Search

Abstract: Environmentally extended multi-regional input–output (EE-MRIO) models provide us with a wealth of data relating to consumption-based environmental impacts at a national level. The results can identify the categories of consumption and sectors of production that contribute most to environmental impact allowing policy makers to prioritise intervention into particular areas. However, these data are not readily accessible to policy makers and civil society, making it difficult to extract and communicate the important messages it contains. The web-based tool — EUREAPA — was created as a usable, task-oriented interface to improve access to environmental and economic data held within a complex EE-MRIO model and make it more relevant to policy makers and civil society. The project team of scientists and IT specialists used an iterative, agile and participatory approach to engage potential end-users in the specification and testing of the tool. The engagement process identified two principal functions that were essential for the EUREAPA tool: viewing data and creating scenarios. The viewing data function allows users to analyse the wealth of data held within the model and present results from a range of perspectives. This helps to understand the causes of environmental pressure and identify priorities for policy intervention. The scenario function helps to communicate how changes in consumption and production might affect the future environmental impact of citizens of the EU, and facilitates long-term planning. Through this dialogue process the project has been able to ensure EUREAPA is relevant, user-friendly and fit-for-purpose. It is intended that EUREAPA will be adopted by policy makers and civil society as an important policy planning and assessment aid in the complex field of sustainable consumption and production. [Copyright &y& Elsevier]

• "Predict and act" decision making is ineffective in the face of deep uncertainty. • Decision making under deep uncertainty (DMDU) methods support robust and adaptable decisions. • However, DMDU methods do not fully reflect the context in which decisions are made. • This influences the availability of feasible options and limits capacity for change. • Decision contexts signpost the selection of full or "hybrid" DMDU approaches. Deep uncertainties like environmental and socio-economic changes create challenges to decision making. Decision Making under Deep Uncertainty (DMDU) methods are recognised approaches to navigate deep uncertainties and support robust and adaptable decisions. However, their ability to fully reflect the context in which these decisions are made has been criticised. This paper presents a synthesis across cases and methods to provide a holistic understanding of the application of DMDU methods to support long-term decision making. We carried out a structured literature review and analysed 37 infrastructure DMDU case studies. The analysis shows that DMDU methods are effective at developing plans to address a range of deep uncertainties and in some cases, reflecting the institutional context of the decision. However, they largely overlook the organisational and individual contexts in which decision making happens. We argue that the use of existing DMDU methods in practice should start with a better understanding of the institutional, organisational and individual contexts. We then suggest modifications to the applications of DMDU methods, i.e. internalising the context at different stages of the decision-making process and developing a decision typology to signpost decision makers to the best approach for a specific context. [ABSTRACT FROM AUTHOR]