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Learning from experts: Energy efficiency in residential buildings

Author

Listed:
  • Billio, Monica
  • Casarin, Roberto
  • Costola, Michele
  • Veggente, Veronica

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.

Suggested Citation

  • Billio, Monica & Casarin, Roberto & Costola, Michele & Veggente, Veronica, 2023. "Learning from experts: Energy efficiency in residential buildings," SAFE Working Paper Series 403, Leibniz Institute for Financial Research SAFE.
    • Handle: RePEc:zbw:safewp:403
    DOI: 10.2139/ssrn.4596682
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    References listed on IDEAS

    as
    1. Ferentinos, Konstantinos & Gibberd, Alex & Guin, Benjamin, 2023. "Stranded houses? The price effect of a minimum energy efficiency standard," Energy Economics, Elsevier, vol. 120(C).
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    4. D Carvalho & S Cardoso Pereira & A Rocha, 2021. "Future surface temperatures over Europe according to CMIP6 climate projections: an analysis with original and bias-corrected data," Climatic Change, Springer, vol. 167(1), pages 1-17, July.
    5. Antonio R. Linero, 2018. "Bayesian Regression Trees for High-Dimensional Prediction and Variable Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 626-636, April.
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    More about this item

    Keywords

    Energy efficiency; Energy Performance Certificate; Machine learning; Tree-based models; big data;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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