Abstract
This paper presents a review and in-depth analysis of the Sustainable Development Goal Track, Trace, and Forecast (SDG-TTF) framework for UN Sustainable Development Goal (SDG) attainment forecasting. Unlike earlier SDG attainment forecasting frameworks, the SDG-TTF framework considers the possibility for causal relationships between SDG indicators, both within a given geographic entity (intra-entity relationships) and between the current entity and its neighbouring geographic entities (inter-entity relationships). The difficulty lies in identifying such causal linkages. Six different mechanisms are considered. The discovered causal relationships are then used to generate multivariate time series prediction models within a bottom-up SDG prediction taxonomy. The overall framework was assessed using three different geographical regions. The results demonstrated that the Extended SDG-TTF framework was capable of producing better predictions than competing models that do not account for the possibility of intra and inter-causal linkages.
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Note that all sub-indicators are not necessarily relevant to all countries, for example sub-indicators concerned with forestation will not be relevant to a desert country, hence all countries do not feature exactly the same number of time series.
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Alharbi, Y., Arribas-Bel, D., Coenen, F. (2023). Forecasting the UN Sustainable Development Goals. In: Fred, A., Sansone, C., Madani, K. (eds) Deep Learning Theory and Applications. DeLTA DeLTA 2020 2021. Communications in Computer and Information Science, vol 1854. Springer, Cham. https://doi.org/10.1007/978-3-031-37320-6_5
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