Learning from experts: Energy efficiency in residential buildings

Monica Billio, Roberto Casarin, Michele Costola and Veronica Veggente

No 403, SAFE Working Paper Series from Leibniz Institute for Financial Research SAFE

Abstract: Measuring and reducing energy consumption constitutes a crucial concern in public policies aimed at mitigating global warming. The real estate sector faces the challenge of enhancing building efficiency, where insights from experts play a pivotal role in the evaluation process. This research employs a machine learning approach to analyze expert opinions, seeking to extract the key determinants influencing potential residential building efficiency and establishing an efficient prediction framework. The study leverages open Energy Performance Certificate databases from two countries with distinct latitudes, namely the UK and Italy, to investigate whether enhancing energy efficiency necessitates different intervention approaches. The findings reveal the existence of non-linear relationships between efficiency and building characteristics, which cannot be captured by conventional linear modeling frameworks. By offering insights into the determinants of residential building efficiency, this study provides guidance to policymakers and stakeholders in formulating effective and sustainable strategies for energy efficiency improvement.

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: 2023
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eec, nep-eff, nep-ene and nep-ure
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Journal Article: Learning from experts: Energy efficiency in residential buildings (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:safewp:403

DOI: 10.2139/ssrn.4596682

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