Forecasting the UN Sustainable Development Goals

Alharbi, Yassir ORCID: 0000-0001-6764-030X, Arribas-Bel, Daniel ORCID: 0000-0002-6274-1619 and Coenen, Frans ORCID: 0000-0003-1026-6649 (2023) Forecasting the UN Sustainable Development Goals. In: Communications in Computer and Information Science. Communications in Computer and Information Science, 1854 C . Springer Nature Switzerland, pp. 88-110. ISBN 9783031373190

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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.

Item Type:	Book Section
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science Faculty of Science and Engineering > School of Environmental Sciences
Depositing User:	Symplectic Admin
Date Deposited:	18 Jul 2023 09:20
Last Modified:	14 Mar 2024 21:44
DOI:	10.1007/978-3-031-37320-6_5
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3171744