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Increasingly frequent urban waterlogging disasters are having profound social and economic consequences. An appropriate and integrated evaluation of the total economic impacts of such disasters is crucial for achieving effective urban disaster risk management and sustainable development. However, existing metrics are inadequate for measuring the economic impacts of rainfall events of different intensities and their ripple effects across regions. Moreover, their ecological impacts have received insufficient attention. To address these gaps, we developed an integrated assessment framework for analyzing urban waterlogging losses and evaluating their various impacts. Taking Beijing as a case study, we used the InfoWorks ICM model to simulate urban waterlogging disaster risks, quantified direct economic losses, and assessed their environmental impacts. Additionally, we estimated indirect economic losses using input-output analysis and explored spillover effects. The results revealed increasing trends of direct economic losses and environmental losses corresponding to a longer return period. We observed synergies between these losses and their spatial heterogeneity. However, indirect impacts far outweighed direct impacts, with the former being 2.43 times larger than the latter. The cascading effect resulting from damage to infrastructure was also particularly pronounced. The industrial and spatial heterogeneity of interregional impacts was striking, with eastern provinces evidencing the most significant effects. By mapping the transmission paths of disaster losses along industrial chains and across regions, this study provides inputs that could assist policymakers in developing more effective measures for preventing and mitigating urban waterlogging disasters. © 2024 Elsevier Ltd
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Journal of Environmental Management
ISSN: 0301-4797
Year: 2024
Volume: 352
8 . 7 0 0
JCR@2022
Cited Count:
WoS CC Cited Count: 0
SCOPUS Cited Count: 4
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 8
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