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Comparing GDP Growth Rates between Countries with High and Low Renewable Energy Usage Based on Neural Networks

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  • Abdelsamiea, Abdelsamiea Tahsin

    (Department of Economics, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia)

  • El-Aal, Mohamed F. Abd

    (Department of Economics, Faculty of Commerce, Arish University, North Sinai, Egypt)

Abstract

This paper utilizes a neural network algorithm to examine the impact of renewable energy consumption on economic growth in countries with both high and low usage rates. The findings indicate a weak inverse relationship between economic growth and renewable energy consumption. In contrast, there is a strong direct relationship between economic growth and the expansion of the industrial and manufacturing sectors. For countries that do not rely heavily on renewable energy, the results are consistent with those of high-consumption countries: renewable energy consumption exhibits a weak inverse relationship with economic growth. Simultaneously, the industrial and manufacturing sectors remain vital to economic growth. The study reveals that the industrial sector is the top contributor to economic growth in all the studied countries, representing 57.2% of Iceland's economy, 52.8% of Russia's, 49.3% of Norway's, and 44.6% of South Africa's. The manufacturing sector ranks next, contributing 36.6% in South Africa, 32.6% in Norway, 30.6% in Russia, and 20.7% in Iceland. In contrast, renewable energy consumption is the least significant contributor, accounting for 22.1% in Iceland, 18.7% in South Africa, 18.1% in Norway, and 16.8% in Russia. Overall, the results suggest that renewable energy consumption has no significant impact on economic growth in the examined countries, regardless of their reliance on renewable sources.

Suggested Citation

  • Abdelsamiea, Abdelsamiea Tahsin & El-Aal, Mohamed F. Abd, 2025. "Comparing GDP Growth Rates between Countries with High and Low Renewable Energy Usage Based on Neural Networks," International Journal of Energy Economics and Policy, Econjournals, vol. 16(1), pages 397-404, December.
  • Handle: RePEc:eco:journ2:v:16:y:2025:i:1:id:20868
    DOI: 10.32479/ijeep.20868
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