Sustainable Development Goal Relational Modelling and Prediction
A methodology for UN Sustainable Development Goal (SDG) attainment prediction is presented, the Sustainable Development Goals Correlation Attainment Predictions Extended framework SDG-CAP-EXT. Unlike previous SDG attainment methodologies, SDG-CAP-EXT takes into account the potential for a causal relationship between SDG indicators both with respect to the geographic entity under consideration (intra-entity) and neighbouring geographic entities to the current entity (inter-entity). The challenge is in the discovery of such causal relationships. A ensemble approach is presented that combines the results of a number of alternative causality relationship identification mechanisms. The identified relationships are used to build multi-variate time series prediction models that feed into a bottom-up SDG prediction taxonomy, which is used to make SDG attainment predictions and rank countries using a proposed Attainment Likelihood Index that reflects the likelihood of goal attainment. The framework is fully described and evaluated. The evaluation demonstrates that the SDG-CAP-EXT framework can produce better predictions than alternative models that do not consider the potential for intra- and inter-causal relationships.