Uncertainty analysis in integrated assessment: the users’ perspective |
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Authors: | Silke Gabbert Martin van Ittersum Carolien Kroeze Serge Stalpers Frank Ewert Johanna Alkan Olsson |
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Institution: | (1) Department of Social Sciences, Environmental Economics and Natural Resources Group, Wageningen University, P.O. Box 8130, 6700 EW Wageningen, The Netherlands;(2) Plant Production Systems Group, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands;(3) Environmental Systems Analysis Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands;(4) Environmental Systems Analysis Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands;(5) School of Science, Open University of The Netherlands, Heerlen, The Netherlands;(6) Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany;(7) Lund University Centre for Sustainability Studies, Lund University, Box 117, 22100 Lund, Sweden |
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Abstract: | Integrated Assessment (IA) models aim at providing information- and decision-support to complex problems. This paper argues
that uncertainty analysis in IA models should be user-driven in order to strengthen science–policy interaction. We suggest
an approach to uncertainty analysis that starts with investigating model users’ demands for uncertainty information. These
demands are called “uncertainty information needs”. Identifying model users’ uncertainty information needs allows focusing the analysis on those uncertainties which users
consider relevant and meaningful. As an illustrative example, we discuss the case of examining users’ uncertainty information
needs in the SEAMLESS Integrated Framework (SEAMLESS-IF), an IA model chain for assessing and comparing alternative agricultural
and environmental policy options. The most important user group of SEAMLESS-IF are policy experts at the European and national
level. Uncertainty information needs of this user group were examined in an interactive process during the development of
SEAMLESS-IF and by using a questionnaire. Results indicate that users’ information requirements differed from the uncertainty
categories considered most relevant by model developers. In particular, policy experts called for addressing a broader set
of uncertainty sources (e.g. model structure and technical model setup). The findings highlight that investigating users’
uncertainty information needs is an essential step towards creating confidence in an IA model and its outcomes. This alone,
however, may not be sufficient for effectively implementing a user-oriented uncertainty analysis in such models. As the case
study illustrates, it requires to include uncertainty analysis into user participation from the outset of the IA modelling
process. |
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