Quantitative Analysis and Forecasting of Industrial CO2 Emissions using Multiple Machine Learning Models

In response to escalating climate concerns, precise industrial Carbon Dioxide (CO2) emissions prediction is paramount. Employing advanced Machine Learning (ML) techniques, this study focuses on forecasting industrial CO2 emissions using global data from the Our World In Data Dataset (containing information on annual emissions from cement, coal, flaring, gas, and oil industries). Various regression models including Support Vector Regression (SVR), Linear Regression, and XGBoost were explored, with a primary emphasis on time series forecasting models for yearly CO2 emissions. Leveraging time series forecasting, intricate temporal trends in emissions data are discerned, offering enhanced predictive insights. CO2 prediction literature was reviewed, data collected and preprocessed, and various ML algorithms implemented, followed by hyperparameter tuning. The models, rigorously trained and evaluated, yield accurate emission predictions. Results highlight the superior performances of the Transformer model and the Neural Prophet Library developed by Stanford University in collaboration with Facebook Inc., with RMSE scores of 416.58 and 470.30, impressively low MAPE scores of both 0.01, and relatively lower MAE of 349.07 and 380.40 compared to other tested models. DeepTCN also demonstrates competitive predictive capabilities but falls short of Transformer model and Neural Prophet model accuracy. Traditional models including ARIMA, Naive Forecasting, Auto Regression (AR), Exponential Smoothing, and SARIMA lag in performance compared to both Neural Prophet and Transformer. These findings underscore the promising role of ML in advancing sustainable environmental management and pave the way for subsequent research endeavors.