Learning from experts: energy efficiency in residential buildings

  • 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.

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Author:Monica BillioORCiDGND, Roberto CasarinORCiD, Michele CostolaORCiD, Veronica VeggenteORCiD
URN:urn:nbn:de:hebis:30:3-715233
URL:https://ssrn.com/abstract=4596682
DOI:https://doi.org/10.2139/ssrn.4596682
Series (Serial Number):SAFE working paper (403)
Publisher:SAFE
Place of publication:Frankfurt am Main
Document Type:Working Paper
Language:English
Year of Completion:2023
Year of first Publication:2023
Publishing Institution:Universit├Ątsbibliothek Johann Christian Senckenberg
Release Date:2023/10/13
Tag:Energy Performance Certificate; Energy efficiency; Machine learning; Tree-based models; big data
Edition:October 2023
Page Number:49
HeBIS-PPN:513091572
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Wissenschaftliche Zentren und koordinierte Programme / House of Finance (HoF)
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Wissenschaftliche Zentren und koordinierte Programme / Sustainable Architecture for Finance in Europe (SAFE)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
JEL-Classification:C Mathematical and Quantitative Methods / C1 Econometric and Statistical Methods: General / C10 General
C Mathematical and Quantitative Methods / C5 Econometric Modeling / C50 General
C Mathematical and Quantitative Methods / C5 Econometric Modeling / C53 Forecasting and Other Model Applications
Sammlungen:Universit├Ątspublikationen
Licence (German):License LogoDeutsches Urheberrecht