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Regional disaster impact analysis: comparing input–output and computable general equilibrium models

Abstract:
A variety of models have been applied to assess the economic losses of disasters, of which the most common ones are input-output (IO) and computable general equilibrium (CGE) models. In addition, an increasing number of scholars have developed hybrid approaches: one that combines both or either of them in combination with noneconomic methods. While both IO and CGE models are widely used, they are mainly compared on theoretical grounds. Few studies have compared disaster impacts of different model types in a systematic way and for the same geographical area, using similar input data. Such a comparison is valuable from both a scientific and policy perspective as the magnitude and the spatial distribution of the estimated losses are born likely to vary with the chosen modelling approach (IO, CGE, or hybrid). Hence, regional disaster impact loss estimates resulting from a range of models facilitate better decisions and policy making. Therefore, this study analyses the economic consequences for a specific case study, using three regional disaster impact models: two hybrid IO models and a CGE model. The case study concerns two flood scenarios in the Po River basin in Italy. Modelling results indicate that the difference in estimated total (national) economic losses and the regional distribution of those losses may vary by up to a factor of 7 between the three models, depending on the type of recovery path. Total economic impact, comprising all Italian regions, is negative in all models though.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.5194/nhess-16-1911-2016

Authors


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Institution:
University of Oxford
Division:
Social Sciences Division
Department:
SOGE; Environmental Change Institute
Role:
Author


Publisher:
Copernicus Publications
Journal:
Natural Hazards and Earth System Sciences More from this journal
Volume:
16
Issue:
8
Pages:
1911-1924
Publication date:
2016-08-16
Acceptance date:
2016-07-15
DOI:
EISSN:
1684-9981
ISSN:
1561-8633


Keywords:
Pubs id:
pubs:708593
UUID:
uuid:51e94d62-735e-4b26-b9c4-d64ea5cc12e8
Local pid:
pubs:708593
Source identifiers:
708593
Deposit date:
2018-05-29

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