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Co-production as an approach to improve the impact of EESM research

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Abstract

As communities around the world face the impacts of socio-environmental problems, there is an urgent need for scientific research that can inform policies and decisions. In this context, there have been several calls for EESM research to be more “engaged” with potential decision-makers and users of research. Co-production is an increasingly popular approach to improve the impact and decision-relevance of environmental research. In co-production, scientists work collaboratively and iteratively with decision-makers, practitioners, communities, or other knowledge-holders to conduct their research. While there are burgeoning examples of how co-production can be used for the development of environmental solutions, there are fewer examinations of how this approach can be applied to projects/programs that probe fundamental earth and environment modeling research.

Here, we present the case study of the HyperFACETS project that has used co-production approaches for probing fundamental understanding of decision-relevant climate processes and enabling credible climate modeling for management-relevant outcomes. Using the 8 years of experience in HyperFACETS, we showcase three distinct ways in which coproduction can improve the impact of EESM research - (1) By identifying fundamental research questions/directions that are both scientifically and societally impactful (2) By improving the rigor and credibility of key modeling choices and decisions, and (3) By developing fundamental research on the “Science of Actionable Knowledge”. In this presentation, we will highlight how HyperFACETS applies the co-production approach in the context of a DOE project, while simultaneously improving engagement with practitioners as well as the scientific innovativeness and impact of the research. Lessons learnt can be useful to other EESM projects that are interested in improving the rigor, actionability, and impact of the work.

Category
Urban
Metrics, Benchmarks and Credibility of model output and data for science and end users
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