• Journal of Environmental Impact Assessment
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• v.9 no.4
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• pp.349-362
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• 2000

In the study, the feasibility of landscape ecological measures as indices system for interpretation and evaluation of regional ecosystem was investigated through the application to Yongin City. Each patch metrics well showed the class structure and supplemented the class metrics, and class metrics also showed well the landscape structure and supplemented the landscape metrics. And the change analysis through subtraction of two set of landscape ecological measurement in two point of time showed the dynamic trends very well. One of the dynamic trends in Yongin City was the rapid fragmentation. While there was no landcover data on Yongin City, using Landsat data and remote sensing techniques were proved to be efficient and effective to produce the digital landcover data.

• New & Renewable Energy
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• v.18 no.2
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• pp.60-72
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• 2022

The environmental, social, and governance (ESG) score is gaining recognition as important nonfinancial investment criteria. With climate change emerging as a global issue, energy companies must pay attention to the ESG impact on corporate performance. In this study, the ESG impact on the performance of energy companies was analyzed based on 23 companies selected from the S&P 500. The panel corrected standard error methodology was used. The Refinitiv ESG score was the independent variable, and financial performance metrics, such as Tobin's Q, return on assets, and return on equity, were the dependent variables. It was found that the ESG score is positively associated with long-term corporate value but not with short-term profitability in the electricity utility industry. Among the subcategories of ESG, the environmental and social scores also showed positive correlations with long-term corporate value. A direct incentive policy is recommended that can offset expenses for ESG activities to reduce carbon emission in the energy sector.

• Environmental Analysis Health and Toxicology
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• v.30 no.sup
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• pp.3.1-3.11
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• 2015

Objectives This study presents the indicators and proxy variables for the quantitative assessment of green chemistry technologies and evaluates the relative importance of each assessment element by consulting experts from the fields of ecology, chemistry, safety, and public health. Methods The results collected were subjected to an analytic hierarchy process to obtain the weights of the indicators and the proxy variables. Results These weights may prove useful in avoiding having to resort to qualitative means in absence of weights between indicators when integrating the results of quantitative assessment by indicator. Conclusions This study points to the limitations of current quantitative assessment techniques for green chemistry technologies and seeks to present the future direction for quantitative assessment of green chemistry technologies.

• Journal of Environmental Impact Assessment
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• v.21 no.3
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• pp.483-501
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• 2012

The purpose of this study was to apply a multi-metric model of Lentic Ecosystem Health Assessments(LEHA) for environmental impact assessments of Cheongpyung Reservoir during 2005 - 2006 and assessed the ecological model values. The ecosystem model of LEHA was composed of eleven metrics such as biological parameters($B_p$), physical parameters($P_p$), and chemical parameters($C_p$), and determined the rank of ecological health by the criteria. The variables of $B_p$ were metrics of % sensitive species($M_2$, NMS) and insectivore species($M_5$, % $I_n$), which decrease as the water quality degradates, and these metric values were low as 1.5% and 32.4%, respectively. In contrast, the proportions of tolerant species and omnivore species as the other $B_p$ parameters were 43% and 62%, respectively, which indicate a degradation and disturbance of the ecosystem. Riparian vegetation coverage($M_9$, % $V_c$) as a variable of $P_p$, were higher in the 2nd than 1st survey, and decreased toward the dam site from the headwaters. This was due to a habitat simplification(modifications) by frequent bottom dredging of sand and rocks. The variables of $C_p$ were two metrics of specific conductivity($M_{10}$, $C_I$) as an indicator of ionic contents(cations and anions) and the Trophic State Index(TSI) based on chlorophyll-a($M_{11}$, $TSI_{CHL}$) as an indicator of trophic state. These metric values of $C_p$ had high temporal variations, but low spatial variations on the main axis of the reservoir along with the ecological health of a good condition. The environmental impact assessments using the LEHA multi-metric model indicated that the model values of LEHA averaged 30.7 in 1st survey(fair - poor condition) vs. 28 in 2nd survey(poor condition), indicating a temporal variation of the ecological health. The model values of LEHA showed a minimum(28) in the lacustrine zone(S5) and ranged from 29 to 30 in the other locations sampled, indicating a low longitudinal variation. Overall, environmental impact assessments, based on LEHA model, suggest that chemical water quality conditions were in good, but biological conditions were disturbed due to habitat modifications by frequent dredgings in the system.

• Journal of Environmental Impact Assessment
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• v.21 no.3
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• pp.391-407
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• 2012

The loss of connectivity and fragmentation of forest landscapes are seriously hindering dispersal of many forest-dwelling species, which may be critical for their viability and conservation by decreasing habitat area and increasing distance among habitats. For understanding their environmental impacts, numerous spatial models exist to measure landscape connectivity. However, general relationships between functional connectivity and landscape structure are lacking, there is a need to develop landscape metrics that more accurately measure landscape connectivity in whole landscape and individual patches. We reviewed functional and structural definition of landscape connectivity, explained their mathematical connotations, and applied representative 13 indices in 3 districts of Seoul having fragmented forest patches with tits, the threshold distance was applied 500m by considering the dispersal of tits. Results of correlation and principal component analysis showed that connectivity indices could be divided by measurement methods whether they contain the area attribute with distance or not. Betweenness centrality(BC), a representative index measuring distance and distribution among patches, appreciated highly stepping stone forest patches, and difference of probability of connectivity(dPC), an index measuring including area information, estimated integrated connectivity of patches. Therefore, for evaluating landscape connectivity, it is need to consider not only general information of a region and species' characteristics but also various measuring methods of landscape connectivity.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.3
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• pp.29-43
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• 2024

Legally protected species are one of the crucial considerations in the field of natural ecology when conducting environmental impact assessments (EIAs). The occurrence of legally protected species, especially 'Endangered Wildlife' designated by Ministry of Environment, significantly influences the progression of projects subject to EIA, necessitating clear investigations and presentations of their habitats. In perspective of statistics, a minimum of 30 occurrence coordinates is required for population prediction, but most of endangered wildlife has insufficient coordinates and it posing challenges for distribution prediction through modeling. Consequently, this study aims to propose modeling methodologies applicable when coordinate data are limited, focusing on Rodgersia podophylla, representing characteristics of endangered wildlife and northern plant species. For this methodology, 30 random sampling coordinates were used as input data, assuming little survey data, and modeling was performed using individual models included in BIOMOD2. After that, the modeling results were evaluated by using discrimination capacity and the reality reflection ability. An optimal modeling technique was proposed by ensemble the remaining models except for the MaxEnt model, which was found to be less reliable in the modeling results. Alongside discussions on discrimination capacity metrics(e.g. TSS and AUC) presented in modeling results, this study provides insights and suggestions for improvement, but it has limitations that it is difficult to use universally because it is not a study conducted on various species. By supporting survey site selection in EIA processes, this research is anticipated to contribute to minimizing situations where protected species are overlooked in survey results.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.407-418
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• 2023

This study conducted an analysis of terrain change indicators in major development projects in Korea, examining the correlation between terrain change indicators to derive foundational terrain change metrics based on different land use and slope types. The aim is to contribute to sustainable development by enhancing the efficiency of land utilization and landscaping, while minimizing environmental impacts in future development endeavors. Additionally, to apply the research findings in practical contexts, domestic regulations related to terrain were surveyed, and the compatibility and usability between these regulations and research analysis results were discussed. Based on this, the study seeks to explore strategies for more accurate and useful utilization of terrain change indicators in future research. As a result, in the tourism development, terrain changes predominantly occur in the order of flat land, hillly land, and mountain land, with the analysis indicating higher terrain changes in undulating hilly and mountainous lands compared to flat land. Furthermore, in industrial complex development, very steep (20°-30°) and extreme (30°-40°) slopes; in urban development projects, steep slope (15°-20°); in athletic service facility and tourist development, steep (15°-20°) and very steep (20°-30°) exhibit higher average terrain change indicators compared to other slope categories. The findings of our study can contribute to the formulation of strategies aimed at minimizing terrain disturbance in future domestic development projects and serve as foundational data for environmental impact assessments.

• Journal of KIBIM
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• v.9 no.1
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• pp.32-41
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• 2019

As population in Sarasota and Manatee Counties, Florida in the United States is projected to increase, land use changes from land development happen continuously. The more land development means the more impervious surfaces and stormwater runoff to Sarasota Bay, which causes critical impact on the resiliency of the ecosystem. In order to decrease its impact on water quality and the ecosystem function of Sarasota Bay, it is important to assess the resilient status of communities that create negative impacts on the ecosystem. Three types of guiding principles of resiliency for Sarasota Bay watershed are suggested. To assess resiliency status, three indexes - vulnerability index, socio-economic index, and ecological index are developed and analyzed by using geographic information system for each census tract in the two counties. Since each indicator for vulnerability index, socio-economic index, and ecological index is measured with different metrics, statistical standardizing method - distance from the best and worst performers is used for this study to directly compare and combine them all to show total resilience score for each census tract. Also, the ten most and the ten least scores for the total resilience index scores are spatially distributed for better understanding which census tracts are most or least resilient. As Sarasota Watershed boundary is also overlaid, it is easy to understand how each census tract attains its resilience and how each census tract impacts to Sarasota Bay ecosystem. Based on results of the resiliency assessment several recommendations, guidelines, or policies for attaining or enhancing resiliency are suggested.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.16 no.4
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• pp.334-345
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• 2006

The objectives of this study were to analyze and compare 24 hrs personal exposure levels of MF at microenvironments such as home, school, educational institute, internet pc game room, transportation, and other places according to time activity patterns using various metrics for children attending the primary schools located near and away from the power lines, and to characterize the major microenvironments and impact factors attributed personal exposure level. The study was carried out for 44 children attending a primary school away from the lines(school A) and 125 children attending a school away from 154 kV power lines(school B), all who aged 12 years and were 6 grade, from July 2003 to December 2003. All participants filled in a questionnaire about characteristics, residence, use of electrical appliances and others. Children wore a small satchel in which EMDEX II and Lite (Enertech, Co. Ltd) and a diary of activity list for period of registration in 20 minutes blocks. All statistical calculations were made with the SAS System, Releas 6.12. The summary of results was presented below. First, about the characteristics of subjects, there no differences between two groups. The subject almost spent about 56 % of their time at home and about 20~25 % of their time at school. Fifty percent of children spent 2 hours at private educational institutes. Second, the personal exposure measurements of children in school B was statistically higher than those of children in school A by various metrics such as arithmetic mean, geometric mean, percentile(5, 25, 50, 75, 95), maximum, rate of change metric, constant field metric. The arithmetic and geometric mean magnetic fields during the time the children were at school B were 0.98 and $0.86{\mu}T$ and were about 23 times higher than those of children were at school A. In conclusion, the significant major determinants of personal exposure level is the distance from the power line to microenvironments.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.