Forecasting National Sustainability Trajectories with Deep Learning: Predictability, Surprise, and Early Predictive Signals

Sustainability monitoring has mainly focused on measuring where countries stand today, rather than anticipating where they are headed. This study develops an AI-based forecasting framework to predict national sustainability outcomes and identify countries whose actual paths deviate from predictions. Using 749 World Development Indicators across 184 countries and regions from 2003 to 2022, a Temporal Fusion Transformer (TFT) is developed using data from 2003 to 2017 (training and validation) and evaluated on a held-out 2018 to 2022 test period, with calibrated prediction intervals constructed retrospectively over the test period. Assuming that historical development patterns remain informative over the forecast horizon, the model achieves mean absolute errors of 1.10 for the Sustainable Development Goals Index (SDGI, 0 to 100 scale) and 0.008 for the Human Development Index (HDI, 0 to 1 scale), reducing error by at least 19 percent for SDGI and 60 percent for HDI relative to linear trend and XGBoost baselines. Of 184 countries and regions, 115 (62 percent) are classified as on-track for both indices. Among the rest, 35 show positive SDGI deviations, mostly developing nations in Sub-Saharan Africa and South Asia that are exceeding their forecast trajectories, while 23 show negative HDI deviations concentrated among nations affected by conflict and economic disruption. We find this asymmetric pattern is consistent with a decoupling between goal-level and capability-level sustainability, in which policy-driven SDG indicators can advance while foundational human development in health and income stalls. Our model identifies economic indicators as the dominant predictors of HDI (7 of the top 10), while SDGI prediction draws on a more balanced mix of economic, social, environmental, and institutional indicators. We also find that better governance is associated with lower prediction error for both SDGI (p = 0.004) and HDI (p < 0.001), suggesting that countries and regions with stronger institutions follow more predictable sustainability trajectories.