Learning from experts: energy efficiency in residential buildings
File(s)
Published version
Author(s)
Billio, Monica
Casarin, Roberto
Costola, Michele
Veggente, Veronica
Type
Journal Article
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.
JEL classification
C10; C53; C50
JEL classification
C10; C53; C50
Date Issued
2024-08-01
Date Acceptance
2024-05-17
Citation
Energy Economics, 2024, 136
ISSN
0140-9883
Publisher
Elsevier
Journal / Book Title
Energy Economics
Volume
136
Copyright Statement
© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by nc-nd/4.0/).
Subjects
Big data
Business & Economics
Economics
Energy efficiency
Energy performance certificate
GREENHOUSE-GAS EMISSIONS
Machine learning
POLICY
REGRESSION
REGULARIZATION
SELECTION
Social Sciences
Tree-based models
Publication Status
Published
Article Number
107650
Date Publish Online
2024-05-22