• Energy Research

The existing building patrimony is responsible for 36% of the global energy use and 37% of the greenhouse gas emissions. It is hence a major challenge to improve its energy performance. According to the Renovation Wave, the average annual renovation rate should be doubled by 2030 up to 3% and deep energy renovations should be encouraged. The Belgian city of Leuven works towards this target and is even more ambitious, setting their goal on becoming climate neutral by 2050. The strategy investigated in this study is to increase the renovation rate by clustering renovations, which is challenging since the Belgian building stock is highly privatised. Based on a thorough literature study, this paper examines various methodologies for building stock modelling. The main focus is comparing the required input data with the data availability, handling the data gaps, and defining their influence on the model’s accuracy. The findings are applied to Leuven by analysing the main drivers to cluster renovation measures. However, many data gaps appeared, leading to the selection of a GIS-enhanced archetype model enriched by energy data as the most suitable approach. To avoid misinterpretation due to differences in data quality, transparent reporting in stock modelling is recommended.

Abstract Various environmental challenges, particularly the rising severity of the impacts of climate change, require a systematic shift in and decarbonization of the global economy. Due to their high environmental impacts, buildings and construction have a special role in decarbonization. Environmental modelling of building stock dynamics can help policy makers and inform decision making. This study presents a systematic review of both the latest scientific literature on environmental modelling of building stocks and related EU policy initiatives. Our findings illuminate the strengths and limitations of existing approaches as well as the potential of such modelling and the required directions for future development to provide effective policy support. Based on the assessment of 104 scientific papers, our study shortlisted and analysed 22 environmental building stock modelling approaches. While promising, these show various limitations on their effectiveness in supporting decarbonization efforts while avoiding burden shifting. Future building stock models should offer extended system boundaries and comprehensive life cycle assessment, improved hotspot analysis and impact monitoring across spatiotemporal scales. A long-term perspective on the entire building stock covering climate and other environmental impacts is needed, as outlined in the latest standards. By linking existing studies to related EU policy objectives, we identify various studies that investigate scenarios and strategies relevant to EU policy makers and highlight research gaps. Future research should enable comprehensive environmental assessment of building stocks across scales and emphasize the monitoring of multiple environmental impacts of building stock development to ensure compliance with environmental targets and minimization of trade-offs.