Managing uncertainty, ambiguity and ignorance in impact assessment by embedding evolutionary resilience, participatory modelling and adaptive management
Journal of Environmental Management
School of Environmental Sciences, University of East Anglia, United Kingdom; School of Geo and Spatial Sciences, North-West University, South Africa; Environmental Science, Murdoch University, Australia; Department of Geography and Planning, School of Environment and Sustainability, University of Saskatchewan, Canada; Integral Sustainability, Australia
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 emphasising 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 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. © 2014 Elsevier Ltd.
adaptive management; environmental impact assessment; environmental planning; numerical model; participatory approach; policy making; uncertainty analysis; adaptive environmental management; ambiguity; Article; ecosystem resilience; environmental impact assessment; quantitative analysis; uncertainty; environmental health; health impact assessment; human; theoretical model; uncertainty; Environmental Health; Health Impact Assessment; Humans; Models, Theoretical; Uncertainty