We suggest that the impact assessment community has lost its way based on our observation that impact assessment is under attack because of a perceived lack of efficiency. Specifically, we contend that the proliferation of different impact assessment types creates separate silos of expertise and feeds arguments for not only a lack of efficiency but also a lack of effectiveness of the process through excessive specialisation and a lack of interdisciplinary practice. We propose that the solution is a return to the basics of impact assessment with a call for increased integration around the goal of sustainable development and focus through better scoping. We rehearse and rebut counter arguments covering silo-based expertise, advocacy, democracy, sustainability understanding and communication. We call on the impact assessment community to rise to the challenge of increasing integration and focus, and to engage in the debate about the means of strengthening impact assessment.
In the context of continuing uncertainty, ambiguity and ignorance in impact assessment (IA) prediction, the case is made that existing IA processes are based on false 'normal' assumptions that science can solve problems and transfer knowledge into policy. Instead, a 'post-normal science' approach is needed that acknowledges the limits of current levels of scientific understanding. We argue that this can be achieved through embedding evolutionary resilience into IA; using participatory workshops; and emphasizing adaptive management. The goal is an IA process capable of informing policy choices in the face of uncertain influences acting on socio-ecological systems. We propose a specific set of process steps to operationalise this post-normal science approach which draws on work undertaken by the Resilience Alliance. This process differs significantly from current models of IA, as it has a far greater focus on avoidance of, or adaptation to (through incorporating adaptive management subsequent to decisions), unwanted future scenarios rather than a focus on the identification of the implications of a single preferred vision. Implementing such a process * corresponding author 2 would represent a culture change in IA practice as a lack of knowledge is assumed and explicit, and forms the basis of future planning activity, rather than being ignored.
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