Extreme temperatures in urban environments exacerbate thermal discomfort and intensify the Urban Heat Island (UHI) effect, particularly during peak warm periods. Pavements, which constitute a significant portion of urban surfaces, contribute significantly to heat retention, whereas soil and vegetated areas aid in cooling through lower heat storage and higher moisture retention. Accurate forecasting of soil and pavement surface temperatures is critical for developing effective UHI mitigation strategies. This paper explores the application of Resistance-Capacitance (RC) models, a type of grey-box model, for soil surface temperature prediction. Unlike purely physics-based and data-driven models, RC models integrate physical principles with data-driven insights, balancing accuracy and interpretability. The proposed methodology is validated using real-world data from a dike in the Netherlands, where an optimal RC model is identified through an iterative process based on the Akaike Information Criterion (AIC). Results demonstrate that a two-node RC model provides a reliable balance between complexity and predictive accuracy, achieving an R² of 0.862 and a mean absolute error (MAE) of 0.675°C. These findings highlight the feasibility of applying RC models for soil temperature prediction while maintaining physical interpretability. Future research could extend this methodology to various soil types and urban surfaces, including pavements, to further enhance predictive capabilities and inform climate-responsive urban design.

The renovation of buildings has a high capacity to influence the environmental impacts and global objectives of climate change mitigation. In the context of designing low-energy buildings with minimized environmental impacts, the life cycle assessment (LCA) has been proven a straightforward method, to evaluate the direct and indirect environmental impacts of a building concept. Even though it is the most energy-intensive element, the use phase is not only a source of environmental concern but also the whole life cycle of the building and its components. However, energy-efficient renovation decisions tend to be financially motivated events, subject to exogenous constraints or barriers, that do not integrate whole life cycle thinking. This study aims to identify how the LCA information can be considered in comparing renovation options. compare renovation options, taking into account the modular envelope system developed as part of the European research project ENSNARE (ENvelope meSh aNd digitAl framework for building Renovation) case study. The study analysed different renovation scenarios, generated according to combinations of renewable energy sources and compare them to the base case and typical renovation scenario. Such information can support the design team in making decisions that consider the whole building and its components' life cycles.