Abstract. Disaster damages have negative effects on the economy, whereas reconstruction investment has positive effects. The aim of this study is to model economic causes of disasters and recovery involving the positive effects of reconstruction activities. Computable general equilibrium (CGE) model is a promising approach because it can incorporate these two kinds of shocks into a unified framework and furthermore avoid the double-counting problem. In order to factor both shocks into the CGE model, direct loss is set as the amount of capital stock reduced on the supply side of the economy; a portion of investments restores the capital stock in an existing period; an investment-driven dynamic model is formulated according to available reconstruction data, and the rest of a given country's saving is set as an endogenous variable to balance the fixed investment. The 2008 Wenchuan Earthquake is selected as a case study to illustrate the model, and three scenarios are constructed: S 0 (no disaster occurs), S 1 (disaster occurs with reconstruction investment) and S 2 (disaster occurs without reconstruction investment). S 0 is taken as business as usual, and the differences between S 1 and S 0 and that between S 2 and S 0 can be interpreted as economic losses including reconstruction and excluding reconstruction, respectively. The study showed that output from S 1 is found to be closer to real data than that from S 2 . Economic loss under S2 is roughly 1.5 times that under S 1 . The gap in the economic aggregate between S 1 and S 0 is reduced to 3 % at the end of government-led reconstruction activity, a level that should take another four years to achieve under S 2 .
The vulnerability to flood disaster is addressed by a number of studies. It is of great importance to analyze the vulnerability of different regions and various periods to enable the government to make policies for distributing relief funds and help the regions to improve their capabilities against disasters, yet a recognized paradigm for such studies seems missing. Vulnerability is defined and evaluated through either physical or economic–ecological perspectives depending on the field of the researcher concerned. The vulnerability, however, is the core of both systems as it entails systematic descriptions of flood severities or disaster management units. The research mentioned often has a development perspective, and in this article we decompose the overall flood system into several factors: disaster driver, disaster environment, disaster bearer, and disaster intensity, and take the interaction mechanism among all factors as an indispensable function. The conditions of flood disaster components are demonstrated with disaster driver risk level, disaster environment stability level and disaster bearer sensitivity, respectively. The flood system vulnerability is expressed as vulnerability = f(risk, stability, sensitivity). Based on the theory, data envelopment analysis method (DEA) is used to detail the relative vulnerability's spatiotemporal variation of a flood disaster system and its components in the Dongting Lake region.
The study finds that although a flood disaster system's relative vulnerability is closely associated with its components' conditions, the flood system and its components have a different vulnerability level. The overall vulnerability is not the aggregation of its components' vulnerability. On a spatial scale, zones central and adjacent to Dongting Lake and/or river zones are characterized with very high vulnerability. Zones with low and very low vulnerability are mainly distributed in the periphery of the Dongting Lake region. On a temporal scale, the occurrence of a vibrating flood vulnerability trend is observed. A different picture is displayed with the disaster driver risk level, disaster environment stability level and disaster bearer sensitivity level.
The flood relative vulnerability estimation method based on DEA is characteristic of good comparability, which takes the relative efficiency of disaster system input–output into account, and portrays a very diverse but consistent picture with varying time steps. Therefore, among different spatial and time domains, we could compare the disaster situations with what was reflected by the same disaster. Additionally, the method overcomes the subjectivity of a comprehensive flood index caused by using an a priori weighting system, which exists in disaster vulnerability estimation of current disasters
Abstract. This study evaluates and compares the indirect economic loss (IEL) resulting from two hypothetical catastrophes occurring in China -in developed Shanghai and in less-developed Sichuan -to provide new measures of disaster reduction. IEL was divided into indirect economic loss due to the disruption of production process (IEL I) and indirect economic loss induced by the disturbance of industrial lines (IEL II). An input-output model was used to assess these two types of IEL. The study showed that (1) developed regions may be more vulnerable with respect to IEL; (2) IEL II is the primary factor contributing to total IEL; (3) decision makers need to focus on IEL II beside IEL I which is usually the main disaster-reduction target after a disaster; and (4) tradeoff between economic growth and disaster prevention is needed to achieve regional sustainable development.
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