Energy-related carbon emissions from the transportation sector are one of the major obstacles to ... more Energy-related carbon emissions from the transportation sector are one of the major obstacles to achieving global carbon reduction targets. Current research on transportation energy mainly focuses on the energy end-use stage, with a lack of simulations covering both the front-end and end-use stages of transportation energy. This study, from a life-cycle perspective, conducts a carbon emission simulation for county-level transportation. Based on the 2019 transportation data of Huadu District, Guangzhou, the study combines life-cycle assessment (LCA) with the Low Emission Analysis Platform (LEAP) to simulate changes in transportation carbon emissions from 2020 to 2050. The results indicate that: (1) The Energy-Saving scenario has the greatest carbon reduction potential, capable of reducing carbon emissions by 75 %. (2) Energy efficiency factors have the most significant carbon reduction effect in transportation. (3) There is a carbon transfer phenomenon from "Tank-to-Wheel" to "Well-to-Tank" in transportation energy. (4) Embodied carbon accounts for 40 %, while operational carbon accounts for 60 % of the entire transportation energy life cycle. Based on the above results, the study suggests that the government should increase policy support and technological innovation, invest in public transportation infrastructure, and strengthen carbon management throughout the entire lifecycle to comprehensively enhance the carbon emission reduction effects of transportation energy.
To address the building decarbonization crisis, the widespread adoption of rooftop photovoltaics ... more To address the building decarbonization crisis, the widespread adoption of rooftop photovoltaics (PV) has been agreed upon globally, with PV potential prediction being a crucial evaluation task. Current methods for predicting rooftop photovoltaic (PV) potential face significant shortcomings, as geospatial approaches struggle with precision at urban scales, historical time-series methods tend to overestimate potential, and urban studies often neglect spatial shading between buildings, thereby inflating predictions. This study addresses these issues by employing a Graph Convolutional Network − Long Short-Term Memory (GCN-LSTM) model to perform spatiotemporal predictions of urban rooftop PV potential, incorporating the spatial shading relationships between buildings to enhance prediction accuracy. The results show that, compared to traditional Long Short-Term Memory (LSTM) models, GCN-LSTM significantly improves prediction accuracy, reducing MAE by 21%, MSE by 22%, RMSE by 13%, and MAPE by 12%. This improvement is particularly evident in winter and summer, validating the interpretability of the GCN-LSTM model. Moreover, clustering analysis of the shading relationship graphs between urban buildings identified three primary types of graph clusters: moderately diverse medium-scale building shading graphs, simple small-scale building shading graphs, and complex large-scale building shading graphs. Factors such as the number of buildings, standard deviation of building heights, and standard deviation of roof slopes were found to collectively influence the complexity and shading intensity of these graphs, leading to variations in PV potential. Based on the findings of this study, it is evident that integrating deep learning models with engineering physics knowledge can substantially enhance the accuracy of urban rooftop PV potential predictions, providing suggestions and bases for the formulation and implementation of PV promotion policies.
The urban heat island effect and extreme high temperatures pose significant challenges to the liv... more The urban heat island effect and extreme high temperatures pose significant challenges to the livability of cities, making it urgent to mitigate urban heat issues. However, uncertainty remains regarding the most effective cooling measures. The differences between single and combined measures have not been clearly defined, and there is limited research comparing their impacts on urban microclimates and energy consumption. Therefore, this study aims to explore the differences between single cooling measures and mixed cooling strategies from the perspectives of temperature, thermal comfort, and building energy consumption to provide a more holistic impact analysis of cooling strategies. Therefore, this study conducted a simulation on a typical street canyon in Melbourne, Australia, on December 20, 2019. Four individual scenarios and six combined scenarios were established. Subsequently, the environmental parameters and building energy consumption were simulated under various cooling scenarios. The results indicated that the combined cooling scenario with three measures achieved the highest comprehensive score, demonstrating the most favorable impact on the thermal environment and building energy consumption. We also found that the green scenario combined with high-reflectivity can reduce building energy consumption by 71.83 kWh within a day, yielding the best results. However, the combination of green infrastructure, permeable surfaces, and misting proved to be the most effective in improving the environmental thermal comfort, with a maximum reduction of 1.86 °C in the UTCI (universal thermal climate index). This study can provide guidance and recommendations for designers in the early stages of decision-making.
Inhabitants of informal settlements face socioeconomic difficulties and suboptimal living conditi... more Inhabitants of informal settlements face socioeconomic difficulties and suboptimal living conditions, where demographic, architectural, and well-being factors interrelate to determine quality of life (QoL). This paper explores how these interactions occur and how architectural interventions can shape these environments in ways that contribute to improved health outcomes. We conducted a demographic and architectural study, with a focus on daylighting among selected inhabitants. We also investigated well-being by utilizing the SF-36 and Pittsburgh Sleep Quality Index (PSQI) surveys to measure health and sleep quality. The results clearly showed that older age and lower education are strongly related to poor health, while natural light exposure and better building orientation are strongly related to improvement in sleeping quality and, hence, good health. Distinct clusters emerged from this population; for instance, the employed and better-educated people presented higher well-being. Interaction effects also showed how age, education, and daylight exposure are interactively determining health and sleep outcomes. The health of the less educated elderly decreases at a much greater rate; accessibility to daylight moderates this. Such findings hint at targeted interventions that might involve optimal window placement, coupled with improvements in building orientation and social support measures to enhance general well-being among vulnerable groups. Merging these architectonic and socioeconomic factors, the interventions could contribute a lot to people living a better life in such informal settlements.
In recent years, the green building industry in Southeast Asia has shown a clear trend of develop... more In recent years, the green building industry in Southeast Asia has shown a clear trend of development. However, the development differences among Southeast Asian countries are generally large, and existing studies lack a comprehensive comparative analysis of green buildings in developed and developing countries in the region. This study takes Singapore and Vietnam as typical representatives of developed and developing Southeast Asian countries and compares their green building rating systems, green building technologies, and the current status of building energy consumption of various types of commercial buildings. The results show that there is a significant difference in the overall energy consumption levels between Singapore and Vietnam. The overall energy consumption level of buildings in Singapore is much higher. And probably due to the standardization of the green building rating system and advanced development of green building technologies in Singapore, the energy consumption level of all types of commercial buildings in Singapore is more stable and less fluctuating than in Vietnam. The results of the study critically point out differences in green building development and the current status of energy consumption between developed and developing countries in Southeast Asia and provide a direction for improving the existing green building evaluation standards, promote the development of green buildings in developing countries, and to narrow the regional gap.
Building facades, especially windows, are essential for indoor lighting and solar energy use, but... more Building facades, especially windows, are essential for indoor lighting and solar energy use, but traditional windows often fail to balance daylighting and energy performance, while semi-transparent building-integrated photovoltaics (BIPVs) offer a promising solution, though research on optimizing their design for energy harvesting, daylight sufficiency, and glare control in office spaces is limited. This study proposes a multi-objective optimization framework for designing semi-transparent building-integrated photovoltaic (BIPV) windows to balance energy efficiency, daylight quantity, and glare control in office buildings. Using a case study of an office on a campus in Wuhan, we developed a parametric model to control key design parameters such as photovoltaic window area and distribution. Simulations evaluated the impact on daylight sufficiency, glare, and photovoltaic efficiency. The results were analyzed using K-means clustering, revealing three design clusters: one maximizing transmittance with lower daylight and glare performance, another optimizing energy production and glare reduction, and a third offering a balanced performance with the best daylighting, though slightly reduced glare. The optimal design from Cluster 3 resulted in a 15.63 % increase in annual illuminance, improved daylight uniformity, and a 6.55 % increase in spatial glare autonomy, while maintaining sufficient daylight levels. The BIPV system generated 5,508.86 kWh annually, meeting the office’s entire nighttime energy demand. This framework provides valuable guidance for early-stage building design, helping to optimize both energy and lighting performance in BIPV-integrated facades.
To address office vacancy and housing shortages, adaptive transformations of office buildings int... more To address office vacancy and housing shortages, adaptive transformations of office buildings into residential spaces are proposed. While renovating old office buildings is prioritized, energy and socio-economic feasibility assessments are lacking. This study fills the research gap by using building performance simulations to create over 500 models, revealing the potential benefits of converting office buildings to residential use through multi-scenario evaluations. The research examined twenty-three pre-1980 office buildings in Los Angeles County with vacancy rates over 50%, establishing a baseline scenario based on actual data. Four scenarios were compared: baseline scenario, office energy efficiency transformation scenario, office-to-residential transformation scenario, and office-to-residential and energy efficiency transformation scenario. The study evaluated energy consumption and costs for each scenario. The research found that functional transformations cut energy use intensity by 46.09% and Life Cycle Cost by 61.61%, saving at least 5,154.84 MWh annually for Los Angeles County. It also found a comprehensive approach that reduces electric equipment loads, improves insulation, and moderately cuts lighting power density offers the best energy and economic benefits. The study suggests prioritizing renovation for small to medium-sized old office buildings with energy consumption below 348.3 kWh/m2·yr, footprint area over 2000 square meters, and height under 26 meters. This study highlights the benefits of functional transformation over energy-saving transformations in reducing operational energy and costs, offering decision-making methods and optimization recommendations for converting non-residential buildings to address the climate and housing crisis.
In the face of global climate warming, outdoor thermal comfort in urban settings is increasingly ... more In the face of global climate warming, outdoor thermal comfort in urban settings is increasingly critical. However, accurately predicting residents' thermal perceptions during outdoor activities remains challenging due to complex environmental dynamics. This study introduces a human-centered digital twin framework that integrates physiological data, atmospheric conditions, and urban building environment features, with multiple machine learning models employed to predict and analyze outdoor thermal comfort in different regions of Singapore. Among these methods, the Bayesian-tuned XGBoost model exhibits the highest accuracy (0.66), notably excelling in categorizing “Prefer cooler” and “Prefer no change” responses. SHAP value analysis identifies key influencing factors such as human activity intensity (heart rate), geographical location (longitude and latitude), meteorological conditions (solar azimuth angle, dew point temperature), and greenery (Normalized Difference Vegetation Index). Based on the most effective machine learning method, this research develops a user-personalized real-time prediction model for urban thermal comfort perception. The extensive hourly grid-based prediction results illustrate the spatiotemporal variations in outdoor thermal comfort, highlighting preference differences across locations, seasons, and activity levels. Results underscore the efficacy of the human-centric digital twin approach and machine learning in managing urban thermal environments, leveraging multi-source data to complement traditional survey methods effectively.
The accuracy of occupancy and energy-consuming equipment schedules significantly influences build... more The accuracy of occupancy and energy-consuming equipment schedules significantly influences building energy simulations. Existing standards provide generalized schedules that do not fully capture variations across different office building types and periods. This study utilizes questionnaire data from office buildings across four Chinese cities to extract refined prototypes for occupancy and equipment usage schedules using hierarchical clustering analysis. Specific schedules are developed for summer workdays, winter workdays, summer weekends, and winter weekends, covering HVAC, lighting, and office equipment. For instance, the extracted lighting schedule increases annual energy consumption intensity by 25.35% compared to standard schedules. Additionally, XGBoost models identify key factors influencing equipment usage; for HVAC, floor number emerges as most significant. The study's prototypes offer more realistic inputs for building energy simulations, enhancing accuracy and guiding energy-efficient building design and management strategies in China.
Harnessing energy surpluses and technological advances from positive energy buildings (PEBs) with... more Harnessing energy surpluses and technological advances from positive energy buildings (PEBs) within a community offers a logical transition pathway from PEBs to positive energy communities (PECs). This study proposes a set of reasonable performance indicators (KPIs) for streamlining site planning assessments based on existing PEBs, aiming to transition to PECs for achieving energy autonomy in off-grid states. The proposed KPIs include energy surplus ratio, PEB area coverage ratio, community energy difference, shared energy matching ratio, and PEB impact coefficient. A sample of 81 PEBs provided by a real database in North America was selected for testifying these KPIs, and relevant geographic analyses and simulations were performed. Four PV installation scenarios and three radius ranges were considered for building energy generation and energy consumption. The results show that the establishment of a PEC through a PEB is promising. In existing communities, the physical boundary of the transition from PEB to PEC can be determined to be between 150 and 250 m. The study also found that educational buildings should be closely integrated with residential buildings in the energy resilience planning process. KPIs offer crucial insights for initial PEC site selection, offering practical guidance for PEC development and informing strategic decisions in planning PECs through PEBs. This study’s findings serve as actionable guidance for stakeholders, such as urban planners, policymakers, developers, and researchers, facilitating the creation of sustainable and energy-positive communities.
The complexity of urban form can have a significant impact on the utilization of solar energy. Wh... more The complexity of urban form can have a significant impact on the utilization of solar energy. While numerous studies have examined the influence of urban form on solar potential, the optimization of solar energy use in cities located at high latitudes remains a challenging subject. In this study, we focus on the high-latitude city of Glasgow, using residential buildings in urban grid cells as our sample. We calculate solar potential and urban form indicators for these buildings using the Digimap database and the ArcGIS Pro platform. Employing eight machine learning algorithms, we analyze the data and extract eight key morphological indicators that affect the solar potential of urban grid cells. Among these indicators, we select four indicators-roof slope, building density, plot ratio, and building perimeter shape factor-for cluster analysis, enabling us to classify urban building forms into five types based on their characteristics and solar potential. Our calculations demonstrate that effective utilization of solar energy offers significant zero energy potential for Glasgow. The findings of this research can provide valuable guidance in the early stages of urban planning and design, assisting policymakers in rationalizing the use of solar energy resources for sustainable urban development. Furthermore, the results help urban stakeholders identify variations in the solar potential of different building forms, aiding them in selecting appropriate building types and zones to maximize solar energy utilization.
Assessing BIPV (Building Integrated Photovoltaic) potential is of great significance for the comp... more Assessing BIPV (Building Integrated Photovoltaic) potential is of great significance for the comprehensive promotion and deployment of solar energy. Traditional models mostly rely on morphological parameters for PV potential assessment, presenting challenges such as subjective knowledge of urban forms and difficulty in generalization within dense urban areas. This study employs Convolutional Neural Network (CNN) for 3D modeling to evaluate BIPV potential at medium and large urban scales, introducing a framework for a multidimensional single-channel one-dimensional CNN model. The model utilizes the Gaussian Mixture Model combined with building point cloud data to extract the building window-to-wall ratio, thereby enhancing individual features in the building cluster point cloud. It also utilizes the 3D physical model to extract building geographic orientation information, integrating point cloud distribution through spatial connectivity to address the issue of missing geographic orientation due to rotational invariance of point cloud convolution. Additionally, it uses the surface area of the 3D model as the weight for surface point cloud sampling and combines it with normal estimation to retain building entity information, solving the disorder of point cloud convolution. This modeling framework enables accurate prediction of PV potentials in urban blocks by utilizing city point cloud data and predicting urban block boundaries. Using Melbourne City as a case study, the model demonstrates superior performance compared to traditional morphological parameter-based prediction models, with a root mean square error of 2415.548 kWh/year and an R2 SCORE of 0.937 in 75 training sets. The proposed modeling framework enables the prediction of multi-scale BIPV potential, which is beneficial for the staged promotion of BIPV and the development of effective energy deployment strategies. This study offers new insights for urban building energy modeling, deep learning, and energy prediction in complex scenarios at medium and large scales for sustainable urban development.
In the midst of a global energy crisis, fuel poverty has become increasingly prevalent. This stud... more In the midst of a global energy crisis, fuel poverty has become increasingly prevalent. This study examines the effectiveness of energy efficiency retrofits (EERs) in alleviating fuel poverty in the United Kingdom. Using the England fuel poverty dataset with XGBoost and interpretive analyses like Individual Conditional Expectation (ICE) and SHapley Additive exPlanations (SHAP), the research investigates the impact of EERs on fuel poverty. Findings reveal that while energy efficiency enhancements hold potential for improving conditions for vulnerable households, their effectiveness diminishes beyond a certain threshold. The study identifies solid wall and cavity wall insulation, condensing boilers, well-insulated lofts, and central heating systems as the most effective retrofit measures. However, the optimal selection of retrofit methods varies depending on household and dwelling conditions due to interactive effects. Additionally, comprehensive cost-efficiency analyses show that installing condensing combination boilers in homes without existing boilers and adding cavity wall insulation to houses with uninsulated cavity walls are more cost-efficient options. These insights shed light on the complex relationship between energy efficiency retrofits and fuel poverty, informing the development of targeted strategies to address this pressing issue in the UK and globally.
With the rapid growth of cloud computing, the number of data centers (DCs) continuously increases... more With the rapid growth of cloud computing, the number of data centers (DCs) continuously increases, leading to a high-energy consumption dilemma. Cooling, apart from IT equipment, represents the largest energy consumption in DCs. Passive design (PD) and active design (AD) are two important approaches in architectural design to reduce energy consumption. However, for DC cooling, few studies have summarized AD, and there are almost no studies on PD. Based on existing international research (2005-2024), this paper summarizes the current state of cooling strategies for DCs. PD encompasses floors, ceilings, and layout and zoning of racks. Additionally, other passive strategies not yet studied in DCs are critically examined. AD includes air, liquid, free, and two-phase cooling. This paper systematically compares the performance of different AD technologies on various KPIs, including energy, economic, and environmental indicators. This paper also explores the application of different cooling design strategies through best-practice examples and presents advanced algorithms for energy management in operational DCs. This study reveals that free cooling is widely employed, with Artificial Neural Networks emerging as the most popular algorithm for managing cooling energy. Finally, this paper suggests four future directions for reducing cooling energy in DCs, with a focus on the development of passive strategies. This paper provides an overview and guide to DC energy-consumption issues, emphasizes the importance of implementing passive and active design strategies to reduce DC cooling energy consumption, and provides directions and references for future energy-efficient DC designs.
This study investigated the climatic risks and vulnerabilities of informal settlements in the Glo... more This study investigated the climatic risks and vulnerabilities of informal settlements in the Global South, as well as the extent to which these risks impact the vulnerabilities. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 methodology and deductive content analysis, this study critically examined 69 documents, including 28 scholarly journal articles obtained from Scopus and ScienceDirect and 41 web-based releases identified through the Google search engine and snowballing technique. Document inclusion criteria focused on the relevance to climate risks and vulnerabilities, excluding non-peer-reviewed, non-English, and unreliable sources, as well as irrelevant studies. Seven major climate risks impacting informal settlements were identified: floods (44), temperature changes (41), storms (31), sea level rise (30), drought (28), rainfall (23), and landslides (14). The primary vulnerabilities highlighted were poor housing conditions (64), health risks (50), lack of basic services (49), inadequate sanitation (41), inadequate hygiene (39), and limited access to water (38). The combination of vulnerabilities and climate risks creates considerable direct, indirect, and low-level threats to informal settlements. Despite Asia, Africa, and Latin America’s vulnerability, most studies focused on formal and developed areas. The findings highlight the critical need for climate adaptation strategies in informal settlements of the Global South to ensure the United Nations Sustainable Development Goals (SDGs) are met.
In this study, an evaluation framework for retrofitting traditional electric vehicle charging sta... more In this study, an evaluation framework for retrofitting traditional electric vehicle charging stations (EVCSs) into photovoltaic-energy storage-integrated charging stations (PV-ES-I CSs) to improve green and low-carbon energy supply systems is proposed. Using existing EVCSs in the "10-minute living circle residential areas" of seven central urban districts in Wuhan city, we comprehensively consider factors such as the site distribution, sky view factor and service population to assess the economic and environmental benefits of PV-ES-I CS systems at different locations and scales. The research results show that the central urban districts have high retrofitting potential, with 44 stations north of the Yangtze River and 20 stations south of the Yangtze River being most suitable for retrofitting. The economic and environmental benefits vary significantly across building types; the retrofitting/construction of PV-ES-I CSs near hotels yields the greatest economic benefit and carbon dioxide emission reduction per unit investment.
In recent years, the urgency to address climate challenges has driven the need to achieve net-zer... more In recent years, the urgency to address climate challenges has driven the need to achieve net-zero carbon goals by 2050. The construction industry, as a major contributor to carbon emissions, plays a vital role in achieving this target. While numerous net-zero energy building approaches have been proposed globally, there is limited comparative research between developed and developing countries. This study aims to address this research gap by selecting the top three carbon-emitting countries in the world-China, India, and the United States (US)-as the research subjects. Through a comparison of their net-zero energy building approaches and typical cases, it is evident that there exists a significant disparity between developed and developing nations. Specifically, differences are observed in the following aspects: net-zero building approaches in the United States have an earlier start and involve collaborations between the government and multiple organizations, while China and India started relatively later and rely primarily on government initiatives. China's consideration of applicable building types in net-zero approaches is not inclusive enough, particularly lacking coverage on industrial buildings. It is encouraging to observe that China and India pay more attention to embodied carbon emissions compared, while their decarbonization strategies for embodied carbon emissions are not as comprehensive as those in the United States when it comes to actual building applications. Regarding carbon offset options, the United States and India demonstrate more comprehensive considerations, while China currently lacks consideration for off-site power generation and negative carbon technologies. The United States boasts a larger number of net-zero buildings, most of which have been certified through the country's own net-zero building approaches. In contrast, China and India have fewer certified net-zero buildings, and only a small percentage have been certified using their own methods, especially in China. The technologies employed in zero-carbon buildings in the United States are more advanced compared to China and India. These findings underline how the disparities in economic levels between developed and developing countries are reflected in net-zero building approaches. Based on these insights, professionals in the field can develop tailored strategies to bridge the gap in net-zero building development between developed and developing nations. This study provides a foundation and recommendations for achieving carbon neutrality in the construction industry for the world's top three economies by 2030.
Occupant behavior plays a crucial role in enhancing indoor thermal comfort and achieving energy e... more Occupant behavior plays a crucial role in enhancing indoor thermal comfort and achieving energy efficiency by influencing the operational modes of Heating, Ventilation, and Air Conditioning (HVAC) systems as well as windows. However, accurately quantifying the impact of occupant behavior on the indoor environment presents significant challenges in practical applications. This study introduces an innovative approach by leveraging the ASHRAE Global Building Occupant Behavior Database and harnessing the power of XGBoost in conjunction with Deep Q Networks (DQN) to construct a reinforcement learning model. This model enables precise prediction of the impact of occupant behavior on the indoor environment at the next time step under varying indoor-outdoor conditions, simultaneously targeting the dual objectives of indoor thermal comfort and energy conservation. By applying the XGB-DQN model in sample rooms of four international cities with distinct features, the results demonstrate a significant increase in indoor thermal comfort duration by 24 %, accompanied by a 24.7 % decrease in air conditioning usage compared to baseline models and actual occupant data. This research represents a pioneering effort in applying reinforcement learning techniques to accurately predict occupant behavior's impact on indoor environments, offering valuable insights for intelligent building design and energy management.
Global warming and the urban heat island effect have led to a decline in the quality of urban ope... more Global warming and the urban heat island effect have led to a decline in the quality of urban open spaces. Although many studies have been conducted on the thermal environment of urban open spaces, there is a lack of a detailed analysis of the time dimension. This study is to propose a method for analyzing long-term time series data on thermal environment, to more comprehensively evaluate the comfort of urban open spaces. We employed thermal environment data from urban open spaces in Melbourne and conducted a time series comfort analysis using a Gaussian mixture model. The research findings indicate that urban open spaces surrounded by low-rise buildings exhibit the highest thermal comfort, whereas those encompassed by a combination of buildings and green spaces exhibit the lowest comfort level. More importantly, we discovered similarities in the comfortable time periods across the five types of urban open spaces, with the majority falling between 6:00 AM and 10:00 AM. These discoveries can assist in achieving a more objective evaluation of the thermal environment in urban open spaces and provide scientific guidance for urban planning and landscape design, thus enhancing the quality of life for urban residents.
Achieving net-zero energy (NZE) in buildings involves laying down photovoltaics (PV) over large b... more Achieving net-zero energy (NZE) in buildings involves laying down photovoltaics (PV) over large building areas, and the issue of dissipating surplus PV capacity has been a challenge. With the popularity of electric vehicles (EVs), parking lots attached to buildings offer such a possible solution. In order to assess the possibility of applying PV surplus power to electric vehicle charging stations (EVCSs) in parking lots, a workflow based on the p-center model was developed in this study using scenario analysis. Considering New York City as the research focus, 100 buildings satisfying the prescribed criteria were screened. Subsequently, the potential of the parking facilities of the selected buildings was evaluated for dissipating surplus PV capacity, focusing on achieving both NZE and green power goals. The results show that the NZE goal is achieved between 3 % and 13 %, while the GP goal is determined as 20 %. The shape factor of the buildings and the goal completion rate from the four clusters reflect the improvements in the four types of parking lots. The average reduction in greenhouse gas (GHG) emissions for the five PV installation scenarios was 67.10, 112.94, 147.41, 171.00, and 187.24 MTCO 2 e, with an average payback period of 0.71, 1.87, 2.21, 2.47, and 2.22 years, respectively. In addition, 13 samples met the NZE goal, and 29 samples met the GP goal, representing 13 % and 29 % of the sample size sample, respectively. The study confirms the advantages of the building-to-vehicle-to-building (B2V2B) model, and the results can be used as a tool and reference for formulating PV development strategies in cities.
Energy-related carbon emissions from the transportation sector are one of the major obstacles to ... more Energy-related carbon emissions from the transportation sector are one of the major obstacles to achieving global carbon reduction targets. Current research on transportation energy mainly focuses on the energy end-use stage, with a lack of simulations covering both the front-end and end-use stages of transportation energy. This study, from a life-cycle perspective, conducts a carbon emission simulation for county-level transportation. Based on the 2019 transportation data of Huadu District, Guangzhou, the study combines life-cycle assessment (LCA) with the Low Emission Analysis Platform (LEAP) to simulate changes in transportation carbon emissions from 2020 to 2050. The results indicate that: (1) The Energy-Saving scenario has the greatest carbon reduction potential, capable of reducing carbon emissions by 75 %. (2) Energy efficiency factors have the most significant carbon reduction effect in transportation. (3) There is a carbon transfer phenomenon from "Tank-to-Wheel" to "Well-to-Tank" in transportation energy. (4) Embodied carbon accounts for 40 %, while operational carbon accounts for 60 % of the entire transportation energy life cycle. Based on the above results, the study suggests that the government should increase policy support and technological innovation, invest in public transportation infrastructure, and strengthen carbon management throughout the entire lifecycle to comprehensively enhance the carbon emission reduction effects of transportation energy.
To address the building decarbonization crisis, the widespread adoption of rooftop photovoltaics ... more To address the building decarbonization crisis, the widespread adoption of rooftop photovoltaics (PV) has been agreed upon globally, with PV potential prediction being a crucial evaluation task. Current methods for predicting rooftop photovoltaic (PV) potential face significant shortcomings, as geospatial approaches struggle with precision at urban scales, historical time-series methods tend to overestimate potential, and urban studies often neglect spatial shading between buildings, thereby inflating predictions. This study addresses these issues by employing a Graph Convolutional Network − Long Short-Term Memory (GCN-LSTM) model to perform spatiotemporal predictions of urban rooftop PV potential, incorporating the spatial shading relationships between buildings to enhance prediction accuracy. The results show that, compared to traditional Long Short-Term Memory (LSTM) models, GCN-LSTM significantly improves prediction accuracy, reducing MAE by 21%, MSE by 22%, RMSE by 13%, and MAPE by 12%. This improvement is particularly evident in winter and summer, validating the interpretability of the GCN-LSTM model. Moreover, clustering analysis of the shading relationship graphs between urban buildings identified three primary types of graph clusters: moderately diverse medium-scale building shading graphs, simple small-scale building shading graphs, and complex large-scale building shading graphs. Factors such as the number of buildings, standard deviation of building heights, and standard deviation of roof slopes were found to collectively influence the complexity and shading intensity of these graphs, leading to variations in PV potential. Based on the findings of this study, it is evident that integrating deep learning models with engineering physics knowledge can substantially enhance the accuracy of urban rooftop PV potential predictions, providing suggestions and bases for the formulation and implementation of PV promotion policies.
The urban heat island effect and extreme high temperatures pose significant challenges to the liv... more The urban heat island effect and extreme high temperatures pose significant challenges to the livability of cities, making it urgent to mitigate urban heat issues. However, uncertainty remains regarding the most effective cooling measures. The differences between single and combined measures have not been clearly defined, and there is limited research comparing their impacts on urban microclimates and energy consumption. Therefore, this study aims to explore the differences between single cooling measures and mixed cooling strategies from the perspectives of temperature, thermal comfort, and building energy consumption to provide a more holistic impact analysis of cooling strategies. Therefore, this study conducted a simulation on a typical street canyon in Melbourne, Australia, on December 20, 2019. Four individual scenarios and six combined scenarios were established. Subsequently, the environmental parameters and building energy consumption were simulated under various cooling scenarios. The results indicated that the combined cooling scenario with three measures achieved the highest comprehensive score, demonstrating the most favorable impact on the thermal environment and building energy consumption. We also found that the green scenario combined with high-reflectivity can reduce building energy consumption by 71.83 kWh within a day, yielding the best results. However, the combination of green infrastructure, permeable surfaces, and misting proved to be the most effective in improving the environmental thermal comfort, with a maximum reduction of 1.86 °C in the UTCI (universal thermal climate index). This study can provide guidance and recommendations for designers in the early stages of decision-making.
Inhabitants of informal settlements face socioeconomic difficulties and suboptimal living conditi... more Inhabitants of informal settlements face socioeconomic difficulties and suboptimal living conditions, where demographic, architectural, and well-being factors interrelate to determine quality of life (QoL). This paper explores how these interactions occur and how architectural interventions can shape these environments in ways that contribute to improved health outcomes. We conducted a demographic and architectural study, with a focus on daylighting among selected inhabitants. We also investigated well-being by utilizing the SF-36 and Pittsburgh Sleep Quality Index (PSQI) surveys to measure health and sleep quality. The results clearly showed that older age and lower education are strongly related to poor health, while natural light exposure and better building orientation are strongly related to improvement in sleeping quality and, hence, good health. Distinct clusters emerged from this population; for instance, the employed and better-educated people presented higher well-being. Interaction effects also showed how age, education, and daylight exposure are interactively determining health and sleep outcomes. The health of the less educated elderly decreases at a much greater rate; accessibility to daylight moderates this. Such findings hint at targeted interventions that might involve optimal window placement, coupled with improvements in building orientation and social support measures to enhance general well-being among vulnerable groups. Merging these architectonic and socioeconomic factors, the interventions could contribute a lot to people living a better life in such informal settlements.
In recent years, the green building industry in Southeast Asia has shown a clear trend of develop... more In recent years, the green building industry in Southeast Asia has shown a clear trend of development. However, the development differences among Southeast Asian countries are generally large, and existing studies lack a comprehensive comparative analysis of green buildings in developed and developing countries in the region. This study takes Singapore and Vietnam as typical representatives of developed and developing Southeast Asian countries and compares their green building rating systems, green building technologies, and the current status of building energy consumption of various types of commercial buildings. The results show that there is a significant difference in the overall energy consumption levels between Singapore and Vietnam. The overall energy consumption level of buildings in Singapore is much higher. And probably due to the standardization of the green building rating system and advanced development of green building technologies in Singapore, the energy consumption level of all types of commercial buildings in Singapore is more stable and less fluctuating than in Vietnam. The results of the study critically point out differences in green building development and the current status of energy consumption between developed and developing countries in Southeast Asia and provide a direction for improving the existing green building evaluation standards, promote the development of green buildings in developing countries, and to narrow the regional gap.
Building facades, especially windows, are essential for indoor lighting and solar energy use, but... more Building facades, especially windows, are essential for indoor lighting and solar energy use, but traditional windows often fail to balance daylighting and energy performance, while semi-transparent building-integrated photovoltaics (BIPVs) offer a promising solution, though research on optimizing their design for energy harvesting, daylight sufficiency, and glare control in office spaces is limited. This study proposes a multi-objective optimization framework for designing semi-transparent building-integrated photovoltaic (BIPV) windows to balance energy efficiency, daylight quantity, and glare control in office buildings. Using a case study of an office on a campus in Wuhan, we developed a parametric model to control key design parameters such as photovoltaic window area and distribution. Simulations evaluated the impact on daylight sufficiency, glare, and photovoltaic efficiency. The results were analyzed using K-means clustering, revealing three design clusters: one maximizing transmittance with lower daylight and glare performance, another optimizing energy production and glare reduction, and a third offering a balanced performance with the best daylighting, though slightly reduced glare. The optimal design from Cluster 3 resulted in a 15.63 % increase in annual illuminance, improved daylight uniformity, and a 6.55 % increase in spatial glare autonomy, while maintaining sufficient daylight levels. The BIPV system generated 5,508.86 kWh annually, meeting the office’s entire nighttime energy demand. This framework provides valuable guidance for early-stage building design, helping to optimize both energy and lighting performance in BIPV-integrated facades.
To address office vacancy and housing shortages, adaptive transformations of office buildings int... more To address office vacancy and housing shortages, adaptive transformations of office buildings into residential spaces are proposed. While renovating old office buildings is prioritized, energy and socio-economic feasibility assessments are lacking. This study fills the research gap by using building performance simulations to create over 500 models, revealing the potential benefits of converting office buildings to residential use through multi-scenario evaluations. The research examined twenty-three pre-1980 office buildings in Los Angeles County with vacancy rates over 50%, establishing a baseline scenario based on actual data. Four scenarios were compared: baseline scenario, office energy efficiency transformation scenario, office-to-residential transformation scenario, and office-to-residential and energy efficiency transformation scenario. The study evaluated energy consumption and costs for each scenario. The research found that functional transformations cut energy use intensity by 46.09% and Life Cycle Cost by 61.61%, saving at least 5,154.84 MWh annually for Los Angeles County. It also found a comprehensive approach that reduces electric equipment loads, improves insulation, and moderately cuts lighting power density offers the best energy and economic benefits. The study suggests prioritizing renovation for small to medium-sized old office buildings with energy consumption below 348.3 kWh/m2·yr, footprint area over 2000 square meters, and height under 26 meters. This study highlights the benefits of functional transformation over energy-saving transformations in reducing operational energy and costs, offering decision-making methods and optimization recommendations for converting non-residential buildings to address the climate and housing crisis.
In the face of global climate warming, outdoor thermal comfort in urban settings is increasingly ... more In the face of global climate warming, outdoor thermal comfort in urban settings is increasingly critical. However, accurately predicting residents' thermal perceptions during outdoor activities remains challenging due to complex environmental dynamics. This study introduces a human-centered digital twin framework that integrates physiological data, atmospheric conditions, and urban building environment features, with multiple machine learning models employed to predict and analyze outdoor thermal comfort in different regions of Singapore. Among these methods, the Bayesian-tuned XGBoost model exhibits the highest accuracy (0.66), notably excelling in categorizing “Prefer cooler” and “Prefer no change” responses. SHAP value analysis identifies key influencing factors such as human activity intensity (heart rate), geographical location (longitude and latitude), meteorological conditions (solar azimuth angle, dew point temperature), and greenery (Normalized Difference Vegetation Index). Based on the most effective machine learning method, this research develops a user-personalized real-time prediction model for urban thermal comfort perception. The extensive hourly grid-based prediction results illustrate the spatiotemporal variations in outdoor thermal comfort, highlighting preference differences across locations, seasons, and activity levels. Results underscore the efficacy of the human-centric digital twin approach and machine learning in managing urban thermal environments, leveraging multi-source data to complement traditional survey methods effectively.
The accuracy of occupancy and energy-consuming equipment schedules significantly influences build... more The accuracy of occupancy and energy-consuming equipment schedules significantly influences building energy simulations. Existing standards provide generalized schedules that do not fully capture variations across different office building types and periods. This study utilizes questionnaire data from office buildings across four Chinese cities to extract refined prototypes for occupancy and equipment usage schedules using hierarchical clustering analysis. Specific schedules are developed for summer workdays, winter workdays, summer weekends, and winter weekends, covering HVAC, lighting, and office equipment. For instance, the extracted lighting schedule increases annual energy consumption intensity by 25.35% compared to standard schedules. Additionally, XGBoost models identify key factors influencing equipment usage; for HVAC, floor number emerges as most significant. The study's prototypes offer more realistic inputs for building energy simulations, enhancing accuracy and guiding energy-efficient building design and management strategies in China.
Harnessing energy surpluses and technological advances from positive energy buildings (PEBs) with... more Harnessing energy surpluses and technological advances from positive energy buildings (PEBs) within a community offers a logical transition pathway from PEBs to positive energy communities (PECs). This study proposes a set of reasonable performance indicators (KPIs) for streamlining site planning assessments based on existing PEBs, aiming to transition to PECs for achieving energy autonomy in off-grid states. The proposed KPIs include energy surplus ratio, PEB area coverage ratio, community energy difference, shared energy matching ratio, and PEB impact coefficient. A sample of 81 PEBs provided by a real database in North America was selected for testifying these KPIs, and relevant geographic analyses and simulations were performed. Four PV installation scenarios and three radius ranges were considered for building energy generation and energy consumption. The results show that the establishment of a PEC through a PEB is promising. In existing communities, the physical boundary of the transition from PEB to PEC can be determined to be between 150 and 250 m. The study also found that educational buildings should be closely integrated with residential buildings in the energy resilience planning process. KPIs offer crucial insights for initial PEC site selection, offering practical guidance for PEC development and informing strategic decisions in planning PECs through PEBs. This study’s findings serve as actionable guidance for stakeholders, such as urban planners, policymakers, developers, and researchers, facilitating the creation of sustainable and energy-positive communities.
The complexity of urban form can have a significant impact on the utilization of solar energy. Wh... more The complexity of urban form can have a significant impact on the utilization of solar energy. While numerous studies have examined the influence of urban form on solar potential, the optimization of solar energy use in cities located at high latitudes remains a challenging subject. In this study, we focus on the high-latitude city of Glasgow, using residential buildings in urban grid cells as our sample. We calculate solar potential and urban form indicators for these buildings using the Digimap database and the ArcGIS Pro platform. Employing eight machine learning algorithms, we analyze the data and extract eight key morphological indicators that affect the solar potential of urban grid cells. Among these indicators, we select four indicators-roof slope, building density, plot ratio, and building perimeter shape factor-for cluster analysis, enabling us to classify urban building forms into five types based on their characteristics and solar potential. Our calculations demonstrate that effective utilization of solar energy offers significant zero energy potential for Glasgow. The findings of this research can provide valuable guidance in the early stages of urban planning and design, assisting policymakers in rationalizing the use of solar energy resources for sustainable urban development. Furthermore, the results help urban stakeholders identify variations in the solar potential of different building forms, aiding them in selecting appropriate building types and zones to maximize solar energy utilization.
Assessing BIPV (Building Integrated Photovoltaic) potential is of great significance for the comp... more Assessing BIPV (Building Integrated Photovoltaic) potential is of great significance for the comprehensive promotion and deployment of solar energy. Traditional models mostly rely on morphological parameters for PV potential assessment, presenting challenges such as subjective knowledge of urban forms and difficulty in generalization within dense urban areas. This study employs Convolutional Neural Network (CNN) for 3D modeling to evaluate BIPV potential at medium and large urban scales, introducing a framework for a multidimensional single-channel one-dimensional CNN model. The model utilizes the Gaussian Mixture Model combined with building point cloud data to extract the building window-to-wall ratio, thereby enhancing individual features in the building cluster point cloud. It also utilizes the 3D physical model to extract building geographic orientation information, integrating point cloud distribution through spatial connectivity to address the issue of missing geographic orientation due to rotational invariance of point cloud convolution. Additionally, it uses the surface area of the 3D model as the weight for surface point cloud sampling and combines it with normal estimation to retain building entity information, solving the disorder of point cloud convolution. This modeling framework enables accurate prediction of PV potentials in urban blocks by utilizing city point cloud data and predicting urban block boundaries. Using Melbourne City as a case study, the model demonstrates superior performance compared to traditional morphological parameter-based prediction models, with a root mean square error of 2415.548 kWh/year and an R2 SCORE of 0.937 in 75 training sets. The proposed modeling framework enables the prediction of multi-scale BIPV potential, which is beneficial for the staged promotion of BIPV and the development of effective energy deployment strategies. This study offers new insights for urban building energy modeling, deep learning, and energy prediction in complex scenarios at medium and large scales for sustainable urban development.
In the midst of a global energy crisis, fuel poverty has become increasingly prevalent. This stud... more In the midst of a global energy crisis, fuel poverty has become increasingly prevalent. This study examines the effectiveness of energy efficiency retrofits (EERs) in alleviating fuel poverty in the United Kingdom. Using the England fuel poverty dataset with XGBoost and interpretive analyses like Individual Conditional Expectation (ICE) and SHapley Additive exPlanations (SHAP), the research investigates the impact of EERs on fuel poverty. Findings reveal that while energy efficiency enhancements hold potential for improving conditions for vulnerable households, their effectiveness diminishes beyond a certain threshold. The study identifies solid wall and cavity wall insulation, condensing boilers, well-insulated lofts, and central heating systems as the most effective retrofit measures. However, the optimal selection of retrofit methods varies depending on household and dwelling conditions due to interactive effects. Additionally, comprehensive cost-efficiency analyses show that installing condensing combination boilers in homes without existing boilers and adding cavity wall insulation to houses with uninsulated cavity walls are more cost-efficient options. These insights shed light on the complex relationship between energy efficiency retrofits and fuel poverty, informing the development of targeted strategies to address this pressing issue in the UK and globally.
With the rapid growth of cloud computing, the number of data centers (DCs) continuously increases... more With the rapid growth of cloud computing, the number of data centers (DCs) continuously increases, leading to a high-energy consumption dilemma. Cooling, apart from IT equipment, represents the largest energy consumption in DCs. Passive design (PD) and active design (AD) are two important approaches in architectural design to reduce energy consumption. However, for DC cooling, few studies have summarized AD, and there are almost no studies on PD. Based on existing international research (2005-2024), this paper summarizes the current state of cooling strategies for DCs. PD encompasses floors, ceilings, and layout and zoning of racks. Additionally, other passive strategies not yet studied in DCs are critically examined. AD includes air, liquid, free, and two-phase cooling. This paper systematically compares the performance of different AD technologies on various KPIs, including energy, economic, and environmental indicators. This paper also explores the application of different cooling design strategies through best-practice examples and presents advanced algorithms for energy management in operational DCs. This study reveals that free cooling is widely employed, with Artificial Neural Networks emerging as the most popular algorithm for managing cooling energy. Finally, this paper suggests four future directions for reducing cooling energy in DCs, with a focus on the development of passive strategies. This paper provides an overview and guide to DC energy-consumption issues, emphasizes the importance of implementing passive and active design strategies to reduce DC cooling energy consumption, and provides directions and references for future energy-efficient DC designs.
This study investigated the climatic risks and vulnerabilities of informal settlements in the Glo... more This study investigated the climatic risks and vulnerabilities of informal settlements in the Global South, as well as the extent to which these risks impact the vulnerabilities. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 methodology and deductive content analysis, this study critically examined 69 documents, including 28 scholarly journal articles obtained from Scopus and ScienceDirect and 41 web-based releases identified through the Google search engine and snowballing technique. Document inclusion criteria focused on the relevance to climate risks and vulnerabilities, excluding non-peer-reviewed, non-English, and unreliable sources, as well as irrelevant studies. Seven major climate risks impacting informal settlements were identified: floods (44), temperature changes (41), storms (31), sea level rise (30), drought (28), rainfall (23), and landslides (14). The primary vulnerabilities highlighted were poor housing conditions (64), health risks (50), lack of basic services (49), inadequate sanitation (41), inadequate hygiene (39), and limited access to water (38). The combination of vulnerabilities and climate risks creates considerable direct, indirect, and low-level threats to informal settlements. Despite Asia, Africa, and Latin America’s vulnerability, most studies focused on formal and developed areas. The findings highlight the critical need for climate adaptation strategies in informal settlements of the Global South to ensure the United Nations Sustainable Development Goals (SDGs) are met.
In this study, an evaluation framework for retrofitting traditional electric vehicle charging sta... more In this study, an evaluation framework for retrofitting traditional electric vehicle charging stations (EVCSs) into photovoltaic-energy storage-integrated charging stations (PV-ES-I CSs) to improve green and low-carbon energy supply systems is proposed. Using existing EVCSs in the "10-minute living circle residential areas" of seven central urban districts in Wuhan city, we comprehensively consider factors such as the site distribution, sky view factor and service population to assess the economic and environmental benefits of PV-ES-I CS systems at different locations and scales. The research results show that the central urban districts have high retrofitting potential, with 44 stations north of the Yangtze River and 20 stations south of the Yangtze River being most suitable for retrofitting. The economic and environmental benefits vary significantly across building types; the retrofitting/construction of PV-ES-I CSs near hotels yields the greatest economic benefit and carbon dioxide emission reduction per unit investment.
In recent years, the urgency to address climate challenges has driven the need to achieve net-zer... more In recent years, the urgency to address climate challenges has driven the need to achieve net-zero carbon goals by 2050. The construction industry, as a major contributor to carbon emissions, plays a vital role in achieving this target. While numerous net-zero energy building approaches have been proposed globally, there is limited comparative research between developed and developing countries. This study aims to address this research gap by selecting the top three carbon-emitting countries in the world-China, India, and the United States (US)-as the research subjects. Through a comparison of their net-zero energy building approaches and typical cases, it is evident that there exists a significant disparity between developed and developing nations. Specifically, differences are observed in the following aspects: net-zero building approaches in the United States have an earlier start and involve collaborations between the government and multiple organizations, while China and India started relatively later and rely primarily on government initiatives. China's consideration of applicable building types in net-zero approaches is not inclusive enough, particularly lacking coverage on industrial buildings. It is encouraging to observe that China and India pay more attention to embodied carbon emissions compared, while their decarbonization strategies for embodied carbon emissions are not as comprehensive as those in the United States when it comes to actual building applications. Regarding carbon offset options, the United States and India demonstrate more comprehensive considerations, while China currently lacks consideration for off-site power generation and negative carbon technologies. The United States boasts a larger number of net-zero buildings, most of which have been certified through the country's own net-zero building approaches. In contrast, China and India have fewer certified net-zero buildings, and only a small percentage have been certified using their own methods, especially in China. The technologies employed in zero-carbon buildings in the United States are more advanced compared to China and India. These findings underline how the disparities in economic levels between developed and developing countries are reflected in net-zero building approaches. Based on these insights, professionals in the field can develop tailored strategies to bridge the gap in net-zero building development between developed and developing nations. This study provides a foundation and recommendations for achieving carbon neutrality in the construction industry for the world's top three economies by 2030.
Occupant behavior plays a crucial role in enhancing indoor thermal comfort and achieving energy e... more Occupant behavior plays a crucial role in enhancing indoor thermal comfort and achieving energy efficiency by influencing the operational modes of Heating, Ventilation, and Air Conditioning (HVAC) systems as well as windows. However, accurately quantifying the impact of occupant behavior on the indoor environment presents significant challenges in practical applications. This study introduces an innovative approach by leveraging the ASHRAE Global Building Occupant Behavior Database and harnessing the power of XGBoost in conjunction with Deep Q Networks (DQN) to construct a reinforcement learning model. This model enables precise prediction of the impact of occupant behavior on the indoor environment at the next time step under varying indoor-outdoor conditions, simultaneously targeting the dual objectives of indoor thermal comfort and energy conservation. By applying the XGB-DQN model in sample rooms of four international cities with distinct features, the results demonstrate a significant increase in indoor thermal comfort duration by 24 %, accompanied by a 24.7 % decrease in air conditioning usage compared to baseline models and actual occupant data. This research represents a pioneering effort in applying reinforcement learning techniques to accurately predict occupant behavior's impact on indoor environments, offering valuable insights for intelligent building design and energy management.
Global warming and the urban heat island effect have led to a decline in the quality of urban ope... more Global warming and the urban heat island effect have led to a decline in the quality of urban open spaces. Although many studies have been conducted on the thermal environment of urban open spaces, there is a lack of a detailed analysis of the time dimension. This study is to propose a method for analyzing long-term time series data on thermal environment, to more comprehensively evaluate the comfort of urban open spaces. We employed thermal environment data from urban open spaces in Melbourne and conducted a time series comfort analysis using a Gaussian mixture model. The research findings indicate that urban open spaces surrounded by low-rise buildings exhibit the highest thermal comfort, whereas those encompassed by a combination of buildings and green spaces exhibit the lowest comfort level. More importantly, we discovered similarities in the comfortable time periods across the five types of urban open spaces, with the majority falling between 6:00 AM and 10:00 AM. These discoveries can assist in achieving a more objective evaluation of the thermal environment in urban open spaces and provide scientific guidance for urban planning and landscape design, thus enhancing the quality of life for urban residents.
Achieving net-zero energy (NZE) in buildings involves laying down photovoltaics (PV) over large b... more Achieving net-zero energy (NZE) in buildings involves laying down photovoltaics (PV) over large building areas, and the issue of dissipating surplus PV capacity has been a challenge. With the popularity of electric vehicles (EVs), parking lots attached to buildings offer such a possible solution. In order to assess the possibility of applying PV surplus power to electric vehicle charging stations (EVCSs) in parking lots, a workflow based on the p-center model was developed in this study using scenario analysis. Considering New York City as the research focus, 100 buildings satisfying the prescribed criteria were screened. Subsequently, the potential of the parking facilities of the selected buildings was evaluated for dissipating surplus PV capacity, focusing on achieving both NZE and green power goals. The results show that the NZE goal is achieved between 3 % and 13 %, while the GP goal is determined as 20 %. The shape factor of the buildings and the goal completion rate from the four clusters reflect the improvements in the four types of parking lots. The average reduction in greenhouse gas (GHG) emissions for the five PV installation scenarios was 67.10, 112.94, 147.41, 171.00, and 187.24 MTCO 2 e, with an average payback period of 0.71, 1.87, 2.21, 2.47, and 2.22 years, respectively. In addition, 13 samples met the NZE goal, and 29 samples met the GP goal, representing 13 % and 29 % of the sample size sample, respectively. The study confirms the advantages of the building-to-vehicle-to-building (B2V2B) model, and the results can be used as a tool and reference for formulating PV development strategies in cities.
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