TY  - UNPD
A1  - Billio, Monica
A1  - Casarin, Roberto
A1  - Costola, Michele
A1  - Veggente, Veronica
T1  - Learning from experts: energy efficiency in residential buildings
N2  - 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.
T3  - SAFE working paper - 403 
KW  - Energy efficiency
KW  - Energy Performance Certificate
KW  - Machine learning
KW  - Tree-based models
KW  - big data
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/71523
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-715233
UR  - https://ssrn.com/abstract=4596682
PB  - SAFE
CY  - Frankfurt am Main
ER  -