To address urgent, complex and intertwined problems like climate change, deforestation and global inequality policymakers need to learn from various attempts to transform these “super wicked” challenges – and to put that learning into practice, fast.
In the case of REDD+ (Reducing Emissions caused by Deforestation and forest Degradation) deep learning is needed because climate action must be substantially transformative in nature to be effective and due to substantial trade-offs, including conflicts between competing objectives.

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