Local Vulnerability Assessment of Climate Change and Its Implications: The Case of Gyeonggi-Do, Korea View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011

AUTHORS

Jaekyung Koh

ABSTRACT

This paper provides a set of vulnerability indicators appropriate for municipalities and gives the results of their application to 31 municipalities in Gyeonggi-Do, the largest provincial government in Korea. The vulnerability assessment aims to identify relative vulnerability across municipalities. Expert consultations and the Analytic Hierarchy Process (AHP) was used to derive indicators and determine their weightings. The definition of vulnerability developed by the IPCC was used, which consists of three components – exposure, sensitivity and adaptive capacity. The vulnerability indices are composed of 3 categories, 11 sectors, and 35 indicators. The results of the AHP reveal that the weighting of adaptive capacity is highest, followed by sensitivity and then climatic stimuli in order of significance. The most vulnerable areas include Yeoju, Yangpyeong, Gimpo, Pocheon, Yeoncheon, and Hanam, which have higher sensitivity and lower adaptive capacity to climate change. The analysis shows that vulnerability indices are correlated negatively with economic capacity and positively with natural hazard damages. The results of the vulnerability assessment indicate the need for a differentiated approach to adaptation based on the local characteristics of the sectors. The study should be considered a first step in understanding vulnerability to climate change. A regional climate model and data collection system for vulnerability assessment should be developed. The indicators could be replicated for other local governments. More... »

PAGES

411-427

Book

TITLE

Resilient Cities

ISBN

978-94-007-0784-9
978-94-007-0785-6

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-94-007-0785-6_42

DOI

http://dx.doi.org/10.1007/978-94-007-0785-6_42

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012465759


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