Predictive Modeling of CO2 Emissions Based on Economic and Demographic Factors

Abstract

This paper develops a predictive model to estimate carbon dioxide (CO₂) emissions based on economic and demographic factors, specifically Gross Domestic Product (GDP) and population. Data from the World Bank Open Data Catalog were collected and processed using machine learning techniques. The Random Forest model was selected for its ability to capture the non-linear relationships between variables. The model’s results reveal a strong correlation between economic growth and increased CO₂ emissions, emphasizing the need for stricter environmental policies to mitigate the adverse impacts of economic expansion on climate change. This research provides a data-driven tool to help policymakers assess the influence of economic and demographic factors on CO₂ emissions and implement more effective mitigation strategies.