Shaping urban form for solar energy self-sufficiency city

Shaping urban form for solar energy self-sufficiency city | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Shaping urban form for solar energy self-sufficiency city Pengjun Zhao, Yanxiu Jin, Haoran Zhang, Zhaoru Liu, Qing Yu, Zhengying Liu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4124110/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of renewable energy into cityscapes is becoming increasingly crucial to climate change since city is main sector of energy consumption. This research estimated daily changes in rooftop photovoltaic (PV) output and building energy demand across different seasons using 3D building data from 32 global cities, investigated the inherent link between urban form and photovoltaic self-sufficiency. We uncovered a universal power-law relationship between building height and PV self-sufficiency, where higher buildings result in nonlinearly decreasing PV sufficiency. Based on this, a highly accurate multiple regression model was constructed to simulate the PV self-sufficiency, incorporating key variables such as climate, geography, and urban form. This model stands out for its unique capability to be applied across varied urban contexts, accommodating the diverse conditions worldwide. Furthermore, our comparative analysis across four urban planning scenarios reveals that cities designed with the "Garden City" concept significantly outperform others in PV self-sufficiency, offering a quintuple increase in potential for solar energy harnessing, a finding especially pronounced in the context of African cities. These findings provide profound insights by suggesting that strategic urban planning could be a transformative tool in combating energy poverty and fostering sustainable urban development. Scientific community and society/Energy and society/Energy supply and demand Scientific community and society/Energy and society/Energy access Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cities, the epicenters of global energy consumption and greenhouse gas emissions, are under increasing pressure to transition toward sustainability 1–3 . One of the most effective measures to achieve this transition is enhancing energy self-sufficiency through the implementation of distributed rooftop photovoltaic (PV) systems 4,5 . These systems, transforming sunlight directly into electricity, can substantially offset a city's dependence on external energy sources, thereby bolstering energy resilience and playing a fundamental role in climate change mitigation 6,7 . Energy self-sufficiency in the context of rooftop PV systems can be defined as the city's ability to meet a significant portion of its electricity demand through solar energy harvested from its own rooftops 8 . A higher self-sufficiency rate implies that the city can supply a larger share of its energy needs from its rooftop PV systems, thereby reducing its reliance on energy imports and decreasing its vulnerability to external energy shocks. This not only contributes to the city's energy security but also promotes the adoption of clean, renewable energy sources, thereby fostering a more sustainable urban future. However, the ability of a city to increase its self-sufficiency from rooftop PV systems is not solely dependent on the availability of sunlight. The urban form—characterized by factors such as building height, density, and orientation—plays a critical role in determining the potential for solar energy harvesting 9–11 . Differing urban forms can lead to substantial variation in solar energy harvesting potential, thereby affecting the achievable level of self-sufficiency. For instance, densely built-up areas with high-rises might have limited roof surface area exposed to sunlight, reducing the potential for rooftop PV deployment. Conversely, lower-density areas with more expansive rooftops might offer greater opportunities for solar energy harvesting. Moreover, the orientation of buildings and the presence of shading elements, such as neighboring buildings, can also significantly impact the amount of sunlight that a rooftop can capture 12,13 . The differences in urban block typology could result in up to a 200% increase in solar energy harvesting potential and electricity generated from rooftop PV under the same planning conditions and design premises 14 . Despite the clear connection between urban form and rooftop PV self-sufficiency, most existing studies have focused on simplified and simulated urban blocks, limiting the applicability of their findings to real-world, large-scale urban scenarios 10,14–16 . This underscores the need for a comprehensive analysis of actual cities to guide urban planning and design strategies that maximize rooftop PV potential. This study aims to address this gap by conducting an in-depth analysis of rooftop PV self-sufficiency in worldwide. Leveraging 3D building data from 32 cities worldwide, our study employs industry-recognized solar power and building energy consumption simulation models to explore how urban form shapes rooftop PV energy self-sufficiency (Fig. 1 ). A key insight from our analysis is a power-law relationship between average building height and the self-sufficiency rate of solar energy. This pattern emerges consistently across all cities studied, indicating a universal principle linking urban form with rooftop solar energy potential. To delve deeper into this pattern, we construct a multiple regression model incorporating variables such as climate, geography, and urban form. In addition, we extrapolate and assess four distinct urban planning scenarios across both developed and developing cities, with the goal of identifying the urban planning policies that best enhance rooftop PV energy self-sufficiency. By examining the interplay between urban form and rooftop PV self-sufficiency, the study will provide valuable insights into how urban form and design can be optimized to shape more sustainable energy landscapes. Results Rooftop PV potential and building energy demand Considering the geographical diversity among cities, we have categorized our 32-city sample into four groups: high-latitude cities (between 60°N and 45°N), mid-latitude cities (between 45°N and 30°N), low-latitude cities (between 30°N and 0°), and cities in the southern hemisphere. We obtained these cities’ 3D building data from ONEGEO company, which provides comprehensive and highly accurate global building data integrated from multiple datasets 17 . Then, we utilized their 3D building data to estimate rooftop shadow coverage for each city to assess both solar energy potential and building energy demand. The estimated rooftop shadow coverage for all 32 cities is presented in Supplementary Fig. 1. Using these estimates, we then evaluated the annual average daily rooftop PV output under ideal conditions—assuming complete rooftop coverage with PV panels—in conjunction with the energy demand at an urban scale. Furthermore, we examined hourly fluctuations in both rooftop PV output and building energy demand across different city groups during four representative months: January, April, July, and October. Figure 2 a presents the annual average daily PV output for each city, arranged in descending order according to latitude. The data reveals that Los Angeles boasts the highest PV output, surpassing 450 W/m², while Dublin registers the lowest at around 180 W/m². A clear trend emerges as we move from north to south: the annual average PV output increases with decreasing latitude. Cities at higher latitudes tend to yield relatively low average annual PV outputs, seldom exceeding 300 W/m². In stark contrast, cities situated at lower latitudes and in the southern hemisphere typically amass higher quantities of solar energy compared to their mid-latitude and high-latitude counterparts. Their geographical advantage, being closer to the equator and subjected to a suitable climate, ensures they benefit from more direct sunlight throughout the year 18 . Thus, the implementation of solar PV systems in these regions could yield optimal renewable energy utilization, aiding in the reduction of their reliance on traditional fossil fuels. Furthermore, a comparison of the interquartile range (represented by the box lengths) across various cities reveals a tighter data clustering in high-latitude cities and certain mid-latitude cities such as Vienna, Milan, and Istanbul. Conversely, the remaining mid-latitude cities, along with low-latitude and southern hemisphere cities, exhibit a wider dispersion of data. This pattern suggests that PV output tends to be more stable in high-latitude areas, while in other city groups, local architectural layout and climate characteristics might introduce greater uncertainties. This insight could be crucial in planning and optimizing photovoltaic installations across different regions. We employed the urban-scale building energy simulation model (DeST-urban) 19 to heating/cooling energy demand and other energy consumption, including lighting and appliances. All thermal-physical characteristics of buildings, such as the U-value of opaque shell materials and the solar heat gain coefficient (SHGC) of windows, were incorporated into the model. We also factored in the occupancy rate, lighting power density, and urban environment (details available in Supplementary Note 1). The average building energy demand varies across cities, as illustrated in Fig. 2 b. Geneva registers the highest average value, exceeding 3500 W/m², whereas Johannesburg records the lowest at approximately 650 W/m². Generally, as latitude decreases, there is a mild downward trend in the average value of building energy demand. This variation is due to the different climate zones encountered from north to south, which directly impact energy demand. Reviewing the maximum and minimum values of the box plots, it is clear that the daily building energy demand across all cities demonstrates significant volatility. Chicago, in particular, experiences the largest fluctuations, with grids requiring 11.8 times more energy for high-demand compared to low-demand grids. Given similar geographical and climatic conditions, the urban form could significantly influence urban total energy demand. Figure 2 c presents the hourly averages of PV output for each city group across the four seasons representing months. It is evident that high-latitude cities are significantly impacted by seasonal changes, as reflected in their PV output curve. For example, even at noon in January, when solar radiation is at its daily peak, PV output dips to less than 10 W/m² per hour compared to the higher photovoltaic outputs of the other three city groups. Conversely, the PV output curve of high-latitude cities in July shows little variation compared to the other three city groups. Southern hemisphere cities, however, maintain a stable performance, exhibiting minimal variation between seasons in their hourly PV output curves. Energy demand follows a clear daily pattern, akin to PV output, but with a notable difference: the peak of building energy demand occurs between 19:00 and 20:00, as opposed to the PV output's midday peak. Figure 2 d highlights significant variations in energy demand fluctuations among different urban clusters throughout the seasons. In cities of the northern hemisphere, daytime energy demand generally increases in summer, while in southern hemisphere cities, energy demand remains relatively constant (Fig. 2 d). Regarding nighttime energy demand, high-latitude cities register much higher demands compared to other city groups, particularly in January and April — a probable consequence of their colder climates necessitating more heating. Moreover, each urban cluster shows internal variability of energy demands. For example, the light-colored lines, representing changes in energy demands for each grid unit within a city, reveal considerable differences in energy demands during specific time periods among different grid units within a single cluster. These disparities could be attributed to factors such as diverse geographical locations, varied building types, and differing technology use within each city. Geographical and seasonal variations in rooftop PV self-sufficiency rate In the context of our previous discussion on the potential of rooftop PV systems and their relationship to building energy demand, we now turn our attention to a critical metric: the rooftop PV self-sufficiency rate. This rate, defined as the proportion of a city's total energy demand that can be met by locally produced solar energy, provides a significant insight into the potential for rooftop PV systems to contribute to urban sustainability. The forthcoming exploration of Fig. 3 provides a detailed examination of this rate across 32 global cities, highlighting the influence of geographical location and seasonal variations. Within Fig. 3 a, a geographical trend becomes apparent. There is a clear increase in self-sufficiency from north to south, indicating that a city's geographical location significantly impacts the effectiveness of solar energy utilization. Notably, Johannesburg, situated in the southern hemisphere, boasts an annual average rate of 1.1. This figure suggests that with further advancements in storage technologies, this city could potentially realize total solar energy self-sufficiency, a significant milestone in urban sustainability. Figure 3 b, a box plot of grid units within the cities, reveals larger variations in self-sufficiency rates among cities located at lower latitudes and in the southern hemisphere. Diverse urban forms and local energy policies contribute significantly to these variations, underscoring the importance of local context in renewable energy implementation. Moving on to Fig. 3 c through Fig. 3 f, we dive into the seasonal variations in self-sufficiency rates, revealing yet another layer of complexity. High-latitude cities, despite enjoying ample sunlight in July, fail to achieve optimal rates, a pattern that closely mirrors the PV output curve. This trend underscores the profound impact of seasonal changes on solar energy generation, an issue that needs to be addressed by developing more efficient solar technologies and storage solutions. In contrast, low and mid-latitude cities exhibit distinct seasonal shifts in their self-sufficiency rates. There is a notable divergence between these two groups in July and October, indicating different responses to seasonal variations in sunlight. This divergence could be attributed to differences in climatic and geographical conditions and emphasizes the need for tailored solar energy strategies. Cities in the southern hemisphere present inconsistent self-sufficiency rates, with a notable drop during the winter month of July. Despite this, they retain PV self-sufficiency for over six hours daily during the four months under study, underlining the considerable potential of solar energy in these regions. The relationship between urban form and PV self-sufficiency rate Having examined the geographical and seasonal influences on the rooftop PV self-sufficiency rates, we now turning to an aspect that plays a crucial role yet often overlooked: the urban form. As mentioned earlier, the design and layout of a city, characterized by its building types, density, orientation, and height, can significantly impact the effectiveness of solar energy harnessing. The interplay between urban form and self-sufficiency rates is complex, as it involves a myriad of factors including shadowing effects, available rooftop area for PV installations, and local energy demand patterns. In the following analysis, we will unpack this relationship, aiming to shed light on how urban form can be optimized to enhance the potential of rooftop PV systems and thereby bolster urban sustainability. The assessment of urban morphological indicators is performed on a grid basis, with each grid unit comprising an area of 1 km². Based on the previous studies 11,14,20–22 , we select eight parameters to characterize the urban form: (i) Building height (measured in meters), (ii) Building footprint area (measured in m²), (iii) Building perimeter (measured in meters), (iv) Surface area to volume ratio (S/V), (v) Floor area ratio (FAR) (expressed as a percentage), (vi) Building coverage ratio (BCR) (expressed as a percentage), (vii) Block roundness, and (viii) Mixed land use. The geometry of the buildings is described with area and perimeter while city skyline is quantified by building height. The surface area to volume (S/V) ratio is an indicator referring to the compactness of building shape. BCR and FAR measure open space versus built-up space. Block compactness is numerically quantified using block roundness and mix land use. These eight indicators comprehensively summarize attributes such as size, shape, and compactness of the urban form. Specific calculation processes are detailed in method section. The values for these morphological indicators within a grid are represented as the average of all buildings or blocks contained within that grid. To discern the factors most significantly affecting PV self-sufficiency, we conducted a correlation analysis between these urban form indicators and the PV self-sufficiency rates. Scatter diagrams for each pair of variables were plotted to provide a visual representation of these relationships (see Supplementary Note 2 for more details). Our analysis reveals that the average building height exhibits the strongest correlation with the self-sufficiency rate. This finding underscores the influence of vertical urban form on solar energy harvesting, prompting the need for further exploration. The subsequent sections will delve deeper into this relationship, aiming to elucidate the role of building height in solar energy self-sufficiency. Our findings also reveal a complex correlation between the average building height (H) and PV self-sufficiency rate (R), best represented by the power-law equation H = aR b , where b < 1. This relationship indicates that as buildings grow taller, PV self-sufficiency rates decrease, but not in a linear fashion. In environments with low average building heights, self-sufficiency rates are significantly impacted by alterations in building height. However, once the building height surpasses a certain threshold, the negative impact on PV self-sufficiency rate begins to decelerate. This non-linear relationship suggests that while taller buildings may present more challenges to solar energy harvesting, these challenges do not increase indefinitely with height. Interestingly, cities situated at similar latitudes present comparable patterns in their fitted curves. This observation hints at the existence of common factors that influence the values of 'a' and 'b' in the power-law equations. These shared factors, possibly related to similar sunlight patterns due to comparable latitudes, further underline the interplay between urban form and geographical location in determining PV self-sufficiency rates. Given the observed similarities in geographical location and climate within cities of the same group, we proceeded to construct a multivariate regression model. This model aimed to explore the relationships between meteorological indicators (air temperature and global horizontal irradiance), urban morphological indicators (FAR and block roundness), as well as geographic data (latitude and longitude), and the two parameters (a and b) derived from our power-law equation for each city. Our results reveal that the parameter 'a' closely aligns with geographic and meteorological indicators, while the parameter 'b' is more attuned to geographic and urban morphological indicators. The precise values of 'a' and 'b' for each city are documented in Supplementary Table 6. The fitting curves for each city group are illustrated in Fig. 4 a-d. Remarkably, the average R-square value of the fitting curve for all 32 cities under study reaches 0.812, denoting that our model explains over 81% of the variability in PV self-sufficiency rates. To further validate our equation, we earmarked one city from each group as a test set and constituted the remaining 28 cities as the training set. As portrayed in Fig. 4 e-h, our model demonstrated robust performance across all city groups, signifying its wide applicability to cities globally. Importantly, the model accurately captured the PV self-sufficiency rates in relation to building heights. The Mean Squared Error (MSE) of three cities was below 0.01, signifying that our estimates was less than 1% off from the actual self-sufficiency rates. This remarkable accuracy underscores the model's utility in simulating PV self-sufficiency rates based on geographic, meteorological, and urban morphological indicators. This model, validated across diverse urban groups and applicable globally, offers a powerful tool for sustainable urban planning and policy-making. For instance, it can be used for building codes can be devised to limit building heights or shape blocks, thereby maximizing the use of solar energy and improving energy efficiency in urban areas. With its worldwide applicability, the model can be customized to suit the specific conditions of different urban environments. Urban planners and policy-makers anywhere in the world can leverage this model, using local geographic, meteorological, and urban morphological data, to predict their potential PV self-sufficiency rates. This adaptability enhances its utility for cities worldwide in achieving their sustainability objectives. Shaping urban form for PV self-sufficiency city Building upon our validated PV self-sufficiency model, we are now poised to explore its practical applications in shaping PV self-sufficiency through urban form. This model, rooted in empirical data, will serve as our roadmap as we navigate its potential influence on urban planning and architectural design, aspiring to augment PV self-sufficiency in cities worldwide. We conceptualized four distinctive urban form scenarios (Fig. 5 a) based on a consistent population size of 1,000 individuals within an area of 0.04 km 2 . These scenarios encompass: Scenario 1: Tower City , characterized by tall but dispersed buildings. Scenario 2: Hybrid City I , a blend of high-rise buildings housing 70% of the population and low-rise buildings accommodating the remaining 30%. Scenario 3: Hybrid City II , with a majority of low-rise buildings sheltering 70% of the population and a sprinkling of high-rises for the rest. Scenario 4: Garden City , an intimate low-rise environment permitting each family to enjoy a private garden. These scenarios depict two polar extremes of all high-rise and all low-rise buildings, as well as two mixed distributions with either high-rise or low-rise buildings being dominant. The details of scenario settings are shown in Method section. The average building heights for these four urban scenarios are 40.0, 11.5, 8.7 and 8.0 meters respectively. Utilizing these scenarios as our framework, we executed simulations for the initially analyzed 32 cities based on the current urban planning situation and broadened our scope to include four African cities - Addis Ababa, Bamako, Kampala, and Nairobi. This extension allowed us to apply our model within a distinct context. African cities, unlike their highly urbanized counterparts previously analyzed, are largely in their urban development infancy 23 . This presents a prime opportunity to guide their urbanization trajectory towards sustainability from the outset. The accelerated rate of urbanization in Africa further underscores the importance of sustainable urban planning strategies. Supplementary Note 3 offers a detailed account of the simulation process employed to adjust the parameters for these African cities. Given the unavailability of block roundness data for these specific cities, we adopted the average block roundness of geographically proximate low-latitude cities. The curve illustrating the relationship between urban height and self-sufficiency rate for these cities is depicted in Supplementary Fig. 5. Our analysis, graphically presented in Fig. 5 b, provides a fresh perspective on the self-sufficiency rate of buildings across various urban scenarios, offering insights for urban renewal across different cities. For cities aligning with the Tower City scenario, the annual self-sufficiency rates are strikingly low, indicating a profound need for reconsideration and remodeling of their existing urban form. One potential approach, as seen in the Hybrid City I scenario, could be reduce building heights to boost self-sufficiency. This adjustment, although seemingly minor, can prompt a significant surge in self-sufficiency rates. On the other hand, cities that already exhibit low-rise characteristics, akin to those in the Hybrid City II and Garden City scenarios, can find affirmation in our findings for their potential for solar energy integration. A promising strategy for these cities could be to maximize the use of rooftops in suburban areas, thus achieving a larger scale Hybrid City form and raising the self-sufficiency rates. However, while these strategies are promising, they may not be universally applicable. Cities like Helsinki, Copenhagen, and Berlin, located in regions where winter solar radiation intensity is exceptionally low, face unique challenges. Sacrificing public space for achieving Garden City scenario may not bring more benefits of solar self-sufficiency. Therefore, the hybrid city concept might be more feasible. Apart from optimizing the urban form for maximum solar energy gains, exploring additional renewable energy sources such as wind or geothermal energy is also a viable option for these cities. As African cities urbanize, integrating PV self-sufficiency with city development presents a unique opportunity to shape their future sustainably. Owing to their proximity to the equator, African cities are well-positioned to exploit solar energy. Simulation results show that all four African cities exhibited robust self-sufficiency potential, particularly Addis Ababa in the Garden City scenario, where the annual self-sufficiency rate exceeds 50%. If all four African cities adopt the planning policy of Garden City scenario, their average annual PV self-sufficiency rate will be about 5 times higher than that of Tower City scenario. Especially in Kampala, the self-sufficiency rate of two extreme scenarios in January differs by about 9 times. The IEA points out that currently, 80% of the power generation capacity in Kampala is based on hydroelectric power 24 . Assuming Kampala adopts the Garden City scenario for development in the future, its reliance on hydroelectric power will decrease from 96% in the tower city scenario to 63.4%. Our model suggests that integrating solar energy into sensible urban design can significantly alleviate the energy security concerns arising from excessive dependence on hydroelectric power, particularly amidst water resource crises resulting from climate change in the future. Moreover, when considering block roundness, the model shows that with extremely compact block shapes, all four cities could achieve full solar energy self-sufficiency during summer in scenarios such as Hybrid City I, Hybrid City II, and Garden City (Supplementary Fig. 6). This insight indicates that fostering compact urban blocks can optimize solar energy utilization, propelling a leap towards energy self-sufficiency while accommodating the pressures of urban growth. Discussion Decade-long contributions to urban morphology and solar energy research have elevated this domain into a critical area of study. Despite the acknowledged discrepancies in research focus between developed countries and those that are developing or with economies in transition, our analysis reveals intricate patterns in urban form and self-sufficiency in solar energy that are far richer and more varied than previously recognized. The results are not only relevant for developing countries undergoing rapid urbanization but also for developed nations striving to renew their existing urban structures. PV self-sufficiency as a design parameter in urban planning Recognizing the interplay between urban form and solar energy utilization has led us to some insights. It Is not solely about the height of the buildings or compactness of city blocks, but a more complex, nuanced interaction involving these factors. For instance, our research points to an inverse relationship between building height and PV self-sufficiency, but this doesn't suggest that all cities should aim to reduce their skyline. Instead, it demonstrates the potential of hybrid urban forms that balance high-rise and low-rise structures, optimizing solar energy harnessing without sacrificing urban density. This nuanced understanding could redefine urban planning paradigms, taking us beyond the common approach of just reducing building height for better solar access. During the urban planning phase, incorporating self-sufficient solar energy into design considerations can greatly help achieve a sustainable balance between urban development and energy efficiency. By integrating the concept of "solar-ready," infrastructure is designed in advance to accommodate future solar installations. The fusion of these two aspects forms a forward-thinking urban development and construction strategy. With our model, urban planners and decision-makers can grasp energy gaps and surplus blocks in the planning stage, opening up innovative opportunities for energy management. For example, implementing community-based energy sharing or trading platforms allows neighborhoods or buildings with excess energy to distribute it to those in deficits. Alternatively, redirecting excess PV output to meet public infrastructure needs such as streetlights, public Wi-Fi networks, and water treatment systems also helps reduce municipal cost savings and promote sustainable development environments. By prioritizing PV self-sufficiency in urban planning, cities can achieve a balance between urbanization and environmental sustainability paving the way for resilient, energy-efficient metropolitan futures. Inspiration for boosting PV self-sufficiency in developed nations In the case of developed nations, the task of increasing solar energy self-sufficiency may appear daunting due to their established urban structures. However, our research paints a more optimistic picture. Instead of a total transformation of existing urban forms, which might seem impossible or prohibitively expensive, there are more feasible, yet highly effective solutions. A promising strategy could be to maximize the use of rooftops in suburban areas, thereby achieving a larger scale Hybrid City structure, and raising the self-sufficiency rates. However, this approach would likely increase the investment required for microgrids and energy storage systems. Subtle alterations, such as reorienting city blocks for better solar exposure or modifying roof structures to accommodate more solar panels, can also be considered to further enhance solar energy utilization without drastic transformations. These strategies suggest a pathway to enhanced energy self-sufficiency that doesn't require comprehensive urban transformation, but instead, depends on smart, targeted changes. This approach suggests that with careful planning and implementation, cities can make significant strides towards solar energy self-sufficiency without necessitating a complete overhaul of their established urban forms. For individual buildings that are unable to install solar panels due to location or policy restrictions, community solar programs offer a viable solution. These programs allow multiple stakeholders to invest in a shared solar energy system, benefiting from the electricity generated at a location other than their property. Great opportunity for achieving PV self-sufficiency in developing nations As for developing nations, take Africa cities as examples, the accelerated pace of urbanization is often viewed as a daunting challenge. However, our findings flip this notion on its head, suggesting that rapid urbanization can serve as a unique opportunity. Specifically, these nations have the potential to bypass the more energy-inefficient stages of urban development that many developed nations experienced in the past. By incorporating energy-conscious urban forms from the outset—including balanced high-rise and low-rise structures and compact city blocks—developing nations could establish urban environments that are inherently better suited for solar energy utilization. Although implementing these urban development concepts may require more land, it is a feasible and attractive development model in the context of African cities. From an economic perspective, the cost of constructing low-rise buildings is often lower in African countries, mainly due to relatively abundant construction materials and labor. In addition, this concept aligns with the common living habits in many African communities, better ensuring essential aspects of daily life such as outdoor activities and community interaction. Therefore, this strategy could balance ecological, economic, and social factors, leading to urbanization that is not only robust in the face of escalating urban population growth, but also highly energy-efficient and environmentally friendly. However, the endeavor to increase solar energy self-sufficiency is not without complexities. Balancing the costs of modifying urban forms against the potential energy gains requires careful consideration. Factors such as fluctuating sunlight conditions due to climate change and environmental pollution, and varying safety regulations and building height restrictions across countries, add to the complexity of this task. These challenges underscore the need for adaptability and localized strategies. In conclusion, our research illuminates a promising yet multifaceted path towards solar energy self-sufficiency in cities. It emphasizes the need for a nuanced understanding of urban form and its impact on solar energy utilization. By moving beyond traditional assumptions and exploring innovative possibilities, cities around the world can make significant strides towards sustainable growth. Method Data Acquisition This study utilizes various types of data, including building footprint, road network, land use, and meteorological data. The building footprint data is obtained from ONEGEO GmbH 17 , which provides high-precision 3D building data on a global scale. The data attributes include name, type, height, levels, and geometry. Building footprint data is used to calculate urban morphological indicators, geometric shadow calculation, and building energy demand estimation. Road network and land use data are obtained from OpenStreetMap for calculating urban morphological indicators. In addition, each city requires an annual weather file to describe local climatic conditions. We use the National Solar Radiation Database (NSRDB) which provides solar radiation and meteorological information for multiple countries and regions at time resolutions of 5, 10, or 15 minute intervals 25 . Since our research focuses on hourly units, only exact hours are selected from the dataset. Meteorological data is utilized for geometric shadow calculation, building energy demand estimation as well as multivariate regression model fitting. Geometric shadow calculation on building roofs The building shadow calculation in this study is based on the trigonometric relations in the triangle defined by the sun light and the wall of the buildings. For a building, the algorithm will first break up the building into a collection of walls and calculates the shadow geometry for each wall independently based on the sun's position. Given the location of the study area and the time, the sun position defined by altitude angle \({\alpha }_{al}\) and azimuth angle \({\alpha }_{az}\) can be calculated by suncalc 26 . For the wall with the height h, the projection distance d on the ground for a given point on the wall can be calculated by Eq. 2 (Supplementary Fig. 8). $$d=h\bullet \text{cot}({\alpha }_{al})$$ 2 Since the azimuth angle \({\alpha }_{az}\) implies the direction of the shadow, the quadrangle geometry of the wall shadow can be generated by calculating the projection position of the two edge points on each wall. Once all wall shadows have been generated, they can be merged to form a vector-based geometric shadow for the building. The above approach of building shadow calculation can be further extended to the case of calculating shadow coverage on the building roof. The shadow coverage of a building's roof can be calculated by creating the shadows of all structures taller than this one, then intersecting the shadows with the building outline. PV potential estimation We made unified assumptions for the solar energy system involved in this study. Based on the coefficients provided by PVSystem from pvlib 27 , performance parameters were determined in Supplementary Note 4. The PV panels are tilted according to the local latitude. By utilizing pvlib, hourly PV output per module ( \({PV}_{m}\) ) could be calculated at a specified location. Based on the area of the module and building footprint area, the placement factor ( \(\eta\) ) can be calculated by Eq. 3 . Considering the solar energy can only be collected within available building areas ( \({A}_{a})\) where are not covered by shadows, we can calculate hourly PV output ( \({PV}_{b})\) for buildings by Eq. 4 . $$\eta =\frac{{A}_{m}}{{A}_{f}}$$ 3 $${PV}_{b}= {PV}_{m}\bullet {A}_{a}\bullet \eta$$ 4 Urban-scale building energy simulation Urban building energy modeling (UBEM) is a physics-based, bottom-up approach to simulate the building energy consumption at the urban scale 28 . In this study, the previously developed UBEM platform (DeST-urban) was used to automatically generate building energy models (BEMs) based on 3D city models and simulate the hourly energy demand of each building in parallel 19 . DeST is utilized as the simulation engine to simulate heating/cooling energy demand and other energy consumption, including lighting and appliances. DeST is a whole-building performance simulation engine that enables physics-based calculation including heat balance, airflow, and other thermal and energy metrics 29 . In urban-scale building energy simulation, the GeoJSON file including the building geometry (footprint and height) was parsed and the BEM of each building was established based on the specific geometry. BEMs were generated by extruding the building footprints to their corresponding heights. The number of floors was estimated based on the building height and floor-to-floor height. One thermal zone per floor was modeled, balancing time and accuracy 30 . Two main building types, residential and commercial, were considered. The BEMs of each building type were configured based on the prototype building models in the previous study 31 , including the properties of building envelopes, the occupancy, and the usage of lighting, appliances and heating, ventilation, and air conditioning (HVAC). The simulations used the same weather data with the PV output simulation to analyze the matching of energy supply and demand. The parameters of the prototype building models are presented in Supplementary Note 1. To consider the impact of urban form on energy consumption of the building blocks, each building was modeled with its surroundings, and the shadow cast on the rooftops and facades in the neighborhood was calculated based on the real direction of sunlight in each hour 32 . Solar heat gain of each building surface could be calculated based on the sunlit area, which would affect the heating/cooling energy demand. Urban morphological indicators As shown in Supplementary Table 10, eight urban morphological indicators were calculated to capture the spatial and geometric characteristics of the city. For the division of blocks, we removed private roads, steps, bike lanes, unnamed roads, service roads, and sidewalks from the road network. The remaining polygons formed by the intersection of roads refer to blocks. Building coverage ratio is calculated as the percentage of the total building footprint area within a block to the block area. The geometry of the buildings is described with area and perimeter while city skyline is quantified by building height. The surface area to volume (S/V) ratio is an indicator referring to the compactness of building shape 22 . Floor area ratio (FAR) represents the percentage of the total building floor area to the block area while Building coverage ratio (BCA) refers to the percentage of the first floor area to the block area 11,20,22 . Roundness is a measure proposed by Richardson 33 that evaluates regularity in urban blocks, with a range from 0 to 1. If a block shape approaches a circle more closely, its roundness value will be closer to 1; otherwise, it will be closer to 0. Land use mix refers to how different types of land uses (e.g., residential, commercial or industrial) are physically and functionally integrated. There are numerous ways to measure the extent of land use diversity, but this article solely employs the entropy index which is commonly used 33 . Multivariate regression model Although we have figured out the universal laws of urban morphological indicators and self-sufficiency rate, it remains unclear what relationship exists between values a and b and urban attributes. To apply universal laws to other cities worldwide, it is necessary to explore the physical meaning of values a and b. We selected indicators that affect the values of a and b through correlation analysis. Meteorological indicators (air temperature and global horizontal irradiance) and geographic information (latitude and longitude) were chosen as the independent variable group that affects value a, and urban morphological indicators (FAR and block roundness) and geographic information (latitude and longitude) were chosen as the independent variable group that affects value b. We used a multivariate regression model to describe the complex interaction between the values of a and b and their respective independent variable groups and obtained the following formulas. $$a=0.0422\text{G}\text{H}\text{I}-0.1897\text{A}\text{i}\text{r}\text{T}\text{e}\text{m}-0.0208\text{L}\text{a}\text{t}+0.01 \text{L}\text{n}\text{g}-0.8781$$ 5 \(\text{b = }-\text{ln[}-\text{0.0136}\text{ln}\left(\text{L}\text{a}\text{t}\right)+0.1785\text{l}\text{n}\left(\text{L}\text{n}\text{g}\right) -0.2823\text{A}\text{v}\text{g}\left(FAR\right)+ 0.105\text{A}\text{v}\text{g}(\text{R}\text{o}\text{u}\text{n}\text{d}\text{n}\text{e}\text{s}\text{s}\) ) + 2.6573] ( 6 ) Simulation Scenario Settings We take the residential density configuration within 0.04 km 2 accommodating 1,000 residents or 200 households as the basic setting. Assuming an average of five people per household for low-rise buildings and four people per household for high-rise buildings. The floor-to-ceiling height of high-rise buildings is set at 3-3.2 meters, while the total height of low-rise buildings is set at 8 meters, meeting the building standards of multiple countries 34–36 . Scenario 1 assumes the construction of fifteen-stories high-rise buildings, requiring four such structures to accommodate 480 individuals (120 people per building). The remaining 520 residents are distributed among five twelve-story high-rise buildings (96 people per building), supplemented by a five-stories building to accommodate the rest of the population. The distance between each building is 65 meters front to back and exceeds 26 meters on either side. In this scenario, the average building height is 38.8 meters. After accounting for elevators and roof decorations, a rounded height of 40 meters is adopted. Scenario 2 proposes to set 70% buildings to accommodate 700 people, requiring six fifteen-stories high-rise buildings, and remaining 30% buildings to accommodate 300 people, requiring sixty low-rise buildings. The high-rise buildings are located north of the low-rise buildings with a front-to-back distance of 65 meters, and the lateral distance between low-rise buildings is 9 meters. In this case, the average building height is 11.5 meters. Scenario 3 allocates 30% of the space to high-rise buildings for 300 people across four twelve-stories buildings, and 70% to low-rise buildings to accommodate 700 individuals, requiring 140 buildings. This results in an average height of 8.7 meters. Lastly, Scenario 4 is exclusively composed of 200 low-rise buildings, with a front-to-back spacing of 10 meters and a side-to-side spacing of more than 6 meters, maintaining the average height at 8 meters. Each scenario reflects a strategic approach to urban form, balancing building height and population distribution to accommodate the needs of 1,000 residents within a finite urban block. Declarations Data availability All data generated or analyzed during this study used in the graphs are included in this published article and its supplementary information files: “Supplementary Information”. Further data are available from the corresponding author on reasonable request. References Fox S, Goodfellow T. Cities and Development . 2nd ed. Routledge; 2016. doi:10.4324/9781315815527 Pulselli RM, Broersma S, Martin CL, Keeffe G, Bastianoni S, Van Den Dobbelsteen A. Future city visions. The energy transition towards carbon-neutrality: lessons learned from the case of Roeselare, Belgium. Renew Sustain Energy Rev . 2021;137:110612. doi:10.1016/j.rser.2020.110612 Wang X, Wang G, Chen T, Zeng Z, Heng CK. Low-carbon city and its future research trends: A bibliometric analysis and systematic review. Sustain Cities Soc . 2023;90:104381. doi:10.1016/j.scs.2022.104381 Creutzig F, Agoston P, Goldschmidt JC, Luderer G, Nemet G, Pietzcker RC. The underestimated potential of solar energy to mitigate climate change. Nat Energy . 2017;2(9):17140. doi:10.1038/nenergy.2017.140 Nijsse FJMM, Mercure JF, Ameli N, et al. The momentum of the solar energy transition. Nat Commun . 2023;14(1):6542. doi:10.1038/s41467-023-41971-7 Khor N. World Cities Report 2022: Envisaging the Future of Cities . United Nations Human Settlements Programme (UN-Habitat); 2022. Tollin N, Vener J, Pizzorni M, et al. Urban Climate Action. The Urban Content of the NDCs: Global Review 2022 . United Nations Human Settlements Programme UN-Habitat; 2022. Masson G, Bosch E, Kaizuka I, et al. Snapshot of Global PV Markets 2023 .; 2023. Hui SCM. Low energy building design in high density urban cities. Renew Energy . 2001;24(3-4):627-640. doi:10.1016/S0960-1481(01)00049-0 Perera ATD, Coccolo S, Scartezzini JL. The influence of urban form on the grid integration of renewable energy technologies and distributed energy systems. Sci Rep . 2019;9(1):17756. doi:10.1038/s41598-019-53653-w Mohajeri N, Upadhyay G, Gudmundsson A, Assouline D, Kämpf J, Scartezzini JL. Effects of urban compactness on solar energy potential. Renew Energy . 2016;93:469-482. doi:10.1016/j.renene.2016.02.053 Ghaleb B, Asif M. Application of solar PV in commercial buildings: Utilizability of rooftops. Energy Build . 2022;257:111774. doi:10.1016/j.enbuild.2021.111774 Li SY, Han JY. The impact of shadow covering on the rooftop solar photovoltaic system for evaluating self-sufficiency rate in the concept of nearly zero energy building. Sustain Cities Soc . 2022;80:103821. doi:10.1016/j.scs.2022.103821 Zhang J, Xu L, Shabunko V, et al. Impact of urban block typology on building solar potential and energy use efficiency in tropical high-density city. Appl Energy . 2019;240:513-533. doi:10.1016/j.apenergy.2019.02.033 Giostra S, Masera G, Monteiro R. Solar Typologies: A Comparative Analysis of Urban Form and Solar Potential. Sustainability . 2022;14(15):9023. doi:10.3390/su14159023 Shareef S. The impact of urban morphology and building’s height diversity on energy consumption at urban scale. The case study of Dubai. Build Environ . 2021;194:107675. doi:10.1016/j.buildenv.2021.107675 ONE GEO. Clean and Comprehensive Worldwide Building Footprints. https://onegeo.co/?gclid=Cj0KCQiAoeGuBhCBARIsAGfKY7yWADmIsy2lZGZMuih4-BWv349eZ7KQ_B--7oDJPRjF7r1prinBEY8aAlIcEALw_wcB Suri,Marcel;, Betak,Juraj;, Rosina,Konstantin;, et al. Global Photovoltaic Power Potential by Country (English). Published online 2020. http://documents.worldbank.org/curated/en/466331592817725242/Global-Photovoltaic-Power-Potential-by-Country Liu Z, Zhou X, Tian W, Liu X, Yan D. Impacts of uncertainty in building envelope thermal transmittance on heating/cooling demand in the urban context. Energy Build . 2022;273:112363. doi:10.1016/j.enbuild.2022.112363 Hermosilla T, Palomar-Vázquez J, Balaguer-Beser Á, Balsa-Barreiro J, Ruiz LA. Using street based metrics to characterize urban typologies. Comput Environ Urban Syst . 2014;44:68-79. doi:10.1016/j.compenvurbsys.2013.12.002 Oh M, Kim Y. Identifying urban geometric types as energy performance patterns. Energy Sustain Dev . 2019;48:115-129. doi:10.1016/j.esd.2018.12.002 Morganti M, Salvati A, Coch H, Cecere C. Urban morphology indicators for solar energy analysis. Energy Procedia . 2017;134:807-814. doi:10.1016/j.egypro.2017.09.533 Gore CD, Gopakumar G. Infrastructure and Metropolitan Reorganization: An Exploration of the Relationship in Africa and India. J Urban Aff . 2015;37(5):548-567. doi:10.1111/juaf.12180 International Energy Agency. Uganda 2023: Energy Policy Review . OECD; 2023. doi:10.1787/1b6b9a5a-en Sengupta M, Xie Y, Lopez A, Habte A, Maclaurin G, Shelby J. The National Solar Radiation Data Base (NSRDB). Renew Sustain Energy Rev . 2018;89:51-60. doi:10.1016/j.rser.2018.03.003 Elmarhraoui A, Thieurmel B. suncalc: Compute Sun Position, Sunlight Phases, Moon Position and Lunar Phase. Published online 2022. https://CRAN.R-project.org/package=suncalc Holmgren W, Anderson K, Hansen C, et al. pvlib/pvlib-python: v0.9.5. Published online March 18, 2023. doi:10.5281/ZENODO.593284 Reinhart CF, Cerezo Davila C. Urban building energy modeling – A review of a nascent field. Build Environ . 2016;97:196-202. doi:10.1016/j.buildenv.2015.12.001 Yan D, Zhou X, An J, et al. DeST 3.0: A new-generation building performance simulation platform. Build Simul . 2022;15(11):1849-1868. doi:10.1007/s12273-022-0909-9 Johari F, Munkhammar J, Shadram F, Widén J. Evaluation of simplified building energy models for urban-scale energy analysis of buildings. Build Environ . 2022;211:108684. doi:10.1016/j.buildenv.2021.108684 Gui C, Yan D, Guo S, An J. Development of Prototype Building Model in Beijing Based on Actual Energy Consumption. In: Wang Z, Zhu Y, Wang F, Wang P, Shen C, Liu J, eds. Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019) . Environmental Science and Engineering. Springer Singapore; 2020:1187-1196. doi:10.1007/978-981-13-9528-4_120 Yan D, Xia J, Tang W, Song F, Zhang X, Jiang Y. DeST — An integrated building simulation toolkit Part I: Fundamentals. Build Simul . 2008;1(2):95-110. doi:10.1007/s12273-008-8118-8 Haggett P. Locational Analysis in Human Geography . London : Edward Arnold; 1965. Shakespeare R. DUBLIN CITY DEVELOPMENT PLAN 2016 - 2022 PROGRESS REPORT . Dublin City Council; 2018. https://www.dublincity.ie/dublin-city-development-plan-2016-2022 Hasegawa T. Introduction to the Building Standard Law -Building Regulation in Japan- . Building Center of Japan; 2013. https://www.bcj.or.jp/upload/international/baseline/BSLIntroduction201307_e.pdf American Society of Heating, Refrigerating and Air-Conditioning Engineers. ANSI/ASHRAE Standard 90.2-2018 Energy-Efficient Design of Low-Rise Residential Buildings. Published online 2018. https://ashrae.iwrapper.com/ASHRAE_PREVIEW_ONLY_STANDARDS/STD_90.2_2018 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Supplementary Information Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4124110","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":289515622,"identity":"a158a41b-685a-4d7c-bd3d-43e73436a670","order_by":0,"name":"Pengjun Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYLACCRjxgSEBxDQgXgvjDKK1wPQx8xCjxZy99/ALy7Y7DPKzmx8+tt2RltjA3rxNgqHmDk4tlj3n0iwk254xMM45ZmyceyYnsYHnWJkEw7FnOLUY3MgxM5BsO8zALJFgJp3bVpHYIJFjJsHYcBi3lvtvIFrYJNK/SVuCtMi/IaDlBo/xA5AWHqDh0oxtQIdJ8ODXYtmTY8Ygce4wj4RETrFh75k04zaetGKLhGO4tZiznzH+LFF2WE5+RvrGBz93JMv2sx/eeONDDR6HMTCwSQOjhAfMY2wAckGMBJwawFqYP36A8UBaRsEoGAWjYBSgAwAV61CXLIvS+wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5373-5551","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Pengjun","middleName":"","lastName":"Zhao","suffix":""},{"id":289515623,"identity":"e581a365-47d1-4c6c-b614-5e475462f52d","order_by":1,"name":"Yanxiu Jin","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yanxiu","middleName":"","lastName":"Jin","suffix":""},{"id":289515624,"identity":"c6a4a3cf-1405-4605-a885-22432b91b364","order_by":2,"name":"Haoran Zhang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Haoran","middleName":"","lastName":"Zhang","suffix":""},{"id":289515625,"identity":"f4703311-3e91-457c-8724-827e9a177a79","order_by":3,"name":"Zhaoru Liu","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Zhaoru","middleName":"","lastName":"Liu","suffix":""},{"id":289515626,"identity":"2c452cef-cf3b-4a29-a15a-f590180567f6","order_by":4,"name":"Qing Yu","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Yu","suffix":""},{"id":289515627,"identity":"b37d0d21-30a5-4dab-a66f-eee1c3c1d4bf","order_by":5,"name":"Zhengying Liu","email":"","orcid":"","institution":"Peking University Shenzhen Graduate School","correspondingAuthor":false,"prefix":"","firstName":"Zhengying","middleName":"","lastName":"Liu","suffix":""},{"id":289515628,"identity":"67c65183-b85b-4a11-a534-6e8fc8a76020","order_by":6,"name":"Zhiling Guo","email":"","orcid":"","institution":"The Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Zhiling","middleName":"","lastName":"Guo","suffix":""},{"id":289515629,"identity":"07326c71-0cb9-45fb-9ebe-5b77901e3603","order_by":7,"name":"Da Yan","email":"","orcid":"https://orcid.org/0000-0003-2399-723X","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Da","middleName":"","lastName":"Yan","suffix":""},{"id":289515630,"identity":"6411a822-44f9-4646-a83e-a34ca2a87b0d","order_by":8,"name":"Ryosuke Shibasaki","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Ryosuke","middleName":"","lastName":"Shibasaki","suffix":""},{"id":289515631,"identity":"8b423d34-7263-4ec1-8faa-0add092dc2ce","order_by":9,"name":"Jinyue Yan","email":"","orcid":"","institution":"The Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Jinyue","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2024-03-18 14:42:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4124110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4124110/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54476439,"identity":"cdb60a4d-3e47-42cc-8256-745b3d04dd7e","added_by":"auto","created_at":"2024-04-11 06:57:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":412384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFramework. \u003c/strong\u003ewe investigate the influence of urban form on the self-sufficiency rate of cities, where the supply is solely provided by rooftop PV. We use 32 cities across six continents as our research sample. Each city is segmented into 1km square grids, each measuring 1m², serving as the unit for subsequent calculations. For each grid, we first calculate the shadow area of buildings using geometric methods. Then, we estimate the solar energy output with pvlib. The Dest-urban is applied to simulate the city-wide building energy demand. This allows us to derive a new indicator—the self-sufficiency rate based on rooftop PV. We further explore this relationship through a multiple regression model that considers factors such as climate, geography, and urban form. Additionally, we devise and test four urban planning scenarios—Tower City, Hybrid City I, Hybrid City II, and Garden City—on four developing African cities to evaluate the impact of different urban construction conditions on energy self-sufficiency. The simulation process is as follows: firstly, input the selected city’s geographical attributes (latitude and longitude) and meteorological data (air temperature and solar irradiance); set assumptions for urban planning conditions (floor area ratio and block roundness). Secondly, substitute them into the multiple regression models to obtain the curve of self-sufficiency rate. Finally, infer its solar energy self-sufficiency based on the average building height set in the planning scenario.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4124110/v1/f6c208dfceb0abc34236017c.png"},{"id":54476443,"identity":"6c92e090-d214-4378-a8eb-49602e0749a3","added_by":"auto","created_at":"2024-04-11 06:57:38","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1112879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative Overview of Rooftop PV Potential and Building Energy Demand Across Various Cities.\u003c/strong\u003e \u003cstrong\u003ea.\u003c/strong\u003e A box plot illustrating the annual average daily PV output. The y-axis lists the cities in descending order from north to south latitude, while the x-axis denotes the cumulative daily PV output per square meter. \u003cstrong\u003eb. \u003c/strong\u003eA box plot showcasing the average daily building energy demand per year. Similar to \u003cstrong\u003ea\u003c/strong\u003e, the y-axis lists the cities from north to south latitude, and the x-axis represents the cumulative daily building energy demand per square meter. \u003cstrong\u003ec. \u003c/strong\u003eThe curves depict the hourly variations in PV output for the four city groups during each season, represented by four distinct months: January, April, July, and October. \u003cstrong\u003ed. \u003c/strong\u003eThe curves illustrate the hourly variations in the average building energy demand for the four city groups during each season, represented by the same four months: January, April, July, and October. In the box plots, each point signifies the PV potential or building energy demand values of individual city grid units. The light-colored lines in the line plots represent the hourly variations of PV output or building energy demand for each city grid unit.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4124110/v1/a23e863d27e93fb198046af1.jpeg"},{"id":54476438,"identity":"216b9667-1dc3-4950-9765-305f207ee920","added_by":"auto","created_at":"2024-04-11 06:57:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":400254,"visible":true,"origin":"","legend":"\u003cp\u003eThe PV self-sufficiency rate in 32 cities. \u003cstrong\u003ea. \u003c/strong\u003eEach point on the world map represents a city, with the color and size denoting the city group and average PV self-sufficiency rate within a year, respectively. The larger the point, the higher the PVself-sufficiency rate. \u003cstrong\u003eb. \u003c/strong\u003eDisplayed the box plot of grids within the cities, arranged from north to south from left to right.\u003cstrong\u003e \u003c/strong\u003eThe hourly variation of the PV self-sufficiency rate in four months: \u003cstrong\u003ec\u003c/strong\u003e January, \u003cstrong\u003ed\u003c/strong\u003e April, \u003cstrong\u003ee\u003c/strong\u003e July, and \u003cstrong\u003ef\u003c/strong\u003e October.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4124110/v1/f36ddc1816bdcb46c4ca208d.png"},{"id":54476440,"identity":"f9f08186-a41d-48b4-bd86-3ad9af1ea3cd","added_by":"auto","created_at":"2024-04-11 06:57:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":265799,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between urban height and \u003c/strong\u003ePV\u003cstrong\u003e self-sufficiency rate. a-d. \u003c/strong\u003eThe fitting curve of the average building height and self-sufficiency rate for each city group. The average building height and PV self-sufficiency rate fit well with the power-law curve H = aR\u003csup\u003eb\u003c/sup\u003e. Some cities, such as Helsinki, Brussels, Istanbul, Singapore, Sydney, etc., have fitted R-squared values exceeding 0.9. Additionally, cities within the same city group also exhibit similar shapes. \u003cstrong\u003ee-h\u003c/strong\u003e. Test set results.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4124110/v1/1cf15bbf5154b5053a1ca6ce.png"},{"id":54476442,"identity":"02e01994-3db9-4897-bc7a-1e1357ba08b1","added_by":"auto","created_at":"2024-04-11 06:57:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":277592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustrations of various scenarios.\u003c/strong\u003e Based on a fixed population size - 1,000 people within an area of 0.04 km\u003csup\u003e2\u003c/sup\u003e: Tower City which is tall but more spread out; Hybrid City Ⅰ which has some high-rise buildings housing 70% of the population and some low-rise buildings housing 30% of the population; Hybrid City Ⅱ which has many low-rise buildings housing 70% of the population and a few high-rise buildings; and Garden City which is low level and close together and allows each family to have a private garden. \u003cstrong\u003eb. \u003c/strong\u003ePV\u003cstrong\u003e self-sufficiency rate by scenario. \u003c/strong\u003eThe figure shows the PV self-sufficiency rate in various cities under four different scenarios. The gray boxed areas represent four African cities used for simulation. The light colors represent the self-sufficiency situation corresponding to the four seasonal representative months (January, April, July, October). Detailed information about each month can be seen in Supplementary Figure 7.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4124110/v1/7386b07470d58825c66ee5c5.png"},{"id":82110449,"identity":"2c5b1752-a3ab-48e0-8d02-552d1aa8212b","added_by":"auto","created_at":"2025-05-07 00:33:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3299635,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4124110/v1/f3e7b82a-556d-4824-b19e-4708b4da1b80.pdf"},{"id":54476441,"identity":"a5a669ac-dadc-42c0-b196-67e2ff47e848","added_by":"auto","created_at":"2024-04-11 06:57:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3603647,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Information\u003c/p\u003e","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4124110/v1/ff5dfa0a5a19c3a59123cdde.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Shaping urban form for solar energy self-sufficiency city","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCities, the epicenters of global energy consumption and greenhouse gas emissions, are under increasing pressure to transition toward sustainability\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. One of the most effective measures to achieve this transition is enhancing energy self-sufficiency through the implementation of distributed rooftop photovoltaic (PV) systems\u003csup\u003e4,5\u003c/sup\u003e. These systems, transforming sunlight directly into electricity, can substantially offset a city's dependence on external energy sources, thereby bolstering energy resilience and playing a fundamental role in climate change mitigation\u003csup\u003e6,7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEnergy self-sufficiency in the context of rooftop PV systems can be defined as the city's ability to meet a significant portion of its electricity demand through solar energy harvested from its own rooftops\u003csup\u003e8\u003c/sup\u003e. A higher self-sufficiency rate implies that the city can supply a larger share of its energy needs from its rooftop PV systems, thereby reducing its reliance on energy imports and decreasing its vulnerability to external energy shocks. This not only contributes to the city's energy security but also promotes the adoption of clean, renewable energy sources, thereby fostering a more sustainable urban future.\u003c/p\u003e \u003cp\u003eHowever, the ability of a city to increase its self-sufficiency from rooftop PV systems is not solely dependent on the availability of sunlight. The urban form\u0026mdash;characterized by factors such as building height, density, and orientation\u0026mdash;plays a critical role in determining the potential for solar energy harvesting \u003csup\u003e9\u0026ndash;11\u003c/sup\u003e. Differing urban forms can lead to substantial variation in solar energy harvesting potential, thereby affecting the achievable level of self-sufficiency. For instance, densely built-up areas with high-rises might have limited roof surface area exposed to sunlight, reducing the potential for rooftop PV deployment. Conversely, lower-density areas with more expansive rooftops might offer greater opportunities for solar energy harvesting. Moreover, the orientation of buildings and the presence of shading elements, such as neighboring buildings, can also significantly impact the amount of sunlight that a rooftop can capture\u003csup\u003e12,13\u003c/sup\u003e. The differences in urban block typology could result in up to a 200% increase in solar energy harvesting potential and electricity generated from rooftop PV under the same planning conditions and design premises\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the clear connection between urban form and rooftop PV self-sufficiency, most existing studies have focused on simplified and simulated urban blocks, limiting the applicability of their findings to real-world, large-scale urban scenarios\u003csup\u003e10,14\u0026ndash;16\u003c/sup\u003e. This underscores the need for a comprehensive analysis of actual cities to guide urban planning and design strategies that maximize rooftop PV potential. This study aims to address this gap by conducting an in-depth analysis of rooftop PV self-sufficiency in worldwide. Leveraging 3D building data from 32 cities worldwide, our study employs industry-recognized solar power and building energy consumption simulation models to explore how urban form shapes rooftop PV energy self-sufficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A key insight from our analysis is a power-law relationship between average building height and the self-sufficiency rate of solar energy. This pattern emerges consistently across all cities studied, indicating a universal principle linking urban form with rooftop solar energy potential. To delve deeper into this pattern, we construct a multiple regression model incorporating variables such as climate, geography, and urban form. In addition, we extrapolate and assess four distinct urban planning scenarios across both developed and developing cities, with the goal of identifying the urban planning policies that best enhance rooftop PV energy self-sufficiency. By examining the interplay between urban form and rooftop PV self-sufficiency, the study will provide valuable insights into how urban form and design can be optimized to shape more sustainable energy landscapes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRooftop PV potential and building energy demand\u003c/h2\u003e \u003cp\u003eConsidering the geographical diversity among cities, we have categorized our 32-city sample into four groups: high-latitude cities (between 60\u0026deg;N and 45\u0026deg;N), mid-latitude cities (between 45\u0026deg;N and 30\u0026deg;N), low-latitude cities (between 30\u0026deg;N and 0\u0026deg;), and cities in the southern hemisphere. We obtained these cities\u0026rsquo; 3D building data from ONEGEO company, which provides comprehensive and highly accurate global building data integrated from multiple datasets\u003csup\u003e17\u003c/sup\u003e. Then, we utilized their 3D building data to estimate rooftop shadow coverage for each city to assess both solar energy potential and building energy demand. The estimated rooftop shadow coverage for all 32 cities is presented in Supplementary Fig.\u0026nbsp;1. Using these estimates, we then evaluated the annual average daily rooftop PV output under ideal conditions\u0026mdash;assuming complete rooftop coverage with PV panels\u0026mdash;in conjunction with the energy demand at an urban scale. Furthermore, we examined hourly fluctuations in both rooftop PV output and building energy demand across different city groups during four representative months: January, April, July, and October.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea presents the annual average daily PV output for each city, arranged in descending order according to latitude. The data reveals that Los Angeles boasts the highest PV output, surpassing 450 W/m\u0026sup2;, while Dublin registers the lowest at around 180 W/m\u0026sup2;. A clear trend emerges as we move from north to south: the annual average PV output increases with decreasing latitude. Cities at higher latitudes tend to yield relatively low average annual PV outputs, seldom exceeding 300 W/m\u0026sup2;. In stark contrast, cities situated at lower latitudes and in the southern hemisphere typically amass higher quantities of solar energy compared to their mid-latitude and high-latitude counterparts. Their geographical advantage, being closer to the equator and subjected to a suitable climate, ensures they benefit from more direct sunlight throughout the year\u003csup\u003e18\u003c/sup\u003e. Thus, the implementation of solar PV systems in these regions could yield optimal renewable energy utilization, aiding in the reduction of their reliance on traditional fossil fuels. Furthermore, a comparison of the interquartile range (represented by the box lengths) across various cities reveals a tighter data clustering in high-latitude cities and certain mid-latitude cities such as Vienna, Milan, and Istanbul. Conversely, the remaining mid-latitude cities, along with low-latitude and southern hemisphere cities, exhibit a wider dispersion of data. This pattern suggests that PV output tends to be more stable in high-latitude areas, while in other city groups, local architectural layout and climate characteristics might introduce greater uncertainties. This insight could be crucial in planning and optimizing photovoltaic installations across different regions.\u003c/p\u003e \u003cp\u003eWe employed the urban-scale building energy simulation model (DeST-urban)\u003csup\u003e19\u003c/sup\u003e to heating/cooling energy demand and other energy consumption, including lighting and appliances. All thermal-physical characteristics of buildings, such as the U-value of opaque shell materials and the solar heat gain coefficient (SHGC) of windows, were incorporated into the model. We also factored in the occupancy rate, lighting power density, and urban environment (details available in Supplementary Note 1). The average building energy demand varies across cities, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb. Geneva registers the highest average value, exceeding 3500 W/m\u0026sup2;, whereas Johannesburg records the lowest at approximately 650 W/m\u0026sup2;. Generally, as latitude decreases, there is a mild downward trend in the average value of building energy demand. This variation is due to the different climate zones encountered from north to south, which directly impact energy demand. Reviewing the maximum and minimum values of the box plots, it is clear that the daily building energy demand across all cities demonstrates significant volatility. Chicago, in particular, experiences the largest fluctuations, with grids requiring 11.8 times more energy for high-demand compared to low-demand grids. Given similar geographical and climatic conditions, the urban form could significantly influence urban total energy demand.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec presents the hourly averages of PV output for each city group across the four seasons representing months. It is evident that high-latitude cities are significantly impacted by seasonal changes, as reflected in their PV output curve. For example, even at noon in January, when solar radiation is at its daily peak, PV output dips to less than 10 W/m\u0026sup2; per hour compared to the higher photovoltaic outputs of the other three city groups. Conversely, the PV output curve of high-latitude cities in July shows little variation compared to the other three city groups. Southern hemisphere cities, however, maintain a stable performance, exhibiting minimal variation between seasons in their hourly PV output curves.\u003c/p\u003e \u003cp\u003eEnergy demand follows a clear daily pattern, akin to PV output, but with a notable difference: the peak of building energy demand occurs between 19:00 and 20:00, as opposed to the PV output's midday peak. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed highlights significant variations in energy demand fluctuations among different urban clusters throughout the seasons. In cities of the northern hemisphere, daytime energy demand generally increases in summer, while in southern hemisphere cities, energy demand remains relatively constant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Regarding nighttime energy demand, high-latitude cities register much higher demands compared to other city groups, particularly in January and April \u0026mdash; a probable consequence of their colder climates necessitating more heating. Moreover, each urban cluster shows internal variability of energy demands. For example, the light-colored lines, representing changes in energy demands for each grid unit within a city, reveal considerable differences in energy demands during specific time periods among different grid units within a single cluster. These disparities could be attributed to factors such as diverse geographical locations, varied building types, and differing technology use within each city.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGeographical and seasonal variations in rooftop PV self-sufficiency rate\u003c/h2\u003e \u003cp\u003eIn the context of our previous discussion on the potential of rooftop PV systems and their relationship to building energy demand, we now turn our attention to a critical metric: the rooftop PV self-sufficiency rate. This rate, defined as the proportion of a city's total energy demand that can be met by locally produced solar energy, provides a significant insight into the potential for rooftop PV systems to contribute to urban sustainability. The forthcoming exploration of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a detailed examination of this rate across 32 global cities, highlighting the influence of geographical location and seasonal variations.\u003c/p\u003e \u003cp\u003eWithin Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, a geographical trend becomes apparent. There is a clear increase in self-sufficiency from north to south, indicating that a city's geographical location significantly impacts the effectiveness of solar energy utilization. Notably, Johannesburg, situated in the southern hemisphere, boasts an annual average rate of 1.1. This figure suggests that with further advancements in storage technologies, this city could potentially realize total solar energy self-sufficiency, a significant milestone in urban sustainability. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, a box plot of grid units within the cities, reveals larger variations in self-sufficiency rates among cities located at lower latitudes and in the southern hemisphere. Diverse urban forms and local energy policies contribute significantly to these variations, underscoring the importance of local context in renewable energy implementation. Moving on to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec through Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef, we dive into the seasonal variations in self-sufficiency rates, revealing yet another layer of complexity. High-latitude cities, despite enjoying ample sunlight in July, fail to achieve optimal rates, a pattern that closely mirrors the PV output curve. This trend underscores the profound impact of seasonal changes on solar energy generation, an issue that needs to be addressed by developing more efficient solar technologies and storage solutions. In contrast, low and mid-latitude cities exhibit distinct seasonal shifts in their self-sufficiency rates. There is a notable divergence between these two groups in July and October, indicating different responses to seasonal variations in sunlight. This divergence could be attributed to differences in climatic and geographical conditions and emphasizes the need for tailored solar energy strategies. Cities in the southern hemisphere present inconsistent self-sufficiency rates, with a notable drop during the winter month of July. Despite this, they retain PV self-sufficiency for over six hours daily during the four months under study, underlining the considerable potential of solar energy in these regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eThe relationship between urban form and PV self-sufficiency rate\u003c/h2\u003e \u003cp\u003eHaving examined the geographical and seasonal influences on the rooftop PV self-sufficiency rates, we now turning to an aspect that plays a crucial role yet often overlooked: the urban form. As mentioned earlier, the design and layout of a city, characterized by its building types, density, orientation, and height, can significantly impact the effectiveness of solar energy harnessing. The interplay between urban form and self-sufficiency rates is complex, as it involves a myriad of factors including shadowing effects, available rooftop area for PV installations, and local energy demand patterns. In the following analysis, we will unpack this relationship, aiming to shed light on how urban form can be optimized to enhance the potential of rooftop PV systems and thereby bolster urban sustainability.\u003c/p\u003e \u003cp\u003eThe assessment of urban morphological indicators is performed on a grid basis, with each grid unit comprising an area of 1 km\u0026sup2;. Based on the previous studies\u003csup\u003e11,14,20\u0026ndash;22\u003c/sup\u003e, we select eight parameters to characterize the urban form: (i) Building height (measured in meters), (ii) Building footprint area (measured in m\u0026sup2;), (iii) Building perimeter (measured in meters), (iv) Surface area to volume ratio (S/V), (v) Floor area ratio (FAR) (expressed as a percentage), (vi) Building coverage ratio (BCR) (expressed as a percentage), (vii) Block roundness, and (viii) Mixed land use. The geometry of the buildings is described with area and perimeter while city skyline is quantified by building height. The surface area to volume (S/V) ratio is an indicator referring to the compactness of building shape. BCR and FAR measure open space versus built-up space. Block compactness is numerically quantified using block roundness and mix land use. These eight indicators comprehensively summarize attributes such as size, shape, and compactness of the urban form. Specific calculation processes are detailed in method section. The values for these morphological indicators within a grid are represented as the average of all buildings or blocks contained within that grid. To discern the factors most significantly affecting PV self-sufficiency, we conducted a correlation analysis between these urban form indicators and the PV self-sufficiency rates. Scatter diagrams for each pair of variables were plotted to provide a visual representation of these relationships (see Supplementary Note 2 for more details). Our analysis reveals that the average building height exhibits the strongest correlation with the self-sufficiency rate. This finding underscores the influence of vertical urban form on solar energy harvesting, prompting the need for further exploration. The subsequent sections will delve deeper into this relationship, aiming to elucidate the role of building height in solar energy self-sufficiency.\u003c/p\u003e \u003cp\u003eOur findings also reveal a complex correlation between the average building height (H) and PV self-sufficiency rate (R), best represented by the power-law equation H\u0026thinsp;=\u0026thinsp;aR\u003csup\u003eb\u003c/sup\u003e, where b\u0026thinsp;\u0026lt;\u0026thinsp;1. This relationship indicates that as buildings grow taller, PV self-sufficiency rates decrease, but not in a linear fashion. In environments with low average building heights, self-sufficiency rates are significantly impacted by alterations in building height. However, once the building height surpasses a certain threshold, the negative impact on PV self-sufficiency rate begins to decelerate. This non-linear relationship suggests that while taller buildings may present more challenges to solar energy harvesting, these challenges do not increase indefinitely with height. Interestingly, cities situated at similar latitudes present comparable patterns in their fitted curves. This observation hints at the existence of common factors that influence the values of 'a' and 'b' in the power-law equations. These shared factors, possibly related to similar sunlight patterns due to comparable latitudes, further underline the interplay between urban form and geographical location in determining PV self-sufficiency rates.\u003c/p\u003e \u003cp\u003eGiven the observed similarities in geographical location and climate within cities of the same group, we proceeded to construct a multivariate regression model. This model aimed to explore the relationships between meteorological indicators (air temperature and global horizontal irradiance), urban morphological indicators (FAR and block roundness), as well as geographic data (latitude and longitude), and the two parameters (a and b) derived from our power-law equation for each city. Our results reveal that the parameter 'a' closely aligns with geographic and meteorological indicators, while the parameter 'b' is more attuned to geographic and urban morphological indicators. The precise values of 'a' and 'b' for each city are documented in Supplementary Table\u0026nbsp;6. The fitting curves for each city group are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-d. Remarkably, the average R-square value of the fitting curve for all 32 cities under study reaches 0.812, denoting that our model explains over 81% of the variability in PV self-sufficiency rates. To further validate our equation, we earmarked one city from each group as a test set and constituted the remaining 28 cities as the training set. As portrayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee-h, our model demonstrated robust performance across all city groups, signifying its wide applicability to cities globally. Importantly, the model accurately captured the PV self-sufficiency rates in relation to building heights. The Mean Squared Error (MSE) of three cities was below 0.01, signifying that our estimates was less than 1% off from the actual self-sufficiency rates. This remarkable accuracy underscores the model's utility in simulating PV self-sufficiency rates based on geographic, meteorological, and urban morphological indicators.\u003c/p\u003e \u003cp\u003eThis model, validated across diverse urban groups and applicable globally, offers a powerful tool for sustainable urban planning and policy-making. For instance, it can be used for building codes can be devised to limit building heights or shape blocks, thereby maximizing the use of solar energy and improving energy efficiency in urban areas. With its worldwide applicability, the model can be customized to suit the specific conditions of different urban environments. Urban planners and policy-makers anywhere in the world can leverage this model, using local geographic, meteorological, and urban morphological data, to predict their potential PV self-sufficiency rates. This adaptability enhances its utility for cities worldwide in achieving their sustainability objectives.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eShaping urban form for PV self-sufficiency city\u003c/h2\u003e \u003cp\u003eBuilding upon our validated PV self-sufficiency model, we are now poised to explore its practical applications in shaping PV self-sufficiency through urban form. This model, rooted in empirical data, will serve as our roadmap as we navigate its potential influence on urban planning and architectural design, aspiring to augment PV self-sufficiency in cities worldwide.\u003c/p\u003e \u003cp\u003eWe conceptualized four distinctive urban form scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) based on a consistent population size of 1,000 individuals within an area of 0.04 km\u003csup\u003e2\u003c/sup\u003e. These scenarios encompass:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eScenario 1: Tower City\u003c/em\u003e, characterized by tall but dispersed buildings.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eScenario 2: Hybrid City I\u003c/em\u003e, a blend of high-rise buildings housing 70% of the population and low-rise buildings accommodating the remaining 30%.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eScenario 3: Hybrid City II\u003c/em\u003e, with a majority of low-rise buildings sheltering 70% of the population and a sprinkling of high-rises for the rest.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eScenario 4: Garden City\u003c/em\u003e, an intimate low-rise environment permitting each family to enjoy a private garden.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese scenarios depict two polar extremes of all high-rise and all low-rise buildings, as well as two mixed distributions with either high-rise or low-rise buildings being dominant. The details of scenario settings are shown in Method section. The average building heights for these four urban scenarios are 40.0, 11.5, 8.7 and 8.0 meters respectively.\u003c/p\u003e \u003cp\u003eUtilizing these scenarios as our framework, we executed simulations for the initially analyzed 32 cities based on the current urban planning situation and broadened our scope to include four African cities - Addis Ababa, Bamako, Kampala, and Nairobi. This extension allowed us to apply our model within a distinct context. African cities, unlike their highly urbanized counterparts previously analyzed, are largely in their urban development infancy\u003csup\u003e23\u003c/sup\u003e. This presents a prime opportunity to guide their urbanization trajectory towards sustainability from the outset. The accelerated rate of urbanization in Africa further underscores the importance of sustainable urban planning strategies. Supplementary Note 3 offers a detailed account of the simulation process employed to adjust the parameters for these African cities. Given the unavailability of block roundness data for these specific cities, we adopted the average block roundness of geographically proximate low-latitude cities. The curve illustrating the relationship between urban height and self-sufficiency rate for these cities is depicted in Supplementary Fig.\u0026nbsp;5.\u003c/p\u003e \u003cp\u003eOur analysis, graphically presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, provides a fresh perspective on the self-sufficiency rate of buildings across various urban scenarios, offering insights for urban renewal across different cities. For cities aligning with the Tower City scenario, the annual self-sufficiency rates are strikingly low, indicating a profound need for reconsideration and remodeling of their existing urban form. One potential approach, as seen in the Hybrid City I scenario, could be reduce building heights to boost self-sufficiency. This adjustment, although seemingly minor, can prompt a significant surge in self-sufficiency rates. On the other hand, cities that already exhibit low-rise characteristics, akin to those in the Hybrid City II and Garden City scenarios, can find affirmation in our findings for their potential for solar energy integration. A promising strategy for these cities could be to maximize the use of rooftops in suburban areas, thus achieving a larger scale Hybrid City form and raising the self-sufficiency rates. However, while these strategies are promising, they may not be universally applicable. Cities like Helsinki, Copenhagen, and Berlin, located in regions where winter solar radiation intensity is exceptionally low, face unique challenges. Sacrificing public space for achieving Garden City scenario may not bring more benefits of solar self-sufficiency. Therefore, the hybrid city concept might be more feasible. Apart from optimizing the urban form for maximum solar energy gains, exploring additional renewable energy sources such as wind or geothermal energy is also a viable option for these cities.\u003c/p\u003e \u003cp\u003eAs African cities urbanize, integrating PV self-sufficiency with city development presents a unique opportunity to shape their future sustainably. Owing to their proximity to the equator, African cities are well-positioned to exploit solar energy. Simulation results show that all four African cities exhibited robust self-sufficiency potential, particularly Addis Ababa in the Garden City scenario, where the annual self-sufficiency rate exceeds 50%. If all four African cities adopt the planning policy of Garden City scenario, their average annual PV self-sufficiency rate will be about 5 times higher than that of Tower City scenario. Especially in Kampala, the self-sufficiency rate of two extreme scenarios in January differs by about 9 times. The IEA points out that currently, 80% of the power generation capacity in Kampala is based on hydroelectric power\u003csup\u003e24\u003c/sup\u003e. Assuming Kampala adopts the Garden City scenario for development in the future, its reliance on hydroelectric power will decrease from 96% in the tower city scenario to 63.4%. Our model suggests that integrating solar energy into sensible urban design can significantly alleviate the energy security concerns arising from excessive dependence on hydroelectric power, particularly amidst water resource crises resulting from climate change in the future. Moreover, when considering block roundness, the model shows that with extremely compact block shapes, all four cities could achieve full solar energy self-sufficiency during summer in scenarios such as Hybrid City I, Hybrid City II, and Garden City (Supplementary Fig.\u0026nbsp;6). This insight indicates that fostering compact urban blocks can optimize solar energy utilization, propelling a leap towards energy self-sufficiency while accommodating the pressures of urban growth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDecade-long contributions to urban morphology and solar energy research have elevated this domain into a critical area of study. Despite the acknowledged discrepancies in research focus between developed countries and those that are developing or with economies in transition, our analysis reveals intricate patterns in urban form and self-sufficiency in solar energy that are far richer and more varied than previously recognized. The results are not only relevant for developing countries undergoing rapid urbanization but also for developed nations striving to renew their existing urban structures.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch3\u003ePV self-sufficiency as a design parameter in urban planning\u003c/h3\u003e\n \u003cp\u003eRecognizing the interplay between urban form and solar energy utilization has led us to some insights. It Is not solely about the height of the buildings or compactness of city blocks, but a more complex, nuanced interaction involving these factors. For instance, our research points to an inverse relationship between building height and PV self-sufficiency, but this doesn\u0026apos;t suggest that all cities should aim to reduce their skyline. Instead, it demonstrates the potential of hybrid urban forms that balance high-rise and low-rise structures, optimizing solar energy harnessing without sacrificing urban density. This nuanced understanding could redefine urban planning paradigms, taking us beyond the common approach of just reducing building height for better solar access.\u003c/p\u003e\n \u003cp\u003eDuring the urban planning phase, incorporating self-sufficient solar energy into design considerations can greatly help achieve a sustainable balance between urban development and energy efficiency. By integrating the concept of \u0026quot;solar-ready,\u0026quot; infrastructure is designed in advance to accommodate future solar installations. The fusion of these two aspects forms a forward-thinking urban development and construction strategy. With our model, urban planners and decision-makers can grasp energy gaps and surplus blocks in the planning stage, opening up innovative opportunities for energy management. For example, implementing community-based energy sharing or trading platforms allows neighborhoods or buildings with excess energy to distribute it to those in deficits. Alternatively, redirecting excess PV output to meet public infrastructure needs such as streetlights, public Wi-Fi networks, and water treatment systems also helps reduce municipal cost savings and promote sustainable development environments. By prioritizing PV self-sufficiency in urban planning, cities can achieve a balance between urbanization and environmental sustainability paving the way for resilient, energy-efficient metropolitan futures.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eInspiration for boosting PV self-sufficiency in developed nations\u003c/h3\u003e\n\u003cp\u003eIn the case of developed nations, the task of increasing solar energy self-sufficiency may appear daunting due to their established urban structures. However, our research paints a more optimistic picture. Instead of a total transformation of existing urban forms, which might seem impossible or prohibitively expensive, there are more feasible, yet highly effective solutions. A promising strategy could be to maximize the use of rooftops in suburban areas, thereby achieving a larger scale Hybrid City structure, and raising the self-sufficiency rates. However, this approach would likely increase the investment required for microgrids and energy storage systems. Subtle alterations, such as reorienting city blocks for better solar exposure or modifying roof structures to accommodate more solar panels, can also be considered to further enhance solar energy utilization without drastic transformations. These strategies suggest a pathway to enhanced energy self-sufficiency that doesn\u0026apos;t require comprehensive urban transformation, but instead, depends on smart, targeted changes. This approach suggests that with careful planning and implementation, cities can make significant strides towards solar energy self-sufficiency without necessitating a complete overhaul of their established urban forms. For individual buildings that are unable to install solar panels due to location or policy restrictions, community solar programs offer a viable solution. These programs allow multiple stakeholders to invest in a shared solar energy system, benefiting from the electricity generated at a location other than their property.\u003c/p\u003e\n\u003ch3\u003eGreat opportunity for achieving PV self-sufficiency in developing nations\u003c/h3\u003e\n\u003cp\u003eAs for developing nations, take Africa cities as examples, the accelerated pace of urbanization is often viewed as a daunting challenge. However, our findings flip this notion on its head, suggesting that rapid urbanization can serve as a unique opportunity. Specifically, these nations have the potential to bypass the more energy-inefficient stages of urban development that many developed nations experienced in the past. By incorporating energy-conscious urban forms from the outset\u0026mdash;including balanced high-rise and low-rise structures and compact city blocks\u0026mdash;developing nations could establish urban environments that are inherently better suited for solar energy utilization. Although implementing these urban development concepts may require more land, it is a feasible and attractive development model in the context of African cities. From an economic perspective, the cost of constructing low-rise buildings is often lower in African countries, mainly due to relatively abundant construction materials and labor. In addition, this concept aligns with the common living habits in many African communities, better ensuring essential aspects of daily life such as outdoor activities and community interaction. Therefore, this strategy could balance ecological, economic, and social factors, leading to urbanization that is not only robust in the face of escalating urban population growth, but also highly energy-efficient and environmentally friendly.\u003c/p\u003e\n\u003cp\u003eHowever, the endeavor to increase solar energy self-sufficiency is not without complexities. Balancing the costs of modifying urban forms against the potential energy gains requires careful consideration. Factors such as fluctuating sunlight conditions due to climate change and environmental pollution, and varying safety regulations and building height restrictions across countries, add to the complexity of this task. These challenges underscore the need for adaptability and localized strategies.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our research illuminates a promising yet multifaceted path towards solar energy self-sufficiency in cities. It emphasizes the need for a nuanced understanding of urban form and its impact on solar energy utilization. By moving beyond traditional assumptions and exploring innovative possibilities, cities around the world can make significant strides towards sustainable growth.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eData Acquisition\u003c/h2\u003e\n\u003cp\u003eThis study utilizes various types of data, including building footprint, road network, land use, and meteorological data. The building footprint data is obtained from ONEGEO GmbH\u003csup\u003e17\u003c/sup\u003e, which provides high-precision 3D building data on a global scale. The data attributes include name, type, height, levels, and geometry. Building footprint data is used to calculate urban morphological indicators, geometric shadow calculation, and building energy demand estimation. Road network and land use data are obtained from OpenStreetMap for calculating urban morphological indicators. In addition, each city requires an annual weather file to describe local climatic conditions. We use the National Solar Radiation Database (NSRDB) which provides solar radiation and meteorological information for multiple countries and regions at time resolutions of 5, 10, or 15 minute intervals\u003csup\u003e25\u003c/sup\u003e. Since our research focuses on hourly units, only exact hours are selected from the dataset. Meteorological data is utilized for geometric shadow calculation, building energy demand estimation as well as multivariate regression model fitting.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eGeometric shadow calculation on building roofs\u003c/h2\u003e\n\u003cp\u003eThe building shadow calculation in this study is based on the trigonometric relations in the triangle defined by the sun light and the wall of the buildings. For a building, the algorithm will first break up the building into a collection of walls and calculates the shadow geometry for each wall independently based on the sun's position. Given the location of the study area and the time, the sun position defined by altitude angle \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{al}\\)\u003c/span\u003e\u003c/span\u003e and azimuth angle \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{az}\\)\u003c/span\u003e\u003c/span\u003e can be calculated by suncalc\u003csup\u003e26\u003c/sup\u003e. For the wall with the height h, the projection distance d on the ground for a given point on the wall can be calculated by Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e (Supplementary Fig.\u0026nbsp;8).\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$d=h\\bullet \\text{cot}({\\alpha }_{al})$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eSince the azimuth angle \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{az}\\)\u003c/span\u003e\u003c/span\u003e implies the direction of the shadow, the quadrangle geometry of the wall shadow can be generated by calculating the projection position of the two edge points on each wall. Once all wall shadows have been generated, they can be merged to form a vector-based geometric shadow for the building.\u003c/p\u003e\n\u003cp\u003eThe above approach of building shadow calculation can be further extended to the case of calculating shadow coverage on the building roof. The shadow coverage of a building's roof can be calculated by creating the shadows of all structures taller than this one, then intersecting the shadows with the building outline.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003ePV potential estimation\u003c/h2\u003e\n\u003cp\u003eWe made unified assumptions for the solar energy system involved in this study. Based on the coefficients provided by PVSystem from pvlib\u003csup\u003e27\u003c/sup\u003e, performance parameters were determined in Supplementary Note 4. The PV panels are tilted according to the local latitude. By utilizing pvlib, hourly PV output per module (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({PV}_{m}\\)\u003c/span\u003e\u003c/span\u003e) could be calculated at a specified location. Based on the area of the module and building footprint area, the placement factor (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\eta\\)\u003c/span\u003e\u003c/span\u003e) can be calculated by Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Considering the solar energy can only be collected within available building areas (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{a})\\)\u003c/span\u003e\u003c/span\u003e where are not covered by shadows, we can calculate hourly PV output (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({PV}_{b})\\)\u003c/span\u003e\u003c/span\u003efor buildings by Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$$\\eta =\\frac{{A}_{m}}{{A}_{f}}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$${PV}_{b}= {PV}_{m}\\bullet {A}_{a}\\bullet \\eta$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eUrban-scale building energy simulation\u003c/h2\u003e\n\u003cp\u003eUrban building energy modeling (UBEM) is a physics-based, bottom-up approach to simulate the building energy consumption at the urban scale\u003csup\u003e28\u003c/sup\u003e. In this study, the previously developed UBEM platform (DeST-urban) was used to automatically generate building energy models (BEMs) based on 3D city models and simulate the hourly energy demand of each building in parallel\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDeST is utilized as the simulation engine to simulate heating/cooling energy demand and other energy consumption, including lighting and appliances. DeST is a whole-building performance simulation engine that enables physics-based calculation including heat balance, airflow, and other thermal and energy metrics\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn urban-scale building energy simulation, the GeoJSON file including the building geometry (footprint and height) was parsed and the BEM of each building was established based on the specific geometry. BEMs were generated by extruding the building footprints to their corresponding heights. The number of floors was estimated based on the building height and floor-to-floor height. One thermal zone per floor was modeled, balancing time and accuracy\u003csup\u003e30\u003c/sup\u003e. Two main building types, residential and commercial, were considered. The BEMs of each building type were configured based on the prototype building models in the previous study\u003csup\u003e31\u003c/sup\u003e, including the properties of building envelopes, the occupancy, and the usage of lighting, appliances and heating, ventilation, and air conditioning (HVAC). The simulations used the same weather data with the PV output simulation to analyze the matching of energy supply and demand. The parameters of the prototype building models are presented in Supplementary Note 1.\u003c/p\u003e\n\u003cp\u003eTo consider the impact of urban form on energy consumption of the building blocks, each building was modeled with its surroundings, and the shadow cast on the rooftops and facades in the neighborhood was calculated based on the real direction of sunlight in each hour\u003csup\u003e32\u003c/sup\u003e. Solar heat gain of each building surface could be calculated based on the sunlit area, which would affect the heating/cooling energy demand.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eUrban morphological indicators\u003c/h2\u003e\n\u003cp\u003eAs shown in Supplementary Table\u0026nbsp;10, eight urban morphological indicators were calculated to capture the spatial and geometric characteristics of the city. For the division of blocks, we removed private roads, steps, bike lanes, unnamed roads, service roads, and sidewalks from the road network. The remaining polygons formed by the intersection of roads refer to blocks. Building coverage ratio is calculated as the percentage of the total building footprint area within a block to the block area. The geometry of the buildings is described with area and perimeter while city skyline is quantified by building height. The surface area to volume (S/V) ratio is an indicator referring to the compactness of building shape\u003csup\u003e22\u003c/sup\u003e. Floor area ratio (FAR) represents the percentage of the total building floor area to the block area while Building coverage ratio (BCA) refers to the percentage of the first floor area to the block area\u003csup\u003e11,20,22\u003c/sup\u003e. Roundness is a measure proposed by Richardson\u003csup\u003e33\u003c/sup\u003e that evaluates regularity in urban blocks, with a range from 0 to 1. If a block shape approaches a circle more closely, its roundness value will be closer to 1; otherwise, it will be closer to 0. Land use mix refers to how different types of land uses (e.g., residential, commercial or industrial) are physically and functionally integrated. There are numerous ways to measure the extent of land use diversity, but this article solely employs the entropy index which is commonly used\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003eMultivariate regression model\u003c/h2\u003e\n\u003cp\u003eAlthough we have figured out the universal laws of urban morphological indicators and self-sufficiency rate, it remains unclear what relationship exists between values a and b and urban attributes. To apply universal laws to other cities worldwide, it is necessary to explore the physical meaning of values a and b. We selected indicators that affect the values of a and b through correlation analysis. Meteorological indicators (air temperature and global horizontal irradiance) and geographic information (latitude and longitude) were chosen as the independent variable group that affects value a, and urban morphological indicators (FAR and block roundness) and geographic information (latitude and longitude) were chosen as the independent variable group that affects value b. We used a multivariate regression model to describe the complex interaction between the values of a and b and their respective independent variable groups and obtained the following formulas.\u003c/p\u003e\n\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ4\" class=\"mathdisplay\"\u003e$$a=0.0422\\text{G}\\text{H}\\text{I}-0.1897\\text{A}\\text{i}\\text{r}\\text{T}\\text{e}\\text{m}-0.0208\\text{L}\\text{a}\\text{t}+0.01 \\text{L}\\text{n}\\text{g}-0.8781$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{b = }-\\text{ln[}-\\text{0.0136}\\text{ln}\\left(\\text{L}\\text{a}\\text{t}\\right)+0.1785\\text{l}\\text{n}\\left(\\text{L}\\text{n}\\text{g}\\right) -0.2823\\text{A}\\text{v}\\text{g}\\left(FAR\\right)+ 0.105\\text{A}\\text{v}\\text{g}(\\text{R}\\text{o}\\text{u}\\text{n}\\text{d}\\text{n}\\text{e}\\text{s}\\text{s}\\)\u003c/span\u003e \u003c/span\u003e) + 2.6573] (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003eSimulation Scenario Settings\u003c/h2\u003e\n\u003cp\u003eWe take the residential density configuration within 0.04 km\u003csup\u003e2\u003c/sup\u003e accommodating 1,000 residents or 200 households as the basic setting. Assuming an average of five people per household for low-rise buildings and four people per household for high-rise buildings. The floor-to-ceiling height of high-rise buildings is set at 3-3.2 meters, while the total height of low-rise buildings is set at 8 meters, meeting the building standards of multiple countries\u003csup\u003e34\u0026ndash;36\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eScenario 1 assumes the construction of fifteen-stories high-rise buildings, requiring four such structures to accommodate 480 individuals (120 people per building). The remaining 520 residents are distributed among five twelve-story high-rise buildings (96 people per building), supplemented by a five-stories building to accommodate the rest of the population. The distance between each building is 65 meters front to back and exceeds 26 meters on either side. In this scenario, the average building height is 38.8 meters. After accounting for elevators and roof decorations, a rounded height of 40 meters is adopted. Scenario 2 proposes to set 70% buildings to accommodate 700 people, requiring six fifteen-stories high-rise buildings, and remaining 30% buildings to accommodate 300 people, requiring sixty low-rise buildings. The high-rise buildings are located north of the low-rise buildings with a front-to-back distance of 65 meters, and the lateral distance between low-rise buildings is 9 meters. In this case, the average building height is 11.5 meters. Scenario 3 allocates 30% of the space to high-rise buildings for 300 people across four twelve-stories buildings, and 70% to low-rise buildings to accommodate 700 individuals, requiring 140 buildings. This results in an average height of 8.7 meters. Lastly, Scenario 4 is exclusively composed of 200 low-rise buildings, with a front-to-back spacing of 10 meters and a side-to-side spacing of more than 6 meters, maintaining the average height at 8 meters. Each scenario reflects a strategic approach to urban form, balancing building height and population distribution to accommodate the needs of 1,000 residents within a finite urban block.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eAll data generated or analyzed during this study used in the graphs are included in this published article and its supplementary information files: \u0026ldquo;Supplementary Information\u0026rdquo;. Further data are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFox S, Goodfellow T. \u003cem\u003eCities and Development\u003c/em\u003e. 2nd ed. Routledge; 2016. doi:10.4324/9781315815527\u003c/li\u003e\n\u003cli\u003ePulselli RM, Broersma S, Martin CL, Keeffe G, Bastianoni S, Van Den Dobbelsteen A. Future city visions. The energy transition towards carbon-neutrality: lessons learned from the case of Roeselare, Belgium. \u003cem\u003eRenew Sustain Energy Rev\u003c/em\u003e. 2021;137:110612. doi:10.1016/j.rser.2020.110612\u003c/li\u003e\n\u003cli\u003eWang X, Wang G, Chen T, Zeng Z, Heng CK. Low-carbon city and its future research trends: A bibliometric analysis and systematic review. \u003cem\u003eSustain Cities Soc\u003c/em\u003e. 2023;90:104381. doi:10.1016/j.scs.2022.104381\u003c/li\u003e\n\u003cli\u003eCreutzig F, Agoston P, Goldschmidt JC, Luderer G, Nemet G, Pietzcker RC. The underestimated potential of solar energy to mitigate climate change. \u003cem\u003eNat Energy\u003c/em\u003e. 2017;2(9):17140. doi:10.1038/nenergy.2017.140\u003c/li\u003e\n\u003cli\u003eNijsse FJMM, Mercure JF, Ameli N, et al. The momentum of the solar energy transition. \u003cem\u003eNat Commun\u003c/em\u003e. 2023;14(1):6542. doi:10.1038/s41467-023-41971-7\u003c/li\u003e\n\u003cli\u003eKhor N. \u003cem\u003eWorld Cities Report 2022: Envisaging the Future of Cities\u003c/em\u003e. United Nations Human Settlements Programme (UN-Habitat); 2022.\u003c/li\u003e\n\u003cli\u003eTollin N, Vener J, Pizzorni M, et al. \u003cem\u003eUrban Climate Action. The Urban Content of the NDCs: Global Review 2022\u003c/em\u003e. United Nations Human Settlements Programme UN-Habitat; 2022.\u003c/li\u003e\n\u003cli\u003eMasson G, Bosch E, Kaizuka I, et al. \u003cem\u003eSnapshot of Global PV Markets 2023\u003c/em\u003e.; 2023.\u003c/li\u003e\n\u003cli\u003eHui SCM. Low energy building design in high density urban cities. \u003cem\u003eRenew Energy\u003c/em\u003e. 2001;24(3-4):627-640. doi:10.1016/S0960-1481(01)00049-0\u003c/li\u003e\n\u003cli\u003ePerera ATD, Coccolo S, Scartezzini JL. The influence of urban form on the grid integration of renewable energy technologies and distributed energy systems. \u003cem\u003eSci Rep\u003c/em\u003e. 2019;9(1):17756. doi:10.1038/s41598-019-53653-w\u003c/li\u003e\n\u003cli\u003eMohajeri N, Upadhyay G, Gudmundsson A, Assouline D, K\u0026auml;mpf J, Scartezzini JL. Effects of urban compactness on solar energy potential. \u003cem\u003eRenew Energy\u003c/em\u003e. 2016;93:469-482. doi:10.1016/j.renene.2016.02.053\u003c/li\u003e\n\u003cli\u003eGhaleb B, Asif M. Application of solar PV in commercial buildings: Utilizability of rooftops. \u003cem\u003eEnergy Build\u003c/em\u003e. 2022;257:111774. doi:10.1016/j.enbuild.2021.111774\u003c/li\u003e\n\u003cli\u003eLi SY, Han JY. The impact of shadow covering on the rooftop solar photovoltaic system for evaluating self-sufficiency rate in the concept of nearly zero energy building. \u003cem\u003eSustain Cities Soc\u003c/em\u003e. 2022;80:103821. doi:10.1016/j.scs.2022.103821\u003c/li\u003e\n\u003cli\u003eZhang J, Xu L, Shabunko V, et al. Impact of urban block typology on building solar potential and energy use efficiency in tropical high-density city. \u003cem\u003eAppl Energy\u003c/em\u003e. 2019;240:513-533. doi:10.1016/j.apenergy.2019.02.033\u003c/li\u003e\n\u003cli\u003eGiostra S, Masera G, Monteiro R. Solar Typologies: A Comparative Analysis of Urban Form and Solar Potential. \u003cem\u003eSustainability\u003c/em\u003e. 2022;14(15):9023. doi:10.3390/su14159023\u003c/li\u003e\n\u003cli\u003eShareef S. The impact of urban morphology and building\u0026rsquo;s height diversity on energy consumption at urban scale. The case study of Dubai. \u003cem\u003eBuild Environ\u003c/em\u003e. 2021;194:107675. doi:10.1016/j.buildenv.2021.107675\u003c/li\u003e\n\u003cli\u003eONE GEO. Clean and Comprehensive Worldwide Building Footprints. https://onegeo.co/?gclid=Cj0KCQiAoeGuBhCBARIsAGfKY7yWADmIsy2lZGZMuih4-BWv349eZ7KQ_B--7oDJPRjF7r1prinBEY8aAlIcEALw_wcB\u003c/li\u003e\n\u003cli\u003eSuri,Marcel;, Betak,Juraj;, Rosina,Konstantin;, et al. Global Photovoltaic Power Potential by Country (English). Published online 2020. http://documents.worldbank.org/curated/en/466331592817725242/Global-Photovoltaic-Power-Potential-by-Country\u003c/li\u003e\n\u003cli\u003eLiu Z, Zhou X, Tian W, Liu X, Yan D. Impacts of uncertainty in building envelope thermal transmittance on heating/cooling demand in the urban context. \u003cem\u003eEnergy Build\u003c/em\u003e. 2022;273:112363. doi:10.1016/j.enbuild.2022.112363\u003c/li\u003e\n\u003cli\u003eHermosilla T, Palomar-V\u0026aacute;zquez J, Balaguer-Beser \u0026Aacute;, Balsa-Barreiro J, Ruiz LA. Using street based metrics to characterize urban typologies. \u003cem\u003eComput Environ Urban Syst\u003c/em\u003e. 2014;44:68-79. doi:10.1016/j.compenvurbsys.2013.12.002\u003c/li\u003e\n\u003cli\u003eOh M, Kim Y. Identifying urban geometric types as energy performance patterns. \u003cem\u003eEnergy Sustain Dev\u003c/em\u003e. 2019;48:115-129. doi:10.1016/j.esd.2018.12.002\u003c/li\u003e\n\u003cli\u003eMorganti M, Salvati A, Coch H, Cecere C. Urban morphology indicators for solar energy analysis. \u003cem\u003eEnergy Procedia\u003c/em\u003e. 2017;134:807-814. doi:10.1016/j.egypro.2017.09.533\u003c/li\u003e\n\u003cli\u003eGore CD, Gopakumar G. Infrastructure and Metropolitan Reorganization: An Exploration of the Relationship in Africa and India. \u003cem\u003eJ Urban Aff\u003c/em\u003e. 2015;37(5):548-567. doi:10.1111/juaf.12180\u003c/li\u003e\n\u003cli\u003eInternational Energy Agency. \u003cem\u003eUganda 2023: Energy Policy Review\u003c/em\u003e. OECD; 2023. doi:10.1787/1b6b9a5a-en\u003c/li\u003e\n\u003cli\u003eSengupta M, Xie Y, Lopez A, Habte A, Maclaurin G, Shelby J. The National Solar Radiation Data Base (NSRDB). \u003cem\u003eRenew Sustain Energy Rev\u003c/em\u003e. 2018;89:51-60. doi:10.1016/j.rser.2018.03.003\u003c/li\u003e\n\u003cli\u003eElmarhraoui A, Thieurmel B. suncalc: Compute Sun Position, Sunlight Phases, Moon Position and Lunar Phase. Published online 2022. https://CRAN.R-project.org/package=suncalc\u003c/li\u003e\n\u003cli\u003eHolmgren W, Anderson K, Hansen C, et al. pvlib/pvlib-python: v0.9.5. Published online March 18, 2023. doi:10.5281/ZENODO.593284\u003c/li\u003e\n\u003cli\u003eReinhart CF, Cerezo Davila C. Urban building energy modeling \u0026ndash; A review of a nascent field. \u003cem\u003eBuild Environ\u003c/em\u003e. 2016;97:196-202. doi:10.1016/j.buildenv.2015.12.001\u003c/li\u003e\n\u003cli\u003eYan D, Zhou X, An J, et al. DeST 3.0: A new-generation building performance simulation platform. \u003cem\u003eBuild Simul\u003c/em\u003e. 2022;15(11):1849-1868. doi:10.1007/s12273-022-0909-9\u003c/li\u003e\n\u003cli\u003eJohari F, Munkhammar J, Shadram F, Wid\u0026eacute;n J. Evaluation of simplified building energy models for urban-scale energy analysis of buildings. \u003cem\u003eBuild Environ\u003c/em\u003e. 2022;211:108684. doi:10.1016/j.buildenv.2021.108684\u003c/li\u003e\n\u003cli\u003eGui C, Yan D, Guo S, An J. Development of Prototype Building Model in Beijing Based on Actual Energy Consumption. In: Wang Z, Zhu Y, Wang F, Wang P, Shen C, Liu J, eds. \u003cem\u003eProceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019)\u003c/em\u003e. Environmental Science and Engineering. Springer Singapore; 2020:1187-1196. doi:10.1007/978-981-13-9528-4_120\u003c/li\u003e\n\u003cli\u003eYan D, Xia J, Tang W, Song F, Zhang X, Jiang Y. DeST \u0026mdash; An integrated building simulation toolkit Part I: Fundamentals. \u003cem\u003eBuild Simul\u003c/em\u003e. 2008;1(2):95-110. doi:10.1007/s12273-008-8118-8\u003c/li\u003e\n\u003cli\u003eHaggett P. \u003cem\u003eLocational Analysis in Human Geography\u003c/em\u003e. London : Edward Arnold; 1965.\u003c/li\u003e\n\u003cli\u003eShakespeare R. \u003cem\u003eDUBLIN CITY DEVELOPMENT PLAN 2016 - 2022 PROGRESS REPORT\u003c/em\u003e. Dublin City Council; 2018. https://www.dublincity.ie/dublin-city-development-plan-2016-2022\u003c/li\u003e\n\u003cli\u003eHasegawa T. \u003cem\u003eIntroduction to the Building Standard Law -Building Regulation in Japan-\u003c/em\u003e. Building Center of Japan; 2013. https://www.bcj.or.jp/upload/international/baseline/BSLIntroduction201307_e.pdf\u003c/li\u003e\n\u003cli\u003eAmerican Society of Heating, Refrigerating and Air-Conditioning Engineers. ANSI/ASHRAE Standard 90.2-2018 Energy-Efficient Design of Low-Rise Residential Buildings. Published online 2018. https://ashrae.iwrapper.com/ASHRAE_PREVIEW_ONLY_STANDARDS/STD_90.2_2018 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4124110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4124110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of renewable energy into cityscapes is becoming increasingly crucial to climate change since city is main sector of energy consumption. This research estimated daily changes in rooftop photovoltaic (PV) output and building energy demand across different seasons using 3D building data from 32 global cities, investigated the inherent link between urban form and photovoltaic self-sufficiency. We uncovered a universal power-law relationship between building height and PV self-sufficiency, where higher buildings result in nonlinearly decreasing PV sufficiency. Based on this, a highly accurate multiple regression model was constructed to simulate the PV self-sufficiency, incorporating key variables such as climate, geography, and urban form. This model stands out for its unique capability to be applied across varied urban contexts, accommodating the diverse conditions worldwide. Furthermore, our comparative analysis across four urban planning scenarios reveals that cities designed with the \"Garden City\" concept significantly outperform others in PV self-sufficiency, offering a quintuple increase in potential for solar energy harnessing, a finding especially pronounced in the context of African cities. These findings provide profound insights by suggesting that strategic urban planning could be a transformative tool in combating energy poverty and fostering sustainable urban development.\u003c/p\u003e","manuscriptTitle":"Shaping urban form for solar energy self-sufficiency city","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-11 06:57:33","doi":"10.21203/rs.3.rs-4124110/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54904de6-91c0-49b0-b0d5-a7e597424382","owner":[],"postedDate":"April 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30484684,"name":"Scientific community and society/Energy and society/Energy supply and demand"},{"id":30484685,"name":"Scientific community and society/Energy and society/Energy access"}],"tags":[],"updatedAt":"2025-05-07T00:25:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-11 06:57:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4124110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4124110","identity":"rs-4124110","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}