon emissions at a high resolution on a city scale by integrating multi-source data step by step.

[0007] To achieve the above-mentioned purpose, the present application provides a town residential space carbon emission monitoring method, comprising the following steps: S1, accounting for the carbon emissions in the energy consumption of town residents; S2, identifying the types and living spaces of town residential buildings; S3, grouping the residences according to the building age and assigning different carbon emission allocation weights to different groups; S4, introducing building features 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 emissions of the energy consumption of the town residents to each residential land; S6, identifying residential rooftop photovoltaics based on high-precision remote sensing images, calculating the carbon emission reduction effect of photovoltaic power generation, and obtaining the carbon emission detection and simulation results of each residential land considering the photovoltaic emission reduction.

[0008] Preferably, in S1, based on the functional attributes and core emission sources of town residential land, the accounting object is set as the energy consumption carbon emissions directly related to the daily life of residents in the town living space; The types of residential life energy include direct fossil fuel consumption and indirect fossil fuel consumption, the direct fossil fuel consumption includes coal, oil and natural gas, and the indirect fossil fuel consumption includes electricity and heat energy. The carbon emission coefficient method is used to account for the carbon emissions generated by different energy consumption, and the calculation formula is: ; In the formula, represents the total carbon emissions of urban town residents; E j represents the j consumption of the th energy;j The carbon emission coefficient corresponding to each energy source This indicates the number of energy types selected.

[0009] 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. 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.

[0010] 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.

[0011] 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: 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. 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. 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: ; ; 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 n Residential land.

[0012] 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: ; In the formula, Residential land Carbon emissions from residential consumption The total carbon emissions from urban residents' consumption. , and Residential land Carbon emission weights corresponding to each factor.

[0013] 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; 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.

[0014] Preferably, in S6, the specific method for calculating carbon emission reduction during the photovoltaic power generation operation phase is as follows: An estimate of the annual potential power generation is made by combining the potential of solar radiation and the technological potential of photovoltaic systems: ; 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: ; 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; 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: ; 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. 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 : ; 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: ; 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.

[0015] Therefore, the present invention employs the above-mentioned method for monitoring carbon emissions in urban residential spaces, and the beneficial effects are as follows: 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.

[0016] 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

[0017] 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; 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

[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] 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.

[0020] 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: S1. Calculate the total carbon emissions from urban residents' energy consumption. The specific process is as follows: 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.

[0021] 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: ; 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.

[0022] 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.

[0023] 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.

[0024] S3. Group residential buildings according to their building age and assign differentiated carbon emission allocation weights to different groups, as follows: 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.

[0025] 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: 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.

[0026] 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.

[0027] 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: ; ; 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.

[0028] 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: ; In the formula, Residential land Carbon emissions from residential consumption The total carbon emissions from urban residents' consumption. , and Residential land Carbon emission weights corresponding to each factor.

[0029] 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: 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.

[0030] 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.

[0031] 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: An estimate of the annual potential power generation is made by combining the potential of solar radiation and the technological potential of photovoltaic systems: ; 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: ; 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.

[0032] 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: ; 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. Based on annual power generation and different power gridsRPV System carbon emission reduction coefficient And calculate the first one using the following formula. Carbon emission reduction from photovoltaic power generation on residential land : ; 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: ; In the formula, To consider residential land under the condition of rooftop photovoltaic emission reduction i The final carbon emissions, and Residential land Carbon emissions from residential consumption and carbon emission reductions from photovoltaic power generation.

[0033] 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 residential 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.

[0034] 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 for monitoring carbon emissions from urban residential spaces, characterized in that, Includes the following steps: S1. Calculate carbon emissions from urban residents' energy consumption; S2. Identify urban residential building types and living spaces; S3. Group residential buildings according to their building age and assign different carbon emission allocation weights to different groups; S4. Introduce building features as a supplementary factor in carbon emission weight allocation, and assign corresponding weights to them respectively; 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. 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.

2. The method for monitoring carbon emissions from urban residential spaces according to claim 1, characterized in that, 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. 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: ； 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.

3. The method for monitoring carbon emissions in urban residential spaces according to claim 2, characterized in that, In S2, urban residential building types 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. 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.

4. The method for monitoring carbon emissions in urban residential spaces according to claim 3, characterized in that, In S3, residential buildings are grouped based on 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 for monitoring carbon emissions in urban residential spaces according to claim 4, characterized in that, In S4, building features include building height and residential occupancy rate. Residential occupancy rate is estimated using nighttime light remote sensing imagery, specifically through the following method: 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. 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. 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: ； ； 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 n Residential land.

6. The method for monitoring carbon emissions from urban residential spaces according to claim 5, characterized in that, In S5, by weighting various factors, the total carbon emissions from the city's residential consumption are allocated to each residential land use: ； In the formula, Residential land Carbon emissions from residential consumption The total carbon emissions from urban residents' consumption. , and Residential land Carbon emission weights corresponding to each factor.

7. The method for monitoring carbon emissions from urban residential spaces according to claim 1, characterized in that, 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. 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.

8. The method for monitoring carbon emissions in urban residential spaces according to claim 7, characterized in that, In S6, the specific method for calculating carbon emission reduction during the operation phase of photovoltaic power generation is as follows: An estimate of the annual potential power generation is made by combining the potential of solar radiation and the technological potential of photovoltaic systems: ； 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: ； 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; 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: ； 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. 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 : ； 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: ； 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.

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