dc.contributor.author | Pata, Uğur Korkut | |
dc.contributor.author | Kartal, Mustafa Tevfik | |
dc.contributor.author | Kılıç Depren, Serpil | |
dc.date.accessioned | 2025-01-06T06:45:08Z | |
dc.date.available | 2025-01-06T06:45:08Z | |
dc.date.issued | 2025 | en_US |
dc.identifier.citation | Pata, 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.2024.104084 | en_US |
dc.identifier.issn | 2213-1388 | |
dc.identifier.uri | https://doi.org/10.1016/j.seta.2024.104084 | |
dc.identifier.uri | https://hdl.handle.net/11436/9799 | |
dc.description.abstract | The 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.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Energy R&D investments; | en_US |
dc.subject | Energy transition | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Renewable energy | en_US |
dc.subject | SL algorithm | en_US |
dc.title | How are energy R&D investments beneficial in ensuring energy transition: Evidence from leading R&D investing countries by novel super learner algorithm | en_US |
dc.type | article | en_US |
dc.contributor.department | RTEÜ, İktisadi ve İdari Bilimler Fakültesi, İktisat Bölümü | en_US |
dc.contributor.institutionauthor | Pata, Uğur Korkut | |
dc.identifier.doi | 10.1016/j.seta.2024.104084 | en_US |
dc.identifier.volume | 72 | en_US |
dc.identifier.startpage | 104084 | en_US |
dc.relation.journal | Sustainable Energy Technologies and Assessments | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |