How are energy R&D investments beneficial in ensuring energy transition: Evidence from leading R&D investing countries by novel super learner algorithm

PATA U. K., Kartal M. T., KILIÇ DEPREN S.

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, vol.72, 2024 (SCI-Expanded, Scopus) identifier identifier

  • Nəşrin Növü: Article / Article
  • Cild: 72
  • Nəşr tarixi: 2024
  • Doi nömrəsi: 10.1016/j.seta.2024.104084
  • jurnalın adı: SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
  • Jurnalın baxıldığı indekslər: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Geobase, INSPEC
  • Açar sözlər: Energy transition, Machine learning, Renewable energy, SL algorithm, Energy R&D investments
  • Açıq Arxiv Kolleksiyası: Məqalə
  • Adres: Yox

Qısa məlumat

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.