CO₂ Emissions Projections for 2100: A Comparative Machine Learning Study of U.S. and Multimodal Approach of Global Trends

The continuous rise in CO₂ emissions is a major contributor to climate change, affecting ecosystems, economies, and public health. Predicting future emissions accurately is crucial for designing effective policies and mitigation strategies. This study explores multiple machine learning models for CO₂ emissions forecasting, comparing traditional methods like Support Vector Machines (SVM), Linear Regression, and Decision Trees with advanced deep learning techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Multi-Layer Perceptron (MLP). Using a time-series approach, we forecast emissions up to 2100 and assess model performance through key metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R² score. Our results indicate that deep learning models, especially LSTM and GRU, outperform traditional methods in capturing complex patterns and trends in emissions data. Additionally, we generate geospatial visualizations to highlight regions facing the highest risks. These insights provide valuable guidance for policymakers and environmental researchers, enabling data-driven decisions for emission reduction, resource allocation, and long-term sustainability planning in the fight against climate change.