With the growing trend of developing and applying innovative bio-based materials in the construction industry, evaluating their performance across various building typologies and climatic conditions is crucial for their wider adoption. Traditional approaches to building energy simulation often require a large number of scenarios, leading to an exponential increase in simulations that demand significant computational resources and time. These challenges can be mitigated through data-driven methods.

This study presents a predictive modelling framework applied to the Kádár-kocka, one of the most representative residential building types in Hungary's building stock. Multiple regression algorithms were implemented for predictive modelling to enable rapid estimation of key performance indicators related to energy demand and thermal comfort. The models' robustness was evaluated through a comprehensive set of evaluation metrics, including mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2).

Using state-of-the-art simulation tools such as EnergyPlus and Grasshopper, a diverse range of renovation scenarios was developed, serving as data sources for predictive model development. The simulations considered a range of factors, including building envelope characteristics, shading, orientation and occupant behaviour. The building envelope scenarios were based on the "Existing State", "Usual Refurbishment" and "Advanced Refurbishment" options provided by the Tabula Webtool, alongside solutions developed as part of the BIO4EEB project. The results highlight the scalability of predictive modelling in guiding sustainable refurbishment strategies.

This study investigates the precision and potential application of national building register data for identifying exterior wall types among detached houses constructed between 1941 and 1990. As Building Renovation Passports (BRPs) are envisioned to become key instruments in achieving EU-wide energy performance goals, the need for scalable, data-based digital pre-audit tools becomes evident. By constructing a catalogue of 68 historically prevalent wall types and analyzing over 85,000 entries in the Estonian Building Register, the study quantifies the data's usefulness in estimating wall types through a process of elimination and evaluation of precision. Findings indicate that while 21% of houses contain ambiguous or unusable exterior wall data, over 65% exhibit exterior wall data where at least 73% of all potential wall types may be assumed unlikely. A perfect EBR-based identification, where all but one wall type are excluded, is possible as well, however these data types appear rarely in practice. This study provides validation for developing digital tools that generate preliminary BRPs and guide owners of detached houses in the early phases of renovation planning based on national register data.

The Green roofs function as passive physical systems that rely on natural physical processes to mitigate the negative effects of climate change in urban environments. This paper provides a comprehensive review of the environmental and climatic benefits of green roofs, emphasizing key physical mechanisms such as thermal insulation, solar reflectivity (albedo effect), evapotranspiration, acoustic attenuation, and stormwater retention. By analyzing scientific and technical literature, both from experimental studies and simulation-based models, the paper outlines the potential of green roofs to contribute to energy efficiency, reduction of greenhouse gas emissions, improvement of urban microclimates, and mitigation of the urban heat island effect.

Special attention is paid to differences among extensive, semi-intensive, and intensive green roofs with regard to their physical performance and ecological impact. The study also explores the role of green roofs in improving indoor thermal comfort, filtering air and water pollutants, and supporting biodiversity in densely built environments. As passive systems, green roofs operate without the need for external energy input, instead utilizing the inherent properties of substrate layers, vegetation, and structural configurations to achieve functional outcomes. Although numerous simulation studies demonstrate promising results, there remains a notable lack of long-term empirical research under real-world climatic conditions. The paper concludes by identifying critical research gaps and suggests directions for future studies that would allow for more accurate physical evaluation and broader integration of green roofs into urban climate adaptation strategies and policy frameworks

Glass façades are increasingly favored in modern architecture, not only for their aesthetic appeal but also for their ability to enhance natural lighting by offering exceptional transparency and a nearly weightless appearance, creating a seamless integration between interior and exterior spaces. A well-designed and executed façade significantly influences both the building's energy performance and its visual impact, serving as a defining feature of contemporary structures, while also addressing the growing demand for energy-efficient and environmentally sustainable building solutions.

Despite their non-load-bearing role, point-fixed glass façades, where thermal insulating glass is fixed to the supporting structure solely at points, must meet energy performance and structural requirements. However, currently there are no comprehensive or standardized methodology exists for evaluating the thermal behavior of these, often called spider facades. Although EN ISO 12631 standard describes the thermal performance of curtain walls and outlines the calculation of thermal transmittance, but it is limited to line-supported structures. Addressing this gap, this paper focuses on the thermal performance modeling of point-fixing systems using three-dimensional numerical thermal modeling. The aim is to provide a comprehensive understanding of the thermal bridges of point-fixed glass façade systems and to investigate how varying design parameters affect thermal performance.

To explore the thermal transmittances and point thermal bridges of point-fixed glass façades comprehensively, parametrized three-dimensional geometric models were created incorporating a variety of fixing types, material properties, and geometric configurations to simulate real-world scenarios, ensuring their suitability across a wide range of building applications. Finite element analysis, following a mesh independence study, was employed to ensure accuracy and reliability.

The results show that the differences are significant among the three fitting types, especially where the glass is fully drilled through. The cylindrical head fitting performs the worst in terms of heat loss, with a point thermal transmittance value of 0.047–0.066 W/m²K. The countersunk head fitting is similar but slightly better, with a value of 0.043–0.061 W/m²K. The hidden fitting type performs the best, with a point thermal transmittance of 0.003–0.014 W/m²K.

This research bridges a critical gap in these façade systems, and the findings can serve as a foundation for informed design decisions and contribute to the development of a thermal bridge catalog for these systems, incorporating multiple influencing factors.