Assessing the Economic Effects of Energy Access Inequalities between Rural and Urban Areas in Egypt Based on the Random Forest Algorithm

Abdelsamiea Tahsin Abdelsamiea (), Hasan Amin Mohamed Mahmoud () and Mohamed F. Abd El-Aal ()
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Abdelsamiea Tahsin Abdelsamiea: Department of Economics, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
Hasan Amin Mohamed Mahmoud: Department of Economics, Faculty of Commerce, Aswan University, Egypt
Mohamed F. Abd El-Aal: Department of Economics, Faculty of Commerce, Arish University, North Sinai, Egypt

International Journal of Energy Economics and Policy, 2025, vol. 15, issue 5, 59-64

Abstract: This research aims to determine the impact of energy entry in rural and urban areas on industrial growth in Egypt. The study relied on the Random Forest algorithm as one of the machine learning algorithms to determine this. The research concluded that the RF algorithm is more accurate than the remaining algorithms. The paper found that access to electricity in rural areas has the most significant impact on the growth of the industrial sector in Egypt, increasing by 85%, compared to a 15% increase in access to electricity in urban areas. Additionally, the paper confirmed a positive relationship between the growth of the industrial sector in Egypt and the rate of electricity access in both rural and urban areas. Hence, the paper finds that the electricity access to rural areas supports the Egyptian industrial sector and, consequently, development. This indicates the spread and concentration of small projects in rural areas.

Keywords: Machine Learning Algorithms; Random Forest; Electricity Access in Urban Areas; Electricity Access in Rural Areas; Industrial Value Added (search for similar items in EconPapers)
Date: 2025
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DOI: 10.32479/ijeep.18905

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