[논문]민간기업을 위한 물리적 기후리스크 추정 연구

최용상; 유창현; 공민정; 조민정; 정해수; 이윤경; 박선기; 안명환; 황재학; 김성주

doi:10.14191/atmos.2024.34.1.001

민간기업을 위한 물리적 기후리스크 추정 연구
Estimation of Physical Climate Risk for Private Companies 원문보기

대기 = Atmosphere, v.34 no.1, 2024년, pp.1 - 21

최용상 (이화여자대학교 기후.에너지시스템공학과) , 유창현 (이화여자대학교 기후.에너지시스템공학과) , 공민정 (이화여자대학교 기후.에너지시스템공학과) , 조민정 (이화여자대학교 기후.에너지시스템공학과) , 정해수 (이화여자대학교 기후.에너지시스템공학과) , 이윤경 (이화여자대학교 기후.환경변화예측연구센터) , 박선기 (이화여자대학교 기후.에너지시스템공학과) , 안명환 (이화여자대학교 기후.에너지시스템공학과) , 황재학 (금융감독원 지속가능금융팀) , 김성주 (금융감독원 지속가능금융팀)

Abstract ▼ AI-Helper

Private companies are increasingly required to take more substantial actions on climate change. This study introduces the principle and cases of climate (physical) risk estimation for 11 private companies in Korea. Climate risk is defined as the product of three major determinants: hazard, exposure, and vulnerability. Hazard is the intensity or frequency of weather phenomena that can cause disasters. Vulnerability can be reflected in the function that explains the relationship between past weather records and loss records. The final climate risk is calculated by multiplying the function by the exposure, which is defined as the area or value of the target area exposed to the climate. Future climate risk is estimated by applying future exposure to estimated future hazard using climate model scenarios or statistical trends based on weather data. The estimated climate risks are developed into three types according to the demand of private companies: i) climate risk for financial portfolio management, ii) climate risk for port logistics management, iii) climate risk for supply chain management. We hope that this study will contribute to the establishment of the climate risk management system in the Korean industrial sector as a whole.

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표/그림 (18)

표 Table 1. Variable of some extreme climate index (Source: Korea Meteorological Administration, http://www.climate.go.kr/ home/CCS/contents_2021/info/how_use.html?m1=1&m2=2).
표 Table 2. Definition of RCP (Representative Concentration Pathways) and SSP (Shared Socioeconomic Pathways) scenarios (Source: Korea Meteorological Administration).
그림 Fig. 1. A conceptual diagram of the relationship between hazard (e.g., precipitation in mm/year, blue) and climate risk (e.g., loss in 10⁷ won, red) according to Eq. (2). The solid and dashed lines indicate the probability distributions for the present and future climates, respectively. The black line indicates that the loss is the exponential cost function of precipitation.
표 Table 3. Curve estimation of each regression model (NDMI, 2013).
표 Table 4. Average coefficient of determination for each variable. The coefficient of determination is the average over 229 values for each region.
그림 Fig. 2. (a) Estimated flood risk (unit: 10⁷ won), (b) maximum consecutive 5-days precipitation (RX5DAY) for SSP1 scenario (2021~2030, 2031~2040, 2041~2050 average) and (c) difference of RX5DAY between present (2000~2020 average), and future for SSP1 scenario. Regions with positive values will increase RX5DAY in the future compared to the present level, and regions with negative values decrease RX5DAY.
그림 Fig. 3. Same as Fig. 2 except for SSP5.
그림 Fig. 4. Flood risk loss by scenario and RX5DAY in (a) Buk-gu, Gwangju, and (b) Gwacheon, Gyeonggi-do.
그림 Fig. 5. Distribution of normalized RX5DAY (blue) and flood risk loss (red) in SSP5 scenarios at Buk-Gu, Gwangju (left) and Gwacheon, Gyeonggi-do (right). The definition of RX5DAY is explained in Table 1. The middle of the box in the box-whisker plot represents the median, and the top and bottom lines of the box represent the 25th and 75th percentiles, respectively. The horizontal line and the x-symbol inside the box represent the 5th and 95th percentiles, respectively. The ○-symbol represents the limit value, or outlier.
그림 Fig. 6. Estimated typhoon risk for (a) 1.5-degree, and (b) 2-degree scenario (in order of 2030, 2040, and 2050).
표 Table 5. Trends in economic growth rates from 2018 to 2022.
그림 Fig. 7. 5-Regions with the highest estimated loss for typhoon.
그림 Fig. 8. Scatter plot and corresponding linear regression line between critical wind speed duration and time on demurrage in each month. The * symbol means a significant value for the 95% confidence interval.
그림 Fig. 9. Estimated demurrage charge (unitless) for future scenarios (a) RCP2.6, (b) RCP4.5, (c) RCP6.0, (d) RCP8.5, and (e) future climate data from historical observations.
그림 Fig. 10. Standard deviation of 95 percentile heat wave days (1979~2020) from ERA5 reanalysis data (ECMWF: European Centre for Medium Range Forecast). The number of heat wave days is calculated year by year using daily 2-m temperature.
$Fig. 11. Example of cost function (V):<TEX>$\overline{H}$</TEX>: 20 days, <TEX>$\tilde{H}$</TEX> : 10 days, s: 1, 2, 4. X-axis denotes the H in eq. (5), and y-axis denotes the V. Where, s indicates the parameter that determines the form of the cost function, <TEX>$\overline{H}$</TEX> is the average number of heat wave days that occurred in the past for each point, and <TEX>$\tilde{H}$</TEX> is the standard deviation for each point.$ 그림 Fig. 11. Example of cost function (V):$\overline{H}$: 20 days, $\tilde{H}$ : 10 days, s: 1, 2, 4. X-axis denotes the H in eq. (5), and y-axis denotes the V. Where, s indicates the parameter that determines the form of the cost function, $\overline{H}$ is the average number of heat wave days that occurred in the past for each point, and $\tilde{H}$ is the standard deviation for each point.
그림 Fig. 12. (Top panel) past climate, (bottom panel) future climate, (left panel) the change of number of heat wave days, and (right panel) the change of heat wave risk. The future climate is calculated based on ensemble average of integrated 10 models using SSP3 scenarios.
그림 Fig. 13. (a) The number of heat waves days and (b) the risk of heat waves in the past climate (black line) and the SSP 3 future climate (green line) shown in Fig. 12 [Korean Peninsula (37.5N, 127E)]. Number of heat wave days and heat wave risk for SSP1 (yellow line) and SSP5 (blue line). The trend calculated from each data is marked in the upper left, and the * table means a significant value for the 95% confidence interval.

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