• Environmental and Resource Economics Review
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• v.16 no.3
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• pp.723-738
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• 2007

This paper draws some implications from Logarithmic Mean Weight Divisia Method (LMWDM) on the sources of $CO_2$ emission changes in the manufacturing sectors of Korea, UK, and USA. The sources of change in industrial $CO_2$ emission of a country, as manifested by production scale factor, structural factor, and technical factor, summarizes the forces behind the change in $CO_2$ emissions in each country's manufacturing sector. There are three observations. First one is that Korea's emission is increasing while USA and UK are experiencing reduction or stabilization of $CO_2$ emission in the manufacturing sector. Second implication is that the technical factor affecting $CO_2$ emission in Korea does not help much, or even hinder, the reduction of $CO_2$ emissions, comparing to USA and UK. Third one, which is the combined result of the first and the second one, is that Korea's increasing trend in aggregate $CO_2$ emission throughout the periods in consideration is mainly due to the failure in technical progress, or the deterioration in the structure of within subcategories, or both. The policy implications is clear. The obvious prescription is to launch a nation-wide policy drive which can revert these adverse trends.

• Environmental and Resource Economics Review
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• v.26 no.1
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• pp.1-35
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• 2017

This study investigates a decomposition analysis of the determinants of the reduced $CO_2$ emissions in seven OECD countries that implemented carbon taxes from 1995 to 2013. Recent studies on decomposition analysis of changes in $CO_2$ emissions focused on technology-based physical factors; however, this study analyzes the effects of a carbon tax as an economic factor. According to the results obtained by using the Log Mean Divisia Index, the energy intensity effect and the carbon tax effect contributed the most towards the reduction of total $CO_2$ emissions in the seven OECD countries. The results for each country show that the emissions decreased due to the energy intensity effect, while the effects of carbon tax and carbon tax revenues differed by policy and environment of the countries.

• Proceedings of the KSEEG Conference
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• 1999.04a
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• pp.181-183
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• 1999

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.319-330
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• 2006

A signal in real world usually composes of multiple signals having different scales of frequencies. For example sun-spot data is fluctuated over 11 year and 85 year. Economic data is supposed to be compound of seasonal component, cyclic component and long-term trend. Decomposition of the signal is one of the main topics in time series analysis. However when the signal is subject to nonstationarity, traditional time series analysis such as spectral analysis is not suitable. Huang et. at(1998) proposed data-adaptive method called empirical mode decomposition (EMD) . Due to its robustness to nonstationarity, EMD has been applied to various fields. Huang et. at, however, have not considered denoising when data is contaminated by error. In this paper we propose efficient denoising method utilizing cross-validation.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.65-73
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• 2001

Until recently, we use only weekly and monthly adjustment factors in order to estimate the AADT. By the way. we can suppose that the traffic is time series data related to flow of time. So we tried to analyse traffic patterns using time series analysis and apply them to estimate the AADT. We could divide traffic patterns into trend, cyclic variation, seasonal variation and irregular variation like as time series data. Also, in order to reduce random error components, we have looked for the weather conditions as an influential factor. There are many weather conditions such as rainfalls, but, temperatures, and sunshine hours among others but we selected rainfalls and lowest temperatures. And then, we have estimated the AADT using time series factors. To compare the results of, we have applied both irregular variation joined to weather factors and that not joined to. RMSE and U-test were opted at methods to appreciate results of AADT estimation.

• 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 Labor Studies
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• v.23 no.2
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• pp.47-77
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• 2017

This paper analyzes the changes in wage inequality and its contributing factors since the mid-2000s. Although trends vary by data and wage indices, the Gini coefficient of the total wage of all workers shows an increasing trend due to the part-time increase of less than 35 hours per week, while the wage Gini coefficient of hourly wages and the total wage Gini coefficient of full-time workers showed a declining trend. Part-time increases have increased inequality based on total wages, but part-time hourly wage increases can be considered to have reduced hourly wage inequality. Therefore, as a result of decomposing the factor of Gini coefficient reduction only for full-time workers, factors that contributed absolutely to inequality reduction were variables such as job tenure, career, and occupation, and employment type variable has little effects, and the establishment size variable deepens inequality. The variables such as industry, age, and education did not contribute significantly to the inequality change. This is attributed to the decline in wage premiums for job tenure and management and professional jobs and the increase in wage premiums for large-scale businesses.

• Environmental and Resource Economics Review
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• v.21 no.2
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• pp.211-235
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• 2012

This paper presents an alternative decomposition technique to identify the relative importance of factors associated with changes in $CO_2$ emissions by using directional distance function to model the joint production of desirable and undesirable outputs. The key feature of the proposed approach is the introduction of fossil and non-fossil fuel energy input efficiencies, productivity change and emission intensity change. For the 27 OECD countries as a whole, the empirical results indicate that economic growth is the most important contributor to $CO_2$ emissions increase, while efficiency change is the most important component to $CO_2$ emissions reduction between 1980 and 2007. For more extensive insights, this paper divided 3 groups according to the emission growth rate and find out that high emission countries show relatively low production efficiencies and technical changes contributing $CO_2$ emissions increase. The results also provide that more strict environmental regulations are needed to improve the pollution intensity in these countries.

• Korean Journal of Social Welfare
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• v.61 no.4
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• pp.333-354
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• 2009

This study decomposes the gender differences on poverty to explain the causes of the poverty gap between male- and female-headed households. In order to do this, we start from examining the extent of the poverty gap between maleand female-headed families and then conduct decomposition of poverty differences by gender using the Oaxaca method. This paper uses the (Urban) Family Budget Survey data from 1982 to 2008 and measures poverty using 50% of the median income poverty line. Major findings of this study are as follows: First, in 2008, the coefficient effect explains 70% or more of the total gender-poverty gap. Second, the trend of gender-poverty gap in the period of 1982~2008 shows that the poverty gap by gender increased in the 1980s', decreased in the 1990s', and a re-increased in 2000s'. Third, comparing the decomposition results in 1982, 1989, 1999, 2008, we found that the share of characteristic effect of the total gender poverty gap has been increased gradually over time. It means the characteristics of the female-headed households have become worse than those of the male-headed households in urban working families. At the same time, the still large coefficient effect suggests that the problems such as the discrimination against matriarchs or the lack of social support for them still play important roles among urban working families in Korea.

• Journal of Korea Water Resources Association
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• v.48 no.12
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• pp.981-993
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• 2015

The analysis of hydrologic time series data is crucial for the effective management of water resources. Therefore, it has been widely used for the long-term forecasting of hydrologic variables. In tradition, time series analysis has been used to predict a time series without considering exogenous variables. However, many studies using decomposition have been widely carried out with the assumption that one data series could be mixed with several frequent factors. In this study, the empirical mode decomposition method was performed for decomposing a hydrologic time series data into several components, and each component was applied to the time series models, autoregressive moving average (ARMA). After constructing the time series models, the forecasting values are added to compare the results with traditional time series model. Finally, the forecasted estimates from ARMA model with empirical mode decomposition method showed better performance than sole traditional ARMA model indicated from comparing the root mean square errors of the two methods.