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The Relationship between Renewable Energy and Economic Growth in a Time of Covid-19: A Machine Learning Experiment on the Brazilian Economy

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  • Cosimo Magazzino

    (Department of Political Sciences, Roma Tre University, 00145 Rome, Italy)

  • Marco Mele

    (Department of Political Sciences, Roma Tre University, 00145 Rome, Italy)

  • Giovanna Morelli

    (Faculty of Political Sciences, University of Teramo, 64100 Teramo, Italy)

Abstract

This paper examines the relationship between renewable energy consumption and economic growth in Brazil, in the Covid-19 pandemic. Using an Artificial Neural Networks (ANNs) experiment in Machine Learning, we tried to verify if a more intensive use of renewable energy could generate a positive GDP acceleration in Brazil. This acceleration could offset the harmful effects of the Covid-19 global pandemic. Empirical findings show that an ever-greater use of renewable energies may sustain the economic growth process. In fact, through a model of ANNs, we highlighted how an increasing consumption of renewable energies triggers an acceleration of the GDP compared to other energy variables considered in the model.

Suggested Citation

  • Cosimo Magazzino & Marco Mele & Giovanna Morelli, 2021. "The Relationship between Renewable Energy and Economic Growth in a Time of Covid-19: A Machine Learning Experiment on the Brazilian Economy," Sustainability, MDPI, vol. 13(3), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1285-:d:487234
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    5. Marco Mele & Cosimo Magazzino & Nicolas Schneider & Antonia Rosa Gurrieri & Hêriş Golpira, 2022. "Innovation, income, and waste disposal operations in Korea: evidence from a spectral granger causality analysis and artificial neural networks experiments," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 39(2), pages 427-459, July.
    6. Deng, Wei & Akram, Rabia & Mirza, Nawazish, 2022. "Economic performance and natural resources: Evaluating the role of economic risk," Resources Policy, Elsevier, vol. 78(C).
    7. Khan, Zeeshan & Badeeb, Ramez Abubakr & Nawaz, Kishwar, 2022. "Natural resources and economic performance: Evaluating the role of political risk and renewable energy consumption," Resources Policy, Elsevier, vol. 78(C).
    8. Samuel-Soma M. Ajibade & Festus Victor Bekun & Festus Fatai Adedoyin & Bright Akwasi Gyamfi & Anthonia Oluwatosin Adediran, 2023. "Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012–2021)," Clean Technol., MDPI, vol. 5(2), pages 1-21, April.
    9. Toto Gunarto & Ukhti Ciptawaty & Dedy Yuliawan & Ahmad Mahyudin & Ahmad Dhea Pratama & Heru Wahyudi, 2024. "Comparison of Energy Consumption to Economic Growth in Developing Asian and Developed Asian Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 264-271, January.
    10. Chopra, Ritika & Magazzino, Cosimo & Shah, Muhammad Ibrahim & Sharma, Gagan Deep & Rao, Amar & Shahzad, Umer, 2022. "The role of renewable energy and natural resources for sustainable agriculture in ASEAN countries: Do carbon emissions and deforestation affect agriculture productivity?," Resources Policy, Elsevier, vol. 76(C).
    11. Kien, Pham Van & Ou, Jenho Peter & Sadiq, Dr Muhammad & Nguyen, Tran Thai Ha & Huy, Pham Quang & Tran, Trung Kien, 2023. "What role financial inclusion, green trade and natural resources utilization play in ASEAN economic growth: Evidence from post COVID era," Resources Policy, Elsevier, vol. 85(PB).
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