Machine Learning-Based Prediction of CO2 Emissions per Capita and per GDP in France: A Comparative Study of ANN, Decision Tree, and Interpolation Approaches

Accurate estimation of carbon dioxide (CO₂) emissions, both per capita and per GDP, is essential for effective environmental policy and sustainable development. This study proposes a data-driven framework based on machine learning techniques to improve the prediction of CO₂ emissions per capita and per unit of Gross Domestic Product (GDP) in France. Artificial Neural Networks (ANN), Decision Trees (DT), and a mathematical interpolation approach were developed and evaluated using datasets incorpo