Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/279413 
Year of Publication: 
2023
Series/Report no.: 
SAFE Working Paper No. 403
Publisher: 
Leibniz Institute for Financial Research SAFE, Frankfurt a. M.
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.
Subjects: 
Energy efficiency
Energy Performance Certificate
Machine learning
Tree-based models
big data
JEL: 
C10
C53
C50
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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