• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1468-1471
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• 1999

This paper presents a method of the regional long-term distribution planning considering economic indicator with the assumption that energy demands proportionally increases with the economic indicators. For the practical distribution planning, it is necessary to regional load forecasting, distribution substation planning, distribution feeder planning. Accordingly, in this paper, after performing regional load forecasting considering economic indicator, it is performed distribution substation planning and distribution feeder planning in order by using this result. For accurate distribution planning, it is very important to scrutinize the correlation among the regional electric power demands, economic indicator and other characteristics because distribution planning results may vary depending on many different factors such as electric power demands, gross products, social trend and so on. In this paper, various steps microscopically and macro scopically are used for the regional long-term distribution planning in order to increase the accuracy and practical use of the results

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.384-384
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• 2022

• 노동경제논집
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• 제37권1호
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• pp.87-116
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• 2014

노동시장 위기관리 시스템의 일환으로 국내외 경제상황 변동으로 야기되는 고용변화를 사전에 감지하는 단기고용변동의 상시적 예측이 요구된다. 이를 위해 본 논문은 경기선행지수 작성방식을 준용하여 비농림 민간부문 임금근로자 변동을 단기적으로 예측하는 고용선행지수를 개발하였다. 고용선행지수는 고용수준 그 자체보다 고용 동향의 국면 및 전환 시점, 변동 속도등 고용의 변화 방향을 조기 탐지하는 것에 중점을 두어 작성되었다. 개발된 지수에 대해 국면 전환 선행성 평가와 고용수준 변동 예측에 대한 모의실험을 통해 검증하고 활용방안을 제시한다.

• 자원환경지질
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• 제55권6호
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• pp.617-632
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• 2022

In recent years, environmental pollution and determining the main factors causing this pollution have become an important issue. This study investigates the relationship between the agricultural sector and environmental pollution in Azerbaijan for 1992-2018. The dependent variable in the study is the agricultural greenhouse gas emissions (CO₂ equivalent). Eight variables were selected as explanatory variables: four agricultural inputs and four agricultural macro indicators. Unit root tests, ARDL boundary test, FMOLS, DOLS and CCR long-term estimators, Granger causality analysis, and variance decomposition analyses were used to investigate the effect of these variables on agricultural emissions. The results show that chemical fertilizer consumption, livestock number, and pesticide use positively and statistically significantly affect agricultural emissions from agricultural input variables. In contrast, agricultural energy consumption has a negative and significant effect. From agricultural macro indicator variables, it was found that the crop and animal production index had a positive and significant effect on agricultural emissions. According to the Granger causality test results, it was concluded that there are a causality relationship from chemical fertilizer consumption, livestock number, crop and livestock production index variables towards agricultural emissions. Considering all the results obtained, it is seen that the variables that have the most effect on the increase in agricultural emissions in Azerbaijan are the number of livestock, the consumption of chemical fertilizers, and the use of pesticides, respectively. The results from the research will contribute to the information on agricultural greenhouse gas emissions and will play an enlightening role for policymakers and the general public.

• Asian Journal of Business Environment
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• 제13권2호
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• pp.9-20
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• 2023

Purpose: This study aims to map the spatial distribution of poverty using nighttime light satellite images as a proxy indicator of economic activities and infrastructure distribution in D.I Yogyakarta, Indonesia. Research design, data, and methodology: This study uses official poverty statistics (National Socio-economic Survey (SUSENAS) and Poverty Database 2015) to compare satellite imagery's ability to identify poor urban areas in D.I Yogyakarta. National Socioeconomic Survey (SUSENAS), as poverty statistics at the macro level, uses expenditure to determine the poor in a region. Poverty Database 2015 (BDT 2015), as poverty statistics at the micro-level, uses asset ownership to determine the poor population in an area. Pearson correlation is used to identify the correlation among variables and construct a Support Vector Regression (SVR) model to estimate the poverty level at a granular level of 1 km x 1 km. Results: It is found that macro poverty level and moderate annual nighttime light intensity have a Pearson correlation of 74 percent. It is more significant than micro poverty, with the Pearson correlation being 49 percent in 2015. The SVR prediction model can achieve the root mean squared error (RMSE) of up to 8.48 percent on SUSENAS 2020 poverty data.Conclusion: Nighttime light satellite imagery data has potential benefits as alternative data to support regional poverty mapping, especially in urban areas. Using satellite imagery data is better at predicting regional poverty based on expenditure than asset ownership at the micro-level. Light intensity at night can better describe the use of electricity consumption for economic activities at night, which is captured in spending on electricity financing compared to asset ownership.

• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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• pp.909-917
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• 2020

The research aims to analyze the firm-specific and macroeconomic factors that affect insurance company's financial performance. The research explores the variables that influence the financial performance of the United Arab Emirates (UAE)' insurance companies. The analysis for determining financial performance considers the following variables: the firm's age, retention ratio, capital adequacy, underwriting risk/loss ratio, financial-leverage, reinsurance dependency, and macro-economic factors such as GDP per capita, inflation rate considered as independent factors. The return-on-asset (ROA) is the key measuring indicator; it is regarded as the dependent variable for financial performance measures. The research focuses on secondary information obtained from insurance companies' financial statements. The researcher targeted 18 insurance companies listed on the UAE stock exchanges for study purposes. The research examines the overall factors that influence the financial performance of an insurance company. For analysis of data, software package of social sciences (SPSS version 20) is used. The studies used correlation and multiple linear regression analysis to determine financial performance and their effects. The analysis suggests that there are important and constructive relationships between the size, capital adequacy, and reinsurance dependency, while loss ratio, retention ratio, and financial leverage indicate a major negative relationship. And there's no link between GDP per capita and inflation.

• 한국항만경제학회지
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• 제22권4호
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• pp.81-98
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• 2006

본 논문에서는 항만요율의 실거래 데이터를 수집, 분석하여 항만간 요율비교 및 요율변화의 추이를 분석하고, 회귀분석을 이용하여 항만요율과 항만별 처리 물동량, GDP 및 무역량과의 관계를 분석하여 거시적인 항만요율의 동향을 제시함을 연구의 목적으로 한다. 구체적인 연구방법으로는, 최근 5년(2001-2205)간 글로벌 서비스선사가 컨테이너선 선형별로 실제 지불한 세계 주요항만의 항만요율 자료를 수집하였으며, 2005년 기준 세계 20대 항만에 속하는 14개 주요항만과 일본의 도쿄항과 고베항의 항만요율 자료를 TEU당 요금으로 환산하여 항만간 요율수준을 비교 분석하였다. 또한, 이 TEU당 항만요율과 항만서비스 수요대리변수인 처리물동량, GDP 및 무역량과의 관계를 회귀분석을 통하여 분석 및 고찰하였다. 연구결과, "입항료 및 항만시설 사용료"와 "하역관련요금"을 합한 세계 주요 컨테이너항만의 최근 5년간의 항만요율의 추이는 홍콩을 제외하고 요율이 대체로 완만한 증가세를 보이며, 싱가포르와 대만의 카오슝은 요율이 감소하고 있다. 또한, 2005년 항만요율을 보면, 롱비치와 홍콩이 선두를 달리고 있으며, 한국은 동북아 경쟁항만에 비해 낮은 요율을 보이고 있음을 확인하였다. 또한, 항만요율을 종속변수로, 물동량 및 GDP, 무역량을 설명변수로 하여 회귀분석 실시한 결과, 물동량이 1% 더 많은 항구에서 항만요율이 0.088-0.204% 높아지며, GDP 1% 높은 국가에서는 항만요율이 0.276% 높아지고, 무역량이 1% 많은 국가에서 항만요율이 0.503% 높아짐을 확인하였다. 더불어, 물동량, GDP, 무역량의 변수의 계수가 양(+)으로 나타나 항만수요가 많을수록 항만요율이 높아짐을 확인하였다.

• 한국조경학회지
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• 제30권2호
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• pp.39-49
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• 2002

The purpose of this study is to propose an objective and rational methodology for the selection of proposed sites far the National Trust(NT), which is the new alterative proposal far the conservation of natural environments destroyed by injudicious land development and economic growth. That is to enforce many analysis for the effective estimation of rare ecological and landscape resources and to propose a model based on estimation and united indicators. Using the estimative model, we apply it to the selection of the proposed site in micro scale and simultaneously offer the basic methodology of effective and systematic land conservation in macro scale. The results of this study are as follows: 1) The results of analysis for the reliability of estimative items and indicators, presented no problem in that the coefficient of reliability was over 0.7. 2) The correlation measure of the estimative indicator indicated that 'succession'and 'regenerating restorability' were highly correlative in the item of plants. Another three items showed a tendency to be alike. 3) The results of factor analysis on the characteristics of indicators, classified plants into four categories including a stable factor. The item of animals was classified as a stable and rare factor. The item of landscape was classified as a physical and mental factor and the environment as a pollutional and conditional factor. 4) The model of estimation created through factor analysis was valid for the approval of the regression model because significant probability was 0.00. When we consider the NT proposed site as a complex body that is composed of diverse natural and manmade resources, certainly the synthetic methodology of estimation is needed. If these studies are carried out, NT sites will be selected more rationally and effectively than at present. Consequently, they have the potential to play a core role of natural ecosystem conservation in Korea.

• The Journal of Asian Finance, Economics and Business
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• 제6권4호
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• pp.53-63
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• 2019

The paper examines the influence of profitability on distance to default (DD) in Vietnam securities market. The investigated sample consists of 211 companies listed on HOSE during 18 years from 2010 to 2017. We apply KMV model to calculate distance to default and use both macroeconomics factors and firm specific factors as independent variables. Using General Least Squared (GLS) method, we find evidence to confirm the positive relationship between profitability and distance to default. This result showed that, although profitability did not directly reflect the cash flow generated, a good profitable enterprise would be an important factor to help facilitate and generate cash flow and at the same time debt was guaranteed when it was due. Besides, the test results revealed that the financial structure and sales on assets have the inverse effect on the distance to default at the significance level of 5%. The results also revealed that a group of macro factors had an influence on the distance to default of businesses, including spread, GDP and trade balance (via exchange rates). Gross domestic income had certain impacts on the distance to default of businesses. This was also a basic indicator measuring the national economic cycle.

• 대한임베디드공학회논문지
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• 제17권1호
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• pp.51-58
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• 2022

Various studies have been conducted from the past to the present because stock price forecasts provide stability in the national economy and huge profits to investors. Recently, there have been many studies that suggest stock price prediction models using various input data such as macroeconomic indicators and emotional analysis. However, since each study was conducted individually, it is difficult to objectively compare each method, and studies on their impact on stock price prediction are still insufficient. In this paper, the effect of input data currently mainly used on the stock price is evaluated through the predicted value of the deep learning model and the error rate of the actual stock price. In addition, unlike most papers in emotional analysis, emotional analysis using the news body was conducted, and a method of supplementing the results of each emotional analysis is proposed through three emotional analysis models. Through experiments predicting Microsoft's revised closing price, the results of emotional analysis were found to be the most important factor in stock price prediction. Especially, when all of input data is used, error rate of ensembled sentiment analysis model is reduced by 58% compared to the baseline.