The influence of building energy performance prediction accuracy on retrofit rates

Christian Wiethe and Simon Wenninger

Energy Policy, 2023, vol. 177, issue C

Abstract: The residential building sector bears enormous CO2 emission reduction potential. However, uncertainty connected to financial savings of retrofits, stemming largely from inaccurate building energy performance predictions, forms a significant investment barrier. So far, literature does not sufficiently cover the relationship between prediction accuracy and retrofit rates. Thus, this paper aims to provide guidance for policymakers by setting up an agent-based building stock model to derive this relationship for the German residential building sector assuming rational decision-making. Results indicate that higher prediction accuracies positively affect the retrofit rate. Using data-driven prediction methods established in research significantly increases the retrofit rate by over 70%, from around 0.98% – 1.68% compared to the legally prescribed engineering method. This equals CO2 emission reductions of almost 45 Mt by 2050 for Germany, leading to a surplus in consumer rent of 310.19 mn €, while investments in retrofits increase by about 1.195 bn €. The government benefits from tax payments and saves opportunity costs. The findings allow to estimate the effect of revising current legislation on building energy performance predictions and thereby clearly guide policymakers.

Keywords: Building stock model; Energy quantification methods; Energy efficiency investments; Machine learning algorithms; Data analytics; Risk (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:enepol:v:177:y:2023:i:c:s0301421523001271

DOI: 10.1016/j.enpol.2023.113542

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