This paper compares two scenarios for forecasting aggregated indices using univariate and multivariate SSA. The methodology is applied to a time series of CO2 emission intensity in Europe, considering its values in terms of the segmentation of Europe into Eastern Europe, Southern Europe, and Western Europe. The results confirm that incorporating detailed information to forecast and then combining the results, significantly improves the forecast accuracy. This highlights the benefits of using multivariate approaches in forecasting, which can lead to more accurate and informed decision-making in environmental policy and climate change mitigation efforts.
