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Development of a Model for Analylzing and Evaluating the Suitability of Locations for Cooling Center Considering Local Characteristics

  • Journal of Environmental Impact Assessment
  • Abbr : J EIA
  • 2024, 33(4), pp.143-154
  • Publisher : Korean Society Of Environmental Impact Assessment
  • Research Area : Engineering > Environmental Engineering
  • Received : April 9, 2024
  • Accepted : August 22, 2024
  • Published : August 31, 2024

Jieun Ryu 1,  Kyeong Doo Cho 1,  Chanjong Bu 1,  Kyungil Lee ORD ID 2

1인천연구원
2서울과학기술대학교 AI·반도체연구소

Accredited

ABSTRACT

Heat waves caused by climate change are rapidly increasing health damage to vulnerable groups, and to prevent this, the national, regional, and local governments are establishing climate crisis adaptation policy. A representative climate crisis adaptation policy to reduce heat wave damage is to expand the number of cooling centers. Because it is highly effective in a short period of time, most metropolitan local governments, except Jeonbuk, include the project as an adaptation policy. However, the criteria for selecting a cooling centers are different depending on the budget and non-budget, so the utilization rate and effectiveness of the cooling centers are all different. Therefore, in this study, we developed logistic regression models that can predict and evaluate areas with a high probability of expanding cooling centers in order to implement adaptation policy in local governments. In Incheon Metropolitan City, which consists of various heat wave-vulnerable environments due to the coexistence of the old city and the new city, a logistic model was developed to predict areas where heat waves can be cooling centered by dividing it into GanghwaOngjin-gun and other regions, taking into account socioeconomic and environmental differences. As a result of the study, the statistical model for the GanghwaOgjin-gun region showed that the higher the ground surface temperature and the more and more the number of elderly people over 65 years old, the higher the possibility of location of cooling centers, and the prediction accuracy was about 80.93%. The developed logistic regression model can predict and evaluate areas with a high potential as cooling centers by considering regional environmental and social characteristics, and is expected to be used for priority selection and management when designating additional cooling centers in the future.

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