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

Álvarez Díez, Susana; Baixauli Soler, Juan Samuel; Lozano Reina, Gabriel; Rodríguez-Linares Rey, Diego

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

dc.contributor.author	Álvarez Díez, Susana
dc.contributor.author	Baixauli Soler, Juan Samuel
dc.contributor.author	Lozano Reina, Gabriel
dc.contributor.author	Rodríguez-Linares Rey, Diego
dc.contributor.department	Organización de Empresas y Finanzas
dc.contributor.department	Métodos Cuantitativos para la Economía y la Empresa
dc.contributor.other	Facultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Organización de Empresas y Finanzas	es
dc.contributor.other	Facultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Métodos Cuantitativos para la Economía y la Empresa	es
dc.date.accessioned	2024-03-18T11:02:27Z
dc.date.available	2024-03-18T11:02:27Z
dc.date.issued	2024-04-01
dc.description	© 2024. The authors. This document is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0 This document is the published version of a published work that appeared in final form in REVISTA. To access the final work, see DOI: https://doi.org/10.1016/j.apenergy.2024.122725	es
dc.description.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.	es
dc.format	application/pdf	es
dc.format.extent	15	es
dc.identifier.citation	Applied Energy Volume 359, 1 April 2024, 122725
dc.identifier.doi	https://doi.org/10.1016/j.apenergy.2024.122725
dc.identifier.issn	Print: 0306-2619
dc.identifier.issn	Electronic: 1872-9118
dc.identifier.uri	http://hdl.handle.net/10201/140282
dc.language	eng	es
dc.publisher	Elsevier Ltd.	es
dc.relation	This work was supported by the Fundación Cajamurcia.	es
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/pii/S0306261924001089?via%3Dihub	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Energy efficiency	es
dc.subject	Public investment subsidies	es
dc.subject	SMEs	es
dc.subject	Random forest	es
dc.subject	Unbalanced samples	es
dc.title	Subsidies for investing in energy efficiency measures: Applying a random forest model for unbalanced samples	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es
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