Air pollution is one of the most significant environmental hazards. The elderly, young, and poor are more vulnerable to air pollution. The risk of air pollution was assessed based on the risk framework published by the Intergovernmental Panel on Climate Change (IPCC) in terms of three aspects: hazard, exposure, and vulnerability. This study determined the concentrations of hazardous pollutants using satellite images from 2015 at 1 km² spatial resolution. In addition, the study identified vulnerable groups who are exposed to hazardous air pollutants. The study highlighted the degree of vulnerability based on environmental sensitivity and institutional abilities, such as mitigation and social adaption policies, using statistical data. Based on the results, Seoul City and Gyeonggi Province have low air pollution risk owing to good institutional abilities, while the western coastal area has the highest air pollution risk. Three adaption pathway scenarios were assessed in terms of the effect of increases in the budget for social adaptation policies on the level of risk. The study found that the risk can be reduced when the social adaptation budget of 2015 base level is increased by 20% in Gyeonggi Province and by 30% in the western coastal area. In conclusion, this risk assessment can support policy-making to target more vulnerable groups based on scientific evidence and to ensure environmental justice at the national level.

Climate change is one of the greatest challenges in Kyrgyzstan. There have been negative spillover effects in agriculture. This study aims to assess the climate change impacts on cropland suitability in Kyrgyzstan. We used the random forest algorithm to develop a model that captures the effects of multiple climate and environment factors at a spatial resolution of 1 km². The model was then applied in the scenario analysis for an understanding of how climate change affects cropland distribution. The potential high-quality cropland was found to be included in existing croplands, while the remaining were distributed around the Chu-Talas valley, the Issyk-kul area, and the Fergana valley. These potential high-quality croplands comprise grasslands (47.1%) and croplands (43.7%). In the future, the potential high-quality cropland exhibited inland trends at the periphery of original cropland category, with grassland and cropland as the primary land components. Due to climate change, potential high-quality cropland is expected to gradually reduce from the 2050s to the 2070s, exhibiting the largest reduction in potential high-quality areas for the Representative Concentration Pathway 8.5 scenario. Therefore, the short- and long-term adaptation strategies are needed for prioritizing the croplands to ensure food security and agricultural resilience.

Land Use, Land-Use Change and Forestry includes the only carbon sink among greenhouse gas (GHG) inventory, and the significance of accurate statistics has increased. However, the exact amounts of GHG absorption and emissions cannot be calculated due to the absence of definition and activity data in the settlement sector. Therefore, this study established the concept and activity data of settlement area in South Korea by reviewing the definition suggested by IPCC and Annex 1 countries and calculated GHG absorption. Considering the possibility of connecting current MRV guidelines, exisiting national statistics, country and urban planning data, and unifying management authority, settlement can be defined in three ways, as a Land category, Urban area, and Urban & Plan management. The study built activity data based on concept 1 and 2, which rarely overlap with the other land categories, and the study area is Seoul, with the largest proportion of settlements in the country. Of the area of Korea, 10% was used as activity data based on systematic sampling, and GHG absorption was estimated applying the factors identified by the IPCC. The result showed GHG absorption by ‘land category’ and ‘urban area’ of 72,990.7 tCO₂·yr^-1 and 201,287.73 tCO₂·yr^-1, respectively. This means that CO₂ absorption can vary depending on the factors that influence the area of vegetation within the settlement. The study identified the share of GHG statistics in settlement sector based on outcomes and contributed to the basis for calculation at the national level.

Drought is a natural disaster that occurs globally and is a main trigger of secondary environmental and socio-economic damages, such as food insecurity, land degradation, and sand-dust storms. As climate change is being accelerated by human activities and environmental changes, both the severity and uncertainties of drought are increasing. In this study, a diagnostic drought prediction model (DDPM) was developed to reduce the uncertainties caused by environmental diversity at the regional level in Kyrgyzstan, by predicting drought with meteorological forecasts and satellite image diagnosis. The DDPM starts with applying a prognostic drought prediction model (PDPM) to 1) estimate future agricultural drought by explaining its relationship with the standardized precipitation index (SPI), an accumulated precipitation anomaly, and 2) compensate for regional variances, which were not reflected sufficiently in the PDPM, by taking advantage of preciseness in the time-series vegetation condition index (VCI), a satellite-based index representing land surface conditions. Comparing the prediction results with the monitored VCI from June to August, it was found that the DDPM outperformed the PDPM, which exploits only meteorological data, in both spatiotemporal and spatial accuracy. In particular, for June to August, respectively, the results of the DDPM (coefficient of determination [R²] = 0.27, 0.36, and 0.4; root mean squared error [RMSE] = 0.16, 0.13, and 0.13) were more effective in explaining the spatial details of drought severity on a regional scale than those of the PDPM (R² = 0.09, 0.10, and 0.11; RMSE = 0.17, 0.15, and 0.16). The DDPM revealed the possibility of advanced drought assessment by integrating the earth observation big data comprising meteorological and satellite data. In particular, the advantage of data fusion is expected to be maximized in areas with high land surface heterogeneity or sparse weather stations by providing observational feedback to the PDPM. This research is anticipated to support policymakers and technical officials in establishing effective policies, action plans, and disaster early warning systems to reduce disaster risk and prevent environmental and socio-economic damage.

Central Asian countries, which are included the Mid-Latitude Region (MLR), need to develop regional adaptive strategies for reducing Sand and Dust Storm (SDS)-induced negative damages based on adequate information and data. To overcome current limitation about data and assessment approaches in this region, the macroscale verified methodologies were required. Therefore, this study analyzed environmental conditions based on the SDS impacts and regional differences of SDS sources and receptors to support regional SDS adaptation plans. This study aims to identify environmental conditions based on the phased SDS impact and regional differences of SDS source and receptor to support regional adaptation plans in MLR. The Normalized Difference Vegetation Index (NDVI), Aridity Index (AI), and SDS frequency were calculated based on satellite images and observed meteorological data. The relationship among SDS frequency, vegetation, and dryness was determined by performing statistical analysis. In order to reflect phased SDS impact and regional differences, SDS frequency was classified into five classes, and representative study areas were selected by dividing source and receptor in Central Asia and East Asia. The spatial analysis was performed to characterize the effect of phased SDS impact and regional distribution differences pattern of NDVI and AI. The result revealed that vegetation condition was negatively correlated with the SDS frequency, while dryness and the SDS frequency were positively correlated. In particular, the range of dryness and vegetation was related to the SDS frequency class and regional difference based on spatial analysis. Overall, the Aral Sea and the Caspian Sea can be considered as an active source of SDS in Central Asia, and the regions were likely to expand into potential SDS risk areas compared to East Asia. This study presents the possibility of potential SDS risk area using continuously monitored vegetation and dryness index, and aids in decision-making which prioritizes vegetation restoration to prevent SDS damages with the macrolevel approach in the MLR perspective.

Using the nexus approach is essential to achieve sustainable development goals. But inter-linkage research based on forest goals is insufficient. In this study, K-SDGs and some policies such as laws and plans related to forest sectors were reviewed to extract keywords and inter-linkages were identified through the keywords. Among K-SDGs, 15 goals are linked to forests except for Goal 5 and Goal 10. In addition, targets with the most positive impact are sustainable forest management (15.2), restoration of devastated land (15.3), conservation of protected areas (15.1), ecological restoration (15.8), conservation of endangered species (15.4), share the benefit of genetic resources (15.5), prevention of capture and smuggling (15.6), and eradication of invasion by genetic resources (15.7). In order to achieve goals related to forest sectors, an understanding that includes society, environment, and economy is directly or indirectly needed. In the future, studies that investigate major circumstances such as the inter-linkage of each target or indicator will be needed that use quantitative methods or modeling.