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

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 tradeoffs that are anticipated to be likely to arise from a set of existing or proposed future policy actions. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).