Global forecasting of atmospheric CO₂ concentrations using a hybrid STL-Prophet-LSTM model

The increasing concentration of atmospheric carbon dioxide (CO₂) poses a significant global challenge, underscoring the need for accurate predictions to better understand and mitigate climate change. While existing CO₂ forecasting models are often restricted to specific scenarios or localized regions, a comprehensive global prediction framework remains lacking. To address this gap, we propose the SPL (STL-Prophet-LSTM) model, an integrated approach combining Seasonal-Trend decomposition using Loess (STL), the Prophet forecasting model, and Long Short-Term Memory (LSTM) networks. Leveraging monthly average CO₂ concentration data from six globally distributed monitoring stations (NOAA), we forecasted CO₂ trends over the next decade. Our results demonstrate the SPL model’s superior predictive performance, achieving an average RMSE of 0.67, MAE of 0.53, and R² of 0.99—outperforming benchmark models (ARIMA, SARIMA, Prophet, and standalone LSTM). Projections reveal a concerning upward trajectory, with Northern Hemisphere CO₂ levels expected to reach 450 ppm by 2032, compared to 438 ppm in the Southern Hemisphere, highlighting persistent hemispheric disparities. This study provides a robust methodological framework for global-scale CO₂ concentration forecasting, offering critical insights for climate policy and mitigation strategies.