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Quantitative Vulnerability Assessment for Economic Systems

Vulnerability and the Process of Recovery for Households and Companies in Phang-Nga and Phuket Provinces in Thailand


Philipp Willroth

In 2004 tsunami waves caused huge economic losses along the coastline of Southern Thailand. These resulted from direct damages and the following economic downturn. This study investigates the factors that led to this vulnerable situation. One of the greatest challenges in vulnerability research is the quantification. To answer this question, a wide database has been used, encompassing highly accurate remote sensing data, quantitative surveys and qualitative focus group discussion data. These data have been integrated in a structural equation model to reproduce factors and relations leading to the hazard induced effects and the capabilities to cope with. The model showed that the impact was almost completely compensated for by households’ and companies’ internal and external resilience capabilities.


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2 Vulnerability of coastal regions to naturalhazards: Theoretical framework


6 scientific or applied backgrounds, such as geography, economics, sociology and risk management, addressing the different topics and interests of these subjects. The results of these differing perspectives on vulnerability are included in a guiding framework for this study and answer the theory-related questions T1 to T3. This framework for the micro-level economic vulnerability assessment will be applied to the tsunami of 2004 as a reference event for vulnerability assess- ment in this study. The majority of these vulnerability concepts were developed as mainly theory-driven frameworks for qualitative research. Despite this, the major goal of this study is to adapt the concept for quantitative vulnerability as- sessment. In order to do so, there will be a discussion as to which indicators are suitable for representing the different perspectives of micro-level vulnerability considering the characteristics of households and companies in the study area. These indicators are to be derived from remote sensing data, secondary statistics where possible, or a household and company survey conducted in the study area. Additionally, these methods will be supported by remote sensing data in order to extend the coverage of the assessment to the entire group of households and companies in the region and to benefit from this method which is relatively effi- cient in terms of both time and cost. The following section will discuss which statistical method is suitable for reproducing and analysing the process of vul- nerability in all its dimensions and quantifying the interrelations between the dimensions of vulnerability. This part addresses...

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