Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 444509
Title A spatially explicit scenario-driven model of adaptive capacity to global change in Europe
Author(s) Acosta, L.; Klein, R.J.T.; Reidsma, P.; Metzger, M.J.; Rounsevell, M.D.A.; Leemans, R.
Source Global environmental change : human and policy dimensions 23 (2013)5. - ISSN 0959-3780 - p. 1211 - 1224.
DOI https://doi.org/10.1016/j.gloenvcha.2013.03.008
Department(s) Plant Production Systems
Environmental Systems Analysis
PE&RC
WIMEK
Publication type Refereed Article in a scientific journal
Publication year 2013
Keyword(s) klimaatverandering - scenario-analyse - modellen - klimaatadaptatie - europa - climatic change - scenario analysis - models - climate adaptation - europe - quantitative vulnerability assessment - fuzzy-set-theory - land-use change - climate-change - environmental-change - adaptation - indicators - growth - susceptibility - uncertainty
Categories Climatic Change
Abstract Traditional impact models combine exposure in the form of scenarios and sensitivity in the form of parameters, providing potential impacts of global change as model outputs. However, adaptive capacity is rarely addressed in these models. This paper presents the first spatially explicit scenario-driven model of adaptive capacity, which can be combined with impact models to support quantitative vulnerability assessment. The adaptive capacity model is based on twelve socio-economic indicators, each of which is projected into the future using four global environmental change scenarios, and then aggregated into an adaptive capacity index in a stepwise approach using fuzzy set theory
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