A statistical analysis of the energy effectiveness of building refurbishment
Owing to the rapid urban growth of past decades, the refurbishment of buildings has become a central topic of city development. A key aspect of building renovations deals with energy saving, both for economic and environmental concerns. The present literature mainly focuses on technological solutions for buildings, and the related data are studied with descriptive statistics. Instead, this paper aims to evaluate the energy effectiveness of refurbishment interventions from a global sector viewpoint. This implies building representative datasets, developing a synthetic cost indicator, estimating a proper regression model, evaluating the meaning of results and outline proper support policies. Two relevant case-studies are considered: the first is a published dataset of European service buildings, which contains detailed information on the undertaken interventions. The cost indicator is built by averaging standard costs per square meter; next, a Beta regression model is fitted to the data. This belongs to the class of generalized linear models (GLM) and it is suitable when the dependent variable (the saving rate) has an asymmetrical distribution on the interval [0,1]. The second case study is a survey on the retrofitting decisions of households in an urban area of Venice; the related dataset includes information on the cost of investment, the energy saving, and the comfort improvement. Comfort may be a subjective perception, including physical, psychological and economic wellness; however, it is also a drive for housing renovation and for energy saving itself. Statistical analyses show a significant positive dependence between all variables, confirming the energy saving effectiveness of refurbishment interventions. On the base of these results, proper refurbishment policies, both for public and private actors, are finally proposed.