Evaluating the robustness of the ARIO model for a local disaster: 2021 flooding in Germany
Given the interconnectedness of modern economies and the widespread adoption of just-in-time production methods, even minor disruptions caused by natural disasters can lead to substantial indirect economic impact. A substantial body of literature has explored this phenomenon, using input-output anal...
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IOP Publishing
2024-01-01
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| Series: | Environmental Research: Infrastructure and Sustainability |
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| Online Access: | https://doi.org/10.1088/2634-4505/ad8375 |
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| author | Samuel Juhel Adrien Delahais Vincent Viguié |
| author_facet | Samuel Juhel Adrien Delahais Vincent Viguié |
| author_sort | Samuel Juhel |
| collection | DOAJ |
| description | Given the interconnectedness of modern economies and the widespread adoption of just-in-time production methods, even minor disruptions caused by natural disasters can lead to substantial indirect economic impact. A substantial body of literature has explored this phenomenon, using input-output analysis, computable general equilibrium and agent-based models. However, these models (i) heavily rely on parameters and data that often lack empirical grounding or (ii) exhibit considerable uncertainty, making it challenging to assess their reliability. The ARIO model has been widely used in the literature and has provided theoretical foundation for several related models. Using the July 2021 floods in Germany as a case study, we assess the sensitivity of the results of this model by varying key parameters, as well as the multi-regional input-output tables (MRIOTs), which constitute its primary input data. To facilitate this analysis, we introduce a new, resource-efficient Python implementation of the ARIO model, enabling the execution of a large number of simulations. Our findings highlight the substantial impact of data source and parameter selection on model outcomes, especially so when post-disaster rebuilding is costly. To ensure the robustness of their results, future studies on indirect economic impacts should be careful about recovery dynamics, consider multiple scenarios and compare results using MRIOTs from various sources. |
| format | Article |
| id | doaj-art-5a65ab12e8564ee0965d2b27343afbb0 |
| institution | OA Journals |
| issn | 2634-4505 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
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| series | Environmental Research: Infrastructure and Sustainability |
| spelling | doaj-art-5a65ab12e8564ee0965d2b27343afbb02025-08-20T02:12:24ZengIOP PublishingEnvironmental Research: Infrastructure and Sustainability2634-45052024-01-014404500310.1088/2634-4505/ad8375Evaluating the robustness of the ARIO model for a local disaster: 2021 flooding in GermanySamuel Juhel0https://orcid.org/0000-0001-8801-3890Adrien Delahais1https://orcid.org/0009-0007-6822-902XVincent Viguié2https://orcid.org/0000-0002-8994-2648CIRED, Ecole des Ponts, AgroParisTech, EHESS, CIRAD, CNRS, Université Paris-Saclay , Nogent-sur-Marne, France; LMD, Ecole Normlale Supérieure , Paris, FranceCIRED, Ecole des Ponts, AgroParisTech, EHESS, CIRAD, CNRS, Université Paris-Saclay , Nogent-sur-Marne, FranceCIRED, Ecole des Ponts, AgroParisTech, EHESS, CIRAD, CNRS, Université Paris-Saclay , Nogent-sur-Marne, FranceGiven the interconnectedness of modern economies and the widespread adoption of just-in-time production methods, even minor disruptions caused by natural disasters can lead to substantial indirect economic impact. A substantial body of literature has explored this phenomenon, using input-output analysis, computable general equilibrium and agent-based models. However, these models (i) heavily rely on parameters and data that often lack empirical grounding or (ii) exhibit considerable uncertainty, making it challenging to assess their reliability. The ARIO model has been widely used in the literature and has provided theoretical foundation for several related models. Using the July 2021 floods in Germany as a case study, we assess the sensitivity of the results of this model by varying key parameters, as well as the multi-regional input-output tables (MRIOTs), which constitute its primary input data. To facilitate this analysis, we introduce a new, resource-efficient Python implementation of the ARIO model, enabling the execution of a large number of simulations. Our findings highlight the substantial impact of data source and parameter selection on model outcomes, especially so when post-disaster rebuilding is costly. To ensure the robustness of their results, future studies on indirect economic impacts should be careful about recovery dynamics, consider multiple scenarios and compare results using MRIOTs from various sources.https://doi.org/10.1088/2634-4505/ad8375ARIOfloodingindirect impactsinput-output modelsnatural disastersreconstruction |
| spellingShingle | Samuel Juhel Adrien Delahais Vincent Viguié Evaluating the robustness of the ARIO model for a local disaster: 2021 flooding in Germany Environmental Research: Infrastructure and Sustainability ARIO flooding indirect impacts input-output models natural disasters reconstruction |
| title | Evaluating the robustness of the ARIO model for a local disaster: 2021 flooding in Germany |
| title_full | Evaluating the robustness of the ARIO model for a local disaster: 2021 flooding in Germany |
| title_fullStr | Evaluating the robustness of the ARIO model for a local disaster: 2021 flooding in Germany |
| title_full_unstemmed | Evaluating the robustness of the ARIO model for a local disaster: 2021 flooding in Germany |
| title_short | Evaluating the robustness of the ARIO model for a local disaster: 2021 flooding in Germany |
| title_sort | evaluating the robustness of the ario model for a local disaster 2021 flooding in germany |
| topic | ARIO flooding indirect impacts input-output models natural disasters reconstruction |
| url | https://doi.org/10.1088/2634-4505/ad8375 |
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