Subsidies for investing in energy efficiency measures: Applying a random forest model for unbalanced samples

Susana Álvarez-Diez, J. Samuel Baixauli-Soler, Gabriel Lozano-Reina and Diego Rodríguez-Linares Rey

Applied Energy, 2024, vol. 359, issue C, No S0306261924001089

Abstract: Investing in energy efficiency measures is a major challenge for SMEs, both for environmental and economic reasons. However, certain barriers often make it difficult to invest in such measures. Although public financial support helps to overcome economic barriers, public bodies face the challenge of identifying which SMEs display the greatest potential to invest in energy efficiency measures. By applying a random forest technique and by using sampling balancing techniques, this paper identifies the profile of industrial SMEs that might be potential beneficiaries of public aid, thereby helping public institutions to target their calls and direct their efforts towards this group of SMEs. Specifically, liquidity and indebtedness are found to be the most useful predictors for SMEs in the industrial sector. The results are robust and reveal that applying a random forest approach for unbalanced samples offers greater predictive capacity and statistical power than applying traditional estimation techniques. By identifying potentially benefiting firms, this work helps to boost the effectiveness of public subsidies and to improve the channeling of public funds, which ultimately favors investment in energy efficiency.

Keywords: Energy efficiency; Public investment subsidies; SMEs; Random forest; Unbalanced samples (search for similar items in EconPapers)
JEL-codes: H2 M2 Q2 Q4 Q5 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2024.122725

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