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.

A methodological framework is provided for characterizing large-scale forest resource distribution in South Korea, along with a baseline for sustainable forest management practices. This study aimed to establish a baseline framework that integrates satellite and ground-based data for nationwide growing stock volume (GSV) estimation. Several machine learning models were applied and compared for estimating GSV across South Korea using Sentinel-2 imagery, national forest inventory data, and topographic information. Four algorithms, namely, k-nearest neighbors (kNN), random forest (RF), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost), were evaluated. The ensemble methods outperformed kNN, with RF demonstrating the highest accuracy (coefficient of determination and root mean squared error of 0.56 and 66.9 m³/ha, respectively). Accuracy assessment shows that kNN performed relatively well near the mean GSV (≒200 m³/ha), but its accuracy decreased sharply toward the extremes, failing to represent plots above 400 m³/ha. Estimation accuracy also varied substantially with stand height, which was identified as the primary predictor, and kNN was the most affected. These findings suggest that the structural complexity and mountainous terrain of South Korean forests may amplify the limitations of distance-based methods, reinforcing the need for improved 3D structural predictors such as satellite-derived stand height.