The construction sector contributes approximately 39% of global carbon emissions, with embodied carbon—emissions from material extraction, manufacturing, transportation, and construction—representing a systematically underestimated yet increasingly critical component of building life cycle environmental impacts. Traditional Life Cycle Assessment (LCA) methods suffer from static database dependencies, delayed feedback cycles, and limited integration with active construction decision-making, creating a fundamental gap between environmental assessment and construction operations. This paper presents the Digital Twin-Enabled Life Cycle Assessment Framework (DT-LCAF), a dynamic construction-phase embodied carbon accounting system aligned with the EN 15978 standard (stages A1–A5) that integrates Building Information Modeling (BIM), Internet of Things (IoT) sensor networks, and machine learning designed to support real-time sustainability decision-making during smart building construction, with computational performance validated through the offline processing of historical datasets. The framework introduces two enabling mechanisms: (1) a Multi-Scale Carbon Prediction Network (MSCPN) employing hierarchical graph attention networks to capture material interdependencies across component, system, and building scales; and (2) a Reinforcement Learning-based Carbon Optimization Engine (RL-COE) that generates constraint-aware recommendations for material substitution, supplier selection, and construction sequencing while respecting structural, economic, and temporal constraints. Experimental evaluation employs two complementary validation strategies using proxy embodied carbon labels (not ground-truth construction measurements): embodied carbon prediction accuracy is assessed using proxy carbon labels derived from the CBECS dataset (5900 commercial buildings) combined with the ICE Database v3.0 emission factors, achieving a 10.24% MAPE, representing a 23.7% improvement over the best-performing baseline in predicting these proxy estimates; temporal responsiveness and streaming data ingestion capabilities are validated using the Building Data Genome Project 2 (1636 buildings, 3053 m). The RL-COE optimization engine demonstrates an 18.4% mean carbon reduction rate within the proxy label framework across building types while maintaining cost and schedule feasibility. A BIM-based case study illustrates the framework’s construction-phase update loop, showing how embodied carbon estimates evolve dynamically as construction progresses. The limitations regarding the proxy-based nature of embodied carbon labels and the absence of ground-truth construction-phase measurements are explicitly discussed. The framework contributes to smart city sustainability by enabling scalable, data-driven embodied carbon intelligence across building portfolios. All quantitative results are based on proxy embodied carbon estimates derived from building characteristics and standard emission factor databases, rather than measured project data. The reported performance therefore demonstrates a proof-of-concept within the proxy system, and real-project, measurement-based validation remains future work.

Impinging jet ventilation (IJV) systems have attracted significant attention due to their potential to augment indoor thermal comfort and airflow distribution. Previous studies have primarily investigated corner and mid-wall IJV installations; however, comparative analyses focusing on different diffuser geometries remain limited. Accordingly, this study examines the combined effects of IJV diffuser geometry and installation location on thermal comfort indices and airflow characteristics. A full three-dimensional computational fluid dynamics (CFD) model, without the use of symmetry, is developed to simulate a realistic office environment (3 × 3 × 2.9 m³), operating in cooling mode under hot summer climatic conditions. Three IJV diffuser cross-section geometries—triangular, square, and circular—are evaluated at four installation locations (two corners and two mid-wall positions), assuming a fixed occupant location. A combined return and exhaust outlet configuration is adopted. The results indicate that the IJV location influences airflow and temperature distributions more strongly than the diffuser geometry. Nevertheless, the circular diffuser exhibits superior performance compared to the triangular and square geometries. The mid-wall location placed behind the occupant and away from the hot exterior wall demonstrates reduced thermal stratification, improved airflow characteristics, and weaker vortex formation, making it the most favorable configuration. From an architectural perspective, these findings highlight the importance of early coordination between ventilation design and office spatial planning, as diffuser placement directly influences occupant comfort zones and furniture layout. Moreover, the preference for mid-wall installations supports a more flexible façade design and allows for greater freedom in organizing workspaces without compromising thermal performance.

This study is the first to numerically evaluate the thermal performance of MPCM-enhanced autoclaved aerated concrete (AAC) bricks combined with an auxiliary heater plate for passive space heating in cold and freezing climates. A two-dimensional finite-volume CFD model with time-dependent outdoor boundary conditions and an apparent-heat-capacity formulation for phase change is developed and implemented in a custom C++ solver. The effects of MPCM volume fraction (φ = 0, 4, 8, 12%) and heater-plate heat flux (q″_hp = 0, 50, 100, 150 W/m²) are examined over a 24 h cycle with outdoor temperature varying between −15 and 15 °C. Indoor surface temperature (T_is), indoor heat flux (q_is), MPCM melting fraction (MF), and daily power consumption (DPC) are employed as performance indicators. For q″ = 100–150 W/m², φ = 8–12% reduces peak T_is by about 1–2 °C and peak q_is by roughly 15–25%, while raising minimum values and delaying the peak by 1 h. At these heater powers, DPC drops from about 0.31–0.34 kWh/m² for plain AAC to 0.24–0.28 kWh/m² (0–30% saving) with MPCM, demonstrating the potential of the proposed AAC–MPCM–heater configuration for peak-load mitigation and energy-efficient envelope design in cold climates. Moreover, over a 40-year service life, the techno-economic analysis shows that 4 vol% MCPCM delivers the highest returns, with NPV = $11,689–$15,970 and IRR = 13.55–17.45% across q’'=0–150 W/m².

Construction projects generate substantial material waste and embodied carbon losses, yet no integrating framework currently transforms routine project documentation into stage-specific decision signals during building delivery. This paper proposes Construction Material Flow Intelligence (CMFI), a project-level decision-support framework that applies mass-balance principles within a four-layer processing architecture to convert standard procurement records, delivery logs, installation data, and waste records into six performance indicators and structured operational signals. CMFI operates at the project-stage-material scale and produces three classes of output: procurement-adjustment signals, waste reduction triggers, and storage optimisation recommendations. An illustrative application to a five-storey commercial building across five material categories and three construction stages demonstrates that CMFI generates seven decision signals and identifies approximately 27.2 tCO₂e of avoidable embodied carbon loss from routine project documentation, without bespoke sensing infrastructure. Component sensitivity analysis confirms that waste classification, stage-based tracking, and embodied carbon integration are each load-bearing elements of the framework. CMFI addresses the gap between aggregate material flow analysis and project-level operational management by shifting construction material monitoring from retrospective waste reporting to stage-resolved sustainability intelligence. Limitations include the use of illustrative rather than empirical data, single-period stage accounting, and uncalibrated trigger thresholds; empirical validation and digital implementation are identified as priority future directions.

Benchmarking embodied carbon in residential building stock accurately would involve a high volume of data sharing and would pose serious privacy and competitive issues among building construction stakeholders. This study introduces a new federated learning-based building stock modeling system (FedCarbon) that can allow embodied carbon to be evaluated collaboratively without data aggregation at a central place. The architecture proposed enables construction firms, cities, and providers of construction materials to collectively train predictive models at the same time as data sovereignty is achieved via a hierarchical federated aggregation mechanism with attention-based client weighting. A differentiated privacy scheme that is adaptively calibrated on noise guarantees the privacy of individual projects and allows for statistically significant benchmarking based on heterogeneous building portfolios. The framework also includes a gradient compression scheme based on momentum, which incurs an 82.6% reduction in communication overhead over traditional federated averaging-based methods and still maintains model convergence. The effectiveness of the approach is demonstrated with the help of comprehensive validation with the UCI Energy Efficiency Dataset, which includes 768 residential building configurations, and the Embodied Carbon in European Buildings Database, which includes 2340 residential units in 12 European jurisdictions. It has been experimentally shown that FedCarbon has a 94.2% prediction accuracy (R2) on embodied carbon intensity, with a mean absolute error of 21.4 kgCO₂e/m², and that (ε, δ) differential privacy can be guaranteed with ε = 1.0 and −δ = 10⁻⁵. This structure opens up building stock knowledge and hastens industry-wide implementation of low-carbon building strategies.

Global transition toward sustainable micro-mobility is an essential aspect of Saudi Vision 2030; however, high car dependency remains a significant barrier to public health and safety targets. In this context, this study explores the factors determining the adoption of electric bicycles (e-bikes) in Al-Qunfudhah, Saudi Arabia. The present research used a convenience sampling strategy through an online survey conducted via social media and texting, utilizing a designed questionnaire of 10 sections delivered to 171 participants, alongside a 5-point Likert scale. Additionally, the scientific validation and analysis were conducted utilizing internal consistency, validity and scale reliability via statistical analysis. The findings indicated a significant intention–action disparity; while respondents demonstrate a strong psychological intention to adopt e-bikes within 12 months (an average of 3.51), real household ownership was relatively low at 11.1%. In addition, a significant 71.9% of participants use private vehicles for short-distance travel (<5 km), influenced by an average bus stop distance of 21.22 km. The hierarchy of barriers indicates infrastructure and security as the main barrier, particularly the absence of dedicated bike lanes, and concerns regarding traffic safety. In contrast, a perception of physical fitness, and interpersonal interaction behave as significant facilitators. Public health data reveals an average weekly activity of 109.77 min, significantly lower than worldwide recommendations; however, 66.7% of individuals believe e-bikes may address the difference. The statistical evaluation acknowledged the questionnaire’s robustness, with significant Pearson correlation coefficients (p < 0.01) demonstrating internal consistency validity and Cronbach’s alpha values between 0.71 and 0.88 indicating high scale reliability, demonstrating a scientifically stable framework for assessing the measured behavioral determinants. The research recommends the establishment of shaded, dedicated micro-mobility networks and the enforcement of safety regulations to promote a healthy, multi-modal urban ecosystem.