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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 and Alkan Olsson, Johanna LU (2010) In Regional Environmental Change 10(2). p.131-143
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
SEAMLESS Integrated Framework, Uncertainty information needs, Integrated Assessment models, Effective uncertainty analysis, Science-policy interaction
in
Regional Environmental Change
volume
10
issue
2
pages
12 pages
publisher
Springer
external identifiers
  • wos:000278096700005
  • scopus:77953025380
ISSN
1436-3798
DOI
10.1007/s10113-009-0100-1https://doi.org/10.1007/s10113-009-0100-1
language
English
LU publication?
yes
id
3a79f408-4ff0-45c5-a74a-711df78c9f7d (old id 1616625)
date added to LUP
2010-06-22 14:25:20
date last changed
2018-07-01 03:15:05
@article{3a79f408-4ff0-45c5-a74a-711df78c9f7d,
  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.},
  author       = {Gabbert, Silke and Van Ittersum, Martin and Kroeze, Carolien and Stalpers, Serge and Ewert, Frank and Alkan Olsson, Johanna},
  issn         = {1436-3798},
  keyword      = {SEAMLESS Integrated Framework,Uncertainty information needs,Integrated Assessment models,Effective uncertainty analysis,Science-policy interaction},
  language     = {eng},
  number       = {2},
  pages        = {131--143},
  publisher    = {Springer},
  series       = {Regional Environmental Change},
  title        = {Uncertainty analysis in integrated assessment: the users’ perspective. Regional Environmental Change},
  url          = {http://dx.doi.org/10.1007/s10113-009-0100-1https://doi.org/10.1007/s10113-009-0100-1},
  volume       = {10},
  year         = {2010},
}