• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.04a
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• pp.203-205
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• 2000

본 연구의 목적은 우리 나라의 환경기초시설 중 하·폐수처리시설에서 발생하는 온실가스 배출량(inventory)을 산출하는 것이다. 이를 위하여 IPCC에서 제안하고 있는 지침을 중심으로 온실가스 배출량 산출방법을 검토하고, 우리 나라의 특성을 반영할 수 있도록 관련변수를 조사하였다. 또한 관련 변수의 적합성을 판단하고 기초 자료를 확보하기 위하여 일부 관련 시설에 대한 배출농도 측정을 실시하였다. (중략)

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6B
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• pp.399-406
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• 2012

The objective of this research is to develop greenhouse gas generation models and estimation method of their parameters for solid waste landfills. Two models obtained by differentiating the Modified Gompertz and Logistic models were employed to evaluate two parameters of a first-order decay model, methane generation potential ($L_0$) and methane generation rate constant (k). The parameters were determined by the statistical comparison of predicted gas generation rate data using the two models and actual landfill gas collection data. The values of r-square obtained from regression analysis between two data showed that one model by differentiating the Modified Gompetz was 0.92 and the other model by differentiating the Logistic was 0.94. From this result, the estimation methods showed that $L_0$ and k values can be determined by regression analysis if landfill gas collection data are available. Also, new models based on two models obtained by differentiating the Modified Gompertz and Logistic models were developed to predict greenhouse gas generation from solid waste landfills that actual landfill generation data could not be available. They showed better prediction than LandGEM model. Frequency distribution of the ratio of Qcs (LFG collection system) to Q (prediction value) was used to evaluate the accuracy of the models. The new models showed higher accuracy than LandGEM model. Thus, it is concluded that the models developed in this research are suitable for the prediction of greenhouse gas generation from solid waste landfills.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.04a
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• pp.197-199
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• 2000

본 연구의 목적은 우리 나라의 환경기초시설 중 소각시설에서 발생하는 온실가스 배출량(inventory)을 산출하는 것이다. 이를 위하여 IPCC에서 제안하고 있는 지침을 중심으로 온실가스 배출량 산출방법을 검토하고, 우리나라의 특성을 반영할 수 있도록 관련변수를 조사하였다. 또한 관련 변수의 적합성을 판단하고 기초 자료를 확보하기 위하여 일부 관련 시설에 대한 배출농도 측정을 실시하였다. (중략)

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1409-1416
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• 2015

The change of temperature influences on the various aspect, especially human health, plant and animal's growth, economics, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly temperature data at the Suwon monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). Among five meteorological variables, radiation, amount of cloud, and wind speed are more influence on the temperature. The radiation influences during spring, summer and fall, whereas wind speed influences for the winter time. Also, among four greenhouse gas variables and five pollution variables, chlorofluorocarbon, methane, and ozone are more influence on the temperature. The monthly ARE model explained about 43-69% for describing the temperature.

• Journal of Korean Society of Environmental Engineers
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• v.35 no.12
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• pp.867-876
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• 2013

To promote the carbon emission trading scheme and reduce greenhouse gas (GHG) emission as following 'Korean GHG & Energy Target Management System', GHG emissions should be accurately determined in each industrial sector. For the estimation method of GHG emission from waste landfill, there are several error parameters, therefore we reviewed the estimation method and proposed a revised method. Methane generation from landfill must be calculated by the selected method based on methane recovery rate, 0.75. However, this methodology is not considered about uncertainty factor. So it is desirable that $CH_4$ generation is estimated using first order decay model and methane recovery should use field monitoring data. If not, $CH_4$ recovery could be applied from other study results; 0.60 of operational landfill with gas vent and flaring system, 0.65 of operational site with landfill gas recovery system, 0.90 of closed landfill with final cover. Other parameters such as degradable organic carbon (DOC) and fraction of DOC decompose ($DOC_f$) need to derive the default value from studies to reflect a Korean waste status. Proper application of MCF that is selected by operation and management of landfill requires more precise criteria.

• Journal of Energy Engineering
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• v.23 no.4
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• pp.150-159
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• 2014

Since the release of mid-term domestic GHG goals until 2020, in 2009, some various GHG reduction policies have been proposed. There are two types of modeling approaches for identifying options required to meet greenhouse gas (GHG) abatement targets and assessing their economic impacts: top-down and bottom-up models. Examples of the bottom-up optimization models include MARKAL, MESSAGE, LEAP, and AIM, all of which are developed based on linear programming (LP) with a few differences in user interface and database utilization. In this paper, we suggest a simplified LP formulation and how can build it through step-by-step procedures.

• Environmental and Resource Economics Review
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• v.20 no.3
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• pp.489-529
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• 2011

When greenhouse gas mitigation policies are implemented, energy intensive manufacturing industries are influenced much due to an increase in cost. However, industries that have price setting power are damaged less by the policies. Therefore, this paper analyzes vulnerability of energy intensive manufacturing industries to the policies by measuring price setting power of the industries. We analyzed price setting power model through ECM, employing the import prices and wages as independent variables. The industries that their prices react to import prices are price takers, which their prices are determined by rival's ones. On the other hand, the industry that their prices react to wages that mean domestic cost are price setters, and they will be less vulnerable to the policies. In addition, fluctuation of energy prices would be reflected in import prices because it influences other countries than my one. Thus, we employed energy prices as control variable to measure the net effects of import prices. As empirical results, petroleum products, chemical products, non-metallic mineral products, textiles, and motor vehicles sector have price setting power, so the industries have competitiveness on greenhouse gas mitigation policies.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1293-1300
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• 2014

The temperature data influences on various policies of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly and seasonal temperature data at the northern part of the Chungcheong Namdo, Seosan monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). The result showed that the monthly ARE model explained about 39-63% for describing the temperature. However, the ARE model will be expected better when we add the more explanatory variables in the model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.4
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• pp.1251-1259
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• 2014

This study presents the linkage method combining the existing Port Management Information System (PORT-MIS) DB with the scattered vessel activity data sets including the hotelling and maneuvering characteristics and specification information of the vessels arriving and departing from the port of Busan from January 2009 to June 2010. By linking the data sets, this study made three types of vessel activity databases: L-PORT-MIS DB with low-level vessel activities, M-PORT-MIS DB with medium-level vessel activities such as hotelling time, H-PORT-MIS DB with high-level vessel activities such as hotelling time, engine power, etc. The greenhouse gas (GHG) emissions estimation results show that total GHG emissions decreases when the detailed vessel activities are employed. This decrease in the total GHG emissions by the level of vessel activities implies that the GHG emissions from the low and medium level vessel activities are overestimated due to the aggregated hotelling/maneuvering times and speeds resulting from the past vessel specifications. Therefore, the GHG emissions using the H-PORT-MIS DB are more reliable GHG emission estimates in that the vessel specifications and the observed hotelling time of each vessel are employed in the estimation process. Hence, the high-level vessel activity dataset should be constructed to implement more suitable countermeasures for reducing the GHG emissions in the port of Busan.

• Life and Agrochemicals
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• s.277
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• pp.26-27
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• 2012

혁신적인 농업기술의 광범위한 보급으로 단위 면적당 수확량을 높이는 반면 온실가스의 발생을 줄여 기후변화가 초래할 수 있는 농업에 대한 악영향을 최소화해야 한다.