Search Results (696)

This study presents a bibliometric and systematic review of 480 articles meeting the following inclusion criteria: English-language articles, reviews, or proceeding papers focusing on building topics with full text available, retrieved from the Web of Science Core Collection on 9 Jannary 2026 to map the intellectual landscape of smart-sustainable building (SSB) research. Employing the PRISMA framework combined with scientometric mapping (VOSviewer), thematic classification, and qualitative synthesis (no risk of bias assessment was performed as this was a bibliometric review), the analysis reveals exponential publication growth since 2022, identifying three dominant thematic clusters: digital enabling technologies (41.0%), energy systems (30.8%), and advanced building envelopes and materials (28.3%). Keyword analysis identifies “smart buildings,” “green buildings,” and “energy efficiency” as central conceptual anchors, while temporal trends indicate increasing attention to artificial intelligence, digital twins, and blockchain. Notably, 51.4% of articles address two or more themes simultaneously, confirming the field’s interdisciplinary character. Critical analysis reveals persistent fragmentation: sustainable building rating tools (e.g., BREEAM, LEED) and smart building evaluation methods (e.g., Smart Readiness Indicator). Seven challenges, including assessment fragmentation, high costs, and cybersecurity vulnerabilities, are identified as barriers to SSB adoption. Limitations include reliance on a single database (Web of Science) and subjective thematic classification. This review provides a roadmap for future research emphasizing integrated assessment frameworks and interdisciplinary collaboration. Registration: Not pre-registered. Funding: National Key R&D Program of China (2025YFF0521003). Full article

Universities compete for funding, and their positions depend on the results of national assessments and rankings, which are expensive to produce and based on difficult-to-predict expert opinions. Assessment results have a significant impact on a university’s reputation, funding levels, attractiveness to faculty and staff, and success in recruiting top-tier students. Expert assessments and forecasts are widely used, but additional support from trusted AI tools is desirable. Several attempts have been made to use various machine learning methods, but confidence in such solutions is limited due to perceived difficulties in clearly and reliably justifying the resulting predictions. This research aims to present a proposal for using neural network models, accompanied by explanations of their predictions, to support trustworthy and sustainable assessment of university competitiveness. This methodological contribution enhances the transparency and interpretability of the assessment process and is further supported by empirical studies based on data from selected universities. A Fully Connected Neural Network (FCNN) is used for the calculations, and the local interpretable model-agnostic explanations (LIME) method is applied to explain the prediction results. The results confirm the usefulness of the proposed model and provide a solid foundation for improving evaluation systems and building trust in AI applications for assessing universities’ competitive position and the benefits of scientific research for society. Full article

The global construction sector is undergoing a major shift driven by Construction 4.0, where traditional structural design methods are increasingly complemented or replaced by advanced digital technologies. This systematic review evaluates how Artificial Intelligence (AI), Generative Design (GD), and Building Information Modeling (BIM) contribute to sustainable development in architecture and civil engineering. Using the PRISMA protocol, the study synthesizes current evidence on the role of algorithmic intelligence in supporting UN Sustainable Development Goals (SDGs), particularly Goals 9, 11, 12 and 13. Findings indicate that transitioning from deterministic engineering approaches to AI-based heuristic methods enables significant optimization of material use and structural mass, thereby reducing embodied carbon in the built environment. Performance-driven generative workflows and physics-informed neural networks (PINNs) emerge as key enablers of circularity and early-stage Life Cycle Assessment (LCA) integration. However, the review also identifies gaps, such as limited applications of genetic algorithms in sustainable steel structure design and the substantial energy consumption associated with large-scale AI models. The study concludes that while digital tools provide transformative potential for decarbonizing the construction sector, future research should focus on improving algorithm transparency, reducing black-box limitations, and standardizing performance metrics to support broader adoption in engineering practice. The review can be a framework to help researchers, engineers, and policymakers integrate emerging AI-tools into sustainable design and advancing decarbonized, resilient built environments. Full article

Climate change, soil degradation, and the disruption of global nutrient cycles are placing unprecedented pressure on agricultural systems and global food security. These challenges are increasingly recognized as central concerns for planetary health, as agriculture simultaneously depends upon and alters critical Earth system processes. Microbe-based agricultural inputs (including biofertilizers, biostimulants, and biocontrol agents) have been widely promoted as climate-smart solutions capable of enhancing productivity, resilience, and environmental sustainability. However, despite rapid scientific and commercial advances, their performance in the field remains highly variable and strongly context-dependent. This review critically examines the evidence base underpinning climate-smart microbial solutions, with a particular focus on their capacity to confer climate resilience across diverse crops, soils, and climatic conditions. We synthesize current knowledge on the functional roles of beneficial microorganisms, including extremophilic and stress-adapted taxa, while highlighting key biological, technological, ecological, and socio-economic constraints that limit predictability and scalability. Special attention is given to evidence gaps related to long-term field performance, ecosystem-level impacts, and the trade-offs associated with widespread microbial deployment. We further assess recent innovations such as synthetic microbial consortia, microbiome engineering, advanced formulations, and data-driven decision tools. Then we highlight how these new technologies may address context dependency but still need validation under real-world conditions. Finally, we discuss policy, regulatory, and capacity-building considerations required to responsibly integrate microbial solutions into climate-smart agriculture frameworks. Overall, this review argues that microbial inoculants should be viewed not as universal inputs but as context-specific tools whose successful deployment depends on robust evidence, ecological sensitivity, and system-level integration. Advancing microbial solutions for agriculture will therefore require aligning technological innovation with broader planetary health objectives, ensuring that efforts to enhance agricultural productivity also support long-term ecosystem stability and resilience. Full article

This case study aimed to benchmark the healthiness, equity, and environmental sustainability of a large, urban Australian university food environment through two audits conducted in 2022 and 2025. Two cross-sectional audits were completed at a large urban university campus using the Uni-Food tool, which assesses 68 best practice indicators across three components: policy, campus facilities, and food retail outlets. Four assessors independently conducted the audits with excellent inter-rater reliability (Cohen’s Kappa = 0.89). Final scores out of 100 were calculated using weighted domains. Descriptive and inferential statistics were used to compare changes over time. In 2025, the university achieved a score of 52%, up from 48% in 2022, indicating medium compliance with best practice standards. Findings highlight that scores differed modestly but there were persistent gaps in university food policy and practice. Specifically, the policy component remained low (48%), demonstrating strong overall planning but a lack in food retail policy and monitoring systems. The campus component scored moderately (63%), with various nutrition knowledge-building opportunities and environmental sustainability initiatives available but heavy promotion of unhealthy foods at campus events. The food retail component scored lowest overall (36%), especially as there was a lack of adequate nutrition information provided at food outlets. Continued investment in policy development, campus-wide strategies, and food retail innovation is essential to create healthier, more equitable, and environmentally sustainable food environments in tertiary settings. Full article

Despite an ageing society emerging as a global challenge, urban spaces still do not adequately address the spatial needs of older citizens. Numerous studies analyse built environment characteristics in relation to the mobility of older citizens, yet studies on the quality of older pedestrians’ perception of spatial attributes with their levels are scarce. This scoping review of 2855 records from 2013 to 2023, exported from Scopus and Web of Science, aimed to identify common patterns with respect to the aspects used in the assessment of the quality of urban spaces for older adults, with the emphasis placed on spatial attributes measured through different levels. Following PRISMA-ScR, the analysis was conducted in AsReview, a scientific tool using ML. Inclusion criteria were: peer-reviewed English-language journal articles and conference papers; the inclusion of spatial attributes in urban planning, measuring the perception of pedestrians, using a conjoint experiment, or urban digital twins; and taking into account an ageing society. The author performed the coding of 115 eligible records in four iterative rounds with the use of Atlas.ti. The findings show that Land Use & Buildings/Destinations, Sidewalk and Amenities, and Aesthetics/Urban Form were the most frequently occurring aspects. Attribute levels were proposed only in 10 records. No study incorporated stated preference and 3D walk-through environments to quantify older adults’ perception of walkability-related attributes. This represents a methodological gap for future research on older adults’ walkability perception. Urban planners and other decision-makers may use the findings of this study to support the design and management of age-friendly, sustainable, and inclusive street environments. Full article

Forest fires are an increasing environmental challenge in Southern Europe, requiring reliable tools for assessing both fire-induced disturbances and subsequent ecosystem recovery. This study presents an integrated satellite-based system for automated monitoring of post-fire forest dynamics. The system combines multispectral data from Sentinel-2 and Landsat (TM, ETM+, OLI, OLI-2) with thermal anomaly information from MODIS and VIIRS within a unified processing framework. It is structured into two modules: Post-Fire Disturbance (PFDMO) and Post-Fire Recovery (PFRMO). The methodology builds on a validated algorithm integrating the Disturbance Index (DI), Vector of Instantaneous Condition (VIC), and Direction Angle (DA), enabling automated multi-temporal analysis from fire detection to recovery assessment. The system was applied to three wildfire-affected areas in Bulgaria under different environmental conditions. Results reveal substantial spatial variability in disturbance and recovery, with PFDMO values ranging from −5.17 to +10.16 and PFRMO values from −2.25 to +7.40. The results demonstrate the applicability of the proposed system for monitoring post-fire forest dynamics and illustrate its potential to support informed decision-making in forest management, biodiversity conservation, and sustainable resource use. The main contribution of the system lies in the integration of disturbance and recovery assessment within a single automated and scalable workflow based on freely available satellite data. Full article

This study introduces a domain-specific multimodal deep learning framework, centered on a Vision Transformer (ViT), to accelerate the prediction of operational carbon emissions in residential buildings. Our approach uniquely fuses two data modalities: the geometric information captured in floorplan images and the quantitative data from vector-based building parameters. By training and testing on a comprehensive dataset of 17,000 residential samples derived from a large-scale open-source Chinese database, the proposed model demonstrates exceptional predictive capability. On the test set, it achieved a mean bias error of 1.75%, a mean absolute percentage error of 2.14%, and a coefficient of determination (R²) of 0.95. Further validation through comparative analysis shows that our framework significantly outperforms established deep learning architectures, including ResNet-18, Inception-V4, and VGG-19, in both accuracy and robustness. The developed tool provides architects with a reliable and rapid method for assessing the carbon footprint of design options, thereby offering crucial scientific support for sustainable building design. Full article

The goal of this paper is to present the design and preliminary development of a prototype for an inclusive evaluation tool aimed at gathering audience feedback in entertainment, cultural contexts and live events. The prototype consists of a flexible questionnaire and was designed as part of the SAFINA VITALITY research project. This research (“ACCESSMAP: design of a flexible system for feedback collection in accessibility to media, entertainment and cultural events”) was funded by PNRR ECS00000041 “Innovation, digitalisation and sustainability for the diffused economy in Central Italy”—PNRR—Missione 4, Componente 2—Investimento 1.5 “Creazione e rafforzamento di “ecosistemi dell’innovazione”—CUP D83C22000710005. Its aim consists of assessing visitors’ comprehension, appreciation and overall experience, with particular attention to accessibility. Building on the existing seminal research in reception studies and accessible design, this study reports on a pilot implementation of the questionnaire. The tool was adapted for being used at the Museo Tattile Statale Omero, a leading institution in tactile, multisensory and inclusive museum experiences. The questionnaire was designed to be accessible to a wide demographic, including visitors with and without disabilities. A preliminary site visit and consultation with the museum staff was made to inform the contextual adaptation of the tool, leading to a shift in focus to tactile engagement and reflecting the museum’s core strengths and needs of its audience. The instrument includes both quantitative and qualitative measures to explore, among others, memory retention, clarity of information, appreciation and perceived usefulness of accessibility features. This article discusses the rationale behind the tool’s design, its contextual adaptation, and its potential applications for museums and live events with an eye to accessibility. Rather than providing a definitive validation of the tool, this pilot study offers an initial exploration of its applicability to a multisensory museum context. In addition, it discusses the design rationale behind the tool, its contextual adaptation, and its potential for supporting accessible feedback collection in cultural settings. Full article

International research and policy frameworks underscore the value of mandatory energy regulations in reducing energy demand and greenhouse gas (GHG) emissions in the built environment. However, Global South (GS) countries experience several challenges in effectively implementing building energy efficiency codes (BEECs), as codes are either absent, unevenly adopted or inconsistently enforced. A poor alignment with the specific climatic, socio-economic and construction realities further limits the potential of BEECs to support GS climate resilience. This research aims to identify opportunities to enhance building energy regulatory practices by exploring recent progress in the field. It also systematically evaluates existing mandatory BEECs in the GS to identify models and principles that could guide the development of more effective codes, specifically for GS countries without BEECs. It is hypothesised that the mandatory BEECs currently implemented in GS countries can be analysed using contextually relevant criteria to reveal common regulatory patterns, strengths, and shortcomings, thereby informing a climate-responsive framework suited to GS realities. This research implemented a two-tiered literature review. After determining the broad regulatory context, an exploratory review of the current state of the art in BEEC research was conducted. These publications (primarily 2016–2025) were obtained via a systematic query in Scopus. Following the exploratory review, this study performed a Systematic Quantitative Literature Review (SQLR) to assess mandatory BEECs from 18 GS countries. The findings reveal that BEECs are useful for delivering energy-efficient buildings in the real world. However, ample opportunities exist to improve their comprehensiveness in context and coverage. Improving regulatory implementation systems and structures, along with robust stakeholder engagement, can support better BEEC design and enforcement. To address the need for contextualised BEECs, the SQLR helped develop a taxonomy by comparing the mandatory codes. This research also introduces the Sustainable Level Indicator Model, Matrix, and Map (SLIM³) prototype, proposed as a decision-support tool, and hosted on an interactive online platform, thereby potentially contributing to real-world building energy regulatory practices. The SLIM³ tool organises the mandatory BEECs into a coherent, accessible framework that could assist GS decision-makers in benchmarking existing and new codes, identifying gaps and prioritising contextually appropriate improvements, thus contributing to a more resource-efficient built environment. Full article

Artificial Intelligence (AI) and Digital Twins (DTs) can support the digital and energy transition in the construction sector; however, their application to the built environment presents both opportunities and limitations. This study aims to give a critical perspective on the topic analyzing the related key challenges, including error assessment, model interpretability, data availability, cybersecurity risks, organizational constraints, and lifecycle costs. Where AI is nowadays developed as a context-dependent tool set, it is most effective when embedded within integrated socio-technical systems rather than adopted as a universal solution. Instead, DTs can be intended as an enabling framework, integrating AI, Internet of Things (IoT), Big Data, and Building Management Systems (BMS) to enhance energy performance, indoor environmental quality, safety, maintenance, and decision-making at both building and urban scales. The direction international research on these topics is facing is clear as evidenced by the wide number of research papers published. The future of these technologies moves towards a simulative approach oriented towards the sustainable and fair development goals and will bring a broad transformation of the building environment where they are ever more integrated into each social and technical aspect. The work is supported by a case study developed at Sapienza University of Rome founded by the Italian National Recovery and Resilience Plan within Flagship Project 2 (FP2), “Energy Transition and Digital Transition in Urban Regeneration and Construction,” of the Rome Technopole ecosystem. Full article

The accelerating pace of technological innovation has exacerbated the spatial misalignment between the static, supply-driven provision of educational facilities and the dynamic, demand-driven patterns of contemporary pedagogical activities. Assessing and quantifying spatial demand and the operational consumption of teaching environments pose critical challenges for facility asset management in higher education. Accordingly, rigorous investigation into the determinants of classroom spatial utilization efficiency and the formulation of evidence-based spatial optimization strategies are essential to advancing the sustainable evolution of campus infrastructure. This study takes the Zhengxin Building at Harbin Institute of Technology as a descriptive case, integrating timetable data with spatial syntax at the building scale. The scheduling data for 2943 courses in the Spring semester of 2023 was selected as the research basis. Using architectural spatial analysis tools—including space syntax theory, statistical correlation methods, and in situ observational surveys—this study extracts spatial attribute variables such as classroom area (A), seating capacity (S), floor level (F), integration (I), and space utilization efficiency metrics as primary quantitative measures. The interrelationships among these variables are examined to elucidate the principal drivers of teaching space performance. The empirical results indicate that the Overall Space Utilization Rate (OSUR) of the Zhengxin Building ranged from 20% to 50% during the study. The key findings include the following: (1) spatial utilization efficiency is positively associated with classroom scale but shows no significant relationship with integration (I); (2) after controlling for classroom type (T), per capita area index (PCAI), and integration (I), floor level (F) no longer exerts a statistically significant influence on utilization outcomes; (3) teaching spaces with higher integration and spatial entropy are more adaptable to heterogeneous instructional and extracurricular uses. The classroom type (T) directly mediates occupancy patterns and activity programming. Full article

The transition to a circular economy (CE) is a critical challenge for the urban built environment, particularly within the Building and Construction sector. This study explores the application of circularity micro-indicators to assess the use of plastic materials in two Portuguese construction approaches: traditional (CS1) and modular (CS2). A set of four relevant micro-indicators—Material Circularity Indicator (MCI), Recycled Content Performance Indicator (RPI), Circular Design Guidelines (CDG), and Circular Economy Implementation Potential (CEIP)—was selected to evaluate material circularity at the product and system levels. The novelty of this study lies in selecting and applying the most relevant circularity indicators from the literature to plastic products in construction, providing the first practical demonstration of their use and offering actionable tools to support stakeholders in material selection, design decisions, and circularity assessment. Findings show that modular construction demonstrates a stronger alignment with CE principles compared to traditional methods, although both cases present low overall levels of circularity. For instance, the Material Circularity Indicator (MCI) ranged from 8.4% in the traditional building (CS1) to 15.2% in the modular building (CS2), quantitatively illustrating the circularity potential of modular construction. Key strategic opportunities for improvement include design for disassembly, the elimination of toxic or scarce materials, improved recyclability of plastic components, and the integration of on-site material separation and recovery zones. Strengthening waste management systems is also essential to enhance the quality and reliability of recycled plastic materials. The study highlights the value of micro-indicators as practical tools to support evidence-based decision-making in material selection, design strategies, and circular construction planning. By guiding more sustainable practices in urban construction, these indicators can play a pivotal role in accelerating the sector’s transition toward a circular and resource-efficient economy. Methodologically, the study adopts a collaborative case-study approach with an industry partner, involving brainstorming sessions with experts in CE and circularity indicators to select representative buildings, and identify indicators from the literature based on data availability and relevance for construction plastic products, which are then applied to real project data, complemented by exploratory improvement scenarios. Full article

The increasing demand for efficient and sustainable building design has led to the development of nD BIM, which integrates multiple dimensions such as time (4D), cost (5D), and environmental performance (6D and beyond). However, existing approaches often lack an integrated decision-making framework that supports the simultaneous evaluation of these criteria, particularly in the early design phase of building envelope systems. This study proposes a unified nD BIM-based decision-making framework for building assemblies, using authoring tools, namely the BIM approach and LCA methodology. The proposed framework is applied in an empirical case study, where several design variants of a multi-residential building are developed and analyzed through 4D and 5D BIM models to assess construction time and costs, while environmental impacts are evaluated using selected key indicators, e.g., Global Warming Potential (GWP), Acidification Potential (AP), and the consumption of non-renewable primary energy (PENRT). The outcomes of these analyses are integrated into a multi-criteria decision-making model based on a weighting system. The results demonstrate that an nD BIM-based unified weighted decision model enhances decision-making by enabling transparent comparison of design alternatives and identification of trade-offs, thereby supporting more efficient and sustainable building envelope design while improving decision quality and reducing uncertainty for designers, engineers, and project investors. Full article

Addressing the prevalent issues of scattered data sources, reliance on multi-software collaboration, and low integration efficiency between Building Information Modeling (BIM) and Life Cycle Assessment (LCA) in current building life cycle carbon emission accounting, this study aims to construct a BIM-driven, data-traceable automated method for building life cycle carbon accounting. This paper proposes a life cycle carbon accounting framework based on Revit secondary development. By defining unified data mapping rules and constructing a scalable localized carbon emission factor database, this framework achieves a seamless workflow from BIM model information extraction and intelligent factor matching to phased accounting and report generation. Taking an office building in Nanning as an empirical case study, the results indicate that the operational stage and the building material production stage are the primary emission sources, accounting for 78.82% and 24.13% of the total emissions, respectively; the transportation stage accounts for 1.68%; the construction stage accounts for 0.40%; and the demolition and recycling stage exhibits negative emissions of −3.53% due to material recovery benefits. The accounting results of the developed plugin exhibit a relative error of 6.67% compared to traditional methods, and the robustness of the accounting framework is verified through uncertainty analysis. Sensitivity analysis further reveals that the grid emission factor, key material factors, and building design service life are the core variables affecting carbon emissions. The contribution of this study lies in proposing an operable and scalable BIM-LCA integrated solution. Its practical value resides in providing a real-time data feedback tool for low-carbon optimization during the building design stage, as well as offering a highly transparent methodological reference for carbon accounting in engineering practice, thereby supporting data-driven decision-making in the pursuit of sustainable urban development. Full article