Umudike Journal of Engineering and Technology

Nigeria's reliance on fossil fuels for energy generation and consumption in many sectors of the economy has resulted in the release of a substantial amount of carbon dioxide (CO2), which is one of the most common gaseous pollutants found in the environment. The high concentration of CO2 has negative consequences on human health and the environment. The World Bank provided CO2-emission data for Nigeria over a multi-decade period (1980 – 2017).  This research paper explores the application of machine learning (ML) techniques, specifically linear regression and regularization methods, to predict carbon dioxide (CO2) emissions using a dataset from Nigeria.  The study explores the relationship between CO2 emissions and various factors such as petroleum and other liquids, coal and coke, and consumed natural gas and evaluates the performance of different regularization techniques in improving the accuracy of the predictive model.