Purpose This study aims to evaluate the thermal performance of green roofs as an urban heat island mitigation strategy under arid coastal climate conditions by using Bahrain as a case study. Design/methodology/approach A scenario-based parametric simulation study was conducted using ENVI-met v5.7.1. Eighteen green roof scenarios were developed by varying key parameters, including leaf area index, plant height, substrate thickness and soil moisture. The simulations were validated against field measurements and compared with a conventional bare roof configuration under extreme summer conditions. Findings Results indicate that, under peak daytime conditions (12:00), green roof scenarios reduced roof surface temperature (Ts) by approximately 28.5–29.3°C relative to the conventional bare-roof reference case. By contrast, reductions in near-roof air temperature (Ta) at 2 m above the roof were considerably smaller, ranging from approximately 0.2°C–0.8°C relative to the bare roof. The results further show that substrate moisture is the dominant factor influencing green roof thermal performance, whilst variations in leaf area index and plant height produced smaller incremental differences between green roof scenarios. Practical implications The findings provide guidance for climate-responsive green roof design in arid environments, highlighting the critical role of irrigation strategies and vegetation selection in achieving optimal thermal performance. Social implications This study demonstrates how green roofs can moderate harsh microclimatic conditions in Bahrain's dense urban areas. The resulting temperature reductions contribute to improved outdoor thermal comfort, reducing heat stress risks for pedestrians and roof users. Common implementation supports healthier and more liveable communities by introducing greenery into highly built-up neighbourhoods lacking natural shading. The findings also highlight the need for sustainable water-use practices, stimulating community-level discussions on balancing cooling benefits with responsible irrigation. Overall, green roofs can support long-term public health resilience and strengthen social adaptation to rising temperatures in arid Gulf cities. Originality/value This study contributes to the limited body of research on green roof performance in arid coastal climates by combining scenario-based parametric simulation and validation to identify key design parameters influencing rooftop cooling efficiency.
Purpose Masonry construction is well suited to robotic automation because it involves the sequential assembly of standardized blocks. However, existing robotic masonry systems depend heavily on manual programming. This study develops a curiosity-driven reinforcement learning (RL) framework that allows robots to learn control policies through environmental interaction rather than task-specific programming, with a focus on dry-stack masonry block assembly within a physics-based simulation environment. Design/methodology/approach A curiosity-driven RL framework is proposed by integrating an Intrinsic Curiosity Module (ICM) into the Proximal Policy Optimization (PPO) algorithm. Intrinsic novelty-based rewards are combined with extrinsic task rewards to enhance exploration and learning efficiency. A physics-based simulation environment is developed using PyBullet. Multiple dry-stack masonry wall configurations are evaluated under dense and sparse reward conditions. Findings For simple tasks with dense rewards, PPO and PPO + ICM exhibit comparable performance. As task complexity increases, PPO + ICM consistently outperforms PPO; for the most demanding 4 × 3 Running Wall, it achieves up to 20.2% higher average rewards and 27.7% higher average success rates. Under sparse reward conditions, PPO fails to learn effective policies, whereas PPO + ICM maintains stable learning and high task success. Research limitations/implications The simulation employs a restricted action space limited to joint rotations and does not model collision dynamics. Future work should incorporate extended action spaces, collision effects and physical-robot validation to assess real-world applicability. Practical implications The proposed method reduces reliance on task-specific programming by enabling robots to adapt to varying block arrangements and construction sequences. This supports more flexible deployment of robotic systems for masonry construction. Originality/value This study presents a novel application of curiosity-driven RL to long-horizon dry-stack masonry block assembly in simulation. It offers a transferable paradigm for addressing sparse rewards and long-horizon decision-making in construction robotics.
Purpose The purpose of this study is to explore gender-sensitive approaches towards occupant response to indoor environmental quality (IEQ) parameters (thermal comfort, indoor air quality, lighting quality and acoustic comfort) in educational spaces, with the aim of understanding gender-based perception differences that may support improved comfort strategies and energy-efficient operation. Design/methodology/approach This exploratory field study was conducted at MNIT Jaipur, involving 734 participants (416 males and 318 females) belonging to different academic backgrounds. Objective environmental measurements of selected IEQ parameters were combined with subjective surveys using validated instruments and questionnaires. Statistical analysis included two-way ANOVA and odds ratio analysis to identify associations and trends in gender-based differences across naturally occurring IEQ conditions. Findings The results indicate the presence of gender-based differences in perceived satisfaction under varying IEQ conditions. Significant gender differences were found in perceptions of thermal comfort and lighting quality, while indoor air quality (IAQ) and acoustic comfort showed similar trends across genders. Males were more dissatisfied in cooler environments (<21°C SET), whereas females were more dissatisfied in warmer conditions (>25°C SET). Females also showed a higher dissatisfaction rate at lower illuminance levels (below 150 lux) and were 3.25 times more likely to report dissatisfaction. In contrast, statistical analysis showed that gender had minimal independent effects on IAQ and acoustic satisfaction; however females exhibited greater dissatisfaction under poorer air quality, and males exhibited greater dissatisfaction in higher noise levels. These findings represent observed perception trends under the measured conditions rather than definitive comfort thresholds. Research limitations/implications The study assessed a limited set of IEQ variables, with IAQ measurements restricted to PM2.5, PM10 and CO2, and without detailed acoustic frequency analysis. In addition, the analysis does not explicitly consider intersectional factors beyond gender, such as age or socio-economic background. Future research should examine the combined effects of gender, age and socio-economic background across seasons and broader pollutant profiles, while incorporating personalised adaptive comfort models to refine gender-sensitive IEQ management strategies. The study benefits male and female occupants, guides inclusive institutional planning, supports societal equity and promotes environmental sustainability through energy-efficient IEQ strategies. Originality/value This study is among the few to comprehensively assess gender-specific IEQ perceptions across four domains within the Indian climatic context. While most of the existing research focuses on thermal comfort, limited studies on gender differences across other IEQ factors were found. Although IEQ perception by gender varies significantly depending on geographical location, climatic conditions and socio-cultural differences, such influences are often underexplored in the context of developing countries like India.
Purpose The increasing adoption of Green Building Rating Systems (GBRSs) has introduced significant methodological diversity into sustainability assessments, raising concerns about the comparability and robustness of evaluation outcomes. This study aimed to evaluate the environmental dimensions of the Moroccan Building Sustainability Assessment Framework (MBSAF(Env)) and examine its methodological positioning relative to established international systems, namely, the Building Research Establishment Environmental Assessment Method (BREEAM), Leadership in Energy and Environmental Design (LEED) and High Environmental Quality (HQE). Design/methodology/approach A comparative multi-assessment approach was employed, applying the MBSAF(Env) and three international GBRSs (BREEAM, LEED and HQE) to two residential buildings. The analysis focused on cross-system variations at both category and overall rating levels, with particular attention paid to scoring mechanisms, normative references, and calculation procedures. Findings The results revealed substantial variability across systems. An inter-project performance gap of up to 52 percentage points was observed, whereas cross-system differences exceeded 100 percentage points in categories such as water and waste. In contrast, the energy performance exhibited relative convergence, with variations limited to approximately three percentage points. The analysis further highlights the influence of penalty-based scoring mechanisms, with negative contributions observed in the MBSAF(Env) (down to −1.15%). These findings demonstrate that assessment outcomes are not solely determined by building performance but are strongly influenced by the structural configuration of the evaluation methodologies. Originality/value This study proposes a tripartite analytical framework based on scoring architecture, normative anchoring, and methodological institutionalization to support the interpretation of variability across GBRSs. It provides novel empirical and conceptual insights into the interactions between local and international frameworks and challenges the validity of direct cross-system comparisons.
Purpose Extreme heat exposure is globally a concern. The Southern African region is particularly vulnerable to heat stress exposure due to macro-climatic patterns and poor built environments. This study investigates indoor heat stress exposure of pregnant women living in rural and urban dwellings in Southern Africa. It focuses on urban informal dwellings in central South Africa, and rural informal dwellings in northern Zimbabwe. The study reports on the thermal performance of a purposive sample of dwellings to define informal urban and rural building typologies, assess their associated thermal performance and identify synergistic low-cost adaptation packages to reduce their overheating risks. Design/methodology/approach The study employed a mixed method approach combining indoor thermal monitoring and building performance simulation. The dwellings were monitored using thermal loggers for up to 91 days. Additionally, using an observational survey method, the dwellings' spatial and material characteristics were documented. The data were used to develop digital simulations of the worst-performing dwellings to test synergistic heat adaptation packages. Findings Through descriptive statistical analyses, the study identified high-thermal mass typologies without adequate thermal insulation, resulting in excessively high indoor temperatures. These typologies represent maladaptations where inappropriate building upgrades increase heat stress exposure during hot periods. However, the study also identifies locally adopted vernacular response strategies incorporating synergistic solutions which cumulatively improve indoor thermal conditions. Originality/value The paper contributes by providing empirical and simulation data related to the living environments of a vulnerable population group in Southern Africa. By including urban and rural typologies, the findings can potentially contribute to regional heat adaptation planning.
Purpose This study develops a data-driven severity-classification approach for maritime construction, relating equipment activity from incident narratives to localized hydro-meteorological conditions to complement static safety systems. Design/methodology/approach The research analyzed 1,139 maritime construction incidents from OSHA databases (2015–2025). A rule-based Natural Language Processing module classified unstructured narratives into 15 distinct equipment categories. Additionally, an interface to a historical weather API reconstructed micro-climate conditions at the incident locations. Sixteen machine-learning algorithms were comprehensively compared for injury severity classification using chronological hold-outs, stratified cross-validation and feature-ablation. Findings A Logistic Regression classifier provided a strong balance of discrimination and interpretability, achieving a cross-validated AUC of 89.2% and a hold-out AUC of 95.6% for post-incident classification based on narratives, while a pre-incident configuration using only prior operational factors achieved an AUC of 68.7%, with an F1-score of 95.4% and a Brier score of 0.062. The predictive signal originates primarily in the incident narrative; weather and employer history contributed modestly. A sensitivity analysis confirmed performance was robust to employer-history exclusion, and a localized wind-speed association near 30 km/h was cautiously identified. Practical implications The approach offers a conceptual triage aid for site superintendents. If integrated into Construction Safety Management Systems, this logic could prioritize hazard reviews and inform daily planning, pending operational validation. Originality/value The study integrates unstructured narratives with quantitative geospatial weather metrics in maritime construction. It reports a highly transparent classifier as an alternative to opaque models, offering an applied integration of established methods for safety-critical risk analysis.
Purpose This study develops a simulation-based cyber-physical framework that integrates bio-inspired adaptation and artificial intelligence (AI) within a digital twin environment to improve building energy efficiency, indoor environmental performance, and adaptability. Design/methodology/approach A scientometric and narrative review was conducted to examine trends in intelligent building control, followed by the development of a MATLAB/Simulink simulation. The framework combines bio-inspired thermoregulation logic, AI-based predictive control, and a simulation-based feedback loop for adaptive building performance assessment. It was evaluated across temperate, tropical, and arid climates using representative building configurations under these climate conditions. Findings The AI-augmented system achieved the best performance, reducing HVAC energy use by up to 27% in tropical climates (from 15.2 to 11.1 kWh/day) and improving the temperature-based comfort proxy by reducing deviation from the setpoint from 2.9°C to 1.3°C. It also showed faster thermal recovery (25–30 min) and stable learning convergence, with mean squared error decreasing from 0.09 to 0.005 over 50 epochs. Scientometric analysis indicates rapid growth in this field between 2016 and 2024, with AI, sustainability and IoT as dominant themes. Research limitations/implications Results are based on simulation using standardised parameters. Real-world validation is required. Practical implications The framework supports adaptive, energy-efficient building control using biologically inspired and data-driven strategies. Originality/value The study presents an integrated simulation framework combining bio-inspired control, AI, and digital twin modelling for adaptive building performance.
Purpose The construction industry is increasingly exploring the metaverse as a transformative digital paradigm to enhance collaboration, efficiency and project delivery. However, the current body of knowledge remains fragmented, lacking a comprehensive synthesis of adoption barriers, research trends and implementation pathways. This study aims to systematically investigate (1) the evolution and trends of metaverse-related research in construction, (2) key barriers to its adoption and (3) strategic pathways for its effective implementation. By integrating these dimensions, the study seeks to provide a holistic understanding of the metaverse ecosystem within the construction industry. Design/methodology/approach A systematic literature review was conducted following the PRISMA protocol to identify relevant studies from the Scopus database. A total of 34 records were selected through rigorous screening and snowballing. Scientometric analysis was employed to examine publication trends, collaboration networks and research hotspots. A novel hybrid multi-criteria decision-making (MCDM) framework, combining evaluation based on distance from average solution (EDAS) and criteria importance through intercriteria correlation (CRITIC), was developed to assess the quality and impact of the selected studies. Furthermore, barriers were extracted and analysed, and a conceptual framework for metaverse adoption was proposed. Findings The findings reveal a rapidly growing research interest in metaverse applications in construction, particularly after 2020. Key adoption barriers are categorised into political, economic, social, technological and cultural dimensions, with major challenges including high initial costs, lack of standardisation, regulatory uncertainty, technological immaturity and resistance to change. Interoperability issues, immature business models and limited awareness emerged as the most interconnected barriers. The proposed framework outlines a structured pathway from stakeholder awareness to industry-wide adoption, emphasising policy support, infrastructure development and technological integration. Originality/value This study contributes to the literature by integrating scientometric analysis, barrier identification, quality assessment and framework development within a unified analytical approach. Unlike prior studies that focus on isolated aspects, this research provides a comprehensive and systematic evaluation of metaverse adoption in construction. The findings offer actionable insights for policymakers, industry practitioners and researchers to facilitate strategic decision-making and accelerate digital transformation in the construction sector.
Purpose The performance of the construction industry is hampered by safety issues arising from hazardous working conditions, including accidents and injuries, often linked to unsafe worker behaviours. While safety behaviour is based on safety compliance and safety participation. This study employs machine learning to develop a model to predict safety participation behaviour. Design/methodology/approach A comparative machine learning framework using eight classification algorithms was employed to identify key behavioural, cognitive and organisational determinants influencing safety participation behaviour. Findings Random Forest model achieved superior performance with 87.72% accuracy and 84.21% after tuning, significantly outperforming other methods. Subsequent model interpretability analyses using SHAP values and partial dependence plots identified safety motivation, safety attitudes and the application of safety knowledge as forming a tripartite foundation for safety participation behaviour. These findings demonstrate how predictive analytics can serve as a diagnostic and decision-support tool within safety management. Originality/value Using a comparative machine learning approach, this study evaluated the determinants of safety participation behaviour among construction workers. It provides predictive analytics to support safety management by enabling the timely identification of workers exhibiting high levels of unsafe behaviour, enabling proactive interventions.
Purpose This study explores the enhancement of the building permitting process through the introduction of dynamic building codes, enabled by an adaptive digital technology framework. By leveraging innovations such as Artificial Intelligence, Internet of Things and integrated digital twins, the framework enables real-time monitoring and updating of building codes to address the complexities of modern urban environments and support climate change adaptation in smart, sustainable cities. Design/methodology/approach The study adopts a qualitative approach to develop a conceptual framework based on an integrated literature review of the key themes. The framework developed is structured into three interconnected layers and five distinct aspects. The role of each layer and its integration within a smart city environment is explored in the framework development section. The conceptual framework is demonstrated through an illustrative scenario in Lusail, Qatar, a state-of-the-art smart city. The proposed framework operationalizes adaptability within regulatory systems, contributing to United Nations Sustainable Development Goal (SDG) 13 by promoting resilient urban development. Findings The findings suggest that emerging technologies can significantly improve sustainability compliance and climate adaptation. It reimagines building permitting as an ongoing process rather than a one-off process to strictly ensure the buildings' continued sustainability compliance throughout their existence. The sequence and logic of the proposed system are explained using an example of temperature rise. Key challenges identified include data interoperability, information security risks and resistance to regulatory change. Practical implications This approach improves institutional capacity for climate adaptation and disaster risk reduction. The research proposes an innovative technological approach building on the latest advancements in the industry that can effectively result in consumption optimization throughout the life cycle of the building, further promoting SDG 12. Originality/value The study proposes future research directions to address current gaps in digital sustainability tracking and to advance the implementation of dynamic building codes for truly resilient urban environments.
Purpose Despite the growing interest in tools for designing-out construction and demolition waste (DoC&DW), information needs associated with these tools remain underexplored. This study aims to identify the information needs of the tools for DoC&DW to streamline the waste minimisation process at the design stage. Design/methodology/approach A systematic literature review (SLR) was conducted using the PRISMA method, analysing 46 peer-reviewed articles published between 2004 and 2024. The selected articles underwent descriptive and thematic analysis. Findings The study highlights the increasing adoption of BIM-enabled tools for DoC&DW, recognised for their automation, robust databases, and interoperability. The information needs of these tools were categorised using the input-process-output (IPO) model. A conceptual framework was proposed to map the BIM-enabled architecture to the IPO model, distinguishing input, process, and output information aligning with designing-out waste (DoW) principle. This provides a foundation for future development of BIM-enabled tools to predict and manage waste at the design stage. Originality/value This study is the first of its kind to map input, process, and output information needs relating to identified BIM-enabled tools for DoC&DW, aligned with each DoW principle. Unlike previous studies that focus on the functionalities of the tools or technology applications, this study uniquely and holistically maps information requirements across IPO model and proposed a conceptual framework that clarifies the information architecture needed to support DoW principles. This facilitates the specific information needs and standardisation of the development of new tools for DoC&DW, enabling informed decision-making, enhanced resource efficiency and contributes to sustainability in the built environment.
Purpose Although many demolished buildings contain concrete elements in good condition, these are rarely reused due to the perceived high costs of deconstruction. Design for Disassembly (DfD) has been proposed as a design approach to enable the cost-effective reuse of building components. This article investigates the impact of DfD practices on the end-of-life operational costs of a prefabricated building with a concrete inner frame and wooden envelope and compares them with current built-as-usual (BAU) design practices. Design/methodology/approach A computational analysis was conducted to compare the costs of deconstruction with the traditional crushing demolition. Moreover, the resale value ratio relative to the initial cost required to make disassembly economically feasible was examined. Findings The results demonstrate that current building design practices do not support the efficient and economically feasible reuse of building components. Under both the BAU and DfD design practices, the costs incurred by deconstruction were significantly higher than those of demolition by crushing. Even with the ideal DfD solution, a 50% resale value ratio for reusable concrete components was insufficient to fully offset the additional costs associated with deconstruction. The results indicate that improvements in disassembly practices, a significant increase in crushing costs, or changes in the market environment would be required to make DfD economically feasible. Originality/value This study provides a novel assessment of a concrete building using a detailed cost framework that captures key cost components and the economic implications of DfD. It offers new insights into the cost competitiveness of deconstruction compared with demolition and the valuation of reusable components.
Purpose This review examines how data-driven technologies are being applied to improve indoor environmental quality (IEQ) while enhancing energy efficiency in buildings. It further highlights the need for intelligent solutions that balance occupant comfort and environmental impact. Design/methodology/approach A PRISMA-based systematic review identified studies integrating AI, machine learning, and digital twins for IEQ monitoring, prediction, and control, yielding 152 reviewed papers. Findings The review indicates that data-driven research largely concentrates on monitoring and predicting IEQ with particular emphasis on thermal comfort and air quality. Considerable attention is also given to enhancing energy efficiency. A wide spectrum of artificial intelligence and machine learning techniques has been applied, including regression and classification models, to represent continuous and categorical IEQ variables. Several studies further integrate AI with BIM and IoT platforms to develop digital twin frameworks enabling real-time performance assessment and adaptive control, though adoption is constrained by data quality, interoperability, and scalability challenges. Research limitations/implications The review is limited by database scope and keyword selection, suggesting opportunities for broader future investigations. Practical implications Findings support the development of intelligent building strategies that enhance occupant well-being, reduce emissions, and promote sustainable indoor environments. Originality/value This review provides a consolidated perspective on how emerging data-driven technologies simultaneously support IEQ improvement and energy efficiency, highlighting the growing role of digital twin systems in intelligent building management.
Purpose Construction professionals encounter a range of psychosocial hazards that can significantly impact their health, well-being, and work performance. This systematic review examines and categorises psychosocial hazards affecting construction professionals, the associated risks and consequences, and how these risks vary across different demographics. It also identifies several research gaps and future research directions. Design/methodology/approach Following the PRISMA guidelines, the review systematically identified and assessed 75 relevant peer-reviewed journal articles. The literature search, guided by keywords shaped by the CoCoPop framework, encompassed five databases: Scopus, Web of Science, ProQuest Central, PubMed, and PsycINFO, along with the Google Scholar search, to ensure comprehensive coverage of articles published from 1989 to 2025. Scientometric and content analyses were conducted to identify underlying patterns, followed by an in-depth discussion. Findings Five levels at which psychosocial hazards affecting construction professionals manifest are: (1) task, (2) organisational, (3) interpersonal, (4) personal, and (5) environmental. These hazards culminate in three primary risk domains: (1) stress, (2) mental health impacts, and (3) physical health impacts, particularly when exposure is prolonged and coping mechanisms are inadequate. The review also highlighted unique hazards across various demographics based on gender, age, experience, and work settings. Additionally, it identified theoretical frameworks or models underpinning psychosocial research on construction professionals. Originality/value The paper reveals both patterns in the existing literature and gaps for future research, thereby contributing to the theory and practice concerning psychosocial risk management in the construction industry.
Purpose This study examines how integrating demand-side flexibility into the planning stage of residential photovoltaic (PV)-battery systems influences optimal system sizing and operational performance.Design/methodology/approach A combined planning-operation optimisation framework is developed in which household electricity demand is decomposed into fixed and flexible components. The model simultaneously determines optimal PV capacity, battery size, and the scheduling of flexible loads. Twelve demand-flexibility scenarios are analysed based on different allowable load-shifting windows to evaluate how flexible demand affects system sizing and energy management.Findings Results show that incorporating flexibility systematically shifts demand toward solar generation periods, improving PV self-consumption and reducing reliance on battery storage. Battery capacity decreases by up to 33%, while PV capacity increases modestly by approximately 2.7%. Compared with a two-stage benchmark model, the proposed approach reduces total system cost by an average of 7%, with improvements reaching up to 9% at higher flexibility levels.Research limitations/implications The model assumes deterministic demand and PV generation over a 20-year horizon. Future research should incorporate stochastic modelling and extend the analysis to diverse climatic and socio-economic contexts.Social implications The results provide actionable insights for homeowners and energy planners by showing that incorporating demand flexibility can reduce storage requirements and improve investment efficiency.Originality/value Unlike conventional approaches that treat demand flexibility only at the operational stage, this study integrates flexibility directly into system planning. The findings demonstrate that co-optimising system sizing and demand scheduling yields more cost-efficient and technically effective PV-battery configurations.
Purpose Indoor environmental quality (IEQ) influences occupants' satisfaction, health, and performance, and is especially consequential in educational settings where it can affect well-being and cognitive outcomes. This study aims to evaluate whether a multimodal artificial intelligence approach, specifically a Multimodal Transformer (MulT), can estimate current IEQ conditions in real-world educational spaces more effectively than conventional approaches that rely primarily on single-modality physical measurements. The work targets real-time, holistic IEQ estimation that better reflects how multiple environmental cues co-occur in occupied rooms.Design/methodology/approach Data were collected in four educational-space scenarios: a faculty conference room, a hybrid laboratory with machinery, and two standard classrooms. Time-lapse RGB images and synchronized sensor measurements (air temperature, relative humidity, CO2, TVOCs, PM1, PM2.5, PM10, and occupancy rate) were recorded at 5-min intervals for 7-8 days per scenario. A MulT architecture was trained to fuse images and sensor streams and estimate IEQ-related variables in a single forward pass. The pipeline, model design, training regimen, and evaluation protocol were specified to support reproducibility.Findings Across 4,945 paired image-sensor samples, the proposed MulT model achieved approximately mean squared error (MSE) = 2.99 and mean absolute error (MAE) = 0.88 on a held-out test set. Test performance closely matched validation results, indicating robust generalization across the measured scenarios. The results show that multimodal fusion can accurately estimate concurrent IEQ factors under real operational conditions, supporting the feasibility of near real-time IEQ assessment in educational environments. The reported workflow and evaluation setup enable direct comparison in future studies and benchmarking across alternative architectures or sensing configurations.Originality/value This work contributes a reproducible, real-world demonstration of MulT modeling for concurrent, real-time estimation of IEQ factors in educational settings using synchronized visual and environmental sensing. Unlike conventional single-modality approaches, the method integrates room imagery with physical measurements to capture contextual cues that accompany IEQ variation. The approach is transferable to other indoor environments and can serve as a foundation for operational deployment, including alert and decision-support frameworks when combined with time-series forecasting and explicit performance thresholds. The study provides a structured baseline for multimodal IEQ research and practical monitoring systems.
PurposeThis study examines the roles and interrelationships of key stakeholders in Sustainable Building Designs and Practices (SBDPs) and how their collaboration influences the integration of Energy Efficiency (EE), Water Efficiency (WE), and Indoor Environmental Quality (IEQ). It proposes a framework that combines Stakeholder Theory with Habermas' critical social theory to align objectives, bridge performance gaps, and advance sustainable building outcomes. Design/methodology/approachAn integrative literature review (2013–2025) was employed using Scopus and Google Scholar (n = 86). Thematic analysis identified five primary stakeholder groups, including investors, producers, policymakers, users, and academics, and examined their interactions, collaborative mechanisms, and barriers. Insights informed the development of a stakeholder engagement framework grounded in both descriptive and normative theory. FindingsStakeholders hold distinct yet interdependent roles. Effective collaboration is crucial to closing the gap between design intent and realised performance, but is hindered by fragmented communication, conflicting priorities, and regulatory limitations. The proposed framework facilitates inclusive dialogue, shared objectives, and transparent decision-making. Research limitations/implicationsReliance on secondary data and the predominance of studies from developed countries limit context-specific insights. Future research should incorporate empirical investigations across diverse regions to address institutional barriers and power imbalances. Practical implicationsThe framework offers actionable strategies, such as stakeholder forums, participatory design processes, and lifecycle monitoring, to align EE, WE, and IEQ goals, improve efficiency, and enhance compliance of buildings with sustainability rating systems. Social implicationsSBDPs contribute to public health, resource conservation, and climate resilience, while inclusive engagement ensures equitable access to sustainable living and working environments. Originality/valueThe study introduces a novel framework that integrates descriptive and normative approaches, offering ethically grounded, practical strategies for enhancing global SBDP collaboration and stakeholders' collaboration.
Purpose This paper proposes the building circularity performance (BCP) model to address fragmentation in existing building circularity assessments, which lack comprehensive coverage of circular economy (CE) aspects, standardized KPI weightings and multi-level evaluation across buildings. Design/methodology/approach Using a design science research approach, a holistic and weighted assessment framework spanning five building levels (material, subcomponent, component, system and building) was developed. The BCP integrates 52 validated and weighted key performance indicators (KPIs) covering material flows, waste, energy, water, CO2 emissions and design strategies, aligned with ISO 20887 and ISO 59020. The model was demonstrated through a single case study, and evaluated through an expert focus group, four design scenarios and sensitivity analyses of KPIs and weights. Findings Results show that circularity performance improves most through strategies extending building lifespan, promoting modularity and optimizing renewable resource use. The findings reveal that circularity emerges from the interaction of material, environmental and design strategies rather than isolated interventions. Sensitivity analyses confirm that the model is both responsive and robust, with bounded variations in outputs and consistent scenario rankings under input and weight changes. Originality/value The novelty of BCP lies in its systematic integration and weighting of multiple KPIs in various CE dimensions through a five-level structure. BCP advances existing approaches by integrating previously overlooked dimensions, including locality, hazardous content, environmental performance and design strategies such as repairability and take-back systems, within a single building assessment framework. It also contributes to the Sustainable Development Goals by supporting climate-resilient, resource-efficient and circular building practices.
Purpose This study aims to assess the real-world effectiveness of commercially available CO2-absorbing paints in improving indoor air quality in educational buildings. By evaluating their performance under normal classroom conditions, the research examines whether these materials can meaningfully influence indoor CO2 concentrations and thus contribute to healthier and more comfortable learning environments. Design/methodology/approach The study monitored five commercially available CO2-absorbing paints applied to classroom walls in a school in Palma (Spain). Continuous measurements of CO2 concentration, temperature and relative humidity were collected over two consecutive academic years. Initial small-scale panel tests were followed by larger wall-scale application of the most promising paint. Comparative analyses, including weekly interannual evaluations, were used to quantify the paints' performance under real operating conditions. Findings Some paints were associated with localized differences in CO2 concentrations, with levels up to 53.4% lower at specific monitoring points during the panel testing phase. However, when the best-performing product was applied on larger wall surfaces, similar reductions were not consistently observed (in several rooms, mean CO2 levels even increased by up to 11%). Weekly interannual comparisons generally showed variations within +/- 10%, with occasional peaks above 1000 ppm, reflecting the strong influence of classroom occupancy patterns and ventilation behavior. Overall, the results indicate that classrooms where these paints were applied did not consistently exhibit lower indoor CO2 concentrations during the monitoring period. Originality/value This research provides one of the first long-term, real-world evaluations of commercially available CO2-absorbing paints in occupied classrooms. By combining continuous monitoring with multi-year analysis, it highlights the gap between laboratory claims and real operational performance. The findings provide practical evidence that such coatings should be considered, at best, complementary measures rather than standalone solutions for improving indoor air quality in educational environments.
