• Environmental and Resource Economics Review
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• v.17 no.2
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• pp.255-286
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• 2008

In this paper, it is tried to combine the decomposition method and econometric analysis for the extension of the decomposition method. Since two approaches approach the energy efficiency problem in the different perspectives, it is believed that it is hard to reconcile the results of two approaches. In the results of energy intensity effect analysis with the econometric method, it is found that the increase in the energy price results in the improvement of energy intensity effect. In enconometric analysis of energy efficiency, the coefficient of a time trend measured as a proxy of energy efficiency is significant and has a negative effect on the energy consumption. This finding implies the energy efficiency improves very slowly over time. In addition, the directions of energy efficiency improvement in the decomposition method are consistent with those in the econometric analysis in four industries. This finding indicates that two methods may be in complementary cooperation for the analysis of energy efficiency. Therefore, it is needed the efforts to seek the complementarity between two methods for the enhancement of academic and policy implications.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.195-209
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• 1997

The joint modelling of mean and dispersion in quasi-likelihood models which greatly extend the scope of generalized linear models, is required in case that the dispersion parameter, the variance component of response variables, is not constant but changes by depending on any covariates. In this paper, by using statistical package GENSTAT(release 5.3.2, 1996) which makes a easily analyze real data through this joint modelling, we mention necessities that must consider this joint modelling rather than existing mean models through model checking based on graphic methods for esterase assay data introduced by Carrol and Ruppert(1987, pp.46-47), and then study methods finding reasonable joint model of mean and dispersion for this data.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.271-272
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• 2017

본 논문은 모순문제를 해결하기 위한 나비 모형에서 다중시스템을 이루는 구성요소 분할-결합에 기반을 둔 문제해결 전략과 단일시스템을 분해하는 구성요소 분할-결합 문제해결 전략을 소개하고 이에 대한 적절한 사례를 제시한다. 또한, 이를 나비 다이어그램으로 정의함으로써 기법간의 차이와 적용을 기술하여 향후 자동화기술로 가기 위한 기반을 다진다.

• Journal of Korea Port Economic Association
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• v.27 no.2
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• pp.27-38
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• 2011

The paper aims at examining the behavioral characteristics of the passenger car export of Gunsan, Pyeongtaek, and Ulsan port. This is accomplished by modelling export demand as exchange rate and the Unites States industrial production. All series span the period January 2001 to December 2010. I first show that both the series and the residuals are stationary at the 5 percent significance level. The result cannot reject the null hypothesis of a unit root in each of the level variables and of a unit root for the residuals from the cointegration regression at the 5 percent significance level. I hitherto make use of forecast error decomposition and historical decompositions The forecast error decomposition indicates that car export is endogenous to industrial production and exchange rate. The historical decompositions for the export show that the entire difference between actual export and the base forecast can be attributed to industrial production shocks since exchange rate moves closer to the actual data or the base forecast. It indicates that industrial production outperforms exchange rate in explaining the passenger car exports.

• Environmental and Resource Economics Review
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• v.23 no.2
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• pp.305-330
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• 2014

There has been a lot of studies to identify the driving forces of energy consumption. Many of them decomposed the final energy consumption into the intensity effect, structural effect, and production effect. Those approach, however, could not consider the transformation loss during the electric power generation. Therefore, in this study, we conducted a decomposition analysis on the primary energy use basis to reflect that transformation loss. Log mean Divisia index and refined Laspeyres methods were used for the index decomposition. As results, we could find out that the difference between two approaches were definite. The intensity effect in 2011 is -0.607 times against 1981 in the final energy case, but -0.236 times in the primary energy case. The structure effect in 2011 is 0.227 times against 1981 in the final energy case, but 0.434 times in the primary energy case. Therefore, an analysis on the primary energy basis is essential when conducting a decomposition analysis.

• Korean Journal of Environmental Agriculture
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• v.16 no.1
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• pp.31-36
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• 1997

This study was carried out to analyze the composition of landfill generation gas using vertical pipe wells installed at landfill. The characteristics of composed waste were examined by the open-cut test at H. landfill in Pusan. The waste compositions of landfill layer by Open-cut test indicated that organic matter was average $4.6{\sim}8.78%$ in each landfill. $CH_4$ compositions of gas in each landfill were $49.71{\sim}50.45%$(A-point), $50.39{\sim}53.74%$(B-point), and $58.76{\sim}61.62%(C-point), respectively. The chemical formula of organic matter left in the underground was $C_{36.3}H_{76}O_{30}N_{0.3}S_{0.1}$ Underground temperatures were changed to $18.8{\sim}25.8^{\circ}C$ when the ambient temperature was about $13.4^{\circ}C$. Temperatures with passed times in A, B and C-lysimeter were about $21.1{\sim}22.5^{\circ}C,\;30{\sim}32.5^{\circ}C$ and $35{\sim}38.5^{\circ}C$, respectively. After about 65 day, decomposition rates of organic matter in A, B and C-lysimeter were 9.9%, 14.9% and 22.3%, respectively.

• Environmental and Resource Economics Review
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• v.15 no.1
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• pp.27-50
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• 2006

We estimate $CO_2$ emissions in Korea industry, 1990 and 2000 using a commodity- by-industry IO model ($CO_2$ hybrid IO mode]). Estimated $CO_2$ emissions in industries include both $CO_2$ emissions from direct and indirect consumption. The results show that total $CO_2$ emissions has increased by 51.6 million TC (Tonne of Carbon) from 64.4 million TC in 1990 to 115.5 million TC in 2000. By applying the structural decomposition analysis technique, we decompose change of $CO_2$ emissions in Korea industry between the period 1990~2000. In the decomposition, we figure out two contributing factors, changes in $CO_2$ coefficient and changes in final demand. The latter is further decomposed as growth effects and structural effects. We also estimated each factor's contribution to the changes in $CO_2$ emissions in industries between 1990~2000. The analysis can be used as a useful resource for policy makers in improving the effectiveness of $CO_2$ emissions mitigation policy.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.365-365
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• 2020

최근 기후변화로 인한 강우, 온도, 유량과 같은 수문학적 요소의 불확실성 증가와 더불어 산업화, 도시화로 인한 물 수요가 커짐에 따라 물부족 발생 위험이 증가하고 있다. 이에 따라, 안정적인 물 공급을 위한 하천유량과 취수량의 균형을 목적으로 하는 취수량의 예측 및 모의에 대한 중요성이 강조되고 있다. 본 연구에서는 과거 하천 취수량 자료로부터 미래 취수량을 예측하기 위해 딥러링 기법 중 하나인 순환신경망(LSTM) 모형과 시계열분해법을 결합하여 취수량 예측 모형을 개발하였다. 시계열분해법을 통해 자료의 경향성과 계절적 변동성 등 다양한 스케일의 시계열을 분해하여 전처리를 수행하였으며 불확실성을 의미하는 잔차(residual)에 LSTM을 적용하여 예측하였다. 결과적으로 LSTM 취수량 예측 모형은 높은 정확도를 보였으며, 월단위 전망 시 관측값에 대하여 신뢰성이 있는 결과를 나타내었다. 본 연구에서 개발한 모형에 따른 결과는 수자원 관리를 위해 활용이 가능할 것으로 기대된다.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.155-160
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• 2014

When we collect marketing data, we can only obtain the bimonthly or quarterly data but the monthly data be available. If we evaluate or predict monthly market condition or establish monthly marketing strategies, we need to disaggregate these bimonthly or quarterly data to the monthly data. In this paper, for bimonthly or quarterly data, we introduce some methods of disaggregation to monthly data. These disaggregation methods include the simple average method, the growth rate method, the weighting method by the judgment of experts, and variable decomposition method using 12 month moving cumulative sum. In this paper, we applied variable decomposition method to disaggregate for bimonthly data of sum of electronics sales in a European country. We, also, introduce how to use this method to predict the future data.

• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.59-71
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• 2016

This study empirically examines the dynamic specification of the ship price model based on a vector autoregressive model and data covering from January 2000 to October 2014. Our results are summarized as follows: first, the relationship between ship price and interest rate shows significantly negative and the relationship between ship price and freight rate shows positive. It provides consistent implication that ship price depends on interest rate and freight rate under the dynamic Gordon model. Second, we apply an impulse response analysis to ship price and find the responses of the ship price from both factors, interest rate and freight rate, which affect during seven periods approximately. Finally, the results of a variance decomposition indicate that freight rate is more important than interest rate on the ship price.