Abstract:
This article introduces and demonstrates a data-driven systems mapping approach designed
to contextualise, communicate, and embed the insights of complexity economics in real
world policy questions. This approach allows us to: build networks representing empirical
regularities between a broad range of factors, analyse these networks in policy-relevant
ways, and embed complexity economics insight in them. In using this approach to connect
complexity economics with policy questions and a more rounded view of policy landscapes,
we hope to help address a range of calls in recent literature for more usable, interpretable,
and inclusive complexity economics outputs. We demonstrate the approach with the policy
topic of the energy transition and its relationship with the Sustainable Development Goals
(SDGs). We consider how the approach can be tuned to different purposes and contexts and
explore two applied questions emerging from existing modelling results and policy topics:
(i) the impact of the energy transition on SDGs and the role of biofuels, and (ii) the nature
of climate impacts on the economy.
Citation:
de Moura, F.S. & Barbrook-Johnson, P. (2022). 'Using data-driven systems mapping to contextualise complexity economics insights'. INET Oxford Working Paper No. 2022-27.