Construction method of roof greening module full life cycle carbon emission reduction evaluation model

Abstract

The invention relates to a construction method of a roof greening module full life cycle carbon emission reduction evaluation model. The method is suitable for carbon emission reduction. According to the technical scheme, the construction method of the roof greening module full-life-cycle carbon emission reduction evaluation model comprises the steps that the carbon emission amount and the carbon sink amount of multiple sets of roof greening modules in the full life cycle are obtained, and the carbon emission amount comprises the operation stage carbon emission amount and the maintenance stage carbon emission amount; obtaining a carbon emission factor index and a carbon sink factor index of each roof greening module, wherein the carbon emission factor index comprises an operation stage carbon emission factor index and a maintenance stage carbon emission factor index; and performing correlation analysis and principal component analysis on the basis of the carbon emission amount and the carbon sink amount of the whole life cycle as well as the carbon emission factor indexes and the carbon sink factor indexes, and constructing a whole life cycle carbon emission reduction evaluation model of the roof greening module on the basis of a correlation analysis result and a principal component analysis result.

Description

TECHNICAL FIELD

[0001] The present application relates to a construction method of a roof greening module full life cycle carbon emission reduction evaluation model. BACKGROUND

[0002] The design of roof greening modules, as an important part of ecological architecture, can greatly promote the development of ecological building design concepts by organically combining greening elements with buildings. As an effective carbon sink measure, roof greening modules can absorb carbon dioxide through photosynthesis to reduce greenhouse gas emissions, significantly improve urban green space ratio, increase carbon sink capacity, and thus directly contribute to the realization of carbon emission reduction targets. At the same time, they can also effectively improve air quality, absorb harmful gases and particulate matter, and reduce urban noise. Therefore, roof greening modules can play a unique advantage for commercial buildings, public buildings, and residential buildings, providing new solutions for urban construction and management to promote the development of green buildings.

[0003] In the design of roof greening modules, two major factors need to be focused on: carbon emission benefits and carbon emission reduction benefits in the full life cycle. The full life cycle includes material selection, construction, use, maintenance, and final demolition and disposal stages. Specifically, the design of roof greening modules needs to focus on the impact of soil substrates on carbon emissions, the impact of selected plants on carbon emissions, and the impact of the full life cycle on carbon emissions. At the same time, it also needs to focus on the impact of different soil substrates and plants on carbon emission reduction benefits.

[0004] However, due to the influence of various factors on carbon emissions of roof greening modules in different stages of the full life cycle, the interaction between these factors and their impact on the full cycle carbon emission effect of roof greening modules is not clear. This leads to the inability to accurately quantify the carbon emissions of different roof greening modules in the full life cycle, and existing technologies cannot accurately quantify the carbon emission benefits and carbon emission reduction benefits of different soil substrates and corresponding plants. The above problems have resulted in many difficulties in the design and application of roof greening modules. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a construction method of a roof greening module full life cycle carbon emission reduction evaluation model in view of the above-mentioned problems.

[0006] The technical solution adopted by the present application is: a construction method of a roof greening module full life cycle carbon emission reduction evaluation model, comprising:

[0007] S100, obtaining a plurality of sets of carbon emissions and carbon sinks of roof greening modules in the full life cycle, the carbon emissions including operating stage carbon emissions and maintenance stage carbon emissions;

[0008] S200, acquiring carbon emission factor indexes and carbon sink factor indexes of each roof greening module, the carbon emission factor indexes including operation stage carbon emission factor indexes and maintenance stage carbon emission factor indexes;

[0009] S300, performing correlation analysis and principal component analysis based on the life cycle carbon emission and carbon sink, and the carbon emission factor indexes and the carbon sink factor indexes, and constructing a roof greening module life cycle carbon emission reduction evaluation model based on the correlation analysis result and the principal component analysis result.

[0010] The correlation analysis comprises:

[0011] The operation stage carbon emission, the maintenance stage carbon emission or the carbon sink of different roof greening modules is defined as a first variable, and each index of different roof greening modules is defined as a second variable.

[0012] The correlation coefficient of each index with the operation stage carbon emission, the maintenance stage carbon emission and the carbon sink is calculated based on the Pearson correlation coefficient calculation formula.

[0013] The principal component analysis comprises:

[0014] The covariance matrix of different indexes is calculated, the eigenvalues and corresponding eigenvectors are obtained by performing eigenvalue decomposition on the covariance matrix, a plurality of eigenvectors with eigenvalues greater than 1 are taken as principal components, and the correlation coefficients of each index and the principal components are calculated.

[0015] Before the principal component analysis, KMO and / or Bartlett sphericity test is performed on each index, and when the statistical value of KMO is greater than a KMO set value or the significance value of Bartlett sphericity test is less than a Bartlett set value, the principal component analysis is performed on the index.

[0016] The operation stage carbon emission factor indexes comprise plant height, stomatal resistance, roughness, soil substrate thickness, solar absorption rate, leaf reflectivity, roof heat transfer coefficient, leaf area index LAI, soil thermal conductivity, plant species, irrigation frequency, irrigation water consumption and irrigation power consumption.

[0017] The maintenance stage carbon emission factor indexes comprise fertilizer consumption, maintenance equipment use frequency, maintenance equipment energy consumption, maintenance time and greening waste amount.

[0018] The carbon sink factor indexes comprise carbon sequestration capacity, plant annual growth, plant photosynthesis rate, plant biomass, soil organic carbon content and plant diversity index.

[0019] An application method of the roof greening module life cycle carbon emission reduction evaluation model constructed by the construction method comprises:

[0020] Based on the known factor index and the whole life cycle carbon emission reduction evaluation target, the factor index that can meet the whole life cycle carbon emission reduction evaluation target is determined from the selected scheme of the remaining factor index.

[0021] The beneficial effects of the present application are: the present application analyzes the carbon emission reduction benefit and its influencing factor index through correlation analysis and principal component analysis, and constructs a whole life cycle carbon emission reduction evaluation model of the roof greening module based on the analysis result, quantitatively studies the relationship between each factor index of the roof greening module and the carbon emission and carbon sink, and provides a reference for subsequent related research. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 It is a flow chart of the construction method of the whole life cycle carbon emission reduction evaluation model of the roof greening module according to the embodiment of the present application.

[0023] Figure 2 It is a technical roadmap of the construction method of the whole life cycle carbon emission reduction evaluation model of the roof greening module according to an embodiment of the present application.

[0024] Figure 3 It is a correlation analysis result graph of the normalized index and the corresponding correlation coefficient according to the embodiment of the present application.

[0025] Figure 4 It is a schematic diagram of the plant carbon sequestration carbon sink of different plant combinations according to the embodiment of the present application. DETAILED DESCRIPTION

[0026] The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of the specification. Instead, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of the specification as detailed in the appended claims.

[0027] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in the specification. In some other embodiments, the steps included in the method can be more or less than described in the specification. In addition, a single step described in the specification may, in other embodiments, be divided into multiple steps for description; and multiple steps described in the specification may, in other embodiments, be combined into a single step for description.

[0028] The full life cycle of the roof greening module refers to the full stage of material production, material transportation, construction, daily use and maintenance management, however, there are many influencing factors affecting carbon emission reduction in the full stage of the roof greening module and the interaction relationship is complex, the embodiment aims to comprehensively and fairly systematically study the carbon emission reduction influencing factors of the roof greening module in the full life cycle, to determine the main driving factors, the mutual relationship and the comprehensive influence on carbon emission, to analyze and study the influencing factors by using correlation analysis method and principal component analysis method, to construct a roof greening module full life cycle carbon emission reduction evaluation model which can quantitatively evaluate the carbon emission reduction benefit of the roof greening module full life cycle, to provide scientific basis and reasonable data support for the evaluation of subsequent carbon emission reduction strategies.

[0029] The embodiment is a construction method of a roof greening module full life cycle carbon emission reduction evaluation model, which specifically comprises the following steps:

[0030] S100, obtaining carbon emission and carbon sink of multiple groups of roof greening modules in the full life cycle, the carbon emission including operation stage carbon emission and maintenance stage carbon emission.

[0031] S200, obtaining carbon emission factor index and carbon sink factor index of each roof greening module, the carbon emission factor index including operation stage carbon emission factor index and maintenance stage carbon emission factor index, and performing normalization processing on the operation stage carbon emission factor index, the maintenance stage carbon emission factor index and the carbon sink factor index to obtain multiple normalized indexes.

[0032] S300, based on the carbon emission and carbon sink in the full life cycle, and the carbon emission factor index and the carbon sink factor index, performing correlation analysis and principal component analysis, and constructing a roof greening module full life cycle carbon emission reduction evaluation model based on the correlation analysis result and the principal component analysis result.

[0033] Different thicknesses of soil substrates have different heat capacities and thermal conductivities, thereby affecting the heat preservation and insulation effect of the roof, and different plants have different carbon sink amounts, therefore, in order to consider different combinations of plants and different thickness characteristics of soil substrates, the multiple groups of roof greening modules selected in step S100 are selected as roof greening modules with different plants and different soil substrate thicknesses, wherein the different plants are different combinations of multiple ground cover plants and herbaceous plants, and the different soil substrate thicknesses include thin layer soil substrate thickness, relatively thin layer soil substrate thickness, medium layer soil substrate thickness and thick layer soil substrate thickness.

[0034] In some specific embodiments, the selected plant combination is any combination of 3 ground cover plants (sedum lineare, sedum lineare, and thymus serpyllum) and 2 herbaceous plants (iris and clover), and the specific plant combination table is shown in Table 1 as follows:

[0035] Table 1: Plant combination table

[0036]

[0037]

[0038] In some embodiments, the different soil substrate thicknesses selected include a thin layer soil substrate thickness of 0.1 m, a relatively thin layer soil substrate thickness of 0.2 m, a medium layer soil substrate thickness of 0.3 m, and a thick layer soil substrate thickness of 0.4 m. The different plant combinations are arranged with the different soil substrate thicknesses, and symmetrical repetitions are removed to obtain 120 groups of roof greening modules.

[0039] In some embodiments, the carbon sink amount of each group of roof greening modules in the full life cycle in step S100 is the sum of the plant carbon fixation carbon sink amount and the soil carbon fixation carbon sink amount.

[0040] The net carbon emission amount of the roof greening module is defined as the difference between the total carbon sink amount of the full life cycle carbon emission amount, and the sum of the plant carbon fixation carbon sink amount and the soil carbon fixation carbon sink amount. The specific calculation formula is as follows:

[0041] CE = CQ - CS;

[0042] CS = C + SOCS;

[0043] Wherein CE is the net carbon emission amount, CQ is the full life cycle carbon emission amount, CS is the carbon sink amount, C is the plant carbon fixation carbon sink amount, and SOCS is the soil carbon fixation carbon sink amount, all in kg.

[0044] It should be noted that the full life cycle carbon emission amount includes the operation phase carbon emission amount, the maintenance phase carbon emission amount, the material production phase carbon emission amount, the transportation phase carbon emission amount, and the construction phase carbon emission amount. However, the material production phase carbon emission amount, the transportation phase carbon emission amount, and the construction phase carbon emission amount only occur during the construction period of the roof greening module construction project, and once completed, no carbon emission amount will be generated. The operation phase carbon emission amount and the maintenance phase carbon emission amount are affected by many index factors, and new carbon emission amounts are generated every year after the roof greening module is completed. Therefore, only the operation phase carbon emission amount and the maintenance phase carbon emission amount are considered when constructing the roof greening module full life cycle carbon emission model, but the full life cycle carbon emission amount is calculated when calculating the full life cycle carbon emission amount.

[0045] Regarding the calculation of the plant carbon fixation carbon sink amount: obtain the same plant total biomass of the same kind of plant in a unit area, and take the sum of the product of the plant total biomass of all kinds of plants and the corresponding carbon content coefficient to obtain the plant carbon fixation carbon sink amount.

[0046] The biomass of each individual plant is calculated and the total biomass of the same plant species in the unit area is obtained by summing up the biomass of all individual plants of the same species in the unit area, and the calculation formula is as follows:

[0047] W=a(D 2 H) b

[0048] wherein W is the biomass of an individual plant, a and b are estimated parameters in the equation, D is the diameter at breast height or ground diameter, m; and H is the plant height;

[0049] The total biomass of the same plant is obtained by taking 0.5 m x 0.5 m quadrat as the unit area, summing up the biomass of all individual plants of the same species in the unit area, and obtaining B. The plant carbon sequestration carbon sink amount of the current species is obtained by multiplying the total biomass of all plants of all species and the corresponding carbon content factor. The plant carbon sequestration carbon sink amount of the roof greening module is obtained by summing up the plant carbon sequestration carbon sink amount of all species. When calculating the carbon sequestration carbon sink amount of plants, the carbon content factors of different plants are usually not the same. The carbon content factor (Carbon Content Factor, CCF) refers to the proportion of carbon content in unit dry weight of biomass, which reflects the concentration of carbon in plant tissues.

[0050] Regarding the calculation of the soil carbon sequestration carbon sink amount: the soil carbon sequestration carbon sink amount is obtained by multiplying the soil organic carbon content rate, the soil depth, and the soil bulk density, and the calculation formula is as follows:

[0051] SOCS=SOCxDxB

[0052] wherein SOCS is the soil carbon sequestration carbon sink amount, kg; SOC is the soil organic carbon content rate; D is the soil depth, m; and B is the soil bulk density, kg / m.

[0053] In some specific embodiments, the carbon emission factor index in the operation stage refers to factors affecting the carbon emission amount of the roof greening module in the operation stage, including but not limited to plant height, stomatal resistance, roughness, soil substrate thickness, solar absorption rate, leaf reflectivity, roof heat transfer coefficient, leaf area index LAI, soil thermal conductivity, plant species, irrigation frequency, irrigation water quantity, and irrigation electricity quantity.

[0054] In some specific embodiments, the carbon emission factor index in the maintenance stage refers to factors affecting the carbon emission amount of the roof greening module in the maintenance stage, including but not limited to fertilizer usage, maintenance equipment usage frequency, maintenance equipment energy consumption, maintenance time, and greening waste quantity.

[0055] In some specific embodiments, carbon sink factor indicators refer to factors that affect the amount of carbon sink in the rooftop greening module throughout its entire life cycle, including but not limited to carbon sequestration capacity, annual plant growth, plant photosynthetic rate, plant biomass, soil organic carbon content, and plant diversity index.

[0056] In some specific embodiments, a total of 24 indicators were compiled, including carbon emission factor indicators during the operation phase, carbon emission factor indicators during the maintenance phase, and carbon sink factor indicators. The indicator tables for the carbon emission factor indicators during the operation phase, carbon emission factor indicators during the maintenance phase, and carbon sink factor indicators are shown in Table 2 below:

[0057] Table 2 Indicator Table

[0058]

[0059]

[0060]

[0061] As shown in Table 2 above, different indicators have different dimensions. To avoid the influence of dimensions and physical units on the analysis, the indicators are standardized to obtain normalized indicators. The standardization formula is as follows:

[0062]

[0063] In the formula: x' is the normalization index after data standardization; x is the index before data standardization; min x is the minimum value of the original data; max x is the maximum value of the original data.

[0064] In some specific embodiments, step S300 uses correlation analysis or principal component analysis to analyze the normalized index to obtain the correlation coefficient between the normalized index and the final carbon emission reduction benefit, and then builds a full life cycle carbon emission reduction model for the roof greening module based on the normalized index and the correlation coefficient.

[0065] Correlation analysis is used to explore the relationships and degree of correlation between two or more variables. In this study, correlation analysis explores the correlation coefficients between different normalized indicators and carbon emissions during the operation phase, carbon emissions during the maintenance phase, and carbon sinks. This allows for the determination of the linear relationship between different normalized indicators and the corresponding factors affecting the carbon reduction benefits of rooftop greening modules, and the assessment of the strength and direction of this relationship. The correlation coefficient is the core indicator for measuring this relationship, with a value ranging from -1 to 1. Specifically: a positive correlation coefficient indicates a positive relationship between the normalized indicator and the corresponding factor affecting the carbon reduction benefits of the rooftop greening module, meaning that an increase in one variable leads to an increase in the other; a negative correlation coefficient indicates a negative relationship, meaning an increase in one variable leads to a decrease in the other; and a correlation coefficient close to zero indicates no significant linear relationship between the normalized indicator and the corresponding factor affecting the carbon reduction benefits of the rooftop greening module.

[0066] In some specific embodiments, the Pearson correlation coefficient method is used to perform correlation analysis on the normalized index to obtain the Pearson correlation coefficients between the normalized index and carbon emissions during the operation phase, carbon emissions during the maintenance phase, and carbon sinks as correlation coefficients.

[0067] Correspondingly, in the correlation analysis step, the carbon emissions during the operation phase, carbon emissions during the maintenance phase, or carbon sink of different rooftop greening modules are defined as the first variable, and each normalized index of different rooftop greening modules is defined as the second variable. Based on the first and second variables, the correlation coefficient between each normalized index and the carbon emissions during the operation phase, carbon emissions during the maintenance phase, and carbon sink is calculated according to the Pearson correlation coefficient calculation formula.

[0068] Specifically, when the carbon emissions during the operation phase of different rooftop greening modules are defined as the first variable, the correlation coefficient between each normalized indicator and the carbon emissions during the operation phase is calculated based on the first and second variables using the Pearson correlation coefficient formula. When the carbon emissions during the maintenance phase of different rooftop greening modules are defined as the first variable, the correlation coefficient between each normalized indicator and the carbon emissions during the maintenance phase is calculated based on the first and second variables using the Pearson correlation coefficient formula. When the carbon sink of different rooftop greening modules is defined as the first variable, the correlation coefficient between each normalized indicator and the carbon sink is calculated based on the first and second variables using the Pearson correlation coefficient formula.

[0069] Specifically, the formula for calculating the Pearson correlation coefficient is as follows:

[0070]

[0071] Where r xyX is the correlation coefficient between the first and second variables. i Let y be the i-th sample in the second variable x, corresponding to a normalized index of the i-th rooftop greening module; i Let represent the i-th sample in the first variable y, where the first variable is the carbon emissions during the operation phase, the carbon emissions during the maintenance phase, or the carbon sink. Let represent the carbon emissions during the operation phase, the carbon emissions during the maintenance phase, or the carbon sink of the i-th rooftop greening module. This represents the average of the second variable. This represents the average value of the first variable.

[0072] In this embodiment, a correlation coefficient of 0.8-1.0 indicates a very strong correlation, 0.6-0.8 indicates a strong correlation, 0.4-0.6 indicates a moderate correlation, 0.2-0.4 indicates a weak correlation, and 0-0.2 indicates a very weak correlation or no correlation.

[0073] In some specific implementations, Principal Component Analysis (PCA) reveals the intrinsic structure and characteristics of data by reducing high-dimensional data to low-dimensional data. Its core idea is to transform the original normalized indices into principal components through linear transformation. These principal components are sorted according to the magnitude of the variance explained in the data, thereby reducing the dimensionality of the data and retaining as much information as possible. PCA projects the original high-dimensional data into a low-dimensional space by identifying the most important directions in the data, forming a new set of variables. These new variables (principal components) are linear combinations of the original normalized indices and are orthogonal (uncorrelated) to each other. PCA can effectively reduce the dimensionality of normalized indices, retain key information, and simplify the data structure.

[0074] Specifically, in the principal component analysis step, the same normalized index of different roof greening modules is standardized to obtain a standard normalized index. The covariance matrix of different standard normalized indices is calculated. The covariance matrix is ​​decomposed into eigenvalues ​​to obtain eigenvalues ​​and corresponding eigenvectors. Several eigenvectors with eigenvalues ​​greater than 1 are selected as principal components. The correlation coefficient between the normalized index and the principal components is calculated.

[0075] In this embodiment, the purpose of standardizing the same normalized index for different rooftop greening modules to obtain the standard normalized index is to ensure that all standard normalized indices are on the same scale. The covariance matrix is ​​calculated to obtain the correlation between the standard normalized indices. Eigenvalue decomposition is used to obtain the principal components and their importance. The eigenvalues ​​represent the variance explained by each principal component, and the eigenvectors define the direction of the principal components.

[0076] In some specific implementations, Mr. Huang calculated the mean and standard deviation of the normalized index for different rooftop greening modules, and then calculated the difference between each normalized index and the mean, and made a quotient with the standard deviation to obtain the standard normalized index.

[0077] Specifically, the standardized index for different rooftop greening modules is obtained by standardizing the same normalized index as follows:

[0078]

[0079] Where x i It is a normalized index, where m is the current number of normalized indices, and Z is the number of indices. i It is a standard normalization indicator;

[0080] The covariance matrix of different standard normalization indices is represented as an n-column, m-row matrix:

[0081] Z = [z i ]

[0082] Where n is the number of different normalization indicators, and m is the number of each normalization indicator.

[0083] Eigenvalue decomposition of the covariance matrix yields eigenvalues ​​and corresponding eigenvectors. Several eigenvectors with eigenvalues ​​greater than 1 are selected as principal components. The correlation coefficient between the normalization index and the principal components is calculated. Ultimately, the principal components can be expressed as:

[0084]

[0085] Where Y d Let u be the d-th principal component. There is no correlation between the principal components. dn The correlation coefficient is the nth standard normalized index.

[0086] In addition, prior to the principal component analysis step, the KMO and / or Bartlett's test of sphericity are performed on the normalization indicators. Principal component analysis is then performed on the normalization indicators only if the KMO statistic is greater than the KMO threshold or the Bartlett's test of sphericity shows a significance level less than the Bartlett's threshold. In other words, the correlation of the normalization indicators is first assessed using the KMO and / or Bartlett's test of sphericity. A strong correlation exists between the various normalization indicators if the KMO statistic is greater than the KMO threshold or the Bartlett's test of sphericity shows a significance level less than the Bartlett's threshold.

[0087] In some specific embodiments, KMO is set to 0.7 and Bartlett is set to 0.01.

[0088] This embodiment constructs a full life cycle carbon emission reduction model for rooftop greening modules after analyzing and obtaining various normalized indicators and their corresponding correlation coefficients. This full life cycle carbon emission reduction model for rooftop greening modules records different normalized indicators and their corresponding correlation coefficients. When it is necessary to adjust the carbon emission reduction scheme, the corresponding normalized indicator can be located and adjusted.

[0089] In some specific embodiments, after conducting actual correlation analysis on 120 groups of rooftop greening modules, the correlation analysis results of the normalized index and the corresponding correlation coefficient are shown in Table 3. Figure 3 As shown,

[0090] Table 3. Results of Correlation Analysis

[0091]

[0092]

[0093]

[0094] Where *p < 0.05, **p < 0.01.

[0095] It is evident that plant height (X1) shows a significant negative correlation with carbon emissions during the operation phase, with correlation coefficients of -0.533; soil matrix thickness (X4) shows significant negative correlations with both operation and maintenance phases, with correlation coefficients of -0.711 and -0.421, respectively; roof heat transfer coefficient (X7) shows significant positive correlations with both operation and maintenance phases, with correlation coefficients of 0.839 and 0.646, respectively; plant species (X10) shows significant correlations with carbon emissions during operation, maintenance, and carbon sinks, with correlation coefficients of -0.830, -0.832, and 0.454, respectively; irrigation frequency (X11) and... Carbon emissions during the operation phase, carbon emissions during the maintenance phase, and carbon sinks showed significant correlations, with correlation coefficients of -0.919, -0.844, and 0.382, respectively. Irrigation water consumption (X12) showed a significant negative correlation with operation and maintenance / dismantling, with correlation coefficients of -0.912 and -0.824, respectively. Irrigation electricity consumption (X13) showed a significant negative correlation with operation and maintenance / dismantling, with correlation coefficients of -0.706 and -0.546, respectively. The frequency of maintenance equipment use (X15) showed significant correlations with operation and maintenance / dismantling, and carbon sinks, with correlation coefficients of -0.828, -0.821, and 0.416, respectively. Energy consumption of maintenance equipment (X16) showed a significant correlation with operation and maintenance / dismantling. The demolition showed a significant negative correlation with operation and use, maintenance and demolition, and carbon sequestration, with correlation coefficients of -0.743 and -0.614, respectively. Maintenance time (X17) showed a significant correlation with operation and use, maintenance and demolition, and carbon sequestration, with correlation coefficients of -0.848, -0.851, and 0.373, respectively. Green waste volume (X18) showed a significant correlation with operation and use, maintenance and demolition, and carbon sequestration, with correlation coefficients of -0.760, -0.777, and 0.432, respectively. Carbon sequestration capacity (X19) showed a significant correlation with operation and use, maintenance and demolition, and carbon sequestration, with correlation coefficients of -0.697, -0.765, and 0.519, respectively. Annual plant growth (X20) showed a significant negative correlation with operation and use, maintenance and demolition, with correlation coefficients of... The correlation coefficients were -0.851 and -0.817, respectively; plant photosynthetic rate (X21) showed a significant correlation with operation, maintenance, dismantling, and carbon sequestration, with correlation coefficients of -0.838, -0.854, and 0.427, respectively; plant biomass (X22) showed a significant correlation with operation, maintenance, dismantling, and carbon sequestration, with correlation coefficients of -0.848 and -0.863, respectively; soil organic carbon content (X23) showed a significant correlation with operation, maintenance, dismantling, and carbon sequestration, with correlation coefficients of -0.781, -0.864, and 0.373, respectively; and plant diversity index (X24) showed a significant negative correlation with operation, maintenance, dismantling, and carbon sequestration, with correlation coefficients of -0.773 and -0.668, respectively.

[0096] In some specific embodiments, after performing principal component analysis on 120 groups of rooftop greening modules, the principal component analysis results are shown in Table 4. Furthermore, the principal component index relationship table, which shows the correlation coefficients between the normalized index and the principal components, is constructed and is shown in Table 5.

[0097] Table 4: Principal Component Analysis Results

[0098]

[0099]

[0100] Table 5: Relationship of Principal Component Indices

[0101]

[0102]

[0103] As shown in Table 4, a total of four principal components (Y1, Y2, Y3, and Y4) were obtained. The eigenvalues ​​of each principal component are all greater than 1. The variance explained by these four principal components are 54.079%, 17.122%, 10.473%, and 8.585%, respectively, with a cumulative variance explained of 90.259%, which can reflect more than 90% of the original variable information. The cumulative contribution of component 5 and subsequent components is 9.741%, which contains less than 10% of the original variable information and can be regarded as noise.

[0104] As shown in Table 5, principal component Y1 had the highest contribution rate (54.079%). Y1 was mainly related to soil matrix thickness (X4), roof heat transfer coefficient (X7), plant species (X10), irrigation frequency (X11), irrigation water consumption (X12), irrigation electricity consumption (X13), maintenance equipment usage frequency (X15), maintenance equipment energy consumption (X16), maintenance time (X17), amount of green waste (X18), carbon sequestration capacity (X19), and annual plant growth (X20). The correlation coefficients for plant photosynthetic rate (X21), plant biomass (X22), soil organic carbon content (X23), plant diversity index (X24), and plant height (X1) were 0.658, -0.781, 0.936, 0.888, 0.877, 0.814, 0.941, 0.868, 0.975, 0.862, 0.804, 0.978, 0.954, 0.949, 0.894, 0.874, and 0.65, respectively; Y2 The main correlation coefficients with irrigation electricity consumption (X13), maintenance equipment energy consumption (X16), plant height (X1), solar energy absorption rate (X5), leaf area index (LAI) (X8), leaf reflectance (X6), soil thermal conductivity (X9), soil roughness (X3), and fertilizer application (X14) were 0.544, -0.457, -0.679, 0.781, 0.569, -0.773, -0.567, -0.415, 0.472, and 0.569, respectively. Y3 is mainly correlated with solar energy absorption rate (X5), stomatal resistance (X2), leaf reflectance (X6), soil thermal conductivity (X9), and fertilizer application (X14), with correlation coefficients of 0.435, 0.765, 0.663, -0.834, and 0.423, respectively. Y4 is mainly correlated with soil matrix thickness (X4), stomatal resistance (X2), roughness (X3), and fertilizer application (X14), with correlation coefficients of 0.438, 0.585, 0.790, and -0.647, respectively. Principal component analysis shows that the principal components with the greatest impact on carbon emission reduction throughout the entire life cycle of green roof modules are factors related to carbon sinks, such as carbon sequestration capacity, annual plant growth, plant photosynthetic rate, plant biomass, soil organic carbon content, and plant diversity index. The second most influential principal components are factors related to carbon emissions during the maintenance and dismantling phase, such as plant species, irrigation frequency, irrigation water consumption, and irrigation electricity consumption.

[0105] It should be noted that the carbon reduction calculation model for rooftop greening modules, constructed using the above methods to quantitatively characterize their carbon reduction benefits, can provide a reference for subsequent related research. For example, future research on building carbon reduction should simultaneously focus on carbon sinks and the maintenance / demolition phases, both of which have significant carbon reduction potential. In terms of specific measures, attention should be paid to the carbon sequestration capacity, growth rate, and photosynthetic rate of plants, as well as soil thickness and roughness. Simultaneously, the heat transfer performance of rooftop greening modules and the design of enhanced insulation should be optimized.

[0106] In addition, since plant carbon sequestration accounts for a larger proportion of carbon sequestration than soil carbon sequestration, carbon reduction measures should focus on plant carbon sequestration. In order to improve carbon sequestration efficiency, the optimal plant combination for carbon reduction should be sought based on different soil matrix thicknesses to achieve the goal of carbon reduction.

[0107] In some specific embodiments, the application method for constructing a full life-cycle carbon emission reduction assessment model for rooftop greening modules includes:

[0108] Based on known factor indicators and life cycle carbon emission reduction assessment targets, and combined with the life cycle carbon emission reduction assessment model for rooftop greening modules, the scheme that can meet the life cycle carbon emission reduction assessment targets is determined from the candidate schemes for other factor indicators.

[0109] Specifically, when the known factors of the rooftop greening module are 0.1m and the soil thickness is 0.1m, based on the full life cycle carbon emission reduction assessment target and the full life cycle carbon emission reduction assessment model of the rooftop greening module, two plant combinations are selected from the candidate options for the remaining factors: Sedum sarmentosum + creeping thyme and Sedum sarmentosum + iris. When the soil thickness of the rooftop greening module is 0.2m, four plant combinations are selected: Sedum sarmentosum + creeping thyme + clover. When the soil thickness of the rooftop greening module is 0.3m, three plant combinations are selected: Sedum sarmentosum + Sedum sarmentosum + clover and Sedum sarmentosum + Sedum sarmentosum + creeping thyme. When the soil thickness of the rooftop greening module is 0.4m, a single-layer ground cover plant, Sedum sarmentosum, is selected.

[0110] This embodiment is based on the net carbon emission calculation formula to obtain the net carbon emissions of different rooftop greening models over their entire life cycle. It should be noted that the carbon sequestration of plants accounts for a larger proportion of the carbon sink composition than that of soil. Therefore, carbon reduction measures should focus on the carbon sink component of plants. In order to improve the carbon sink efficiency, this study seeks the optimal plant combination for carbon reduction based on different soil matrix thicknesses.

[0111] This implementation selected the plant combinations shown in Table 1 as plant combinations. The carbon sequestration capacity of different plant combinations is as follows: Figure 4 As shown, the plant with the largest carbon storage is Sedum aureum (151.14 kg·m³).-2 The second most common plant combination was Sedum lineare + Sedum aureum 'Aureum' + Clover (144.79 kg·m³). -2 Sedum lineare + Sedum aureum 'Aureum' + Climbing thyme (171.73 kg·m) -2 Four plant combinations: Sedum lineare + Sedum adolphii + Climbing thyme + Clover (121.47 kg·m -2 ), and a combination of two plants, golden sedum and creeping thyme (119.04 kg·m -2 Golden Sedum + Iris (115.40 kg·m) -2 Optimizing the carbon sequestration of rooftop greening modules can be achieved by changing the plant combination, replacing plants with lower carbon sequestration values ​​with those with higher carbon sequestration values, thereby improving the carbon sequestration efficiency of rooftop greening modules.

[0112] For soil substrates of different thicknesses:

[0113] 1) Rooftop greening modules with a soil substrate thickness of 0.1m

[0114] The carbon emissions of a rooftop greening module with a soil thickness of 0.1m are relatively low (115.01 kg·m³). -2 You can choose some ground cover plants and ground cover plants adapted to shallow soil. To increase carbon sequestration, based on the previous text, you can choose two plant combinations such as golden sedum + creeping thyme and golden sedum + iris. The net carbon emissions of the overall rooftop greening module are -82.72 kg·m³. -2 -75.45 kg·m -2 .

[0115] 2) Rooftop greening modules with a soil substrate thickness of 0.2m

[0116] The carbon emissions of a rooftop greening module with a soil thickness of 0.2m are 129.1 kg·m³. -2 Although it has better water and fertilizer retention capacity than a 0.1m thick substrate, a variety of plants with shallow root systems but strong adaptability can be selected, such as a combination of herbaceous plants and ground cover, like a four-plant combination of Sedum lineare, Sedum 'Golden Leaf', creeping thyme, and clover. The net carbon emissions of the overall rooftop greening module are -108.29 kg·m³. -2 .

[0117] 3) Rooftop greening modules with a soil substrate thickness of 0.3m

[0118] The carbon emissions of a rooftop greening module with a soil thickness of 0.3m are 143.88 kg·m³. -2This allows for deeper root growth and provides a more stable growing environment for plants. Compared to a thinner substrate layer, a 0.3m thickness can store more water and nutrients, reducing the frequency of watering and fertilization. Three plant types were selected: Sedum lineare + Sedum aureum + Clover and Sedum lineare + Sedum aureum + Creeping Thyme. The net carbon emissions of the overall rooftop greening module were -110.18 kg·m³. -2 -109.75 kg·m -2 .

[0119] 4) Rooftop greening modules with a soil substrate thickness of 0.4m

[0120] The carbon emissions of a rooftop greening module with a soil thickness of 0.4m are 160.45 kg·m³. -2 When selecting plants for the ground cover layer, factors such as carbon sequestration capacity and roof load-bearing capacity need to be considered. The carbon sequestration of a single ground cover plant, *Sedum morganianum*, is 167.39 kg·m³. -2 The net carbon emissions of the overall rooftop greening module are -133.82 kg·m³. -2 .

[0121] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for constructing a life-cycle carbon emission reduction assessment model for rooftop greening modules, characterized in that, include: S100. Obtain the carbon emissions and carbon sinks of multiple rooftop greening modules throughout their entire life cycle. The carbon emissions include carbon emissions during the operation phase and carbon emissions during the maintenance phase. S200. Obtain carbon emission factor indicators and carbon sink factor indicators for each rooftop greening module. Carbon emission factors include carbon emission factor indicators during the operation phase and carbon emission factor indicators during the maintenance phase. S300, based on the carbon emissions and carbon sinks throughout the entire life cycle, as well as the carbon emission factor indicators and carbon sink factor indicators, conducts correlation analysis and principal component analysis, and constructs a full life cycle carbon emission reduction assessment model for roof greening modules based on the correlation analysis results and principal component analysis results.

2. The method for constructing the full life-cycle carbon emission reduction assessment model for rooftop greening modules according to claim 1, characterized in that, The correlation analysis includes: The carbon emissions during the operation phase, carbon emissions during the maintenance phase, or carbon sink of different rooftop greening modules are defined as the first variable, and each indicator of different rooftop greening modules is defined as the second variable. Based on the first and second variables, the correlation coefficients of each indicator with carbon emissions during the operation phase, carbon emissions during the maintenance phase, and carbon sinks were calculated using the Pearson correlation coefficient formula.

3. The method for constructing the full life-cycle carbon emission reduction assessment model for rooftop greening modules according to claim 1, characterized in that, The principal component analysis includes: Calculate the covariance matrix of different indicators, perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors, select several eigenvectors with eigenvalues ​​greater than 1 as principal components, and calculate the correlation coefficient between each indicator and the principal components.

4. The method for constructing the full life-cycle carbon emission reduction assessment model for rooftop greening modules according to claim 1, characterized in that, Before the principal component analysis, KMO and / or Bartlett's test of sphericity are performed on each indicator. When the statistical value of KMO is greater than the KMO set value or the significance value of Bartlett's test of sphericity is less than the Bartlett set value, principal component analysis is performed on the indicator.

5. The method for constructing the full life-cycle carbon emission reduction assessment model for rooftop greening modules according to claim 1, characterized in that: The carbon emission factors during the operation phase include: plant height, stomatal resistance, roughness, soil matrix thickness, solar energy absorption rate, leaf reflectance, roof heat transfer coefficient, leaf area index (LAI), soil thermal conductivity, plant species, irrigation frequency, irrigation water consumption, and irrigation electricity consumption. The carbon emission factors indicators during the maintenance phase include: fertilizer usage, frequency of maintenance equipment use, energy consumption of maintenance equipment, maintenance time, and amount of green waste. The carbon sequestration factors include carbon sequestration capacity, annual plant growth, plant photosynthetic rate, plant biomass, soil organic carbon content, and plant diversity index.

6. A method for applying the life-cycle carbon emission reduction assessment model of a rooftop greening module constructed using the construction method described in any one of claims 1 to 5, characterized in that, include: Based on known factor indicators and life cycle carbon emission reduction assessment targets, and combined with the life cycle carbon emission reduction assessment model for rooftop greening modules, the scheme that can meet the life cycle carbon emission reduction assessment targets is determined from the candidate schemes for other factor indicators.

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