ABSTRACT
Over the last 23 years a wide range of science – policy interfaces have been established across space and political levels, as key elements of environmental governance. Within this context the need for comparative research of the existing SPIs aiming for their classification and mapping, and identification the strengths and shortcomings of their structure and functions, as well as major challenges for further conceptual and operational improvements has been widely accepted. The comparative assessment of a representative sample for the full spectrum of SPIs, supporting the environmental governance, has shown that they have a formal and institutionalized statute or they are informal and more flexible. They can operate at different political levels and at different stages of the policy cycle or they can be closer to policy or scientific processes (Young et al. 2013). Moreover the comparative analysis clearly showed that defining and designing the interfaces aiming for environmental governance, evolved from the initial conceptual framework which looked at science and policy as more or less independent domains of human activity to the model of unidirectional link between science and policy or one way flow of scientific knowledge to policy and decision makers, neglecting traditional knowledge and technical expertise owned by many stakeholders, and to the most recent conceptual framework of two ways flow and dynamic interfaces which can be established at the intersection between science, policy and society (Cash et al. 2003, van den Hove 2007, Perrings et al. 2011, Young et al. 2013). In parallel to the growing interest around SPIs
in terms of practices and discourses, an increasing attention was given, in particular in the recent years, to theoretical aspects regarding: i) the environmental governance and multi-level integration (Paavola 2007); ii) social learning and responsibility as primary governance mechanism (Reed et al. 2010); iii) the domains of intersection between science and policy as well as the critical attributes of scientific knowledge reflecting its complexity, uncertainty and indeterminacy, and the balance between issue – driven vs. curiosity – driven science (van den Hove 2007) and iv) the interpretative approaches in the analysis of science – policy interactions (Wesselink et al. 2013).
