Learning from experts: Energy efficiency in residential buildings

Monica Billio, Roberto Casarin, Michele Costola and Veronica Veggente

Energy Economics, 2024, vol. 136, issue C

Abstract: Reducing energy consumption is a key policy focus for mitigating climate change. This study investigates the determinants of residential building energy efficiency, leveraging expert insights from Energy Performance Certificates (EPCs) to develop a machine learning prediction framework. Datasets from countries at distinct latitudes, the UK and Italy, are analyzed to identify potential regional variations in the factors influencing energy efficiency. Findings reveal the crucial role of factors related to heating systems and insulation materials in the determination of the building’s efficiency. Also, there is evidence of the superior ability of non-linear machine learning models to capture complex relationships between building characteristics and efficiency. A scenario analysis further demonstrates the cost-effectiveness of policies informed by machine learning recommendations.

Keywords: Energy efficiency; Energy performance certificate; Machine learning; Tree-based models; Big data (search for similar items in EconPapers)
JEL-codes: C10 C50 C53 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:136:y:2024:i:c:s014098832400358x

DOI: 10.1016/j.eneco.2024.107650

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