Yeeun Shin

Urban heat island (UHI), a significant environmental issue caused by urbanization, is a pressing challenge in modern society. To mitigate it, urban thermal policies have been implemented globally. However, despite differences in topographical and environmental characteristics between cities and within the same city, these policies are largely uniform and fail to reflect contexts, creating notable drawbacks. This study analyzed three cities in Korea with high land surface temperatures (LSTs) to identify factors influencing LST by applying Extreme Gradient Boosting (XGBoost) with Shapley Additive explanations (SHAP) and Geographically Weighted Regression (GWR). Each variable was derived by calculating the average values from May to September 2020. LST was the dependent variable, and the independent variables were chosen based on previous studies: Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), ALBEDO, Population Density (POP_D), Digital Elevation Model (DEM), and SLOPE. XGBoost-SHAP was used to derive the relative importance of the variables, followed by GWR to assess spatial variation in effects. The results indicate that NDBI, reflecting building density, is the primary factor influencing the thermal environment in all three cities. However, the second most influential factor differed by city: SLOPE had a strong effect in Daegu, characterized by surrounding mountains; POP_D had greater influence in Incheon, where population distribution varies due to clustered islands; and DEM was more influential in Seoul, which contains a mix of plains, mountains, and river landscapes. Furthermore, while NDBI and ALBEDO consistently contributed to LST increases across all regions, the effects of the remaining variables were spatially heterogeneous. These findings highlight that urban areas are not homogeneous and that variations in land use, development patterns, and morphology significantly shape heat environments. Therefore, UHI mitigation strategies should prioritize improving urban form while incorporating localized planning tailored to each region’s physical and socio-environmental characteristics. The results can serve as a foundation for developing strategies and policy decisions to mitigate UHI effects.

As urbanization intensifies, the ecological area ratio (EAR) has become an essential tool for assessing ecological performance in urban development projects. However, conventional EAR systems remain largely prescriptive and surface-oriented, lacking adaptability to diverse planning contexts. This study proposes a probability-based EAR reference table developed from empirical data collected across six representative large-scale urban development districts. EAR values were statistically analyzed for 16 land-use types to construct a reference table using mean and quartile indicators. The table was then applied to seven newly planned towns to evaluate its predictive utility. The results showed that predicted EAR values closely aligned with institutional targets and revealed meaningful internal variation depending on land-use composition. Green space and parks showed the highest ecological contributions, while multi-family housing, despite moderate unit-area performance, played a key stabilizing role due to its large spatial footprint. Correlation analyses further demonstrated that EAR composition varied across housing supply types, shaped by differing regulatory frameworks and design priorities. By transitioning EAR from a uniform ratio to a data-driven, probabilistic guideline, this study offers both a practical estimation tool and a strategic planning aid. The findings provide actionable insights for more adaptive, equitable, and ecologically robust urban development practices.

Rural plans incorporating regional identity are vital for fostering regional revitalization and offering viable policy alternatives. The need for a systematic approach that recognizes both the diversity and shared characteristics of rural areas has become increasingly clear. Although numerous studies have explored rural classification, research examining specific regional characteristics remains limited. Hence, this study aimed to establish a comprehensive standard for developing effective rural plans. To this end, a study was conducted to classify the characteristics of rural areas using topic modeling, which is a text-mining algorithm. An analysis of publications on rural revitalization projects in Korea over the past decade revealed five common factors of success themes across each region. The five success factors, “local cultural experience”, “environment and landscape utilization”, “community activation”, “regional infrastructure development”, and “local economic activation”, should be considered in rural areas with different characteristics when establishing rural plans and policies. This classification of success factors serves as the foundation for establishing rural plans and policies. By applying different weights to the five success factors according to the unique characteristics and conditions of each region, it would open a great number of possibilities to establish more precise and effective customized plans. Future research is required to provide more empirical and broadly applicable results based on the classification framework proposed in this study.

Urban parks, a type of urban green space, help mitigate environmental pollution and climate change by absorbing and storing atmospheric carbon. Optimizing their carbon-sink capacity requires thoughtful plant community design considering multiple factors. This study analyzed South Korean urban parks using QGIS and i-Tree Eco, integrating satellite imagery with field surveys at both spatial and tree scales. Park spaces were classified into six types based on the biotope criteria established in this study. Random forest regression was applied to each type to identify key variables influencing annual carbon sequestration and storage. The relationship between maturity and sequestration was examined for ten dominant tree species, offering insights for plant selection. Higher tree coverage and more deciduous species were linked to efficiency in carbon sequestration and storage. While variable importance varied slightly across biotope types, tree density was most influential for sequestration, and diameter at breast height and age were key for storage. These findings provide integrated insights into short-term sequestration and long-term storage, as well as strategic directions for structuring plant communities in urban ecosystems. The study offers empirical evidence for designing carbon-efficient urban parks, contributing to sustainable landscape strategies.

Sustainable agriculture, including organic farming, offers a potential remedy for addressing environmental pollution. Accordingly, the importance of evaluating the environmental benefits of organic agriculture has become evident. Numerous studies have compared organic and conventional farming or focused on specific crops in environmental studies; however, there is a lack of research on indicators that evaluate the positive impact of organic agriculture on the overall agricultural environment. Therefore, this study aimed to develop comprehensive agri-environmental evaluation indicators by utilizing an analytic hierarchy process (AHP). These methods were employed to determine the importance of factors that evaluate the environmental performance of organic agriculture. This study utilized literature reviews, derived and organized indicators, and prioritized evaluation factors using expert surveys and AHP methods. Based on the analysis of experts, physical and ecological agricultural environments were the most crucial evaluation factors, including biological properties. In addition to the physical ecological agricultural environments, the AHP results demonstrated the need for evaluation indicators that evaluate the overall agricultural environment, including the residential ecological environment and village living and cultural environment. Further empirical studies are required for the derivation of additional valid indicators and policy implementation.

Climate change has been progressing rapidly in recent years; consequently, current plant habitats are expected to change. Therefore, to monitor plant movement caused by changed habitat environments, certain plants are designated as bioindicators and managed accordingly. Monitoring changes in plant habitats is important for protecting vulnerable plant species and establishing suitable measures for vegetation environments with suitable plant species under future climates. As part of this task, South Korea manages climate-sensitive plant species for each biological classification group, including plants. Accordingly, in this study, possible current habitats were identified and future habitats were predicted for nine climate-sensitive species in South Korea under climate change scenarios (representative concentration pathways RCP 4.5 and RCP 8.5) using a species distribution model (SDM) and based on national data acquired through field surveys. The MaxEnt algorithm, with high accuracy, was used for the SDM analysis. The MaxEnt algorithm is a powerful tool that analyzes the effects of environmental variables based on occurrence data and indicates possible habitats. To obtain precise results, environmental variables were utilized by collecting comprehensive climatic and topographic data for South Korea. Based on a current habitat analysis, the model accuracy of nine species yielded a high value of more than 0.9, on average, which indicates the extremely high performance of the model. Under climate change scenarios, evergreen coniferous and deciduous broadleaf plant habitats were predicted to expand inland and to the north of South Korea. The results of this study provide valuable data for establishing future conservation and management strategies for climate-sensitive plant species in South Korea. In addition, the detailed environment variable construction method and SDM analysis method used in this study could be applied to the analysis of changes in comprehensive plant habitats caused by climate change in other countries.

This study analyzed the trends and changes in climate-sensitive plants based on environmental attributes, using on-site survey data collected between 2006 and 2018. In combination with environmental attributes, 16 plant species were chosen as climate-sensitive biological indicator species and were spatially distributed based on national ecological survey data. With the use of regression methods and locally weighted scatterplot smoothing, possible changes in the vegetation distribution trends were simulated. The findings indicated that annual rainfall and temperature are critical environmental attributes, as they mostly affect the plant ecosystem. The simulated results suggest that plant habitats for the selected 16 individual species tend to be found in higher temperature zones, with a few exceptions. However, there was no clear pattern of the effects of annual rainfall on climate-sensitive plant species. The effects of climate change on these species are a risk to the environment; however, depending on the situation, adequate adaptation and management plans could provide opportunities for policy makers to make evidence-based decisions.

Previous research suggests that there should be environmental solutions for the emerging health threats caused by poor air quality, such as particulate matters (PM, including PM_2.5 and PM₁₀). Research related to air quality (measured by PM) using land-use regression and geographically weighted regression shows some patterns among different environmental contexts which could reduce the threats from such elements; however, there is little concrete evidence for such threats. To fill this research gap, this study installed real-time PM sensors at human breathing heights at five locations in Seoul, South Korea, and recorded the PM values collected between November 2021 and January 2023. Three-phase time-series analyses were conducted on the collected data. Lower levels of PM concentration were found in more enclosed spaces. In particular, when a space was surrounded by vegetation, the air quality significantly increased. The purpose of this study is to explore variations in air quality, particularly PMs densities, in different types of land use within urban areas such as Seoul. Greater metropolitan areas such as Seoul have a great number of health problems caused by air quality. This study’s results contribute to policy and decision-making in urban design to tackle such problems and to provide spatial guidelines for public health and welfare.

The close relation between atmospheric pollution and human health has been well documented. Accordingly, various policies have been enacted worldwide to reduce and regulate air pollution, with most countries having established correlated monitoring systems. Notably in South Korea, increasing concerns about particulate matter (PM) concentrations led to the establishment of a nationwide forecasting and warning system in 2014. In this study, the PM trends in South Korea over the past decade were examined, and the correlated social issues were analyzed. In addition, the relationships between PM concentration, the forecasting–warning system, and people’s urban park use were analyzed to assess the efficacy of policy introduction. The results indicated that PM concentrations were an obstacle to outdoor activities, and the PM forecasting–warning system affected urban park use. Whereas the effects of PM forecasting and warning systems have not been sufficiently explored in practical terms in the literature, this study could be significant in proving the validity of environmental policies through the evidence including urban park visitors. This study also suggests future directions for developing PM forecasting and warning systems.

Monitoring and preserving natural habitats has become an essential activity in many countries today. As a native tree species in Korea, Paulownia coreana has periodically been surveyed in national ecological surveys and was identified as an important target for conservation as well as habitat monitoring and management. This study explores habitat suitability models (HSMs) for Paulownia coreana in conjunction with national ecological survey data and various environmental factors. Together with environmental variables, the national ecological survey data were run through machine learning algorithms such as Artificial Neural Network and Decision Tree & Rules, which were used to identify the impact of individual variables and create HSMs for Paulownia coreana, respectively. Unlike other studies, which used remote sensing data to create HSMs, this study employed periodical on-site survey data for enhanced validity. Moreover, localized environmental resources such as topography, soil, and rainfall were taken into account to project habitat suitability. Among the environment variables used, the study identified critical attributes that affect the habitat conditions of Paulownia coreana. Therefore, the habitat suitability modelling methods employed in this study could play key roles in planning, monitoring, and managing plants species in regional and national levels. Furthermore, it could shed light on existing challenges and future research needs.