Detecting Episodes of Mildly Explosive Behavior in the Hurricane Resiliency Index to Examine Community Resilience to Hurricanes

Cui, Yuepeng; Liang, Daan; Bradley, Ewing; Gong, Kuangmin

doi:10.1061/(asce)nh.1527-6996.0000606

Cited by 1 publication

(1 citation statement)

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“…Among all natural disasters, floods are the most common in terms of incidence, affected population, area, and related economic losses [1,2]. As flood disasters have negative effects on real GDP, residents' income, consumption, and several other macroeconomic indicators [3], quantifying the spatial distribution and corresponding effects after extreme shocks is critical for both post-disaster reconstruction and sustainable development objectives [4]. Flood exposure will further increase with the intensification of the global hydrological cycle, posing a serious threat to future generations [5][6][7].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Evaluating Indirect Economic Losses from Flooding Using Input–Output Analysis: An Application to China’s Jiangxi Province

Lyu

Xiang

Wang

2023

IJERPH

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Quantifying total economic impacts of flood disaster in a timely manner is essential for flood risk management and sustainable economic growth. This study takes the flood disaster in China’s Jiangxi province during the flood season in 2020 as an example, and exploits the input–output method to analyze indirect economic impacts caused by the agricultural direct economic loss. Based on regional IO data and MRIO data, a multi-dimensional econometric analysis was undertaken in terms of inter-regional, multi-regional, and structural decomposition of indirect economic losses. Our study reveals that the indirect economic losses caused by the agricultural sector in other sectors in Jiangxi province were 2.08 times the direct economic losses, of which the manufacturing sector suffered the worst, accounting for 70.11% of the total indirect economic losses. In addition, in terms of demand side and supply side indirect losses, the manufacturing and construction industries were found to be more vulnerable than other industries, and the flood disaster caused the largest indirect economic loss in eastern China. Besides, the supply side losses were significantly higher than the demand side losses, highlighting that the agricultural sector has strong spillover effects on the supply side. Moreover, based on the MRIO data of the years 2012 and 2015, dynamic structural decomposition analysis was undertaken, which showed that changes in the distributional structure appear to be influential in the evaluation of indirect economic losses. The findings highlight the spatial and sectoral heterogeneity of indirect economic losses caused by floods, and have significant implications for disaster mitigation and recovery strategies.

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