Uncertainty analysis in integrated assessment: the users’ perspective. Regional Environmental Change

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Uncertainty analysis in integrated assessment: the users’ perspective. Regional Environmental Change. / Gabbert, Silke; Van Ittersum, Martin; Kroeze, Carolien; Stalpers, Serge; Ewert, Frank; Alkan Olsson, Johanna.

In: Regional Environmental Change, Vol. 10, No. 2, 2010, p. 131-143.

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Gabbert, Silke ; Van Ittersum, Martin ; Kroeze, Carolien ; Stalpers, Serge ; Ewert, Frank ; Alkan Olsson, Johanna. / Uncertainty analysis in integrated assessment: the users’ perspective. Regional Environmental Change. In: Regional Environmental Change. 2010 ; Vol. 10, No. 2. pp. 131-143.

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TY - JOUR

T1 - Uncertainty analysis in integrated assessment: the users’ perspective. Regional Environmental Change

AU - Gabbert, Silke

AU - Van Ittersum, Martin

AU - Kroeze, Carolien

AU - Stalpers, Serge

AU - Ewert, Frank

AU - Alkan Olsson, Johanna

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - SEAMLESS Integrated Framework

KW - Uncertainty information needs

KW - Integrated Assessment models

KW - Effective uncertainty analysis

KW - Science-policy interaction

U2 - 10.1007/s10113-009-0100-1

DO - 10.1007/s10113-009-0100-1

M3 - Article

VL - 10

SP - 131

EP - 143

JO - Regional Environmental Change

T2 - Regional Environmental Change

JF - Regional Environmental Change

SN - 1436-3798

IS - 2

ER -