Flood risks in the industrial sector and economic damages are increasing because of climate change. In addition to changes in precipitation patterns due to climate change; factors that increase flood damage include infrastructure deterioration and lack of storage facilities. Therefore; it is necessary for companies and the government to actively establish flood management policies. However; no evaluation method is currently available to determine which items should be invested in first by small and medium-sized enterprises that have limited finances. Because the government should make comprehensive and fair decisions; the purpose of this study is to propose priority investment risk items and an assessment method to decide which companies should be invested in first in flood risk management due to climate change. The multispatial scale of the method takes both the location and characteristics of the company into account. Future climate change scenarios were used to evaluate the changing patterns of flood risks. We developed the relative Flood Risk Assessment for Company (FRAC model) methodology to support the government’s policymaking. This method was applied to four companies belonging to four different industries and three risk items were derived that are likely to harm the company owing to flooding.

As climate change is ongoing, many studies have recently focused on adaptation to climate change from a spatial perspective. However little is known about how changing the spatial composition of landuse could improve climate change resilience. Consideration of climate change impacts when spatially allocating landuse could be a useful and fundamental long term adaptation strategy, particularly for regional planning. Here, we identify climate adaptation scenarios based on existing extents of three landuse classes using multi-objective genetic algorithms for a 9982 km 2 region with 3.5 million inhabitants in South Korea. We selected five objectives for adaptation based on predicted climate change impacts and regional economic conditions: minimization of disaster damage and existing landuse conversion, maximization of rice yield, protection of high species richness areas, and economic value. We generated 17 Pareto landuse scenarios by six weighted combinations of the adaptation objectives. Most scenarios, although varying in magnitude, showed better performance than the current spatial landuse composition for all adaptation objectives, suggesting that some alteration of current landuse patterns could increase overall climate resilience. Given the flexible structure of the optimization model, we expect that regional stakeholders could efficiently generate other scenarios by adjusting model parameters (weighting combinations) or replacing input data (impact maps), and selecting a scenario depending on preference or a number of problem-related factors.

Extreme landslides triggered by rainfall in hilly regions frequently lead to serious damage, including casualties and property loss. The frequency of landslide occurrences may increase under climate change, due to the increasing variability of precipitation. Developing urban areas outside landslide risk zones is the most effective method of reducing or preventing damage; however, planning in real life is a complex and nonlinear problem. For such multi-objective problems, genetic algorithms may be the most appropriate optimization tools. Therefore, in this study, we suggest a comprehensive land-use allocation plan using the Non-dominated Sorting Genetic Algorithm II to overcome multi-objective problems, including the minimization of landslide risk, minimization of change, and maximization of compactness. Our study area is Pyeongchang-gun, the host city of the 2018 Winter Olympics in Korea, where high development pressure has resulted in urban sprawl into the hazard zone where a large-scale landslide occurred in 2006. We obtain 100 Pareto plans that are better than the actual land use data for at least one objective, with five plans that explain the trade-offs between meeting the first and second objectives. The results can be used by decision makers for better urban planning and for climate change-related spatial adaptation.

Meteorological conditions regulate particulate matter concentrations near the earth surface. This study investigates the impacts of meteorological conditions (yellow-dust events, wind-speed, relative humidity, days after rainfall, solar duration, downward short-wave radiation, and diurnal temperature range) on particulate matter concentration less than 10 ㎛ (PM10) and less than 2.5 ㎛ (PM2.5) and their ratio (PM2.5/PM10) by analyzing five years (2015-2019) of in-situ surface observations in Seoul. Our results show apparent impacts of meteorological conditions on PM2.5 and PM10 concentrations and their ratio: 1) decrease in PM2.5/PM10 during yellow-dust events, 2) negative (positive) relationships of PM2.5 and PM2.5/PM10 with wind-speed (relative humidity), and 3) increase in PM10 and PM2.5 concentrations with elapsed days after rainfall.

Global temperature increases due to climate change are intensifying heat-related impacts, particularly in densely populated urban areas. Seoul, Korea, has experienced steady increases in summer temperatures since the 1950s, with more than 30 days of extreme heat recorded in 2018. In response to these challenges, green infrastructure (GI) strategies are increasingly recognized as effective long-term adaptations capable of mitigating urban heat through shading, evapotranspiration, and enhanced surface albedo. While GI’s potential for reducing temperatures is well-documented, most prior research has focused on spatial optimization, overlooking the importance of timing and associated maintenance costs. In this study, we address these gaps by optimizing both the spatial and temporal implementation of GI technologies, such as street trees, green roofs, and greenways, for the period from 2030 to 2050. Using RCP 4.5 climate projections and the Non-dominated Sorting Genetic Algorithm II (NSGA-II), we consider the effects of rising temperatures and the financial costs of GI installation and upkeep. Our findings suggest that establishment of GI areas greater than 49% of the total considered area by 2040 will allow the greatest temperature reduction, particularly through the early introduction of street trees. The rate of GI increase in the later period (2040 ~ 2050) depends on scenario, with variation in the effectiveness of specific technologies. This research provides crucial insights for efficient resource allocation in urban climate adaptation by integrating spatial and temporal factors. We emphasize the importance of early GI investment and offer a robust framework for decision-makers to plan long-term, cost-effective strategies that address the escalating challenges of urban heat.