The United Nations' ambitions to combat climate change and prosper human
development are manifested in the Paris Agreement and the Sustainable
Development Goals (SDGs), respectively. These are inherently inter-linked as
progress towards some of these objectives may accelerate or hinder progress
towards others. We investigate how these two agendas influence each other by
defining networks of 18 nodes, consisting of the 17 SDGs and climate change,
for various groupings of countries. We compute a non-linear measure of
conditional dependence, the partial distance correlation, given any subset of
the remaining 16 variables. These correlations are treated as weights on edges,
and weighted eigenvector centralities are calculated to determine the most
important nodes. We find that SDG 6, clean water and sanitation, and SDG 4,
quality education, are most central across nearly all groupings of countries.
In developing regions, SDG 17, partnerships for the goals, is strongly
connected to the progress of other objectives in the two agendas whilst,
somewhat surprisingly, SDG 8, decent work and economic growth, is not as
important in terms of eigenvector centrality.