SDG interlinkage networks: Analysis, robustness, sensitivities, and hierarchies.

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Author: J.H.P. Dawes
Date: Jan. 2022
From: World Development(Vol. 149)
Publisher: Elsevier Science Publishers
Document Type: Report
Length: 452 words

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Keywords Sustainable Development Goals; Network science; Mathematical modelling; Trade-offs; Co-benefits; Synergies Highlights * SDG interlinkages arise from policy actions and their consequences and can be represented by interaction networks. * This paper develops mathematical techniques to analyse system-level consequences of these interaction networks. * Key results include estimating node centrality and the sensitivity of system-level results to changes in the strength of individual interlinkages in the network. * The paper compares and contrasts three interlinkage networks between the SDGs derived from literature surveys and expert opinion: SDGs 1--3 are usually most reinforced, while SDGs 12--15 are most at risk. * In conclusion, these results may help to guide policy design for interventions that can strengthen outcomes across Agenda 2030. Abstract A growing literature considers the Sustainable Development Goals (SDGs) as a interlinked network, connected by co-benefits and trade-offs between pairs of SDGs. Such network descriptions naturally prompt important questions concerning the emergence and identification of system-level features. This paper develops mathematical techniques to address, quantitatively, the extent to which these interlinkage networks point to the likelihood of greater progress on some SDGs than on others, the sensitivity of the networks to the addition of new links (or the strengthening or weakening of existing ones), and the existence of implicit hierarchies within Agenda 2030. The methods we discuss are applicable to any directed network but we interpret them here in the context of three interlinkage matrices produced from expert analysis and literature reviews. We use these as three specific examples to discuss the quantitative results that reveal similarities and differences between these networks, as well as to comment on the mathematical techniques themselves. In broad terms, our findings confirm those from other sources, such as the Sustainable Development Solutions Network: for example, that globally SDGs 12--15 are most at risk. Perhaps of greater value is that analysis of the interlinkage networks is able to illuminate the underlying structural issues that lead to these systemic conclusions, such as the extent to which, at the whole-system level, the structure of SDG interlinkages favours some SDGs over others. The sensitivity analyses also suggest ways to quantify possible improvements to an SDG interlinkage network, since the sensitivity analyses are able to identify the modifications of the network that would best improve outcomes across the whole of Agenda 2030. This therefore indicates possibilities for informing policy-making, since the interlinkage networks themselves are implicitly descriptions of the overlaps, co-benefits and trade-offs that are anticipated to be likely to arise from a set of existing or proposed future policy actions. Author Affiliation: Centre for Networks and Collective Behaviour and Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK Article History: Accepted 3 September 2021 Byline: J.H.P. Dawes [J.H.P.Dawes@bath.ac.uk]

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Gale Document Number: GALE|A679876604