Integrating Machine Learning and Scenario Modelling for Robust Population Forecasting Under Crisis and Data Scarcity

This study introduces a new ensemble framework for demographic forecasting that systematically incorporates stylised crisis scenarios into rate and population projections. While scenario reasoning is common in qualitative foresight, its quantitative application in demography remains underdeveloped. Our method combines autoregressive lags, global predictors, and robust regression with a trend-anchoring mechanism, enabling stable projections from short official time series (15–20 years in length). Scenario shocks are operationalised through binary event flags for pandemics, refugee inflows, and financial crises, which influence fertility, mortality, and migration models before translating into cohort and population trajectories. Results demonstrate that shocks with strong historical precedence, such as Germany’s migration surges, are convincingly reproduced and leave enduring effects on projected populations. Conversely, weaker or non-recurrent shocks, typical in Norway and Portugal, produce muted scenario effects, with baseline momentum dominating long-term outcomes. At the national level, total population aggregates mitigate temporary shocks, while cohort-level projections reveal more pronounced divergences. Limitations include the short length of the training series, the reduction of signals when shocks do not surpass historical peaks, and the loss of granularity due to age grouping. Nevertheless, the framework shows how robust statistical ensembles can extend demographic forecasting beyond simple trend extrapolation, providing a formal and transparent quantitative tool for stress-testing population futures under both crisis and stability.