Machine learning assessment under the development of green technology innovation: A perspective of energy transition

Chen W., Zou W., Zhong K., Aliyeva A.

RENEWABLE ENERGY, vol.214, pp.65-73, 2023 (SCI-Expanded) identifier identifier

  • Nəşrin Növü: Article / Article
  • Cild: 214
  • Nəşr tarixi: 2023
  • Doi nömrəsi: 10.1016/j.renene.2023.05.108
  • jurnalın adı: RENEWABLE ENERGY
  • Jurnalın baxıldığı indekslər: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, Greenfile, Index Islamicus, INSPEC, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Səhifə sayı: pp.65-73
  • Adres: Bəli

Qısa məlumat

This research investigates the influence of green technology innovation on the energy transition process using machine learning methods and econometric approach. To examine the effect of green technology innovation on the energy transition, we utilize panel data from China covering the years 2005-2020. The research intends to highlight the significance of green technology innovation in fostering the transition to sustainable energy. Our analysis of panel data reveals that green technology innovation significantly benefits China's energy transformation strategy. In particular, the study discovers that financial support for green technology innovation, as indicated by R & D spending and patent filings, has a favorable impact on the energy transition procedure. Additionally, to evaluate the influence of green technology innovation on the energy transition process, the research uses machine learning methods. The study emphasizes the need for increased investment in green technology innovation to support the transition to sustainable energy. Additionally, machine learning methods offer a valuable tool for evaluating how innovations in green technology affect the energy transition process. The research also sheds light on the variables that affect the outcome of energy transition initiatives, particularly emphasizing the role of R & D investments and patent filings in advancing a sustainable energy transition.