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Dynamic interactions between oil prices and renewable energy production in Italy amid the COVID-19 pandemic: wavelet and machine learning analyses
Magazzino, Cosimo Giolli, Lorenzo Gattone, Tulia
Magazzino, Cosimo
Giolli, Lorenzo
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Abstract
This study examines the intricate dynamics between oil prices and renewable energy investments in Italy during the initial phase of the CoronaVirus Disease 2019 pandemic, a period characterized by significant economic and social upheaval. Utilizing advanced empirical techniques, such as Partial Wavelet Coherency analysis, Time-Varying Granger Causality, and Robinson Log-Periodogram tests, as well as Machine Learning (ML) regressions, this research uncovers nuanced insights into the interplay between oil prices and renewable energy series including biomass, solar, hydro, wind, and geothermal. Key findings indicate a predominant in-phase relationship with oil prices leading most renewable energy series, and unidirectional causality from renewables to oil prices in several instances, highlighting the potential influence of renewable energy on oil market dynamics. In robustness checks, ML models further elucidate the impact, with solar, hydro, and geothermal sources showing significant importance scores. These insights are critical for policymakers and stakeholders aiming to enhance energy security and transition towards sustainable energy sources amidst global crises.
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Date
2024
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Springer Nature
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Keywords
Oil prices, Renewable energy, COVID-19, Partial Wavelet Coherency, Time-Varying Granger Causality, Machine Learning
Citation
Magazzino, Cosimo, Tulia Gattone, and Lorenzo Giolli. “Dynamic Interactions between Oil Prices and Renewable Energy Production in Italy amid the COVID-19 Pandemic: Wavelet and Machine Learning Analyses.” Energy, Ecology and Environment, June. 2024.