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dc.contributor.authorPata, Uğur Korkut
dc.contributor.authorKartal, Mustafa Tevfik
dc.contributor.authorKılıç Depren, Serpil
dc.date.accessioned2025-01-06T06:45:08Z
dc.date.available2025-01-06T06:45:08Z
dc.date.issued2025en_US
dc.identifier.citationPata, U. K., Kartal, M. T., & Kılıç Depren, S. (2024). How are energy R&D investments beneficial in ensuring energy transition: Evidence from leading R&D investing countries by novel super learner algorithm. Sustainable Energy Technologies and Assessments, 72, 104084. https://doi.org/10.1016/j.seta.2024.104084en_US
dc.identifier.issn2213-1388
dc.identifier.urihttps://doi.org/10.1016/j.seta.2024.104084
dc.identifier.urihttps://hdl.handle.net/11436/9799
dc.description.abstractThe study examines how critical factors affect the energy transition in the countries that invest the most in energy related R&D (namely, the USA, France, Japan, & Germany). The study empirically analyzes the impact of energy-related R&D investments, income (GDP), primary energy consumption (PEC), and human capital (HUC) by applying a novel super learner (SL) algorithm for the period from 2000/Q1 to 2022/Q4. The outcomes demonstrate that (i) the SL algorithm performs better than all others; (ii) nuclear and renewable energy R&D investments support the energy transition in the USA, while energy efficiency R&D investments are helpful for France and Germany, and no R&D types are beneficial for Japan; (iii) GDP and HUC support the energy transition in almost all countries; (iv) PEC supports the energy transition in France and Japan. Hence, on energy transition, the study proves the dominant effect of renewable energy R&D in the USA, HUC in France, energy efficiency R&D in Japan, and, energy efficiency and nuclear energy R&D in Germany, while other factors have less influence.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEnergy R&D investments;en_US
dc.subjectEnergy transitionen_US
dc.subjectMachine learningen_US
dc.subjectRenewable energyen_US
dc.subjectSL algorithmen_US
dc.titleHow are energy R&D investments beneficial in ensuring energy transition: Evidence from leading R&D investing countries by novel super learner algorithmen_US
dc.typearticleen_US
dc.contributor.departmentRTEÜ, İktisadi ve İdari Bilimler Fakültesi, İktisat Bölümüen_US
dc.contributor.institutionauthorPata, Uğur Korkut
dc.identifier.doi10.1016/j.seta.2024.104084en_US
dc.identifier.volume72en_US
dc.identifier.startpage104084en_US
dc.relation.journalSustainable Energy Technologies and Assessmentsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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