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Regional climate exhibits substantial uncertainty, driven by variables with unreliable probability distributions and unclear future trajectories. However, the energy-efficient and climate-resilient transition of building stocks is undertaken and influenced by regional climates. It is therefore essential to evaluate regional climate uncertainty and its impacts on candidate transition strategies. Traditional approaches that rely on probability distributions or exhaustive climate trajectories are not entirely applicable to regional climate contexts. A deep uncertainty analysis approach is proposed to characterise regional climate uncertainty and support building stock transition. This approach constructs deep uncertainty of regional climate and models their impacts on building stocks, with afterward data-driven learning and data mining to (i) deliver robust strategies even probability distributions are unavailable, and (ii) identify vulnerable scenarios across plausible climate trajectories for adaptive decisions. It was applied to residential buildings of Umeå region, Sweden, to evaluate its advantages and limitations. Results show that delivered robust strategies reduced performance variability by 84.5% for single-family houses, by 55.3% for multi-family houses under northern Sweden regional climate. Identified climate-vulnerable scenarios significantly undermine performance of selected strategies. Overall, this approach enables analysis of regional climate's deep uncertainty and supports building-stock transitions for diverse regions.

Upcoming energy transition initiatives, such as Sweden’s capacity-based electricity tariff in 2027, aims to incentivize changes in occupant behaviour (OB) influencing how occupants schedule and shift energy use. Urban building energy modelling (UBEM) is widely used for city-level energy assessment. However, most UBEM studies assume uniform occupant behaviour and homogeneous building properties due to modelling limitations in incorporating diversity and heterogeneity, which is computational infeasible at large scale. In practice, OB varies across demographics and seasonal routines, while heterogeneous building properties such as U-values, HVAC, archetypes directly affect energy impacts. Oversimplifying OB diversity and urban building heterogeneity can misrepresent energy demand, peak loads, and policy effectiveness of energy transition initiatives.

This study introduces an enhanced framework, referred as DOB-HUBS, that explicitly incorporates OB diversity (e.g., seasonal, demographic) along with urban heterogeneity into UBEM. This enables ability to evaluate behavioural impacts across heterogeneous building clusters and occupants’ seasonal interactions. The approach employs unsupervised K-Means clustering and proportional stratified sampling to identify representative buildings, an automated bottom-up physics simulation workflow programmatically assigning diverse OB schedules and building properties, and an ensemble machine learning model that efficiently extrapolates predictions to the entire stock. This framework is demonstrated in Sweden, by evaluating seasonal impacts of excessive window opening, and behavioural responses to 2027 tariff policy. Results reveal strong seasonal dependence of OB, with deviations of up to 15% with 3.06% (standard deviation) energy use compared to traditional approach. Behavioural responses to 2027 policy could reduce peak loads by 6–17%, with impacts varying across small building clusters. By evaluating OB diversity and urban heterogeneity, DOB-HUBS enhances UBEM, supporting urban planners and policymakers in designing more effective occupant engagement strategies.

Purpose: The outdoor construction performance is significantly influenced by meteorological factors. In prefabricated building projects, onsite assembly tasks, such as alignment, installation and grouting, still require substantial manual involvement, making them sensitive to adverse weather conditions. This study was aimed at examining the effect of key meteorological factors (temperature, wind, and rainfall) on the construction duration, cost and carbon emissions of prefabricated buildings.

Design/methodology/approach: A logical model of the standard floor construction process for prefabricated buildings was developed using Arena software. By quantifying the changes in construction duration, cost and carbon emissions under different weather conditions, the effect of the adverse meteorological factors on worker performance during the prefabricated building construction process was analysed.

Findings: (1) In the simulated case, the wind impact was the highest on construction performance, increasing time by 24.2%, costs by 28.6%, and carbon emissions by 29.7%, followed by high temperatures (18.0%, 21.2% and 22.2%) and rainfall (16.5%, 7.4%, 8.4%), (2) windy weather poses most pronounced risks among the three meteorological conditions, suggesting it may warrant particular attention from managers and (3) performance indicators are significantly positively correlated under different meteorological factors, which allows for the estimation of unknown indicators through established linear relationships.

Originality/value: These findings provide insights that may help construction companies reduce the adverse effects of meteorological factors on worker productivity. They also enable the prediction of unknown performance indicators by unlocking the linear relationship between time, cost and carbon emissions, thereby enhancing proactive decision making.

Projecting building-stock climate resilience is essential for urban climate adaptation. The archetype-based approach, widely used for stock modelling, represents buildings with a small set of archetypes and scales the results to the full building population. However, building climate performance is highly sensitive to detailed building attributes, such as HVAC systems and controls, envelope thermal properties, and window/shading details, which greatly differentiate the buildings’ climate resilience. This sensitivity often conflicts with the core premise of classical archetype methods, which assume uniform attributes and rely on homogeneous modelling within same building groups to enable archetype’s scalability. The absence of climate-sensitive attributes may constrain the identification of resilient or vulnerable buildings and constrains the design of targeted, effective adaptation measures. This study aims to enhance the archetype methods by proposing a general data-augmented framework for building-stock climate modelling, enabling vulnerable buildings clustering and effective adaptation measures identification. The proposed approach is designed to complement archetype methods through integrating multi-source building data, augmenting archetype models, and performing data-driven analysis to support climate adaptation. It is applied to the residential building stock of Umeå, Sweden, under two Nordic future climate scenarios: a near-term extreme heat year (2030) and a mid-term gradual warm year (2050). The results indicate that the vulnerable buildings were successfully clustered and effective adaptation measures were identified. Building renovations such as adjusting to mechanical ventilation and behaviour adaptations like active curtain use were found to reduce overheating by 26% and 5%, respectively. Overall, this approach extends classical archetype methods for stock-level climate modelling, enabling targeted identification of at-risk buildings and selection of effective adaptation actions.

Buildings contribute to global energy consumption and greenhouse gas emissions, making energy-efficient interventions important for sustainable development. In practice, the design and evaluation of such interventions commonly rely on correlation-based predictive models, which describe statistical associations but provide limited insight into the causal mechanisms linking environmental changes, occupant perceptions, and behavioral responses. As a result, interventions may produce outcomes that differ from expectations. This study introduces the Occupant-Centric Building Intervention Framework (OCBIF) designed to assess effectiveness of building energy interventions related to OBI. It establishes causal relationships among environments, occupant characteristics and perceptions, and adaptive actions by adopting the Driver–Need–Action–System (DNAS) concept. It employs Immersive Virtual Environments (IVEs) to simulate building contexts to allow observations related to occupant-building interaction (OBI) for final validation of building interventions. The case study is demonstrated using scenarios related to building interventions aiming to reduce heater uses. The causal analysis yields insights into thermal comfort and OBI. The results show that thermal sensation mediates the effect of indoor temperature on heater interaction, while age and system accessibility are additional causal influences on OBI. This causal structure explains why changes in indoor temperature do not translate directly into behavioral responses and why identical thermal interventions can lead to heterogeneous outcomes across occupants. Findings revealed that OCBIF bridged gaps in building intervention research providing actionable insights for stakeholders to design interventions enhancing energy efficiency while considering OBI.

Climate change is expected to increase the frequency and intensity of extreme weather events, such as heat waves, which pose significant challenges to human thermal comfort and public health. Recently, data-driven thermal comfort models have shown superior performance compared with traditional knowledge-based methods such as the Predicted Mean Vote (PMV) model, highlighting their potential in large-scale application in many local areas for future climate. However, in many regions, thermal comfort data are scarce, making it difficult to develop local thermal comfort prediction models. To address this, a transfer learning approach is proposed. Large-scale source domain data are taken from the ASHRAE RP-884, while small-scale target domain data are collected from local climate chamber experiments simulating real indoor environments. The proposed multilayer perceptron (MLP)-based model achieves an accuracy of 0.719 and a weighted F1-score of 0.616 on the target dataset, demonstrating the effectiveness of transfer learning for thermal comfort prediction in data-scarce regions under future climate conditions.

Human–robot collaborative performance is influenced by physical workplace conditions, which can affect performance indirectly through workers' physiological comfort, cognitive load, and situational awareness. Under summer high-temperature conditions, construction sites impose increased environmental constraints on collaboration. This paper investigates how adverse summer working environments influence human–robot collaboration efficiency, and whether workers' trust in robots mediates this relationship. A pilot experiment was conducted using a representative rebar-tying task to compare human–robot collaboration in two construction environments: a relatively comfortable, factory-like environment enabled by an aerial building machine (ABM) and a conventional non-ABM environment characterized by more adverse working conditions. Results show that the ABM environment improves collaborative efficiency by approximately 19.8% and enhances trust. Trust also significantly mediates the effect of environment on performance. These findings offer insights into human–robot collaboration and guide intelligent construction under high-temperature conditions.

Accelerating climate change is fundamentally transforming indoor thermal environments, intensifying heat exposure and variability that threaten occupant health, productivity, and well-being. In response to changing indoor conditions, occupants adopt adaptive behaviors such as adjusting clothing, opening windows, or operating HVAC systems that in turn reshape indoor environments. Understanding these complex human-environment interactions under future climate scenarios is critical for developing resilient and occupant-centric building strategies. This study proposes a hybrid experimental framework that integrates immersive virtual environments (IVEs) with a climate-controlled physical laboratory to investigate thermal-related occupant behaviors under future climate scenarios. The experimental setup combines visual immersion through virtual scenarios with precise control of thermal stimuli, enabling realistic simulation of future indoor conditions. Behavioral responses, physiological signals (e.g., heart rate, skin temperature), and psychological assessments (e.g., perceived thermal comfort, stress) were systematically collected from participants exposed to varied thermal scenarios. The collected multi-dimensional data provide a basis for modeling occupant behavior patterns and identifying the physiological and psychological factors that drive adaptive responses. The study further outlines the future integration of reinforcement learning-based occupant behavior models with building energy simulations via co-simulation, enabling closed-loop modeling of human-building interactions. This approach contributes to advancing climate-resilient building design, supporting the development of adaptive control strategies grounded in empirical occupant data.

As part of Sweden's commitment to carbon neutrality, various municipalities have established energy efficiency targets. Achieving these targets requires decision-making knowledge at the local level, particularly concerning the energy retrofitting of existing building stocks. It includes understanding of the thermal properties (U-values) of building stock, their impact on energy use, and the potential energy retrofits. Our research focuses on assessing the thermal properties performance and their impact on energy use of residential building stocks in Umeå, Sweden. We employ explainable artificial intelligence (XAI) integrated with machine learning regression framework to elucidate how different building thermal features influence the building's energy use and also the correlations among these features. The findings highlight the significant impact of building floor area on energy use, followed by location, age, etc. Among thermal properties, the exterior walls have high impact and attic floor has the lowest impact on the energy use of Umeå's residential building stock. Ultimately, this study provides municipality-level decision-making insights for planning energy retrofitting initiatives for Umeå building stock.

The occupants influence the building's energy-efficient renovation through energy-related behaviors. The renovation, on the other hand, influences the occupant's behaviors due to the created new indoor environments. However, the consistent understanding and conclusive findings regarding how occupants and renovations influence each other are still lacking. These knowledge gaps result in an inaccurate or oversimplified understanding of the role that occupants can play in energy conservation. An experimental laboratory was established at Umeå University named Intelligent Human-Buildings Interaction (IHBI) lab to investigate the relationship between occupant behaviors and energy-efficient renovations. It integrates virtual technology (virtual reality) and physical technology (climate chamber). The occupants in the laboratory can interact with the renovated buildings virtually; synchronously, they physically perceive the buildings with renovation. Virtual reality increases the virtual immersion, while a climate chamber ensures physical perception. This experimental approach is applied to an office building looking for renovation at Umeå University. It was found that renovation clearly impacts personal heater use and door control but does not impact clothing behavior. The reduction of personal heater use leads to additional energy reduction contributed by occupant behaviors. This laboratory experimental approach provides insights regarding the influences between occupants and renovations, which are essential to engage the public such as general residents in achieving occupant-centric building energy-efficient transitions.