Abstract :
[en] Considering the pressing need to renovate existing buildings across Europe, there exists a shared consensus on the importance of developing targeted strategies. Despite research, regulation and action in all the countries of Western Europe, the rate of energy renovation is stagnating. Turning this consensus into effective action requires models that accurately represent the diverse building stock and its inhabitants. This article focuses on Wallonia, aiming to identify typical combinations of buildings and inhabitants representative of the region’s situation. The methodology employs ordinal logistic regression and beta regression algorithms to analyze correlations, leveraging extensive databases encompassing technical and socio-economic data. Subsequently, K-means clustering is utilized to distill the building stock into several characteristic typologies, offering insights into the diversity of the region. Notably, our findings highlight certain underestimated building types, constituting a significant portion of the Walloon building stock. Firstly, a typology of low energy performance houses, mainly attached and semi-detached, inhabited by low-income households makes up more than 17% of Walloon housing. More unexpectedly, a type of low energy performance house, mostly 4-fronted, inhabited by high-income households, still makes up more than 11% of the built stock. These results underscore the efficacy of our methodology in harmonizing disparate datasets and provide novel insights into the building stock and its occupants. Furthermore, the identified typologies empower researchers and policymakers to address the renovation challenge by directing targeted actions at appropriate scales, whether regional or local. Overall, this article contributes to a deeper understanding of the complexities surrounding building renovation and offers practical implications for policy formulation and implementation.
Scopus citations®
without self-citations
0
