Managing science-policy interfaces for impact : Interactions within the environmental governance meshwork

Science-policy interface organizations and initiatives (SPIORG) are a key component of environmental governance designed to make links between science and society. However, the science­policy interface literature lacks a structured approach to explaining the impacts of context on and by these initiatives. To better understand these impacts on and interactions with governance, this paper uses the concept of the governance ‘meshwork’ to explore how dynamic processes – encompassing prior, current and anticipated interactions – co­produce knowledge and impact via processes, negotiation and networking activities at multiple governance levels. To illustrate the interactions between SPIORGs and governance meshwork we use five cases representing archetypal SPIORGs. These cases demonstrate how all initiatives and organizations link to their contexts in complex and unique ways, yet also identifies ten important aspects that connect the governance meshwork to SPIORGs. These aspects of the meshwork, together with the typology of organizations, provide a comprehensive framework that can help make sense how the SPIORGs are embedded in the surrounding governance contexts. We highlight that SPIORGs must purposively consider and engage with their contexts to increase their potential impact on knowledge co-­production and policy making.Peer reviewe

Adding ‘iterativity’ to the credibility, relevance, legitimacy: a novel scheme to highlight dynamic aspects of science–policy interfaces

Credibility, relevance and legitimacy (CRELE) of knowledge are widely recognized as key attributes of effective science–policy interfaces (SPIs). Yet, notwithstanding efforts to enhance the CRELE attributes of an SPI, it may still lack impact or be dismissed as not being credible, legitimate or relevant both inside, and outside the SPI. We introduce ‘iterativity’ as an additional attribute to the CRELE framework to better capture dynamic, continuous and multi-directional interactions between science, policy and society related to SPIs. Iterativity is understood in the context of an important shift in perspective by which SPIs are viewed as dynamic, evolving processes rather than linear processes or isolated events. Based on empirical material on biodiversity-related SPIs, we identify 14 features and lessons learned that explain the outcomes of SPIs regarding their participants and external audiences, and examine how SPIs’ structures, objectives, processes and outputs help to build CRELE and iterativity (CRELE + IT). The four attributes of CRELE + IT and results related to the features explaining outcomes of SPIs also provide useful practical tools for the design, implementation and revision of effective science–policy interfaces. These lessons regarding CRELE + IT help us understand both when and why SPIs are able to contribute to the pressing social and ecological need to halt biodiversity loss and the further deterioration of ecosystem services

Science-policy interfaces for biodiversity: dynamic learning environments for successful impact

To address the pressing problems associated with biodiversity loss, changes in awareness and behaviour are required from decision makers in all sectors. Science-policy interfaces (SPIs) have the potential to play an important role, and to achieve this effectively, there is a need to understand better the ways in which existing SPIs strive for effective communication, learning and behavioural change. Using a series of test cases across the world, we assess a range of features influencing the effectiveness of SPIs through communication and argumentation processes, engagement of actors and other aspects that contribute to potential success. Our results demonstrate the importance of dynamic and iterative processes of interaction to support effective SPI work. We stress the importance of seeing SPIs as dynamic learning environments and we provide recommendations for how they can enhance success in meeting their targeted outcomes. In particular, we recommend building long-term trust, creating learning environments, fostering participation and ownership of the process and building capacity to combat silo thinking. Processes to enable these changes may include, for example, inviting and integrating feedback, extended peer review and attention to contextualising knowledge for different audiences, and time and sustained effort dedicated to trust-building and developing common languages. However there are no ‘one size fits all’ solutions, and methods must be adapted to context and participants. Creating and maintaining effective dynamic learning environments will both require and encourage changes in institutional and individual behaviours: a challenging agenda, but one with potential for positive feedbacks to maintain momentum