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The production of renewable energy has become one of the important elements in the pursuit of sustainable and environmentally friendly economic development, and countries of the world are increasingly adopting renewable energy sources to reduce the carbon footprint and mitigate the effects of climate change. As a result, the goal of this paper is to use different machine learning methods (Random Forest, Gradient Boosting, Support Vector Machine, Naïve Bayes and K-nearest neighbors) to establish which of these algorithms is the most accurate in predicting the values of Egypt's renewable energy production on the one hand, and recognizing the main determinants of this renewable energy production on the other. the paper proved that the Gradient Boosting model is the most accurate machine learning method. It also showed that the main determinant of Egypt's renewable energy production is Governance indicators (60%), then GDP per capita growth by(13%) and Population growth by(10%).As for the rest of the other variables, such as the price of oil , CO2 emissions, Renewable energy technical innovation, Renewable energy adaptation and Energy imports they have no effect. This paper recommends expanding the use of machine learning methods in macroeconomic models. |
