Making science-based natural hazard risk management work within power networks –from co-production models of knowledge transfer to the Research-Integration-Utilisation (RIU) model

Abstract

Decisions and measures in natural hazard risk management (NHRM) should be based on relevant current scientific information, developed within a high-quality scientific research process, to mitigate risks arising from natural hazards. Though the purportedly most advanced co-production models of knowledge transfer have been applied in many research projects dealing with NHRM, the resulting scientific information has seldom reached practitioners on the ground or political actors at the decision-making level. To overcome these limitations, this study strives first to identify additional factors beyond those factors for success that the co-production model claims. Such claims include “multi-stakeholder involvement”, assurance of actor participation, and enhancement of communication between actors, or maximisation of information sharing with them. Secondly, we want to identify concrete places where knowledge transfer indeed takes place. To do this, the novel Research-Integration-Utilisation (RIU) model for knowledge transfer was applied to the analysis of the case study of the EU Interreg Alpine Space project, GreenRisk4Alps (GR4A), which conducts research on ecosystem-based NHRM strategies. The RIU model assumes that the scientific information must be retrieved actively from practitioners or political actors and integrated into their practices, something which happens in a specific actor setting, namely, that of interest-driven actors in existing power relations. The study's first hypothesis is that the co-production model's intense focus on the multi-stakeholder aspect is inadequate for the integration of actors who are both interested and powerful, and who could implement science-based solutions effectively. It was checked against the GR4A project and its Working Plan, which originate in the co-production models of knowledge transfer. Our results clearly demonstrate that the interests and power of actors, as well as other indicators, were widely ignored by that knowledge transfer model. In contrast, the RIU model builds on these indicators and the related data to characterise actors. Actor settings can then be used to address actors selectively. This takes place in very specific places where practitioners and political actors meet to exchange science-based information. These places were identified in all the case study areas of the GR4A project, and called “integration forums” confirming our second hypothesis, i.e., that the bridges between scientific information and the powerful and interested actors are built upon these integration forums.