3.4.5 Final remarks

This chapter has addressed knowledge production challenges related to ESTPs and their implications for effective science-policy interactions. Tipping processes are features of complex systems that present profound learning challenges that can undermine the development of actionable knowledge among decision makers and slow down urgently needed governance efforts. Attention to tipping points has grown in recent IPCC assessment reports, with the assessed risks of tipping point transgression increasing at lower levels of global warming. However, so far this has had limited effect on policy making processes. There are also significant knowledge gaps regarding ESTPs in the social sciences and humanities, which are most relevant to support governance. This context calls for concerted efforts to expand knowledge production related to ESTPs and corresponding science-policy interactions to foster learning and capacity building.

For it to be useful for governance, knowledge about tipping points needs to be solutions-oriented, actionable, context-specific and actor-relevant. Importantly, the multiple time horizons of tipping processes – from years to millennia – require anticipatory forms of knowing and meaning-making. In a polycentric governance framework, it is important to understand where, by whom, and at what scale relevant knowledge is produced, how knowledge producers and users can be connected, and how different kinds of knowledge can empower governance actors to devise, implement and upscale solutions.

Identifying significant limits to the way knowledge is currently developed at the science-policy interface, we have put forward suggestions for improving future knowledge co-production related to ESTPs with a focus on the international scale. Scientific and non-scientific actors should actively participate in knowledge co-production in distributed networks that enable effective multi-scale information sharing. Novel designs of knowledge-production approaches such as participatory scenario development and roleplay simulations are needed, as well as incentives for developing anticipatory and transformative capacities. These approaches tend to combine qualitative and quantitative information, diverse expertise, and even immersive and game-based processes that leverage art and storytelling to provide multi-modal and multi-sensorial learning.

This type of capacity building at the science-policy interface requires more time investment, openness to active learning (rather than reading or listening), and more frequent (iterative) engagement by decision makers than current approaches. Finally, we outlined the importance of grappling with political contestation around the production and mobilisation of knowledge at the science-policy interface.