Role of fourth industrial revolution on dirty and clean energy under bearish, neutral and bullish market conditions: A quantile-on-quantile Granger causality approach

USMAN O., Ibrahim B., Ozkan O., Ike G. N.

Energy, cilt.322, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 322
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.energy.2025.135582
  • Dergi Adı: Energy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Clean energy markets, Dirty energy markets, Fourth industrial revolution, Quantile-on-quantile granger causality
  • İstanbul Ticaret Üniversitesi Adresli: Evet

Özet

The transformation of the world into a complex system through global exposure to artificial intelligence, digitalization, and other technological advancements may affect the environment, including energy markets. This study investigates the role of the Fourth Industrial Revolution (FIR) on clean and dirty energy markets using daily time series data from December 19, 2017, to March 21, 2024. The empirical results based on the newly developed Quantile-on-Quantile Granger Causality test suggest the following: (i) returns of assets related to the FIR under bearish market conditions induce market oscillations in both clean and dirty energy markets; (ii) the predictive power of assets related to the FIR for dirty energy assets is stronger compared to its predictive power for clean energy assets when markets for the FIR assets are neutral. This implies that clean energy assets can act as diversifiers for FIR assets; (iii) market oscillations occur at both bullish and bearish states of FIR asset returns but are more pronounced at bearish states; (iv) the use of alternative energy market proxies tell a somewhat similar story. Overall, the implication of our findings is that assets related to the FIR have stronger predictive content for dirty energy markets. Policy implications are outlined.