Dynamic interactions between oil prices and renewable energy production in Italy amid the COVID-19 pandemic: wavelet and machine learning analyses

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.

Keywords

Oil prices / Renewable energy / COVID-19 / Partial Wavelet Coherency / Time-Varying Granger Causality / Machine Learning / Italy

References

[1] Adebayo TS, Kirikkaleli D. Impact of renewable energy consumption, globalization, and technological innovation on environmental degradation in Japan: application of wavelet tools. Environ Dev Sustain, 2021, 23: 16057-16082

[2] Adebayo TS, Özkan O. Investigating the influence of socioeconomic conditions, renewable energy and eco-innovation on environmental degradation in the United States: A wavelet quantile-based analysis. J Clean Prod, 2024, 434: 140321

[3] Aktar A, Alam M, Al-Amin AQ. Global economic crisis, energy use, CO ₂ emissions, and policy roadmap amid COVID-19. Sustain Prod Consum, 2020, 26: 770-781

[4] Alaraj M, Kumar A, Alsaidan I, Rizwan M, Jamil M. Energy production forecasting from solar photovoltaic plants based on meteorological parameters for Qassim Region, Saudi Arabia. IEEE Access, 2021, 9: 83241-83251

[5] Alola AA, Adebayo TS. The potency of resource efficiency and environmental technologies in carbon neutrality target for Finland. J Clean Prod, 2023, 389: 136127

[6] Arora V, Shi S. Energy consumption and economic growth in the United States. Appl Econ, 2016, 48: 3763-3773

[7] Awerbuch S, Sauter R. Exploiting the oil–GDP effect to support renewables deployment. Energy Policy, 2006, 34(17): 2805-2819

[8] Barboza F, Kimura H, Altman E. Machine learning models and bankruptcy prediction. Expert Syst Appl, 2017, 83: 405-417

[9] Baum CF, Hurn S, Otero J (2021) The dynamics of U.S. industrial production: a time-varying Granger causality perspective. Econom Stat

[10] Bilgili F, Koçak E, Kuşkaya S. Dynamics and co-movements between the COVID-19 outbreak and the stock market in Latin American countries: an evaluation based on the wavelet-partial wavelet coherence model. Eval Rev, 2023, 47(4): 630-652

[11] Bilgili F, Kassouri Y, Kuşkaya S, Majok Garang AP. The dynamic nexus of oil price fluctuations and banking sector in China: a continuous wavelet analysis. Resour Policy, 2024, 88: 104449

[12] Bloch H, Rafiq S, Salim R. Economic growth with coal, oil and renewable energy consumption in China: prospects for fuel substitution. Econ Model, 2015, 44: 104-115

[13] Brady GL, Magazzino C. The relationship among renewable energy, economic growth, labor and capital formation in Italy. Rivista Di Studi Sulla Sostenibilità, 2018, 1: 35-48

[14] Cao H, Guo L, Zhang L. Does oil price uncertainty affect renewable energy firms’ investment? Evidence from listed firms in China. Financ Res Lett, 2020, 33: 101205

[15] Chang C, McAleer M, Wang Y. Herding behaviour in energy stock markets during the global financial crisis, SARS, and ongoing COVID-19. Renew Sustain Energy Rev, 2020, 134: 110349

[16] Chen YH, Chen JL. Machine learning mechanisms for cyber-phishing attack. IEICE Trans Inf Sys, 2019, 102-D(5): 878-887

[17] Coccia M. Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID. Sci Total Environ, 2020, 729: 138474

[18] Elgammal MM, Ahmed WMA, Alshami A. Price and volatility spillovers between global equity, gold, and energy markets prior to and during the COVID-19 pandemic. Resour Policy, 2021, 74: 102334

[19] Essletzbichler J. Renewable energy technology and path creation: a multi-scalar approach to energy transition in the UK. Eur Plan Stud, 2012, 5(20): 791-816

[20] Faraggi D, LeBlanc M, Crowley J. Understanding neural networks using regression trees: An application to multiple myeloma survival data. Stat Med, 2001, 20(9–10): 1255-1268

[21] Ghiani E, Galici M, Mureddu M, Pilo F. Impact on electricity consumption and market pricing of energy and ancillary services during pandemic of COVID-19 in Italy. Energies, 2020, 13(13): 3357

[22] Gillingham K, Knittel C, Li J, Ovaere M, Reguant M. The short-run and long-run effects of Covid-19 on energy and the environment. Joule, 2020, 4(7): 1337-1341

[23] Granger CWJ. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 1969, 37: 424-438

[24] Grinsted A, Moore JC, Jevrejeva S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes Geophys, 2004, 11(5/6): 561-566

[25] Guevara M, Jorba O, Soret A, Petetin H, Bowdalo D, Serradell K, Tena C, Denier van der Gon H, Kuenen J, Peuch V-H, Pérez García-Pando C. Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns. Atmos Chem Phys, 2021, 21(1): 773-797

[26] Guo Y, Yu C, Zhang H, Cheng H. Asymmetric between oil prices and renewable energy consumption in the G7 countries. Energy, 2021, 226: 120319

[27] Hammoudeh S, Mokni K, Ben-Salha O, Ajmi A. Distributional predictability between oil prices and renewable energy stocks: is there a role for the COVID-19 pandemic?. Energy Econ, 2021, 103: 105512

[28] Hsiao C, Lin W, Wei X, Yan G, Li S. The impact of international oil prices on the stock price fluctuations of China’s renewable energy enterprises. Energies, 2019, 12(24): 4630

[29] Içen H, Tatoğlu FY. The asymmetric effects of changes in price and income on renewable and nonrenewable energy. Renew Energy, 2021, 178: 144-152

[30] Jia Z, Wen S, Lin B. The effects and reacts of COVID-19 pandemic and international oil price on energy, economy, and environment in China. Appl Energy, 2021, 302: 117612

[31] Jiang P, Fan Y, Klemeš JJ. Impacts of COVID-19 on energy demand and consumption: challenges, lessons and emerging opportunities. Appl Energy, 2021, 285: 116441

[32] Kavadi DP, Patan R, Ramachandran M, Gandomi AH. Partial derivative nonlinear global pandemic machine learning prediction of COVID-19. Chaos, Solitons Fractals, 2020, 139: 110056

[33] Kılkış Ş. Transition towards urban system integration and benchmarking of an urban area to accelerate mitigation towards net-zero targets. Energy, 2021, 236: 121394

[34] Kiragu E. Transition into a green economy: Are there limits to government intervention?. World Trade Inst, 2015

[35] Kristoufek L, Janda K, Zilberman D. Comovements of ethanol-related prices: Evidence from Brazil and the USA. GCB Bioenergy, 2016, 8(6): 1082-1096

[36] Kumar UA. Comparison of neural networks and regression analysis: A new insight. Expert Syst Appl, 2005, 29(3): 424-430

[37] Kuşkaya S, Bilgili F, Muğaloğlu E, Khan K, Hoque ME, Toguç N. The role of solar energy usage in environmental sustainability: fresh evidence through time-frequency analyses. Renew Energy, 2023, 206: 858-871

[38] Lamperti F, Dosi G, Napoletano M, Roventini A, Sapio A. Climate change and green transitions in an agent-based integrated assessment model. Technol Forecast Soc Chang, 2020, 153: 119806

[39] Lee T, Lee T, Lee Y. An experiment for urban energy autonomy in Seoul: The one ‘less’ nuclear power plant policy. Energy Policy, 2014, 74: 311-318

[40] Prol JL, O SS. The impact of COVID-19 measures on short-term electricity consumption in the most affected EU countries and USA states. iScience, 2020, 23(10): 101639

[41] Luqman M, Ahmad N, Bakhsh K. Nuclear energy, renewable energy and economic growth in Pakistan: evidence from non-linear autoregressive distributed lag model. Renew Energy, 2019, 139: 1299-1309

[42] Magazzino C. On the Relationship between Disaggregated Energy Production and GDP in Italy. Energy Environ, 2012, 23(8): 1191-1207

[43] Magazzino C. Renewable energy consumption-economic growth nexus in Italy. Int J Energy Econ Policy, 2017, 7(6): 119-127

[44] Magazzino C. GDP, Energy Consumption and Financial Development in Italy. Int J Energy Sect Manage, 2018, 12(1): 28-43

[45] Magazzino C, Giolli L. A time series analysis of the aggregate income-energy consumption nexus: the case of Italy. J Energy Dev, 2014, 39(1–2): 219-227

[46] Magazzino C, Mele M, Morelli G. The relationship between renewable energy and economic growth in a time of Covid-19: a machine learning experiment on the Brazilian economy. Sustainability, 2021, 13(3): 1285

[47] Magazzino C, Mutascu M, Mele M, Sarkodie SA. Energy consumption and economic growth in Italy: a wavelet analysis. Energy Rep, 2021, 7: 1520-1528

[48] Mastoi MS, Munir HM, Zhuang S, Hassan M, Usman M, Alahmadi A, Alamri B. A critical analysis of the impact of the pandemic on China’s electricity usage patterns and the global development of renewable energy. Int J Environ Res Public Health, 2022, 19(8): 4608

[49] Moguerza JM, Muñoz A. Support vector machines with applications. Stat Sci, 2006, 21: 322-336

[50] Ng EK, Chan JC. Geophysical applications of partial wavelet coherence and multiple wavelet coherence. J Atmos Oceanic Tech, 2012, 29(12): 1845-1853

[51] Nieto J, Carpintero Ó, Miguel LJ, Blas ID. Macroeconomic modelling under energy constraints: global low carbon transition scenarios. Energy Policy, 2020, 137: 111090

[52] Norouzi N. Post-COVID-19 and globalization of oil and natural gas trade: challenges, opportunities, lessons, regulations, and strategies. Int J Energy Res, 2021, 45(10): 14338-14356

[53] Peiffer-Smadja N, Maatoug R, Lescure F-X, D’Ortenzio E, Pineau J, King J-R. Machine learning for COVID-19 needs global collaboration and data-sharing. Nat Mach Intell, 2020, 2(6): 293-294

[54] Perera F, Berberian A, Cooley D, Shenaut E, Olmstead H, Ross Z, Matte T. Potential health benefits of sustained air quality improvements in New York City: a simulation based on air pollution levels during the COVID-19 shutdown. Environ Res, 2021, 193: 110555

[55] Phillips PC, Wu Y, Yu J. Explosive behavior in the 1990s NASDAQ: When did exuberance escalate asset values?. Int Econ Rev, 2011, 52(1): 201-226

[56] Phillips PC, Shi S, Yu J. Testing for multiple bubbles: historical episodes of exuberance and collapse in the S&P 500. Int Econ Rev, 2015, 56(4): 1043-1078

[57] Psaradakis Z, Sola M, Spagnolo F. Testing for causality-in-variance: an application to the East Asian markets. Int J Financ Econ, 2005, 10(3): 235-245

[58] Reboredo J, Rivera-Castro M, Ugolini A. Wavelet-based test of co-movement and causality between oil and renewable energy stock prices. Energy Econ, 2017, 61: 241-252

[59] Rita E, Chizoo E, Cyril US. Sustaining COVID-19 pandemic lockdown era air pollution impact through utilization of more renewable energy resources. Heliyon, 2021, 7(7): e07455

[60] Robinson PM. Log periodogram regression of time series with long-range dependence. Ann Stat, 1995, 23: 1048-1072

[61] Rugani B, Conticini E, Frediani B, Caro D. Decrease in life expectancy due to COVID-19 disease not offset by reduced environmental impacts associated with lockdowns in Italy. Environ Pollut, 2021, 292(A): 118224

[62] Rume T, Islam SMDU. Environmental effects of COVID-19 pandemic and potential strategies of sustainability. Heliyon, 2020, 6(9): e04965

[63] Sadorsky P. Modeling renewable energy company risk. Energy Policy, 2012, 40: 39-48

[64] Saif-Alyousfi AYH, Saha A. The impact of COVID-19 and non-pharmaceutical interventions on energy returns worldwide. Sustain Cities Soc, 2021, 70: 102943

[65] Selin N. Lessons from a pandemic for systems-oriented sustainability research. Sci Adv, 2021, 7: 22

[66] Shah I, Hiles C, Morley B. How do oil prices, macroeconomic factors and policies affect the market for renewable energy?. Appl Energy, 2017, 215: 87-97

[67] Shi S, Hurn S, Phillips PCB. Change detection and the causal impact of the yield curve. J Time Ser Anal, 2018, 39(6): 966-987

[68] Shi S, Hurn S, Phillips PCB. Causal change detection in possibly integrated systems: revisiting the money-income relationship. J Financ Economet, 2020, 18(1): 158-180

[69] Swanson NR. Money and output viewed through a rolling window. J Monet Econ, 1998, 41(3): 455-474

[70] Tambari I, Failler P. Determining if oil prices significantly affect renewable energy investment in African countries with energy security concerns. Energies, 2020, 13(24): 6740

[71] Thoma MA. Subsample instability and asymmetries in money-income causality. J Econom, 1994, 64(1–2): 279-306

[72] Troster V, Shahbaz M, Uddin G. Renewable energy, oil prices, and economic activity: a Granger-causality in quantiles analysis. Energy Econ, 2018, 70: 440-452

[73] Tugcu C, Topcu M. Total, renewable and non-renewable energy consumption and economic growth: Revisiting the issue with an asymmetric point of view. Energy, 2018, 152: 64-74

[74] von Lüpke H, Well M. Analyzing climate and energy policy integration: the case of the Mexican energy transition. Climate Policy, 2020, 20(8): 832-845

[75] Zar JH. Biostatistical Analysis, 1996 Prentice Hall

[76] Zhang A, Yan J, Yu Q, Obersteiner M, Li W, Chen J, Zhang Q, Jiang M, Wallin F, Song X, Wu J, Wang X, Shibasaki R. 1.6 Million transactions replicate distributed PV market slowdown by COVID-19 lockdown. Appl Energy, 2021, 283: 116341

Funding

Università degli Studi Roma Tre