Search Results (463)

Urban building energy modeling (UBEM) is crucial for assessing energy consumption patterns at the city-scale and for supporting data driven planning and decarbonization strategies. However, its practical deployment is often hindered by the need to balance detailed physics-based simulations with acceptable computation times when thousands of buildings are involved. This work presents a large-scale real world UBEM case study and proposes a workflow that combines EnergyPlus simulations, high-performance computing (HPC), and open urban datasets to model the energy consumption of the building stock in the Municipality of Bologna, Italy. Geometric data such as building footprints and heights were acquired from the Bologna Open Data portal and complemented by aerial light detection and ranging (LiDAR) measurements to refine elevations and roof geometries. Non-geometrical building characteristics, including wall materials, insulation levels, and window properties, were derived from local building regulations and the European TABULA project, enabling the assignment of archetypes in contexts where granular information about building materials is not available. The pipeline’s modular design allows us to analyze different combinations of retrofitting scenarios, making it possible to identify the groups of buildings that would benefit the most. A key feature of the workflow is the use of Leonardo, the supercomputer hosted and managed by Cineca, which made it possible to simulate the energy consumption of approximately 25,000 buildings in less than 30 min. In contrast to approaches that mainly reduce computation time by simplifying the physical model or aggregating representative buildings, the HPC-based workflow allows the entire building stock to be individually simulated (within the intrinsic simplifications of UBEM) without introducing further compromises in model detail. Overall, this case study demonstrates that the combination of open data and HPC-accelerated UBEM can deliver city-scale energy simulations that are both computationally tractable and sufficiently detailed to inform municipal decision-making and future digital twin applications. Full article

The demolition of historic residential buildings generates substantial construction and demolition waste, the effective management of which is essential for advancing circular economy objectives. This study presents a BIM-based waste management framework developed for European residential buildings constructed around the turn of the 19th and 20th centuries, reflecting their characteristic construction methods and material use. The framework employs a predefined structural and material database to automatically quantify waste streams from BIM data at LOD 300. Demolition materials are classified into eight categories consistent with the waste hierarchy: reuse, recycling, energy recovery, and disposal. The model also accounts for the influence of demolition techniques, enabling comparative scenario analysis of recovery outcomes. A Budapest case study demonstrated that selective manual demolition increases the proportion of high-value reuse from 19.6% to 56.8% compared to mechanical demolition, while preserving 88% of salvaged bricks and 90% of architectural stone elements. Although the framework was tested on a building in Budapest, the results are extendable to the wider Central European (Austro-Hungarian) building stock due to typological similarities. The findings confirm the framework’s capacity to support sustainable, circular waste management strategies in historic building demolition. Full article

Urban neighborhoods experiencing socio-spatial pressures increasingly struggle with sustainability, especially in contexts where top-down redevelopment models dominate. In Turkey, the commonly used “demolish-and-rebuild” approach is often criticized for neglecting urban identity and the continuity of local communities. This study examines the Kükürtlü Neighborhood in Bursa, a “double-aged” area characterized by both an elderly population and aging housing stock. Using a mixed-method approach, the study integrates the EcoDistricts framework with participatory spatial analysis and Interpretative Phenomenological Analysis (IPA) to assess sustainability across three priority areas: place, health and wellbeing, and connectivity. Results reveal that while the neighborhood faces structural limitations and underutilized public spaces, it benefits from strong social sustainability rooted in cultural continuity and intergenerational bonds, especially among long-term residents. Conversely, newcomers demonstrate weaker place attachment. These findings inform a set of inclusive, aging-in-place strategies aimed at balancing physical renewal with community preservation. Building on these insights, the study proposes a context-sensitive and potentially adaptable framework to guide sustainability efforts in similar aging urban contexts. The research contributes to international discussions on urban transformation by emphasizing the importance of integrating local lived experiences with spatial planning tools, offering a model for navigating demographic and physical aging in mid-sized cities. Full article

With urbanization slowing, the world has entered a new phase focused on stock-based development, where urban renewal plays a key role in advancing sustainable urbanization. These projects involve multiple stakeholders—governments, enterprises, and residents—whose conflicting interests often hinder progress and affect policy outcomes, equity, and long-term sustainability. This study is conducted to address existing gaps in understanding the dynamic mechanisms and multi-dimensional relationships underlying stakeholder conflicts in urban renewal projects (URPs). A total of 28 key influencing factors are identified and categorized. Exploratory Factor Analysis (EFA) is then applied to reduce data dimensionality, resulting in five core dimensions: economic, legal, implementation, managerial, and social factors. Building on these findings, a system dynamics (SD)-based model is developed to simulate the interactions and evolutionary pathways of these factors within urban renewal projects. Results show that all five factors contribute to conflict to varying degrees, with economic factors being the primary driver. Drawing on empirical data from Chinese URPs, this study provides both theoretical insights and practical implications for policy formulation and governance strategies aimed at promoting more harmonious and sustainable urban renewal processes. Full article

Urban Building Energy Modeling (UBEM) plays a critical role in supporting data-driven strategies for the energy transition of cities. However, its application is often hindered by the lack of harmonized, high-quality input data representing the building stock. This paper presents the methodology and outputs of a national research project to construct an Italian library of reference buildings suitable for UBEM applications described with scorecards. The methodological workflow included six key phases: definition of a national data classification framework, acquisition and integration of heterogeneous data sources, data harmonization, statistical analysis and clustering, archetype formalization, and dissemination. The result is a library of 380 scorecards covering residential, educational, office, commercial, and catering buildings across multiple climate zones and construction periods. Each scorecard is based on empirical data from public databases, field surveys, or technical standards, and includes detailed descriptions of geometry, envelope characteristics, HVAC systems, internal gains, and ventilation. The scorecards are shared openly on the project’s website and were built to work with different UBEM platforms. Overall, both the method and the results help bring more consistency to UBEM practice and support better, data-driven urban energy planning. Full article

Equitable accessibility to elderly canteens is critical for addressing the challenges of an aging population. Using Nanjing as a case study, this paper constructed an integrated framework that fuses GIS spatial analysis with interpretable machine learning to diagnose, evaluate, and optimize the service network’s spatial layout. The study found that the existing design is a direct manifestation of the conflict between “market logic” and “social demand.” First, Nanjing’s elderly canteen service suffers from a severe spatial mismatch and inequality of opportunity. Approximately 80% of the elderly population resides in areas that share less than 15% of the canteen resources. Second, a multi-modal accessibility analysis revealed the phenomenon of “false equity.” The high service coverage under the car accessibility model masks the systemic service deprivation faced by the majority of seniors who rely on walking and micromobility. Third, this study proposed and validated a data-driven “stock activation” strategy. An XGBoost model, guided by a “demand-oriented and spatially efficient” decision-making logic, identified 161 high-potential optimization sites. At the same time, the framework also diagnosed its own strategic boundaries by identifying “resource vacuums” where a lack of convertible stock necessitates alternative solutions, such as new builds. Full article

The Energy Performance of Buildings Directive (EPBD) requires all EU Member States to establish Whole Life Carbon (WLC) benchmarks by 2030. While progress is being made across Europe, a comprehensive and standardised national methodology remains absent in Italy, raising broader questions about how to ensure comparability of WLC assessments across diverse territorial contexts. Italy represents a particularly complex case, as its building stock is regulated simultaneously by seismic zoning and climatic zoning, complicating the definition of representative archetypes. This study applies Life Cycle Assessment (LCA) to a prototypical residential building in Tuscany, testing scenarios that vary by seismic risk and exposure conditions. Results show that structural components dominate impacts, accounting for approximately 60% of total Global Warming Potential (GWP), and that outcomes are highly sensitive to both location and data source. These findings underscore the importance of data granularity and context-specific modelling in developing robust benchmarks. The novelty of this work lies in proposing a framework that integrates seismic and climatic factors into archetype selection, while also emphasising the adoption of nationally tailored datasets to improve accuracy and policy relevance. By situating the Italian case within the wider European debate, the paper contributes to the urgent task of establishing robust, comparable, and context-sensitive WLC benchmarks that can guide both national regulation and EU-wide decarbonisation strategies. Full article

This study investigates the thermal resilience of naturally ventilated Lebanese residential buildings in the context of future climates, based on four climate zones: coastal (moderate and humid), low mountain (cool and seasonally variable), inland plateau (semi-arid with high summer heat), and high mountain (cold, with significant winter conditions). The aim of the study is to evaluate how passive envelope interventions can enhance indoor thermal resilience under five present and future work scenarios: TMY, SSP1-2.6 (2050 and 2080), and SSP5-8.5 (2050 and 2080). A baseline model was developed for typical building stock in each climate using EnergyPlus-23.2.0. The passive design parameters of window type, shading depth, and building orientation were systematically altered to analyze their effect on thermal comfort and building thermal resilience. Unlike previous studies that assessed either individual passive strategies or a single climate condition, this research combines multi-objective optimizations with overheating resilience metrics, by optimizing passive interventions using the GenOpt-3.1.0 and BESOS (Python-3.7.3 packages to minimize indoor overheating degree (IOD) and maximize climate change overheating resistivity (CCOR) index. Our findings indicate that optimized passive interventions, such as deep shading (0.6–1.0 m), low-e or bronze glazing, and southern orientations, can reduce overheating in all climate zones, reflecting a substantial improvement in thermal resilience. The novelty of this work lies in combining passive envelope optimization with future climate situations and a long-term overheating resilience index (CCOR) in the Mediterranean region. The results provide actionable suggestions for enhancing buildings’ resilience to climate change in Lebanon, thus informing sustainable design practice within the Eastern Mediterranean climate. Full article

Buildings account for approximately one-third of global energy usage and associated carbon emissions, making energy benchmarking a crucial tool for advancing decarbonization. Current benchmarking studies have often been limited to mainly the annual scale, relied heavily on simulation-based approaches, or employed regression methods that fail to capture the complexity of diverse building stock. These limitations hinder the interpretability, generalizability, and actionable value of existing models. This study introduces a hybrid AI framework for building energy benchmarking across two time scales—annual and monthly. The framework integrates supervised learning models, including white- and gray-box models, to predict annual and monthly energy consumption, combined with unsupervised learning through neural network-based Self-Organizing Maps (SOM), to classify heterogeneous building stocks. The supervised models provide interpretable and accurate predictions at both aggregated annual and fine-grained monthly levels. The model is trained using a six-year dataset from Washington, D.C., incorporating multiple building attributes and high-resolution weather data. Additionally, the generalizability and robustness have been validated via the real-world dataset from a different climate zone in Pittsburgh, PA. Followed by unsupervised learning models, the SOM clustering preserves topological relationships in high-dimensional data, enabling more nuanced classification compared to centroid-based methods. Results demonstrate that the hybrid approach significantly improves predictive accuracy compared to conventional regression methods, with the proposed model achieving over 80% $R^2$ at the annual scale and robust performance across seasonal monthly predictions. White-box sensitivity highlights that building type and energy use patterns are the most influential variables, while the gray-box analysis using SHAP values further reveals that Energy Star^® rating, Natural Gas (%), and Electricity Use (%) are the three most influential predictors, contributing mean SHAP values of 8.69, 8.46, and 6.47, respectively. SOM results reveal that categorized buildings within the same cluster often share similar energy-use patterns—underscoring the value of data-driven classification. The proposed hybrid framework provides policymakers, building managers, and designers with a scalable, transparent, and transferable tool for identifying energy-saving opportunities, prioritizing retrofit strategies, and accelerating progress toward net-zero carbon buildings. Full article

The building sector has a significant impact on the environment due to its high resource and energy usage. The refurbishment of the building stock is a measure for reducing emissions. In this context, the neighborhood scale is becoming increasingly important as the level at which urban redevelopment takes place. This study contributes a new perspective and data on the scientific debate on the importance of the neighborhood as a level of action in the transformation of the building sector. It combines horizontal building interaction and a practical refurbishment approach, aiming to reduce material use and balance energy demands. Using scenario modeling, the material savings are calculated for the first time by analyzing five refurbishment scenarios of a synthetic neighborhood. The scenario, modeled with horizontal building interaction, is identified as the favorable compromise among all scenarios when considering material demand and energy efficiency. This is achieved through re-thinking energy-oriented refurbishments and optimizing the usage of locally produced renewable energy sources. The results are embedded into the scientific debate, including the works on the balance of embodied and operational energy in the construction sector. Full article

Building archetypes are essential for advancing architectural theory and energy modeling, providing a foundation for scalable assessments of building performance and sustainability worldwide. In Saudi Arabia, educational buildings, especially those in public universities, are predominantly constructed using standardized and repetitive design templates, such as courtyard and prototype models, which have significant implications for energy efficiency, indoor environmental quality, and sustainability outcomes. Despite their prevalence, there is a notable lack of systematic research on the classification and distribution of these archetypes within the Saudi context, particularly regarding their impact on energy consumption and sustainable campus planning. This study addresses this gap by systematically collecting and analyzing data from 29 public universities across Saudi Arabia, employing GIS mapping to document building characteristics including age, region, urban context, masterplan typology, and architectural design. A cumulative weighting factor was applied to quantify the representativeness of archetypes, while chi-square tests and effect size metrics assessed the statistical concentration and significance of observed patterns. The results reveal a pronounced dominance of a small number of archetypes, especially standardized courtyard and identical design models, across the national stock, with the top 10% of archetype ranks accounting for the majority of buildings. This high degree of standardization enables efficient modeling, benchmarking, and targeted energy interventions, while also highlighting the need for greater contextual adaptation in future campus planning. While this study does not directly simulate building energy performance, it establishes a national-scale typological foundation that can support future simulation, benchmarking, and policy design. The developed GIS-based framework primarily serves managerial and planning objectives, offering a standardized reference for facility management, retrofitting prioritization, and strategic energy-efficiency planning in Saudi public universities. Full article

The seismic vulnerability of existing reinforced concrete (RC) buildings that constitute a large portion of the urban building stock has become a growing concern for urban safety. This situation was once again revealed by the massive destruction that occurred in RC structures following the 2023 Kahramanmaraş earthquakes. Particularly in buildings constructed before 1990 and without adequate engineering services, destruction and damage were much greater. In this paper, structural models were created with inadequate transverse reinforcement, low-strength concrete, and inadequate concrete cover thickness, which all play a critical role in the seismic performance of the buildings. Structural analyses were updated for high-performance concrete jacketing models, considering the deformation status obtained for each inadequate parameter. It has been determined that the high-performance concrete can significantly increase structural performance, especially significant increases in shear strength capacities without the need for transverse reinforcement. Full article

Switzerland’s energy transition toward net-zero greenhouse gas emissions by 2050 presents a critical challenge in managing winter electricity demand, particularly due to the widespread electrification of space heating and domestic hot water. In this study, we assess how targeted measures in the building sector can influence heat demand and thereby also the winter electricity gap. To this end, we extended the existing PowerCheck simulation tool by incorporating a detailed bottom-up representation of the Swiss building stock. We model hourly heat and electricity demand across 60 building categories, defined by climate zone, usage type, and insulation standard. Twelve future scenarios are developed based on variations in four key parameters: building renovation rate, hot water heat recovery, heat sources used by heat pumps, and ambient temperature trends. Our results indicate that renovation of old buildings to current insulation standards has by far the greatest effect out of the studied parameters. Increasing the annual thermal renovation rate of building shells from the currently planned 1.1% to 2% can reduce the winter electricity gap from 10.7 TWh to 6.0 TWh, a 44% reduction. Conversely, achieving only a low renovation rate of 0.5% could increase the gap to 13.9 TWh. Additional measures, such as greater use of ground-source instead of air-source heat pumps and implementation of hot water recovery, offer further potential for reduction. These findings underscore the importance of early and sustained investment in thermal renovation of building shells for achieving Switzerland’s 2050 net-zero climate targets. Full article

The construction industry is a major contributor to global resource consumption and waste. This sector extracts over two billion tons of raw materials each year and contributes over 30% of all solid waste generated annually through construction and demolition debris. The movement toward circularity in the built environment aims to replace linear processes of extraction and disposal by promoting policies favoring building preservation and adaptive reuse, as well as the salvage and reuse of building materials. Few North American cities have implemented explicit policies that incentivize circularity to decouple urban growth from resource consumption, and there remain substantial hurdles to adoption. Nonetheless, existing regulatory and planning tools, such as zoning codes and historic preservation policies, may already influence redevelopment in ways that could align with circularity. This article examines spatial patterns in these indirect pathways through a case study of a college town in New York State, assessing how commonly used local planning tools shape urban redevelopment trajectories. Using a three-stage spatial analysis protocol, including exploratory analysis, Geographically Weighted Regressions (GWRs), and Geographic Random Forest (GRF) modeling, the study evaluates the impact of zoning regulations and historic preservation designations on patterns of demolition, reinvestment, and incremental change in the building stock. National historic districts were strongly associated with more building adaptation permits indicating reinvestment in existing buildings. Mixed-use zoning was positively correlated with new construction, while special overlay districts and low-density zoning were mostly negatively correlated with concentrations of building adaptation permits. A key contribution of this paper is a replicable protocol for urban building stock analysis and insights into how land use policies can support or hinder incremental urban change in moves toward the circular city. Further, we provide recommendations for data management strategies in small cities that could help strengthen analysis-driven policies. Full article

Building healthy communities is crucial for creating healthy cities and improving residents’ well-being. Old residential areas, with their substantial stock and elevated health risks, require urgent environmental upgrades. However, the relationship between community health promotion factors and resident sentiment, a crucial indicator of subjective well-being, in old residential areas remains poorly understood. By integrating big data-based community health promotion factors and Weibo data within the 15-min living circle of old residential areas in Xi’an, we developed an XGBoost model and employed SHAP analyses to interpret predictive outcomes. Results show that healthy facilities were dominant influencing factors in old residential areas. Densities of parking, supermarkets, education, package stations, and scenic spots exhibit nonlinear relationships with positive sentiment, indicating clear threshold effects and saturation effects. Two dominant patterns were observed in interactions between dominant factors and their strongest interacting factors. Four environment–sentiment patterns were identified for targeted planning interventions. It is recommended that planners and policymakers account for density phases and synergistic combinations of the dominant factors to optimize community health within old residential areas. The findings offer empirical support and planning insights for fostering healthy, sentiment-sensitive retrofit in old residential areas within the 15-min living circle. Full article