Abstract
The introduction of electric vehicles (EVs) substantially impacts gasoline sales and the economic sustainability of fuel merchants in South Africa. This study uses machine learning algorithms to systematically assess fluctuations in gasoline consumption and revenue patterns, delivering a strong, data-driven view of market dynamics. The findings show a significant reduction in gasoline demand in countries with high levels of EV adoption, emphasizing the need for fuel merchants to make strategic changes to their business models. Machine learning techniques efficiently anticipate and respond to market variations, providing important insights into emerging fuel market trends. As a result, it is critical for gasoline merchants to invest in EV charging infrastructure and diversify their service offerings in order to offset financial risks connected with the changing energy environment. This study adds to the larger discussion on sustainable energy transitions, suggests future trend models, and uses scenario planning to investigate novel business paradigms. Furthermore, it demonstrates the importance of machine learning in anticipating economic effects and managing regulatory obstacles. Finally, this study underscores the need for gasoline merchants to actively participate in the changing energy landscape by upgrading their infrastructure and overall consumer experience.
Keywords: electric vehicles; business forecasting; machine learning; petroleum retailers; South Africa.