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http://dx.doi.org/10.14249/eia.2016.25.4.296

The Characteristics and Improvement Directions of Regional Climate Change Adaptation Policies in accordance with Damage Cases  

Ahn, Yoonjung (Korea Environment Institute)
Kang, Youngeun (Korea Environment Institute)
Park, Chang Sug (Korea Environment Institute)
Kim, Ho Gul (Seoul National University)
Publication Information
Journal of Environmental Impact Assessment / v.25, no.4, 2016 , pp. 296-306 More about this Journal
Abstract
There is a growing interest in establishing a regional climate change adaptation policy as the climate change impact in the region and local scale increases. This study focused on the analysis of 32 regions on its characteristics of local climate change adaptation plans. First, statistic program R was used for conducting cluster analysis based on the frequency and budgets of adaptation plan. Further, we analyzed damage frequency from newspapers regarding climate change impacts in eight categories which were caused by extreme weather events on 2,565 cases for 24 years. Lastly, the characteristics of climate change adaptation plan was compared with damage frequency patterns for evaluating the adequacy of climate change adaptation plan on each cluster. Four different clusters were created by cluster analysis. Most clusters clearly have their own characteristics on certain sectors. There was a high frequency of damage in 'disaster' and 'health' sectors. Climate change adaptation plan and budget also invested a lot on those sectors. However, when comparing the relative rate among regional governments, there was a difference between types of damage and climate change adaptation plan. We assumed that the difference could come from that each region established their adaptation plans based on not only the frequency of damage, but vulnerability assessment, and expert opinions as well. The result of study could contribute to policy making of climate change adaptation plan.
Keywords
Climate Change Adaptation; local Climate Change Adaptation Plans; Cluster Analysis; K-means; Text-mining;
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