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Abstract
The Sustainable Development Goals (SDGs) are presented as integrated and indivisible.
Therefore, for monitoring purposes, conventional indicator-based frameworks need to
be combined with approaches that capture and describe the links and interdependencies
between the Goals and their targets. In this study, we propose a data-driven Bayesian
network (BN) approach to identify and interpret SDGs interlinkages. We focus our analysis
on the interlinkages of SDG 6, related to water and sanitation, across the whole 2030
Agenda, using SDG global available data corresponding to 179 countries, 16 goals,
28 targets and 44 indicators. To analyze and validate the BN results, we first demonstrate
the robustness of the BN approach in identifying indicator relationships (i.e. consistent
results throughout different country sample sizes). Second, we show the coherency
of the results by comparing them with an exhaustive study developed by UN-Water. As
an added value, our data-driven approach provides further interlinkages, which are
contrasted against the existing literature. We conclude that the approach adopted
is useful to accommodate a thorough analysis and interpretation of the complexities
and interdependencies of the SDGs.