Leveraging Machine Learning to Assess the Impact of Energy Consumption on Global GDP Growth: What Actions should be taken Globally toward Environmental Concerns? - El-Aal, Mohamed F. Abd; Mahmoud, Hasan Amin Mohamed; Abdelsamiea, Abdelsamiea Tahsin; Hegazy, Marwa Samir (International Journal of Energy Economics and Policy, 2024)
The study aims to explore the impact of renewable, nonrenewable, and nuclear energy consumption on global gross domestic product (GDP) growth through machine learning algorithms. The findings reveal that renewable energy consumption is the most influential variable, contributing to a predicted 67.5% global GDP growth. In contrast, nuclear energy consumption contributes 17.8%, and non-renewable energy consumption contributes 14.6%. Notably, the relationship between nuclear energy consumption and global economic growth is positive; there is a negative relation in conjunction with renewable energy consumption. However, the association with non-renewable energy is consistently fixed. These results suggest that an increased reliance on renewable energy may necessitate a trade-off, potentially leading to a reduction in global GDP growth despite the positive contributions from renewable sources.