Enhancing science-policy interface in marine environmental governance: Oil spill response models as boundary objects in the Gulf of Finland, Baltic Sea

Marine Policy(2022)

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摘要
Scientific models provide important input to the governance of complex socio-ecological risks. However, scientific knowledge rarely translates to policy in a simple, direct manner. By focusing on the use of scientific models for oil spill risk assessment and management in the Gulf of Finland (GoF), Baltic Sea, this paper aims to enhance the understanding of the capacity of scientific models to connect science, operational decision-making, and policy. In this study, we conceptualize scientific models as boundary objects, i.e. tools that facilitate interactions between different actors, types of knowledge, and perspectives across system boundaries. The study focuses on the different affordances associated with the models regarding their ability to represent, share, and convey knowledge between science and policy and to link the involved knowledge to action (i.e. changes in practice and in policy). We explore 1) how do the different oil spill models work as boundary objects in the science-policy interface, 2) how do different science-policy contexts affect the model affordances, and vice versa. We also provide recommendations for future research. The study is based on interviews of modelers/researchers, response operators, and policymakers. The results suggest that the existing models lack several of the important affordances that are required to successfully integrate different types of knowledge and transform new knowledge to action. As such, we suggest that currently models remain as instrumental, calculative, tools that support pre-determined policies rather than as means for exploring alternative framings of risks and the possible solutions. Finally, we argue that the co-production of knowledge best supports the plurality of model affordances needed to enable transformative change in policy and practice.
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关键词
Oil spills,Marine risk governance,Risk models,Bayesian networks,Science-policy interface,Boundary objects
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