• The Journal of Fisheries Business Administration
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• v.46 no.1
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• pp.75-91
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• 2015

With the increasing improvement of living standard, people pay more attention to the quality and security of their food. There is an increase in the consumption of aquatic products and a vast prospect of its trade. Fisheries as a major one of the traditional industries in China have significant price advantages and natural resources. However, marine pollution in China is more and more serious and the expecting of aquatic products has been seriously influenced by green barriers in the recent years. This paper tries to examine the effect of Chinese marine pollution on export of aquatic products in China. This paper utilizes cointegration test to estimate long-run equilibrium between marine pollution and fisheries products export. The results indicate that real exchange rate and income variable have positive effects and fish price has negative effect on China's fisheries export to Korea. However, marine pollution variable has no statistically significant effect on dependant variable. And according to the result of China's fisheries export to Japan, exchange rate has positive effect and both fish price and marine pollution variable have negative effects on export. Lastly, marine pollution and income level have effects on dependant variable in the case of Hong Kong, but exchange rate and price variable have no significant effect on aquatic products export from China to Hong Kong. In a word, marine pollution of China is a serious problem and it has negative effect on Chinese export of aquatic products.

• Environmental and Resource Economics Review
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• v.11 no.2
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• pp.195-210
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• 2002

In this paper, we present a nonrenewable resource model including environmental pollution stock as a state variable to analyze the dynamic structure of environmental tax. Based on the optimality conditions of our model, we showed that the optimal time path of the shadow cost for environmental pollution stock is the same as that of the costate variable for environmental pollution stock. We did this by applying the theorem, Continuous Dependence on Initial Conditions (Coddington and Levinston, 1985, pp. 22~27), to the optimal control problem. Thus, this result provides a theoretical basis to determine the magnitude of environmental tax to be imposed over time. In addition, we also identified that the costate variable for environmental pollution stock is solely due to the disutility effect.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.1
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• pp.28-34
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• 2007

Recently, with the rapid growth of industry, environmental condition became worse. In addition to outdoor insulators in seashore are polluted due to salty wind. Also this pollution causes the flashover and failure of electric equipments. Especially the salt contaminant is one of the most representative pollutants, and known as the main source of the accident by contamination. As well known, the pollution has a close relation with meteorological factors such as wind velocity, wind direction, temperature, relative humidity, precipitation and so on. In this paper we have statistically analyzed the correlation between the pollution and the meteorological factors. The multiple regression analysis was used for the statistical analysis; daily measured equivalent salt deposit density(dependent variable) and the weather condition data(independent variable) were used. Also we have developed an expert program to predict the pollution deposit. A new prediction system using this program called SPPP(salt pollution prediction program) has been used to model accurately the relationship between ESDD with the meteorological factors.

• Journal of Sport and Applied Science
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• v.4 no.4
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• pp.27-32
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• 2020

Purpose: This study sought to explore the effects of air pollution on professional sports in South Korea. Research design, data, and methodology: The dependent variable, the number of attendances, was comprised of 2013-2017 K-league, 2015-2017 KBO, 2014-2017 KBL regular season games, resulting in 1,063, 2,121, 810 individual match-level observations, respectively. With the actual data collected from each place across the country, we created a categorical variable which identify the air quality index divided into four categories by K-eco (i.e., good, moderate, unhealthy, hazardous). To analyze data, ANOVA was employed. Results: First, there was a significant group effect on K-league attendance. Second, there was a significant group effect of KBO attendance. Lastly, there was a significant group effect on KBL attendance. Conclusions: Summary of above results showed that each professional sport leagues' attendance was significantly different depending on the levels of air pollution. Implications were also discussed. Keywords: air pollution, sport spectatorship, professional sports.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1802-1804
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• 2004

In seashore, outdoor insulators are polluted due to salty wind and the pollution causes the flashover and failure of electric equipments. As well known, the pollution has a close relation with meteorological factors such as wind velocity, precipitation, wind direction, relative humidity, dew point, etc. In this paper we statistically analyzed the correlation between the pollution and the meteorological factors including snowfall and freezing. The multiple regression analysis was used for the statistical analysis; daily measured equivalent salt deposit density(dependent variable) and the meteorological data(independent variable) were used. From the results of this investigation, we verified the influence of snowfall and freezing on the ESDD, which has been overlooked in the preceding investigation.

• Journal of Families and Better Life
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• v.11 no.2
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• pp.169-181
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• 1993

The purpose of this study was to investigate the implementing level of housewife's manage-ment behavior related to reducing environmental pollution and the effects of three variable-groups on management behavior related to reducing environmental pollution. The research data were collected by structured questionnare and 544 cases were finnally selected. The data analysis was conducted by the method of frequency mean Pearson's correla-tion multiple regression. The major findings were as follows: 1) The implementing level of housewife's overall management behavior slightly high. 2) Wife's value(ecologicalism materialism expediency social-enviromental resources(degree of masscom contact degree of meeting with neighbors. refuse box) were significant predictors of the level of overall management behavior And Background variables(wife's aged and educa-tion level & job household income occupation of husband) had not significant effect on the level of overall management behavior. The most influential variable-group on the level of overall management behavior and buying cleaning & dishwashing cooking laundry activities was Wife's value. But the most influential variable-group on the implementing level of disposing trash was social-environmental resources.

• Journal of the Korean housing association
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• v.8 no.2
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• pp.71-84
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• 1997

The Purpose of this study is to estimate how inclination to move can be appeared by understanding the cognition of a resident on stress due to the residential environment. 240 housewives living in Chiniu were Questioned statistical analysis were used with factor analysis, F-test. Duncan's Multiple range analysis, stepwise regression analysis and stepwise discriminant analysis, The result were summarized as follows 1) The stress of residential environment were clissified by six factors indoor facility, educational environmental. indoor structure, air Pollution noise, traffic convenience. 2) The extent of a stress from residential environment was significantly different in the socio-demographic variable and housing-related variable. 3) The stress of residential environment were affected by the direction of house. 4) The variable discriminating inclination to move were the stress of residential environment(air Pollution). an educational level, the type of housing possession, residential Period and the size of house.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 1999.04a
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• pp.63-64
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• 1999

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1153-1164
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• 2008

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Uijeongbu monitoring site in Korea. The result showed that both overall and monthly ARE models are suited for describing the ozone concentration. In the ARE model, seven meteorological variables and four pollution variables are used as the as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide(SO2), Nitrogen dioxide(NO2), Cobalt(CO), and Promethium 10(PM10). Also, the high level ozone data (over 80ppb) have been analyzed four ARE models, General ARE, HL ARE, PM10 add ARE, Temperature add ARE model. The result shows that the General ARE, HL ARE, and PM10 add ARE models are suited for describing the high level of ozone data.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1117-1124
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• 2010

The PM10 (Promethium 10) data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model has been considered for analyzing the monthly PM10 data at the southern part of the Gyeonggi-Do, Suwon monitoring site in Korea. In the ARE model, six meteorological variables and four pollution variables are used as the explanatory variables for the PM10 data set. The six meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, radiation, and amount of cloud. The four air pollution explanatory variables are sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), carbon monoxide (CO), and ozone ($O_3$). The result showed that the monthly ARE models explained about 13-49% for describing the PM10 concentration.