• Journal of the Korean Regional Science Association
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• v.38 no.2
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• pp.3-20
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• 2022

The purpose of this study is to explore the heterogeneity of compensating wage differentials for outdoor workers, under the threat of climate change and heatwave, by region and by wage quantile. This study conducted Oaxaca-Blinder decomposition, multiple regression analysis by region, and unconditional quantile regression analysis using the Korean Working Conditions Survey, which provides individual-level information on the working environment and worker's characteristics. The implications derived from the results of the study are as follows: For most variables, the endowment effect and the price effect were greater for indoor workers, while experience and gender played a role in narrowing the wage gap; The compensating wage differentials for outdoor workers were confirmed to be 2.4% nationwide, depending on the region however, the compensating wage differentials varied from 5 times of national average to nothing statistically significant; The higher the wage quantile, the greater the compensating wage differentials for outdoor workers, and statistically significant monetary compensation was not identified for some low-level outdoor workers. This study is meaningful as an early study that revealed the heterogeneity of compensating wage differentials for outdoor workers and suggested further research on the topic.

• Journal of the Korean earth science society
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• v.44 no.2
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• pp.169-183
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• 2023

This study analyzed students' polar literacy in an effort to promote polar education based on its high educational value. The polar literacy test items developed for this study consisted of questions about knowledge, skills, attitudes, and beliefs about the polar region, as well as background variables of students. The final test items, which were revised and supplemented several times through the preliminary test, were applied to 323 eighth graders in South Korea. We analyzed the response characteristics of the polar literacy questions for all students. Students were grouped into those with a global citizenship perspective and those with a pragmatic perspective, according to the viewpoint of polar issues and their polar literacy. Analysis showed that the students had a high understanding of climate change and living things in the polar regions, but had a very low understanding of ice, which is a key component of the polar regions. Moreover, they were unable to approach the Earth system thinking when dealing with polar issues. In addition, the global citizenship group had a higher intellectual understanding and deeper sympathy of the polar problem than the pragmatic group. This study is meaningful in that the survey results present a specific direction for future polar education.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.563-574
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• 2023

Recently, climate change has affected functional responses of river basins to meteorological variables, emphasizing the importance of rainfall-runoff simulation research. Simultaneously, the growing interest in machine learning has led to its increased application in hydrological studies. However, it is not yet clear whether machine learning models are more advantageous than the conventional conceptual models. In this study, we compared the performance of the conventional GR6J model with the machine learning-based Random Forest model across 38 basins in Korea using both gauged and ungauged basin prediction methods. For gauged basin predictions, each model was calibrated or trained using observed daily runoff data, and their performance was evaluted over a separate validation period. Subsequently, ungauged basin simulations were evaluated using proximity-based parameter regionalization with Leave-One-Out Cross-Validation (LOOCV). In gauged basins, the Random Forest consistently outperformed the GR6J, exhibiting superiority across basins regardless of whether they had strong or weak rainfall-runoff correlations. This suggest that the inherent data-driven training structures of machine learning models, in contrast to the conceptual models, offer distinct advantages in data-rich scenarios. However, the advantages of the machine-learning algorithm were not replicated in ungauged basin predictions, resulting in a lower performance than that of the GR6J. In conclusion, this study suggests that while the Random Forest model showed enhanced performance in trained locations, the existing GR6J model may be a better choice for prediction in ungagued basins.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.32-39
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• 2023

Using the logarithmic methods and the generalized method of moments (GMM), this study developed carbon storage equations for maple trees (Acer palmatum Thunb.) planted in an urban settlement area. A total of 20 maple trees of various ages and diameters were destructively harvested to determine their dry weight and carbon concentration by component. The allometric equations with DBH and DBH²×H as independent variables were developed to estimate the carbon storage for each tree component. The carbon concentration of tree components was the highest in stem wood (49.8%) and lowest in stem bark (46.5%). Allometric equations to estimate the carbon storage of tree components (stem, root, aboveground, and total) showed a similar coefficient of determinations (R²) between the allometric equations of the logarithmic method (0.7494-0.9036) and the GMM (0.7085-0.8847). However, the R² values of the leaves and branches were in the range of 0.3027 to 0.6380, lower than those of the R² of the other tree components. These results indicate that the carbon storage of maple trees growing in urban settlement areas can be efficiently predicted from the equations of GMM methods in the case of a small sample size or the heteroscedasticity of logarithmic equations.

• Korean Journal of Environmental Biology
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• v.40 no.2
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• pp.214-223
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• 2022

Among migratory insect pests, Mythimna seperata and Cnaphalocrocis medinalis are invasive pests introduced into South Korea through westerlies from southern China. M. seperata and C. medinalis are insect pests that use rice as a host. They injure rice leaves and inhibit rice growth. To understand the distribution of M. seperata and C. medinalis, it is important to understand environmental factors such as temperature and humidity of their habitat. This study predicted current and future habitat suitability models for understanding the distribution of M. seperata and C. medinalis. Occurrence data, SSPs (Shared Socio-economic Pathways) scenario, and RCP (Representative Concentration Pathway) were applied to MaxEnt (Maximum Entropy), a machine learning model among SDM (Species Distribution Model). As a result, M. seperata and C. medinalis are aggregated on the west and south coasts where they have a host after migration from China. As a result of MaxEnt analysis, the contribution was high in the order of Land-cover data and DEM (Digital Elevation Model). In bioclimatic variables, BIO_4 (Temperature seasonality) was high in M. seperata and BIO_2 (Mean Diurnal Range) was found in C. medinalis. The habitat suitability model predicted that M. seperata and C. medinalis could inhabit most rice paddies.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.1-18
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• 2024

This study aimed to identify the optimal street tree planting method to improve the summer thermal environment in Seoul, Republic of Korea. The effects of road direction and street tree planting patterns on urban thermal environments using ENVI-met simulations were analyzed. The 68 scenarios were analyzed based on four road directions and 17 planting patterns. The results showed that tree planting had a reducing air temperature, mean radiant temperature, human thermal sensation (PET and UTCI). The most effective planting pattern among all scenarios was low tree height (6m), wide crown width (9m), high leaf area index (3.0), and narrow planting interval (8m). The largest improvement in the thermal environment was the northern sidewalk of the east-west road. Since this study used computer simulations, the difference from real urban spaces should be considered, and further research is needed through field measurement and consideration of more variables.

• Clean Technology
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• v.30 no.2
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• pp.105-112
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• 2024

Globally, the demand for electric vehicles has surged due to greenhouse gas regulations related to climate change, leading to an increase in the production of used batteries as a consequence of the battery life issue. This study aims to selectively leach and recover valuable metal lithium from the cathode material of spent LFP (LiFePO₄) batteries among lithium-ion batteries. Generally, the use of inorganic acids results in the emission of toxic gases or the generation of large quantities of wastewater, causing environmental issues. To address this, research is being conducted to leach lithium using organic acids and other leaching agents. In this study, selective leaching was performed using the organic acid methane sulfonic acid (MSA, CH₃SO₃H). Experiments were conducted to determine the optimal conditions for selectively leaching lithium by varying the MSA concentration, pulp density, and hydrogen peroxide dosage. The results of this study showed that lithium was leached at approximately 100%, while iron and phosphorus components were leached at about 1%, verifying the leaching efficiency and the leaching rates of the main components under different variables.

• Environmental and Resource Economics Review
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• v.33 no.1
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• pp.1-32
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• 2024

The emissions trading system stands as a pivotal climate policy in Korea, incentivizing abatement equivalent to 87% of total emissions (as of 2021). As the system likely has a far-reaching impact, it is crucial to understand how the real economic activity, energy sector, as well as environment would be influenced by its implementation. Employing a macroeconometric model, this paper is the first study analyzing the effects of the Korean emissions trading policy. It interconnects the Korean Standard Industrial Classification (Economy), Energy Balance (Energy), and National Inventory Report (Environment), enhancing its real-world explanatory power. We find that a 50% increase in emission permit price over four years results in a decrease in greenhouse gas emissions (-0.043%) and downward shifts in key macroeconomic variables, including real GDP (-0.058%), private consumption (-0.003%), and investment (-0.301%). The price increase in emission permit is deemed crucial for achieving greenhouse gas reduction targets. To mitigate transition risk associated with price shocks, revenue recycling using auction could ensure the sustainability of the economy. This study confirms the comparative advantage of expanded current transfers expenditure over corporate tax reduction, particularly from an economic growth perspective.

• Journal of Wetlands Research
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• v.26 no.3
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• pp.279-284
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• 2024

Recently, the frequency and intensity of torential rains caused by climate change are increasing, and the damage to reservoir collapse in local governments continues to occur. Most local government reservoirs are aged reservoirs that have been built for more than 50 years, and there is a high risk of collapse due to recent heavy rainfall. In order to prevent reservoir collapse or overflow caused by heavy rainfall, a decision-making support system that can judge risks due to changes in storage capacity is needed. In this study, a reservoir discharge simulation model was constructed using a system dynamics technique that can dynamically represent causal relationships between various variables. Through discharge simulation, the change in storage capacity due to rainfall was analyzed, and the operation time and termination time of the discharge facility to prevent overflow of the reservoir were analyzed. Using the results of this study, it is possible to determine the timing of the overflow of the reservoir due to torrential rain, and also the capacity and operation timing of the discharge facility to prevent overflow can be known. hrough this, it is expected that local governments will be able to judge the risk of damage to reservoirs and establish a preliminary response plan to prevent damage.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.301-306
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• 2018

Urbanization causes urban floods and urban heat island in the summer, so it is necessary to understanding the changes of the thermal environment through urban climate and energy balance. This can be explained by the energy balance, but in urban areas, unlike the typical energy balance, the storage heat flux saved in the building or artificial land cover should be considered. Since the environment of each city is different, there is a difficulty in applying the method of retrieving the storage heat flux of the previous research. Especially, most of the previous studies are focused on the overseas cities, so it is necessary to study the storage heat retrieval suitable for various land cover and building characteristics of the urban areas in Korea. Therefore, the object of this study, it is to derive the regression formula which can quantitatively retrieve the storage heat using the data of the area where various surface types exist. To this end, nonlinear regression analysis was performed using net radiation and surface temperature data as independent variables and flux tower based storage heat estimates as dependent variables. The retrieved regression coefficients were applied to each independent variable to derive the storage heat retrieval regression formula. As a result of time series analysis with flux tower based storage heat estimates, it was well simulated high peak at day time and the value at night. Moreover storage heat retrieved in this study was possible continuous retrieval than flux tower based storage heat estimates. As a result of scatter plot analysis, accuracy of retrieved storage heat was found to be significant at $50.14Wm^{-2}$ and bias $-0.94Wm^{-2}$.