There are 1,210 Zones vulnerable to Natural Disaster in Korea until 2012. If it is designated as 'the Zones Vulnerable to Natural Disaster' some budgets are assigned to repair and restoration. But it is difficult to predict the natural disaster, so the overall correspond strategies of the natural disasters are needed as aver. We tried to have expedient B/C analysis in different 20 districts and analyzed the difference of qualitative analysis effects". And we try to confirm the key points that need to manage the related works. To know these facts we analyzed how the qualitative elements effect to The B/C score using logistic regression analysis. According to this analysis, the average B/C score was 2.77 and it means there are average 2.77 B/C ratio. We analyzed the effects of the results based on the B/C 3.0. Related to the policy parts, the districts that have above 3.0 B/C score, overcome mental shocks easily, and they supported the policy more than any other districts, and the results was much better. According to the logistic regression analysis when the policy rationality increased, the probability of the B/C 3.0 score was more than 154%. The policy appropriateness was 52%, policy support was 66% and the mental shock overcome was 50% increased. Therefore the B/C results were related to the policy parts more than policy results.Key words : Benefit-Cost ratio, Logistic Regression Analysis, the Zones Vulnerable to Natural Disaster
This research analyzes the PM 10 concentration of fine dust using data provided by the Korea Environment Corporation for the Munpyeong-dong Station located at industrial complexes 3 and 4 of Daejeon, South Korea. The change in PM 10 concentration was analyzed by separating seasonal changes of PM 10 in 2017 and dividing the day's 24 hours into 4 segments for 5 years from 2013 to 2017. The PM 10 concentration was observed to be highest during the morning (6-12 a.m.) followed by dawn (0-6 a.m.), evening (18 p.m.-24 a.m.), and afternoon (12-18 p.m.), which showed the lowest PM10 concentrations. By year, January showed the highest fine dust concentration, except in 2015, and August showed the lowest concentration. Throughout the years, Januarys and Augusts showed a decreasing trend, and April of 2015 showed the highest concentration change. Various results could be obtained by extending and connecting industrial activities related to fine dust. Big data analysis for fine dust is considered necessary. Future research observing the occurrence pattern of fine dust, including ultrafine dust, by year and time segment is also needed.
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