Flat roof photovoltaic-greening deployment carbon reduction optimization method, recording medium and system

Abstract

The invention belongs to the technical field of energy conservation and environmental protection, and particularly relates to a flat roof photovoltaic-greening deployment carbon reduction optimization method, which comprises the following steps: establishing a carbon reduction amount calculation model capable of simultaneously processing roof photovoltaic deployment, roof greening deployment and roof photovoltaic-greening combination deployment, the statistics comprises the statistics of the power generation amount, the carbon sequestration amount and the greenhouse gas emission of each application scene; data required for completing the carbon reduction calculation model is obtained and preprocessed, the synergistic effect between the photovoltaic module and the greening vegetation is considered, and carbon reduction calculation is corrected; and calculating the net carbon reduction amount, and determining the scene parameter corresponding to the maximum net carbon reduction amount in each pixel on the map as an optimization scheme of the pixel. According to the method, when the roof utilization mode is selected, scientific decisions can be made based on quantitative data, and the method is suitable for popularization and application in urban flat-roofed buildings. The invention also provides a non-transitory readable recording medium storing the program of the method and a system comprising the medium, and the program can be called through a processing circuit to execute the method.

Description

Technical Field

[0001] This invention belongs to the field of energy conservation and environmental protection technology, and discloses an optimized method, recording medium and system for carbon reduction in flat roof photovoltaic-greening deployment. Background Technology

[0002] Against the backdrop of escalating global climate change, renewable energy technologies have become a key strategy for mitigating greenhouse gas emissions. China, as the world's largest emitter of carbon dioxide (over 10 billion tons annually), has achieved a 48.4% reduction in carbon intensity compared to 2005, but still faces the formidable challenge of peaking carbon emissions before 2030 and achieving carbon neutrality before 2060. Urbanization has led to a rapid increase in building stock, with urban areas accounting for as much as 85% of the national total carbon emissions, and traditional emission reduction measures are insufficient to address the ongoing loss of net primary productivity and carbon reserves.

[0003] Against this backdrop, the low-carbon and ecological utilization of building rooftops, as underdeveloped green spaces in cities, has become a hot research topic in the industry. Rooftop photovoltaic (RPV) systems, rooftop greening (GR) systems, and the photovoltaic / greening synergistic system (PV-GR) formed by combining the two are currently the core technological paths to achieve building carbon emission reduction and ecological service capacity enhancement, and have received widespread attention and initial application in the fields of urban renewal and green building.

[0004] Rooftop photovoltaic (PV) systems convert solar energy into electricity, directly replacing traditional fossil fuel power generation, reducing carbon emissions in the power production process, and decreasing buildings' dependence on the power grid, resulting in significant carbon reduction benefits. Rooftop greening systems, on the other hand, rely on plant photosynthesis to fix carbon and indirectly reduce carbon emissions by regulating the building's microclimate and reducing air conditioning energy consumption through vegetation cover. They also provide ecological services such as air purification and mitigating the urban heat island effect. A synergistic system combining PV and greening achieves synergistic benefits through complementary technologies: vegetation reduces the operating temperature of PV modules and improves power generation efficiency, while PV panels provide shade and reduce water evaporation. This synergistic effect makes the system superior to either a single PV or greening system in terms of total carbon reduction and ecological service capabilities, making it the preferred direction for low-carbon ecological utilization of rooftops.

[0005] However, existing technologies still face key technological bottlenecks in the planning and application of rooftop low-carbon ecosystems, making it difficult to achieve scientific and efficient configuration and promotion. First, there is a lack of a quantitative carbon reduction assessment system. Current analyses of the carbon reduction benefits of rooftop photovoltaics, green roofs, and combined systems mostly remain at the level of qualitative descriptions or rough estimates from a single dimension, without the formation of a unified and accurate quantitative calculation model. Existing methods struggle to comprehensively cover core indicators such as the direct emission reduction from photovoltaic power generation replacing fossil fuels, the carbon sequestration by vegetation photosynthesis, and the indirect emission reduction from reduced building energy consumption. This prevents users from making scientific decisions based on quantitative data when choosing rooftop utilization modes (pure photovoltaics, pure green roofs, or a combination of photovoltaics and green roofs), thus hindering the rational application of low-carbon technologies.

[0006] Secondly, there is a lack of an optimal configuration mechanism for system synergy benefits. When photovoltaics and greening coexist on rooftops, the area ratio of the two, the density of photovoltaic modules, and the selection of vegetation species directly affect the synergistic emission reduction effect and ecological benefits. However, existing technologies have not established a correlation model between parameters and benefits, making it impossible to maximize synergistic benefits through optimized configuration. Furthermore, different buildings have varying roof structural load-bearing capacities, lighting conditions, and regional climate characteristics. There is a lack of systematic technical support for determining the optimal system mode and configuration parameters for specific building scenarios.

[0007] Third, there is insufficient capacity for differentiated planning at the regional scale. In the development of rooftop resources at the city or regional level, existing technologies struggle to achieve refined spatial planning. Specifically, they cannot accurately match buildings to different pixels on a map based on factors such as building distribution, rooftop conditions, and regional ecological needs. This makes it difficult to determine the most suitable utilization scenario for each building rooftop (pure photovoltaic, pure greening, composite systems, and specific parameters), resulting in the inability to achieve a large-scale, differentiated, and optimized layout of low-carbon ecological rooftop resources within the region. This hinders the improvement of overall carbon emission reduction and ecosystem service capabilities.

[0008] In summary, there is an urgent need to build a technical system that covers quantitative assessment of carbon emission reduction, system-wide collaborative optimization and configuration, and regional-scale spatial planning, in order to solve the scientific decision-making problems in the selection, parameter configuration and regional layout of rooftop photovoltaic and greening systems, and promote the maximization of the low-carbon ecological value of urban rooftop spaces. Summary of the Invention

[0009] To address the above problems, this invention provides an optimized method for carbon reduction in flat-roof photovoltaic-green deployment, comprising the following steps: Establish a carbon reduction calculation model that can simultaneously handle three application scenarios: rooftop photovoltaic, rooftop greening, and rooftop photovoltaic-greening combination. The data preparation for carbon reduction calculation includes statistics on power generation, carbon sequestration, and greenhouse gas emissions for each application scenario. Acquire data including climate, geospatial and administrative divisions, and preprocess the data through spatial coordinate system alignment and resolution resampling; receive the initial values ​​and change rates of carbon emission factors, lifespan, initial investment year, photovoltaic system loss rate and grid baseline emission factors according to application scenario, and complete the data structure required for the carbon reduction calculation model. For rooftop photovoltaic-greening application scenarios, it is necessary to calculate the synergistic effect between photovoltaic modules and green vegetation, and simulate one or more of the following effects: the cooling / solar radiation scattering enhancement effect of vegetation on photovoltaics, and the precipitation / light regulation effect of photovoltaics on vegetation, in order to correct the carbon reduction calculations made for photovoltaics or greening alone. The difference between the total benefits of power generation / carbon sequestration and the total cost caused by greenhouse gas emissions is calculated as the net carbon reduction. The scenario parameters corresponding to the maximum net carbon reduction in each pixel on the map are determined as the optimization scheme for that pixel.

[0010] This bidirectional feedback mechanism forms a closed-loop computing structure, realizing dynamic coupling simulation of PV and GR at the pixel scale, significantly improving the physical reality of the net benefit assessment of the collaborative system, and constituting a microclimate interactive computing module that can be embedded and deployed.

[0011] Preferably, when calculating the power generation of photovoltaic modules, if the sunset time is detected to be earlier than the sunrise time, the sunset time is automatically compensated by +1 day. This algorithm effectively ensures that the integral calculation of the diffuse fraction in the model is performed within the correct solar time interval, significantly improving the model's applicability in China's complex geographical environment. Verification has shown that the improved model is effective in calculating Ro of photovoltaic power generation. 2 It improves performance by 0.21%, reduces RMSE by 3.44%, and has modular data processing capabilities that can be encapsulated.

[0012] Preferably, by analyzing the Earth-Sun geometric relationship and the regional solar altitude angle variation pattern, the receiving ratio of direct and diffuse sunlight is adaptively adjusted, and the tilt angle of the roof photovoltaic module is expressed as a function of the latitude of the grid.

[0013] This model abandons the traditional coarse assumption of a fixed tilt angle (e.g., 30°) and achieves spatially differentiated tilt angle configurations at a 0.1° grid scale across the country, significantly improving the physical accuracy of tilt surface irradiance calculations. The algorithm can be integrated into high-resolution photovoltaic potential assessment systems as a pre-parameter generation module, supporting automated operation of large-scale geographic modeling.

[0014] Preferably, when calculating the synergistic effect between photovoltaic modules and green vegetation, the environmental temperature feedback term is a function that includes the local climate and building height / building density factors.

[0015] This method constructs a parameter set that is nationwide in scale, highly spatially consistent, and fully covers any location in the country. It solves the problem of model input distortion caused by data fragmentation, provides a reliable data foundation for subsequent spatial differentiation modeling of vegetation cooling effects, and has the stability and reusability for engineering deployment.

[0016] Preferably, for the precipitation redistribution problem of PV-GR system, a model structure integrating shading penetration coefficient, diversion ratio, and mass conservation is set up to quantitatively distinguish precipitation input between covered and uncovered areas and import it into the GR carbon sequestration calculation model.

[0017] Another aspect of the present invention is to provide a non-transient readable recording medium for storing one or more programs containing multiple instructions, which, when executed, cause the processing circuit to perform the aforementioned optimized method for carbon reduction in flat roof photovoltaic-greening deployment.

[0018] Another aspect of the present invention provides an optimized system for carbon reduction in flat roof photovoltaic-greening deployment, including a processing circuit and a memory electrically coupled thereto. The memory is configured to store at least one program, the program containing multiple instructions. The processing circuit runs the program and can execute the aforementioned optimized method for carbon reduction in flat roof photovoltaic-greening deployment.

[0019] Compared to existing technologies, the optimized method, recording medium, and system for carbon reduction in flat-roof photovoltaic-green deployment provided by this invention have the following beneficial effects: This method comprehensively covers core indicators such as the direct emission reduction from photovoltaic power generation replacing fossil energy, the carbon sequestration by vegetation photosynthesis, and the indirect emission reduction from reduced building energy consumption. This enables users to make scientific decisions based on quantitative data when choosing rooftop utilization modes (pure photovoltaic, pure greening, or a combination of photovoltaic and greening), ensuring the rational application of low-carbon technologies.

[0020] When photovoltaics and greening coexist in rooftop space, the area ratio between the two, the layout density of photovoltaic modules, and the selection of vegetation species, among other configuration parameters, directly affect the synergistic emission reduction effect and ecological benefits. This method establishes a correlation model between photovoltaics and greening, providing systematic technical support for determining the optimal system mode and configuration parameters for specific building scenarios.

[0021] This method accurately matches buildings corresponding to different pixels in the map, determines the most suitable utilization scenario for each building roof (pure photovoltaic, pure greening, composite system and specific parameters), completes the large-scale and differentiated optimized layout of rooftop low-carbon ecological resources in the region, and improves the overall carbon emission reduction and ecological service capabilities. Attached Figure Description

[0022] Figure 1This is a schematic diagram of data flow in the carbon reduction calculation model in this embodiment of the invention; Figure 2 This is a schematic diagram of the carbon reduction calculation method in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. The described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without innovative effort are within the scope of protection of the present invention.

[0024] This invention provides an optimized method for carbon reduction in flat-roof photovoltaic-greening deployment, comprising the following steps: The specific implementation process is as follows: 1. Estimation based on available roof area 1.1 RPV In calculating the overall installed capacity potential of RPV Before proceeding, it is necessary to calculate the roof area available for installing the RPV. Factors affecting the roof availability of RPV include the building's social function, roof geometry, roof orientation, shading, and existing roof structure and obstructions. It can be calculated using the following formula:

[0025] in, For the roof area, Adjustment factors for building categories This is the roof orientation adjustment factor.

[0026] for To determine the data, we used China's first 2.5m building roof area dataset. This dataset, developed by a team from Beijing Normal University based on the Spatiotemporal Awareness Super-Resolution Segmentation Framework (STSR-Seg), integrates Sentinel-2 satellite imagery and multi-source data. It achieves full coverage of urban and rural areas across China for the first time from 2016 to 2021, with a spatial resolution of 2.5 meters. Validated with independent samples, the F1 score for urban areas reached 62.55%, and the recall rate for rural areas reached 78.94%, demonstrating both spatiotemporal consistency and high accuracy. We selected the 2021 CBRA (China Building Rectification Area Map) as the roof data for this study. Dividing the entire raster by 255 and then multiplying by 6.25 yielded the roof area raster data for China in 2021.

[0027] for To determine the building category, we first used the European Urban Land Use Basic Type (EULUC-China) to identify the rooftops within China. This data is in ESRI Shapefile format and employs a two-level classification system. The first-level classification includes categories such as residential, commercial, and industrial, while the second-level classification further subdivides these. The overall accuracy of the first-level classification is 61.2%, and the overall accuracy of the second-level classification is 57.5%. In 2018, the total area of ​​rooftops in China was 166,338 km². 2 Within the impermeable layer of the city, the accuracy of various land uses is as follows (where PA represents the accuracy of producers and UA represents the accuracy of users): Residential land: PA 63.2%, UA 76.3%; Commercial land: PA 44.9%, UA 38.9%; Industrial land: PA 70.7%, UA 58.3%; Public administration land: PA 56.5%, UA 51.2%. This represents the best performance among the existing basic urban land use types in China. This applies to rooftops covered by EULUUC-China. The building classification factor values ​​for residential, commercial, and industrial building rooftops are determined using the existing correction mapping factors for the six building types in rooftop photovoltaic systems. . Exclusion factors such as existing structure, pipes on walls, or shadows on windows were considered. We mapped six building categories to the EULUUC-China classification system, with residential buildings among them... Using the average of apartments, residences and high-rise buildings The value is 0.6; for commercial buildings. Using the average of high-rise buildings and central buildings The value is 0.575; industrial zone buildings Using factory The value is 0.8. This applies to the roofs of administrative, educational, and medical buildings. Referring to the minimum requirements stipulated in the "Notice on Submitting Pilot Programs for Rooftop Distributed Photovoltaic Development in Entire Counties (Cities, Districts)" issued by the General Office of the National Energy Administration of China in 2021, the values ​​are 0.5, 0.4, and 0.4 respectively. This applies to the rooftops of transportation, sports and cultural, and park and green space buildings. All values ​​were set to 0. The study indicates that buildings with special functions (such as roads and transportation, municipal facilities, and cultural and tourism facilities) are not suitable for RPV deployment. Furthermore, for building rooftops not covered by EULUC-China, including five types of urban buildings and rural buildings constructed after 2018... Based on the national-level conversion factor, the requirements of the notice from the General Office of the National Energy Administration of China, and the results of field investigations, it is uniformly set at 0.35.

[0028] Four typical Chinese cities (Beijing, Wuhan, Guiyang, and Yuxi) were selected, ranked from highest to lowest administrative level. The geometric types, slopes, and orientations of buildings in these cities were statistically analyzed. Building roof orientations were categorized into flat roofs and pitched roofs, with pitched roofs further subdivided into eight orientations. The study indicates that north-facing roofs (including north, northwest, and northeast) are unsuitable for RPV installation due to lower solar energy reception. Therefore, the following cities in Beijing, Wuhan, Guiyang, and Yuxi... The values ​​were set at 0.8616, 0.8137, 0.9160, and 0.8784, respectively. This study aims to analyze the values ​​of these four cities. Extrapolating to prefecture-level cities nationwide, we collected five indicators from across the country: annual precipitation, average annual wind speed, average annual wind direction, total GDP, and urbanization rate. Using Beijing, Wuhan, Guiyang, and Yuxi as initial cluster centers, we employed K-means cluster analysis to extrapolate the data from prefecture-level cities nationwide. The data is divided into four categories to enable extrapolation, allowing each prefecture-level city to obtain the corresponding data. Value. Windward-facing roofs significantly improve rainwater capture efficiency, but suffer from higher evaporation losses; while steep-sloped roofs, although retaining less rainwater, have significantly better runoff efficiency than gently sloping designs. This contradiction indicates that high-rainfall areas need to balance rainwater utilization and drainage needs: areas with heavy rainfall tend to use steep slopes combined with windward orientation to optimize runoff discharge, while areas with moderate rainfall can improve rainwater interception efficiency through flat roofs or gently sloping designs. Wind strength directly affects building structural design, especially roof design. High-wind areas need to consider wind resistance requirements, thus limiting the choice of roof orientation; matching roof orientation with wind direction can improve indoor airflow and reduce energy consumption. In areas with significant monsoons, building designs typically avoid strong wind directions to protect the roof structure; economically developed areas tend to adopt advanced building designs and materials, such as passive design, to fully utilize natural energy and improve energy efficiency by optimizing building orientation, shape, and construction. In areas with high urbanization rates, the increased building density and the rise in high-rise building density have led to significant changes in roof shapes and available space. The urbanization process has driven architectural style diversification, and roof design and orientation exhibit differences related to the level of urbanization. In contrast, rural buildings are constrained by geographical environment (such as mountain range orientation) and folk customs (such as feng shui traditions), and roof orientation often shows regional convergence. However, in urbanized areas, due to intensive land use and lighting regulations for high-rise buildings, the distribution of roof orientations is more diversified.

[0029] For obtaining the raw annual precipitation data, we used the 2021 China 1km resolution monthly precipitation dataset. We converted the original NetCDF4 format monthly precipitation data into raster data and then summed the precipitation for all 12 months of the year to obtain the 2021 China precipitation raster data. Combined with the vector data of the administrative boundaries of prefecture-level cities in China, we used ArcGIS Pro to calculate the average annual precipitation for each prefecture-level city, which was then used as the annual precipitation for each prefecture-level city in China in 2021.

[0030] The wind force and direction data are sourced from Weather.com (www.tianqi.com). The website compiles daily meteorological data for provinces, cities, and counties in China from 2011 to 2021. The data comes from the China Meteorological Administration. We used Python web crawlers to collect the daily wind force and direction data for each prefecture-level city in China from 2011 to 2021 from the website, and calculated the mode of wind force and direction for each prefecture-level city over these 11 years. This data serves as a representative of the wind force and direction characteristics of each prefecture-level city during these 11 years.

[0031] The GDP dataset used is the latest 2020 China GDP Spatial Distribution Kilometer Grid Dataset. This dataset is based on the national county-level GDP statistics and takes into account the spatial interaction between land use types, nighttime light intensity, settlement density data, and GDP data that are closely related to human activities. It is generated by spatial interpolation of 1 km square spatial grid data, and then ArcGIS Pro is used to statistically analyze the GDP of each prefecture-level city by region.

[0032] To obtain the urbanization rate, we adopted the method of measuring urbanization development using remote sensing data, a method widely used by scholars to measure urbanization levels. This paper selects the land cover type dataset provided by the 2021 Moderate Resolution Imaging Spectroradiometer (MODIS). This dataset classifies all pixel units in the global map into 17 land use types, including urban built-up area land use classification. In the MODIS dataset, urban built-up areas are defined as those where the impermeable surface, such as buildings, roads, and vehicles, accounts for more than 30% of the area within a pixel. Existing research has validated the classification results of the MODIS dataset using Hidden Markov Models (HMMs) and neural network methods, and the validation results show that the data classification has high accuracy. The calculation formula of this method is as follows:

[0033] in For urbanization index, The pixel area occupied by the built-up area of ​​a prefecture-level city. This represents the total area of ​​prefecture-level cities. This index will serve as a proxy indicator for measuring the urbanization development of various prefecture-level cities in my country. Compared with traditional accounting methods, the urbanization index based on remote sensing data has three advantages: First, it has high data availability, enabling continuous annual monitoring and covering all prefecture-level cities nationwide, effectively solving the problem of missing samples in non-census years with traditional indicators; second, it has high measurement objectivity, directly capturing the distribution of impermeable surfaces through satellite imagery, avoiding measurement noise caused by statistical biases in household registration and conflicts in land planning; third, it has good spatial resolution adaptability, the MODIS dataset (500-meter resolution) can effectively identify the boundaries of urban built-up areas, and avoid the oversensitivity of high-resolution data to scattered township construction land, making it more suitable for the needs of measuring urbanization levels at the prefecture-level city scale. This method provides a standardized observation framework for cross-regional, long-term urbanization research.

[0034] 1.2 GR In calculating the total GR-available planting area in China First, we collected all the constructive indicators of the current local GR standards in China (DB11 / T 281-2015, DB13 / T 1433-2011, DBJ / T 13-303-2018, DB14 / T2698-2023, DB3501 / T 009-2022, DB4401 / T 23-2019, DB31 / T 493-2010 and DB4201 / T453-2014), which are Beijing, Hebei, Fujian, Shanxi, Fuzhou, Guangzhou, Shanghai and Wuhan, as well as the industry standard "Technical Specification for Green Roofing Engineering" JGJ 155-2013. Among them, DB11 / T 281-2015, DB13 / T 1433-2011, DBJ / T 13-303-2018, DB4401 / T 23-2019, DB31 / T 493-2020, DB 4201 / T 453-2014, and JGJ 155-2013 all specify the proportion of green area to total roof area and the proportion of green planting area to total green area for simple roof layouts, as shown in Table S4. Considering the convergence of standards across regions and the universality of technical specifications, we will use the national proportion of green planting area to total roof area. The value was set to 0.75. This parameter value was chosen based on the following considerations: First, except for Hebei Province, which adopts a higher standard of no less than 0.75, other major provinces and cities all adopt technical specifications (JGJ 155-2013) of no less than 0.72, with an overall standard deviation of only 0.0106, showing a high degree of consistency in technical standards among regions; second, the value of 0.75 not only conforms to the technical feasibility of the current specifications, but also reserves necessary buffer space for construction errors. Furthermore, numerous studies have shown that only flat roofs are suitable for GR (Growth Renovation) systems. For the area of ​​the planting system in each prefecture-level city, based on the conclusions drawn from the roof orientation characteristics of buildings in Beijing, Wuhan, Guiyang, and Yuxi, we calculated the proportion coefficient of flat roofs to total roofs in these four typical cities. The values ​​were 0.6884, 0.5126, 0.7688, and 0.6704, respectively. Based on the clustering results of roof orientation in Chinese prefecture-level cities, these four typical cities were then grouped... This was extrapolated to all prefecture-level cities in China. Finally, the results for each prefecture-level city were combined. , Determined by the following formula:

[0035] 1.3 PV-GR

[0036] 2. Calculation of net carbon emission reduction of RPV 2.1 Potential for Photovoltaic Power Generation To assess the long-term power generation potential of China's RPV system, this study developed an improved version of the Global Rooftop Photovoltaic Estimator (GRPE) model, based on version 0.3.1 of the Global Solar Energy Estimator (GSEE)—a numerical model with physical mechanisms—tailored to China's high-resolution geographic features and regional climate heterogeneity. This improved model significantly enhances the accuracy of power generation predictions by coupling multi-source meteorological data with localized system parameters.

[0037] GSEE is a deterministic model specifically designed to simulate the power output of photovoltaic (PV) systems. By coupling a global solar energy estimation framework with localized meteorological data, this model generates high-resolution PV power time series. Its core mechanisms encompass irradiance calculation, temperature effect correction, and system loss quantification. It has been widely applied in renewable energy planning and grid stability analysis in several European countries. The model has been used to assess the potential for wind and PV complementarity under different weather patterns in Europe, demonstrating its ability to accurately capture the spatiotemporal variations in PV output. Furthermore, the study further validated the reliability of the GSEE model in simulating long-term trends in UK PV power generation, with an average absolute error of less than 12% between its output data and measured values. The GSEE model accurately captures the spatiotemporal dynamics of PV power generation. By integrating meteorological reanalysis data and satellite observations, it can simulate PV output on annual and hourly scales and analyze the impact of panel orientation and tilt angle on power generation efficiency. Model input parameters include total horizontal irradiance (rsds), diffuse irradiance percentage (based on the Boland-Ridley-Lauret: BRL model), ambient temperature (tas), geographical location, tilt angle, azimuth angle, and system capacity. The model ignores the effect of wind speed on panel efficiency because existing studies have shown that its effect is small and has significant local uncertainty.

[0038] While GSEE has performed exceptionally well in studies across many European countries, its superior performance is primarily attributed to the smaller deviation between the European time zone and Greenwich Mean Time (GMT), thus reducing the impact of errors in sunrise and sunset time calculations. However, the model has significant limitations when applied to other regions, particularly the irradiance distribution deviation caused by time zone discrepancies. To overcome this deficiency, we developed GRPE based on GSEE. By optimizing time zones and the cross-day problem, we significantly improved the accuracy of diffuse fraction calculations in the BRL model, greatly enhancing its applicability and reliability in diverse geographical environments.

[0039] The formula for the BRL model is as follows:

[0040] The calculation of diffuse irradiance depends on the accuracy of sunrise and sunset times, and its core parameters include the ratio of diffuse irradiance. (Dimensionless) Hourly Sunshine Index (Dimensionless) Apparent solar time (Unit: h) Solar angle (Unit: rad) Daily Clear Sky Index (Dimensionless) and persistence measures (Dimensionless). Among them, Defined as a piecewise function to account for the daily variation of the sunshine index, its formula is:

[0041] However, the original GSEE model has significant flaws in calculating sunrise and sunset times, limiting its applicability outside of Europe. GSEE relies on the PyEphem library, which generates the UTC sunrise and sunset times for an observer's location by inputting their latitude and longitude coordinates. Without time zone conversion, the results are in UTC time instead of local time, leading to incorrect sunrise and sunset times. Furthermore, improper date settings and uncorrected time zone offsets can cause day-crossing issues, resulting in sunset occurring before sunrise and disrupting the continuity of the time series. For example, in Beijing, China (UTC+8), uncorrected calculations might misjudge sunrise as the previous day's UTC evening (e.g., 23:34:14) and sunset as the current day's UTC morning (e.g., 09:05:00), which, after conversion to local time, would correspond to 07:34:14 the following day and 17:05:00 on the current day, respectively. This results in sunrise being later than sunset, leading to incorrect hourly times at that location. It was incorrectly calculated as 0. The principle behind this part of the GSEE model is to determine the local sunrise and sunset times using coordinates, but it uses UTC time as a reference and fails to adapt to local time zone offsets, thus limiting its accuracy globally.

[0042] To overcome the aforementioned limitations, our developed GRPE model significantly improves its spatiotemporal accuracy through systematic improvements. First, by integrating the Timezonefinder library, which can locate global time zones, the local time zone is automatically determined based on latitude and longitude coordinates, ensuring the accuracy of time zone allocation. Second, UTC sunrise and sunset times are generated using the PyEphem astronomical computing library, preserving their physical basis. Finally, a time zone conversion tool (pytz library) is used to convert UTC time to local time, and date resampling and adjustment eliminate cross-day errors, effectively solving the time series inversion problem. Through this innovative process, GRPE ensures... The accurate calculations during each period of sunshine significantly improve the reliability and applicability of the model in diverse geographical environments.

[0043] The core computational process of the GRPE model consists of four steps: Panel tilt angle calculation: Photovoltaic system panel tilt angle A latitude-based dynamic calculation method is used, rather than fixed values. Based on existing latitude regression models, Defined as a grid Location latitude The function is designed to maximize the annual solar irradiance capture efficiency:

[0044] This method optimizes the ratio of direct to diffuse radiation reception by analyzing the Earth-Sun geometric relationship and regional climate characteristics, thereby significantly improving the system's performance in long-term power generation.

[0045] Calculation of irradiance on an inclined plane: based on a geometric optics model and a mesh. direct irradiance Angle of incidence with the plane of the photovoltaic module The calculation formulas are as follows:

[0046] in, For grid Horizontal direct irradiance, For grid solar altitude angle, and They are grids The panel and the solar azimuth angle, for We assume all plates face due south, which is generally considered the optimal azimuth approximation and has been used in many related studies at 24° and 27°. and The calculations are based on the PyEphem library, performing point-by-point calculations on an hourly scale for specific latitude and longitude, combined with astronomical models and local time zone conversions to ensure accurate solar position analysis. It should be noted that the denominator in the original literature was recorded as... The original GSEE model code is based on the solar altitude angle. The calculation logic is inconsistent. This study verifies the GSEE code and corrects it accordingly. This will more accurately reflect the physical influence of the solar altitude angle on irradiance distribution.

[0047] Grid diffuse irradiance The calculation employs the isotropic assumption and incorporates surface reflection correction:

[0048] In the formula, The average roof albedo is taken as 0.2, where the average roof albedo for Chinese roofs is taken as 0.2, and the average roof albedo for roofs planted with Sedum species is taken as 0.25. For grid The method optimizes the geometric representation of the radiation distribution, enhancing the model's adaptability to complex terrain and climatic conditions.

[0049] Component temperature and instantaneous efficiency correction For RPV systems, mesh Photovoltaic module temperature By grid ambient temperature Determined by both irradiance and radiance, its calculation formula is as follows:

[0050] in, The heating coefficient of the building-integrated photovoltaic module is taken as 0.05 ℃·W. -1 ·m 2 , This represents the total planar irradiance. This formula comprehensively considers the dynamic impact of environmental thermal effects and radiation input on the component temperature.

[0051] For PV-GR systems, the performance of photovoltaic panels significantly benefits under certain climatic conditions from the cooling effect of vegetation beneath the panels on the air above, primarily manifested in a reduction in ambient temperature, which in turn reduces [the impact of these effects on overall performance]. :

[0052] in, The table represents a grid. The ambient temperature after adjustment by the cooling effect of vegetation is determined by the cooling effect. Confirmed, specifically:

[0053] The cooling effect is most significant when specific temperature and humidity conditions are met. Due to the heterogeneity of local climate zones (LCZs), the calculation method needs to be specifically adjusted according to the climatic characteristics of different LCZs in order to accurately reflect the regional differences in vegetation's effect on temperature modulation.

[0054] To achieve this goal, we utilized a global local climate zone map (100 m resolution LCZ map) supporting Earth system modeling and urban-scale environmental science, covering multiple types from LCZ1 to LCZ17, to systematically analyze the LCZ attributes within each 0.1° grid in China. The LCZ category for each grid was determined by statistically analyzing the mode of LCZ1 to LCZ10. For individual grids lacking LCZ1 to LCZ10 attributes, nearest neighbor interpolation was used to fill in the missing attributes, ensuring that all areas containing rooftops had complete LCZ attributes. To further support building height data, we introduced a 150 m global urban building height product based on spaceborne LiDAR, calculating the average building height within each grid. For individual grids lacking building height attributes, inverse distance weighted interpolation was used in ArcGIS Pro's geostatistics wizard. Leave-one cross-validation results showed an average error (ME) of -0.00325 m and a root mean square error (RMSE) of 1.784 m. These data lay a solid foundation for the subsequent quantitative analysis of the cooling benefits of GR.

[0055] Furthermore, accurate estimation of cooling effects requires strict climatic conditions, namely, a localized area with an ambient temperature range of 17°C to 28°C and a relative humidity range of 45% to 75%. Considering China's vast territory and significant climatic diversity, we set the relative change ratio at 10%, accordingly adjusting the lower limit of temperature to 15.3°C and the upper limit to 30.8°C, and the lower limit of humidity to 40.5% and the upper limit to 82.5%. Areas outside this range are defined as "significant deviations," while those within the range are considered "acceptable deviations." For areas with significant temperature or humidity deviations, the cooling effect is conservatively estimated at 0°C; for areas with ambient temperatures between 15.3°C and 30.8°C and relative humidity between 40.5% and 82.5%, the effect is calculated based on the grid. LCZ attributes, building height Or building density Dynamically estimate daily : For the LCZ1 - LCZ3 area:

[0056] For the LCZ4 - LCZ6 region:

[0057] For the LCZ7 - LCZ10 region: when :

[0058] when :

[0059] when Because only 0.83% of the mesh is located between LCZ7 and LCZ10 and The proportion is extremely low. The cooling effect was uniformly set at 0.1℃. Studies have shown that the upper limit of building height is 18 m. This study adjusted it to 19.8 m based on a 10% relative change ratio to adapt to the actual needs of China's diverse urban building patterns.

[0060] These empirical formulas are based on data fitting. The values ​​of 0.8208, 0.8910, 0.7970, and 0.7252, respectively, reflect the model's good explanatory power for the cooling effect.

[0061] Calculation of photovoltaic power generation Photovoltaic power generation It is determined by the following formula:

[0062] in, Under standard test conditions ( = 1000 W / m 2 and The rated power of photovoltaic modules at 25 °C For grid The actual irradiance, For calculation of time intervals, the unit is hours (h), instantaneous relative efficiency. The deviation between actual operating conditions and standard test conditions (STC) is characterized by the following expression:

[0063] coefficient The parameters were determined by fitting experimental data from field measurements and are suitable for crystalline silicon (c-Si) panel technology. Specific parameters are detailed in Table S5.

[0064] Photovoltaic module degradation rate refers to the degree of degradation of a photovoltaic module caused by various factors, including but not limited to ultraviolet radiation, temperature fluctuations, humidity, pollution, and mechanical stress. These factors all lead to the gradual aging of photovoltaic materials, thereby reducing the module's conversion efficiency. According to GB55015-2021, the service life of polycrystalline silicon modules should exceed 25 years, with a power degradation rate of less than 2.5% in the first year and less than 0.7% annually thereafter. Photovoltaic module degradation rate refers to the degradation rate of a photovoltaic module after a period of operation under standard test conditions (AM1.5, module temperature 25 ℃, irradiance 1000 W·m). -2 The ratio of maximum output power to the initial maximum output power during commissioning:

[0065] According to the minimum standard of GB55015-2021, assuming the lifespan of the RPV system deployed nationwide is 25 years, its annual power degradation rate follows the minimum limit in the standard. As shown in Figure X, the cumulative degradation rates in the 1st, 10th, and 25th years of system operation are 2.5%, 8.8%, and 19.3%, respectively. The remaining power generation capacity in the 25th year is very close to 80% of the existing technology, and the annual remaining power generation capacity is [not specified].

[0066] The PVOUT dataset provided by the Global Solar Atlas 2.0 was used as the validation benchmark. This data is of great significance in assessing the global potential of rooftop solar photovoltaic power generation. Directly using PVOUT to assess the current global-scale RPV power generation potential highlights its value in renewable energy research. The dataset is provided as a 1 km resolution raster dataset, with each pixel providing daily kilowatt-hours of power generation per kilowatt (peak) of installed capacity at an annual resolution, in kWh / kWp. It covers western China (1999–2018) and eastern China (2007–2018). High reliability was ensured through simulations of 20 years of solar radiation, power conversion losses, atmospheric effects, and panel aging data.

[0067] To verify the accuracy of the GRPE model, we used PVOUT as a reference to evaluate its performance in predicting China's RPV power generation potential. The inputs to the GRPE model included global horizontal irradiance (GHI) and temperature (TEMP), both sourced from the Global Solar Atlas 2.0. GHI data covered western (1999–2018) and eastern (2007–2018) regions; TEMP data were long-term annual averages from 1994–2018, in °C. After unit conversion, the annual rooftop photovoltaic power generation potential in China calculated by the GRPE model was compared with that calculated using PVOUT data. Simultaneously, the original GSEE model was also used with the same inputs for prediction to compare their performance.

[0068] The accuracy of the results output by GSEE and GRPE was verified based on 94,337 data points in China. The results are shown in Table S6. The GRPE model outperforms the GSEE model on all metrics. 2 The improvement from 0.969392 to 0.971459 (+0.21%) indicates a slight improvement in the fit; the decrease in RMSE by 3.44% and MAE by 5.49% shows a significant reduction in prediction bias. These improvements demonstrate that the GRPE model has improved in both accuracy and stability, especially performing better when dealing with China's complex climate and geographical conditions. GRPE effectively improves prediction reliability through optimized algorithms and data processing (such as time zone resolution and local time conversion), providing a more accurate tool for evaluating and optimizing the RPV portion for China.

[0069] 2.2 Carbon Emission Reduction To quantify the carbon reduction benefits of the RPV portion, we follow the baseline methodology proposed by the United Nations Framework Convention on Climate Change (UNFCCC). This methodology provides a simplified analytical framework, facilitating unified comparisons between different projects and thus promoting relevant policy development. The carbon emission reductions resulting from replacing grid electricity with RPV electricity are calculated using the grid's baseline emission factors. The baseline emission factors include the Operating Margin (OM) and the Building Margin (BM). OM reflects the existing group of power plants most affected (reduced) by the project, typically high-emission or high-cost plants, while BM, based on an assessment of planned and anticipated new generating capacity, represents the construction and operation of power plant groups that may be affected by future renewable energy projects. The Combined Margin (CM) is obtained through a weighted average of OM and BM, representing the overall impact of the grid's existing carbon intensity.

[0070] To assess the carbon emission reduction potential of future photovoltaic systems in China and capture the spatial heterogeneity among provinces, we constructed a computational model based on the combined marginal emission factors of future regional power grids. Taking the lifecycle of RPV, GR, and PV-GR systems as 25 years as an example, the method includes three steps: (1) Calculate the baseline emission factors of each regional power grid in the current year (2021); (2) Estimate the combined marginal emission factors of each province from 2021 to 2095 based on the changing trends of provincial power grid emission factors from 2021 to 2095; (3) Combine the carbon emission reduction potential of the pixel-level rooftop RPV power generation potential to calculate the carbon emission reduction potential of the pixel-level RPV system.

[0071] Taking RPV as an example, for mesh Net carbon emission reduction of internal RPV system (Unit: tCO2) The assessment uses the life cycle assessment (LCA) method, considering greenhouse gas (GHG) emissions during the production, installation, maintenance, and disposal phases:

[0072] In the formula For grid Annual carbon emission reduction (unit: tCO2), , and They are grids Greenhouse gas emissions (tCO2) of an RPV system during its production, transportation, installation, operation, and disposal phases over its life cycle.

[0073] Greenhouse gas emissions per unit installed capacity and grid Total installed capacity Sure:

[0074] in, Data from existing research shows that polycrystalline silicon photovoltaic modules in the production stage generate 2,210 tCO2 GHG emissions per megawatt (MW).

[0075] and The calculations are based on dynamic calculations of electricity consumption and future grid emission factors. Specifically, installing a 1 MW peak (MWp) RPV requires 0.08 MWh of electricity, which only needs to be accounted for in the initial years of each lifecycle, namely 2021, 2046, and 2071, while the maintenance phase requires 0.00667 MWh / MW per year (Yang et al., 2024). These electricity consumptions are multiplied by the provincial grid emission factor for the corresponding scenario year. (Unit: tCO2 / MWh) Calculation:

[0076] GHG emissions during the treatment phase are negligible. .

[0077] Based on existing research, annual values ​​for 2021 to 2095 are generated through linear interpolation and extrapolation. The original study provides three scenarios—the conventional scenario (BAU), the conservative renewable energy development scenario, and the proactive scenario.

[0078] It can be calculated using the following formula:

[0079] In the formula For grid Annual power generation (unit: MWh) For grid The region The annual combined marginal emission factor of the power grid (unit: tCO2 / MWh). The calculation of the PV portion of power generation for PV-GR is similar.

[0080] current The calculation should follow the combined marginal method, the core of which lies in integrating the operational marginal and capacity marginal emission factors. According to China's regional power grid division scheme, the national power grid is divided into six major regional power grids: North China, Northeast China, East China, Central China, Northwest China, and South China. The coverage and emission factors of each power grid are shown in Table S7. The calculation formula is:

[0081] in and Representing the 2021 grid respectively. Operating margin and construction margin emission factors, weighting coefficients and Photovoltaic projects are configured according to the UNFCCC guidelines. Based on China's regional power grid division scheme, the national power grid is divided into six major regional power grids: North China, Northeast China, East China, Central China, Northwest China, and South China. Their coverage areas and emission factors are detailed in Table S7. The North China regional power grid... Highest (0.9714) ), while the Southern Regional Power Grid Minimum (0.1880) This reflects the significant differences in energy structure and power generation technology between regions.

[0082] Due to its unique energy structure, this region requires a specialized estimation model. Data from 2021 shows that clean energy accounted for over 90% of the region's power generation, with local thermal power accounting for only 4.15% (350 million kWh). However, the region relies heavily on inter-provincial power imports, with a total import volume of 752 million kWh in 2021. Of this, the Northwest Power Grid (Gansu and Shaanxi) contributed 532 million kWh (70.7%), and the Central China Power Grid (Sichuan) contributed 220 million kWh (29.3%). From January to February 2021, 298 million kWh, 70 million kWh, and 152 million kWh were imported from Gansu, Shaanxi, and Sichuan respectively. A planned import of 231 million kWh was expected in March, assuming the previous proportions are maintained. This model incorporates the thermal power proportions from Gansu (70.3%), Sichuan (15.31%), and Shaanxi (87.03%), based on regional data and export policies. The OM emission factor for 2021 is calculated as follows: (28)

[0083] in, It is 350 million kWh. It is 0.7605 (Emission factor of the best commercial coal-fired power unit). Electricity transferred from region j (unit: billion) ), For the region OM emission factor (unit: ), For the region The proportion of thermal power in exported electricity. Among them, Gansu, Sichuan, and Shanxi were selected.

[0084] The figure is 70.3%, based on the fact that Gansu Electric Power Investment Group has cumulatively transmitted 6.786 billion kWh of electricity to the region from 2016 to 2023, of which 2.015 billion kWh is from new energy sources and thermal power accounts for 70.3%. Assuming that the proportion in 2021 is consistent with the historical cumulative value, it meets the export target of 610 million kWh for 2021-2023. The figure is 15.31%, based on the fact that hydropower accounts for 77.7% of Sichuan's installed capacity and thermal power accounts for 15.31% of its power generation. Hydropower is the main source of electricity for export, but during the dry season, it is supplemented by "hydropower-thermal power bundling". The figure is 87.03%, based on Shaanxi's total power generation of 261.58 billion kWh, of which thermal power accounts for 227.64 billion kWh. Power transmission is mainly from thermal power units, as there is no large-scale renewable energy transmission channel. Substituting the above data into formula (28) yields... .

[0085] 2021 It can be determined by the following formula:

[0086] in, For the new unit types added in 2021 Net electricity generation (unit: ), For the corresponding unit type Emission factor (unit: In 2021, all new projects in the region (such as the Suwalong Hydropower Station, the Dagu Hydropower Station, and the Cuomei Zhegu Wind Farm) were clean energy projects, with no new thermal power units added. The value is 0, therefore It is 0.

[0087] In summary, substituting into formula (29) yields... .

[0088] For the future By combining the annual change rate of emission factors of provincial power grids, we derive the comprehensive marginal emission factors for future years. With the optimization of the power structure, the comprehensive marginal emission factor will dynamically change over time, and this trend is highly consistent with the trend of provincial power grid emission factors. The rationale for this assumption is that changes in the marginal emission factor are mainly driven by newly added generation capacity and its technology type, factors already reflected in the annual adjustments of provincial power grid emission factors. The specific calculation formula is as follows:

[0089] in, Indicates the first In a certain context, the province The comprehensive marginal emission factor for the year Indicates the first The comprehensive marginal emission factor of the previous year under a certain scenario in the province. Indicates the first The annual rate of change of the provincial power grid emission factor reflects the impact of power structure adjustments on emission intensity. Given the lack of independent original data on the power grid emission factor in this region, we use the national average rate of change as a proxy for its trend. Specifically, under each scenario, Set as all provinces in the country under this scenario The mean.

[0090] This method allows us to quantify the impact of future changes in the power structure on marginal emissions, providing a scientific basis for regional energy policy formulation and carbon reduction target assessment. Particularly for provinces with significant differences in power structure, this method can capture the dynamic characteristics of their emission factors, thereby improving prediction accuracy.

[0091] 3GR's net carbon sequestration potential 3.1 DNDC Model The DNDC (DeNitrification-DeComposition) model is a multi-scale mechanistic model specifically designed to quantify the dynamic responses of carbon and nitrogen cycles in agriculture and natural ecosystems by simulating biogeochemical processes. The model calculates daily carbon fluxes (such as CO2 and CH4) and nitrogen emissions (such as N2O and NO3) at the soil-vegetation-atmosphere interface by coupling thermodynamic-hydrological, plant growth, organic matter decomposition, and denitrification submodules. - Its schematic diagram clearly illustrates the dynamic interaction mechanism of the thermodynamic-hydrological, decomposition, denitrification, and crop cultivation sub-modules. The core of DNDC lies in integrating the influence of multiple factors such as climate, soil physicochemical properties (e.g., texture, pH), agricultural management practices (e.g., fertilization, irrigation, tillage), and vegetation dynamics, enabling carbon and nitrogen flux predictions on a scale from hours to centuries. Through long-term field experiments in multiple locations worldwide (e.g., temperate farmland, tropical grassland, and organic soils), the model demonstrates high accuracy in simulating the evolution of soil organic carbon pools and greenhouse gas emissions. In unconventional scenarios such as GR, DNDC, by adjusting localized parameters (e.g., substrate characteristics and plant type), can accurately assess carbon sequestration potential, providing quantitative evidence for the carbon neutrality contribution of GR systems.

[0092] 3.2 Model Parameters The DNDC model's regional simulation parameter system integrates multi-scale environmental and agricultural management data, covering core modules such as climate, soil physicochemical properties, crop physiological characteristics, and human interventions. Climate data is input in a high spatiotemporal resolution gridded format, including daily precipitation, maximum temperature, minimum temperature, and atmospheric CO2 concentration, and scenario simulation is achieved through precipitation variation coefficients and temperature baseline adjustments. Soil parameters cover organic carbon content (SOCmax / SOCmin), clay ratio (Claymax / Claymin), pH range, and bulk density (Densmax / Densmin), combined with slope and salinity index to construct the physicochemical basis of soil carbon and nitrogen cycling. The crop module defines biomass allocation (grain, leaf-stem, and root ratios), carbon-nitrogen ratio, water requirement, accumulated temperature threshold, and nitrogen fixation capacity for over 70 crops, supporting differentiated modeling of perennial and wetland crops. This study refines agricultural management parameters to characterize fertilization strategies (inorganic fertilizer type and application rate, organic fertilizer C / N ratio), irrigation patterns (flood irrigation, drip irrigation), tillage depth (no-till to 30 cm), and straw return ratio, embedding the impact mechanisms of flooding scenarios (continuous flooding, mid-term drainage, and marginal flooding) on ​​wetland ecosystems. All parameters are coupled through a Geographic Information System (GIS) grid and combined with an uncertainty quantification scheme (combination of maximum / minimum soil conditions and irrigation scenarios) to achieve multi-scenario dynamic simulation of regional-scale carbon and nitrogen fluxes, providing multi-dimensional data support for accurately assessing the greenhouse gas emission reduction potential of agricultural ecosystems. In this study, we explore the carbon emission reduction potential provided by Sedum species GR at the prefecture-level city scale; therefore, we assume that each prefecture-level city shares a set of climate and GIS parameters, and the entire country shares a set of Sedum vegetation parameters.

[0093] Regarding the parameter system for Sedum species, this study systematically integrated domestic and international experimental observations and model simulations based on a multi-source data fusion method. The specific parameter values ​​are as follows: Maximum grain production was set at 2750 kg C / ha, derived from field sampling data at 12 regional growth areas (GRs) in Michigan, USA. This value was calculated by measuring the carbon storage in the aboveground and root parts of *Sedum* plants and converting it to dry matter yield. Researchers collected *Sedum* plants from 20 regions at the end of the growing season (after flowering) and measured the average total aboveground and belowground carbon storage to be 275 g C / m³. 2 The final conversion yielded a total biomass carbon density of 2750 kg C / ha (Environmental Science & Technology, 2008).

[0094] Regarding biomass fraction, the proportions of grain, leaf, stem, and root were 0.01, 0.134, 0.467, and 0.389, respectively. The grain fraction was determined by referencing the default values ​​from studies on grassland vegetation in Northwest China. This study determined the grain fraction to be 0.01 based on actual measurements of biomass fractions in different grassland types (Northwest A&F University, 2019). The leaf and stem fractions were estimated using the leaf area index (LAI) and dry matter content of *Sedum verticillata* from the Chinese Plant Traits Database (CPTDv2). Combined with parameters such as leaf area (LA), specific leaf area (SLA), and leaf mass per unit area (LMA), the leaf dry matter accounted for 13.4% of the total biomass, and the stem dry matter accounted for 46.7%. The root proportion was determined based on the proportion of root carbon storage to total carbon in a US experiment of a Sedum species. This study, by isolating the root system and measuring its carbon content, found that root carbon storage accounted for 38.9% of the total carbon (Environmental Science & Technology, 2008).

[0095] The carbon-to-nitrogen ratio (C / N ratio) parameter was determined by combining measured data with the model's default value. The leaf C / N ratio was 12.253, derived from the ratio of leaf carbon content (459.5 g C / kg) to leaf nitrogen content (37.5 g N / kg) of *Sedum adolphii* in CPTDv2. The stem and seed C / N ratios were the same as those for the leaves, consistent with the DNDC model's assumptions regarding the C / N ratios of seeds, leaves, and stems in herbaceous plants; the root C / N ratio was set at the default value of 50 for herbaceous plants in the DNDC model document (DNDC model document, 2023).

[0096] The nitrogen fixation index was set to 1 based on the following reasons: First, Sedum species are not leguminous and lack symbiotic relationships with nitrogen-fixing bacteria; second, GR microbial community studies have shown that nitrogen fixation is mainly driven by rhizobium bacteria, which are not directly associated with Sedum species (Applied Soil Ecology, 2019). Studies have found that nitrogen-fixing microorganisms in GR mainly live in symbiosis with leguminous plants (such as clover), while no nitrogen-fixing ability has been observed in Sedum species.

[0097] Water requirement was calculated using the relationship between evapotranspiration and biomass, resulting in 322.3 kgwater / kg dry matter. The specific steps were: [Calculate the annual evapotranspiration (177.09 – 247.22 kg / m³)]. 2Converting the values ​​to hectares (1,770,900 – 2,472,200 kg / ha), and combining the maximum biomass (2,750 kgC / ha) with the average carbon content (41.75%), the total dry matter yield was obtained (6586.83 kg / ha) (Environmental Science & Technology, 2008). The final water requirement range was 268.85 – 375.32 kg water / kg dry matter, and the average value of 322.09 kg water / kg dry matter was taken as the parameter value (China Flower & Horticulture, 2020).

[0098] The optimal temperature was determined to be 20℃ based on multiple studies. Cutting experiments on *Sedum aizoon* in the Pearl River Delta region of China showed that 20–25℃ was the optimal growth temperature (China Flower & Horticulture, 2020). Physiological analysis of *Sedum aizoon* after dormancy was also shown that biomass and quality indicators were highest at 20℃ (2019 Second International Conference on Green Building and Environmental Management, 2019). The report "An Exploration Report on Suitable Conditions for Leaf Cuttings of Crassulaceae Plants" found through leaf cutting experiments on *Sedum 'Autumn Beauty'* that the optimal ambient temperature for leaf growth was 20℃. Another study, "Introduction, Cultivation, and Adaptability Study of Crassulaceae Succulents," showed that the optimal growth temperature varied among different Crassulaceae varieties: 10–25℃ for *Sedum morganianum* and 15–25℃ for *Sedum rubrotinctum*.

[0099] The accumulated temperature required for maturity (TDD) was calculated using the establishment time (average 60 days) of six Sedum species in Nanjing and meteorological data during the same period, with a cumulative daily average temperature (>0 ℃) of 1368 ℃. This method equates the establishment time of GR (80% lawn coverage) to the maturity stage of the plants, which meets the needs of practical applications (Nanjing Agricultural University, 2010).

[0100] Vascularity is set to 0. According to the definition of wetland plants in the DNDC model, only species with aeration tissues such as rice and cattail are assigned a value of 1. Sedum plants are xerophytic succulents and have no oxygen transport capacity, so the default value for non-wetland plants is adopted (DNDC model document, 2023).

[0101] Sedum vegetation parameters

[0102] Climate data used include 2-meter daily maximum temperature (TASMAX), daily minimum temperature (TASMIN), precipitation (PR), and annual atmospheric CO2 concentration, all of which are from the same source as RSDS and TAS.

[0103] For PR (Rainfall Reduction), in the PV-GR system, photovoltaic panels alter precipitation distribution through shading and diversion effects. Compared to a pure GR system, the amount of precipitation received by vegetation in the PV-GR system differs significantly. Vegetation in the photovoltaic-covered area receives less precipitation due to shading, while vegetation in the uncovered area receives more precipitation due to diversion effects. To simulate this process, we estimate the vegetation water supply in both covered and uncovered areas: For precipitation in the covered area :

[0104] in, This represents the original precipitation (mm). The penetration coefficient for precipitation is taken as 0.25.

[0105] For precipitation in uncovered areas :

[0106] in, and These represent the areas covered and uncovered by photovoltaic panels, respectively. The rainwater interception distribution ratio reflects the proportion of rainwater intercepted by the GR system's matrix layer relative to the total rainfall. This is to determine... This study considers the relationship between the substrate layer thickness and rainwater interception capacity. Given roof load limitations, the substrate layer thickness is typically thin; in this study, it is set at 100 mm. Referring to the "Design Code for Rainwater Control and Utilization Engineering in Sponge Cities" (DB11 / 685–2021), when the substrate layer thickness is less than 300 mm, the runoff coefficient should be 0.5. = 1 - 0.5 = 0.5. Mass conservation constraint. This ensures the physical consistency of precipitation distribution.

[0107] Annual atmospheric CO2 concentrations are derived from a global monthly atmospheric CO2 concentration dataset based on historical and future scenarios from CMIP6, and from AIRS (R 2 =0.95) and GOSAT (R 2=0.99) The observations were highly consistent. Data preprocessing consisted of three steps: First, the time signatures of TASMAX, TASMIN, and PR were unified to "daily," monthly CO2 concentrations were converted to annual averages, and the resolution was improved to 0.1° using bilinear interpolation to ensure that each city contained at least one effective pixel; Second, through vector overlay, the daily averages of TASMAX, TASMIN, and PR, as well as the annual average CO2, for each prefecture-level city under three scenarios: historical period from 1990 to 2014 and from 2021 to 2095 were calculated. Since the original data for 2014 CO2 concentration was missing, the 2013 value was used based on its relatively small interannual variation; Finally, a Python script was used to convert the data into the txt format required by the DNDC model and store it in a climate database to support simulations.

[0108] The GIS data comprises 10 data files, whose names and meanings are shown in Table S8. The following is the process and basis for obtaining the parameters for each data file: ClimateSoil file: (1) Nitrogen content (N-dep) in precipitation in various prefecture-level cities was obtained using the 1996–2015 China Atmospheric Inorganic Nitrogen Wet Deposition Spatiotemporal Pattern Dataset. Based on the site-year data retrieved from the literature, ammonium nitrogen (NH4) at a resolution of 1 km was generated by Kriging interpolation. + - N), nitrate nitrogen (NO3) - The spatial distribution of NH4+ and soluble inorganic nitrogen (DIN) was analyzed, covering four time periods: 1996–2000, 2001–2005, 2006–2010, and 2011–2015. Data processing followed standardized procedures, employing ion chromatography and spectrophotometry (GB 11894-89) to determine the chemical composition of precipitation. Multi-stage quality control (literature screening, data verification, unit standardization, outlier removal) was implemented, and expert review ensured reliability. Validation results showed that NH4+... + - N, NO3 - - R of N and DIN 2 The values ​​were 0.61, 0.40, and 0.61, respectively, with RMSE values ​​of 3.21, 3.22, and 4.93. The regression coefficients were ≥ 0.67 (p < 0.001), indicating that the interpolation results were reliable.

[0109] This study used DIN data from 2011 to 2015. Preprocessing consisted of two steps: first, vector surfaces of prefecture-level cities were overlaid to calculate the average DIN (kgN / ha / yr) for each city; second, the average annual precipitation from 2011 to 2015 was combined to convert it to the units required by the DNDC model. Calculation assumptions: wet deposition nitrogen is completely dissolved in precipitation; precipitation is uniformly distributed in time and space; 1 mm of precipitation is equivalent to 1 L / m³. 2There was no hydrological loss. The specific calculation method is as follows: Precipitation volume calculation:

[0110] in, For the first Rainfall volume per hectare in each prefecture-level city (unit: L / ha / yr). Annual precipitation (unit: mm / yr).

[0111] Nitrogen concentration calculation:

[0112] in, For the first Nitrogen concentration in each prefecture-level city (unit: ppm). For the first The average DIN in each prefecture-level city.

[0113] For soil parameters of each prefecture-level city, we extracted a 2023 Chinese soil raster dataset with a 1km resolution and a soil depth layer of 0-20cm from the Harmonized World Soil Database 2.0 (HWSD 2.0), which was built with the participation of the Food and Agriculture Organization of the United Nations (FAO). This layer contains at least one and at most 12 soil types, each with more than 40 indicators, such as pH, conductivity, sand content, water content, silt content, and organic carbon content. Based on the parameter requirements in the ClimateSoil file, we selected CLAY, REF_BULK, ORG_CARBON, and PH_WATER from this dataset (representing clay content, soil bulk density, organic carbon content, and pH, respectively), as shown in Table S9. Since each cell may contain multiple soil types, we first statistically analyzed the parameter values ​​for multiple soil types in each cell of the original data, calculating the maximum and minimum values ​​as the output values ​​for each cell. Secondly, at the prefecture-level city scale, histograms of parameter values ​​were constructed for each prefecture-level city. Considering the average soil texture of the entire city, the 75th percentile parameter value was taken as the maximum value for that parameter in that city, and the 25th percentile parameter value was taken as the minimum value. After unit conversion, the maximum and minimum values ​​of each soil parameter for each prefecture-level city were finally obtained, conforming to the requirements of the ClimateSoil document. We assume that the GR for each prefecture-level city is taken from the topsoil located in that city, and that these soils conform to the average soil texture of that city.

[0114] CropArea: Records the planting area of ​​Sedum species in GR in each prefecture-level city. Based on the available roof area of ​​each city (see 2.1.2 for details), it is input into the DNDC model in hectares to ensure the accurate quantification of regional carbon reduction potential.

[0115] CropParameter: Defines three major crop parameters for Sedum species in various prefecture-level cities, including maximum yield (kg C / ha), accumulated temperature (°C), and water requirement (kg water / kg dry matter). The parameter values ​​are uniform nationwide, based on multi-source experimental data and model optimization (see 2.3.2 Sedum Parameters) to reflect their physiological characteristics and GR environmental adaptability, and to support carbon and nitrogen cycle simulations using the DNDC model.

[0116] Fertilization: To determine the appropriate fertilization rate for Sedum species in GR (Growth Retention) systems at the prefecture-level city level, this study used a DNDC (Digital Density Concentration-Distribution) model sensitivity analysis to assess the impact of nitrogen fertilizer application on biomass yield and soil carbon sequestration. Considering the low nutrient requirements of Sedum species, the limited nutrient-holding capacity of the GR substrate, and the regional differences in natural soil nutrient content across Chinese prefecture-level cities (soil fertility is sufficient to support plant growth in some areas without additional fertilization), we tested a fertilization range of 0–300 kgN / ha, ultimately selecting 150 kgN / ha (urea, DNDC model code 3, applied on day 22). This fertilization rate achieved the optimal balance in the simulation: supporting stable biomass accumulation at the beginning of the growing season, optimizing carbon sequestration potential, while reducing the risk of nutrient leaching due to excessive fertilization. This parameter integrates the physiological characteristics of Sedum species, substrate features, and regional soil nutrient variability, making it suitable for assessing the carbon sequestration potential of GR systems at the prefecture-level city level in China.

[0117] Flooding: Given that Sedum species are non-wetland species, adapted to arid environments and intolerant of flooding, this study sets up no flooding events in the GR scenarios of various prefecture-level cities in the DNDC model to reflect their ecological characteristics and the drainage design of the GR system.

[0118] Irrigation: In the GR system for prefecture-level cities, the irrigation ratio of Sedum plants is based on the corresponding city's cultivated land irrigation ratio to reflect the local average irrigation level. This assumption is based on the fact that GR irrigation design often references local agricultural water use patterns, and that the low water requirement of Sedum plants makes cultivated land irrigation ratio suitable as a proxy. We used the 2020 annual map of irrigated farmland in China (CIrrMap250, overall accuracy 0.88) and the high-resolution cultivated land fusion product of China (CCropLand30, overall accuracy 0.88, Kappa coefficient 0.76, F1 score 0.86). The irrigated area of ​​CIrrMap250 can explain 50%-60% of the variance of irrigation water withdrawal in China, outperforming existing similar maps. Both datasets have a consistent definition of cultivated land. After resolution resampling, the cultivated land irrigation ratio of each pixel was estimated using ArcGIS Pro's raster calculator. Subsequently, the average irrigation ratio of each prefecture-level city was obtained through zonal statistics, thus adapting to the DNDC model.

[0119] ManureAmendment: Given the low nutrient requirements and substrate management characteristics of Sedum species in GR, no organic fertilizer was applied in this study, and all parameters were set to 0.

[0120] PlantingHarvestDates: To simulate the continuous existence of Sedum plants as perennials, January 1st of each year is set as the planting date and December 31st as the harvest date, reflecting the long-term coverage status of the GR system.

[0121] Residue Management: To accurately calculate the carbon sequestration potential of Sedum plants in the GR system, this study sets that after the end of each planting cycle, the aboveground parts (leaves and stems) are completely removed, and the plants are replanted in the next cycle, with the straw residue ratio set to 0. This setting is based on the following considerations: (1) Sedum plants are short and do not require traditional agricultural harvesting, so removing the aboveground parts simulates the actual management practice of GR; (2) If straw residue is retained (ratio > 0), the residual carbon will be accumulated in the biomass yield, leading to repeated accounting with each year, interfering with the carbon sequestration estimation based on changes in aboveground biomass and soil organic carbon; (3) The carbon content of root biomass is approximately equal to the carbon input of litter, avoiding repeated calculation of litter carbon contribution. Therefore, setting the residue ratio to 0 ensures that only annual changes in aboveground biomass and soil carbon are included in the carbon sequestration calculation, which is consistent with the carbon balance logic of the DNDC model.

[0122] Tillage: The planting substrate for Sedum plants in the GR system is characterized by its shallowness and lightweight nature, requiring a balance between physical stability and waterproofing, significantly different from traditional farmland soil. Conventional tillage (such as plowing) may damage the substrate structure, leading to the loss of lightweight materials, or mechanical disturbance may damage the waterproofing layer, threatening building safety. Although occasional shallow loosening (< 5 cm) is necessary to improve surface aeration, this operation does not involve overall substrate disturbance. Given the fragility of the rooftop ecosystem and the low-maintenance characteristics of Sedum plants, this study uniformly sets the tillage parameter to "no-till" to ensure the continuity of substrate function and building structural safety.

[0123] 3.3 Net Carbon Solidification Calculation To accurately assess the carbon sequestration potential of GR systems, we employ Life Cycle Assessment (LCA) to calculate net carbon sequestration during the production, installation, operation, maintenance, and disposal phases. We have redefined net carbon sequestration (Net_CS) as the total carbon sequestration during the operation phase minus the greenhouse gas emissions at each phase, as shown in the following formula:

[0124] in, For grid Net carbon sequestration (tCO2) of the GR system; For grid Total carbon sequestration (tCO2) of the GR system during operation. , and They are grids Greenhouse gas emissions (tCO2) from GR systems during the production, transportation, operation, and disposal phases.

[0125] Through annual carbon sequestration density, GR planting area And runtime calculation:

[0126] in, prefecture-level city Annual carbon sequestration density of GR system (tCO2 / km) 2 ); For grid GR planting area (km) 2 ); Based on the aboveground biomass of Sedum plants and changes in soil organic carbon, converted to CO2 equivalent:

[0127] in, and They are prefecture-level cities Sedum seed carbon and leaf / stem carbon (tC / km) 2 / year), prefecture-level city Annual variation of soil organic carbon (tC / km) 2 / Year), It is the molecular weight conversion factor from carbon to CO2.

[0128] Based on existing technical data, the life-cycle greenhouse gas emission intensity is 22,587 tCO2 / km² during the production stage. 2 The energy consumption includes material production (such as high-temperature processing of expanded clay), transportation, and other aspects; there are no carbon emissions during the installation and operation phases; the disposal phase has a carbon emission of -16,152 tCO2 / km. 2 This is due to net emissions reductions from 50% recycling and 50% incineration. The total lifecycle carbon intensity is 6434.7 tCO2 / km². 2 Therefore, the grid Emissions during the production and disposal stages are as follows:

[0129] This method calculates carbon sequestration using changes in aboveground biomass and soil organic carbon, and comprehensively calculates net carbon sequestration through LCA, providing a reliable basis for assessing the carbon neutrality potential of GR systems.

[0130] 4. Net emission reduction and carbon sequestration potential of PV-GR For grid The emission reduction and carbon sequestration potential of PV-GR over its life cycle is determined by the following formula:

[0131] 5. Optimal configurations of RPV, GR, and PV-GR systems and assessment of their potential to maximize emission reduction and carbon sequestration. For grid The maximum net emission reduction and carbon sequestration potential over the life cycle is determined by the following formula:

[0132] For grid The optimal system to be deployed on the roof during its life cycle is the system that can maximize the potential for net emission reduction and carbon sequestration.

[0133] Compiling the above methods and steps into a program and storing it on a hard disk or other non-transitory storage medium constitutes an embodiment of the "non-transitory readable recording medium" of the present invention; while electrically connecting the storage medium to a computer processor and optimizing the carbon reduction of flat roof photovoltaic-greening deployment through data processing constitutes an embodiment of the "optimization system for carbon reduction of flat roof photovoltaic-greening deployment" of the present invention.

[0134] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computers or available storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A flat roof photovoltaic-greening deployment carbon reduction optimization method, characterized in that, The method comprises the following steps: A carbon reduction calculation model capable of simultaneously processing three application scenarios of roof photovoltaic, roof greening, and roof photovoltaic-greening combination is established, and data preparation for carbon reduction calculation includes statistics of power generation, carbon sequestration, and greenhouse gas emissions of each application scenario; Data including climate, geographic space, and administrative division are obtained, and the data are preprocessed through spatial coordinate system alignment and resolution resampling; initial values and change rates of carbon emission factors, service life, initial investment year, photovoltaic system loss rate, and power grid baseline emission factors including the whole life cycle are received according to the application scenario classification to complete the data structure required by the carbon reduction calculation model; For the roof photovoltaic-greening application scenario, the synergistic effect between the photovoltaic module and the green vegetation must be calculated, one or more of the cooling / solar radiation scattering enhancement effect of the vegetation on the photovoltaic and the precipitation / light regulation effect of the photovoltaic on the vegetation are simulated to correct the carbon reduction calculation of the photovoltaic or the green vegetation alone; The difference between the total benefit of power generation / carbon sequestration and the total cost caused by greenhouse gas emissions is calculated as the net carbon reduction, and the scene parameters corresponding to the maximum net carbon reduction in each pixel on the map are determined as the optimization scheme of the pixel.

2. The method of claim 1, wherein the method further comprises: When calculating the power generation of the photovoltaic module, if it is detected that the sunset time is less than the sunrise time, the sunset time is automatically compensated by +1 day.

3. The method of claim 2, wherein the method further comprises: By analyzing the relationship between the sun and the earth and the variation law of the regional solar altitude angle, the receiving ratio of direct and diffuse radiation is adaptively adjusted, and the tilt angle of the roof photovoltaic module is expressed as a function of the latitude of the grid.

4. The method of claim 3, wherein the method further comprises: When calculating the synergistic effect between the photovoltaic module and the green vegetation, the environmental temperature feedback term is a function including the local climate and the building height / building density factors.

5. The method of claim 4, wherein the method further comprises: For the precipitation redistribution problem of the PV-GR system, a model structure is set that comprehensively has a shielding penetration coefficient, a flow diversion ratio, and mass conservation, and the precipitation input of the covered area and the non-covered area is quantitatively distinguished and introduced into the GR carbon sequestration calculation model.

6. A non-transitory, readable recording medium storing one or more programs including a plurality of instructions, wherein the plurality of instructions, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 5. When the instructions are executed, the processing circuit will perform the optimization method for carbon reduction of flat roof photovoltaic-greening deployment according to any one of claims 1-5.

7. An optimization system for flat roof photovoltaic-greening deployment carbon reduction, comprising a processing circuit and a memory electrically coupled to the processing circuit, wherein the processing circuit is configured to: The memory is configured to store at least one program, and the program includes a plurality of instructions, and the processing circuit runs the program to perform the optimization method for carbon reduction of flat roof photovoltaic-greening deployment according to any one of claims 1-5.

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