Machine Learning for Long-Term Forecasting from Historical Data

This paper presents a two-stage forecasting framework for future carbon dioxide (CO2) emissions using historical country-level indicators. The method first extrapolates the future trajectories of the input indicators and then predicts future CO2 emissions with nonlinear regressors trained on the most recent segment of the time series. Two design choices define the approach: recency-weighted training, which increases the influence of modern observations, and a boundary-consistency rule that selects the forecast with the smoother transition from observed to predicted emissions. The resulting forecasts preserve country-level heterogeneity while producing a globally increasing aggregate CO2 trajectory, with reported agreement against an external scientific baseline of 95.4%. The accompanying empirical results also show that the strongest associated indicators are dominated by energy use, fossil-fuel electricity production, transport activity, and trade-related variables.