• Journal of Energy Engineering
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• v.24 no.1
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• pp.137-148
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• 2015

So far, the phenomenon of "electrification" has been deepened in Korean industry and especially direct heating energy which accounted for 44.0%(2010) of total energy consumed in Korean manufacturing has been significantly electrified. This paper decomposed electricity consumption for direct heating in Korean manufacturing from 1992 to 2012 using LMDI(Log Mean Divisia Index). This paper includes 4 different factors such as electricity proportion effect, direct heating proportion effect, energy intensity effect and added value effect. And this paper compared the consumption pattern by business type. As results, electricity proportion effect had contributed the most to the increase of electricity consumption for direct heating in Korean manufacturing. And Petrol-Chemical and Iron & Steel had the most electrification of direct heating.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.229-254
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• 2011

In this paper, we decomposed Greenhouse-Gas emissions of Korea's manufacturing industry using LMDI (Log Mean Divisia Index) method. Changes in $CO_2$ emissions from 1991 to 2007 studied in 5 different factors, industrial production (production effect), industry production mix (structure effect), sectoral energy intensity (intensity effect), sectoral energy mix (energy-mix effect), and $CO_2$ emission factors (emission-factor effect). By results, the structure effect and intensity effect has a role of reducing GHG emissions and The role of structure effect was bigger than intensity effect. The energy mix effect increased GHG emissions and emission-factor effect decreased GHG emissions. By time series analysis, IMF regime affected the GHG emission pattern. the structure effect and intensity effect in that regime was getting worse. After 2000, in the high oil price period, the structure effect and intensity effect is getting better.

• Journal of the Korean Society for Railway
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• v.15 no.3
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• pp.286-293
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• 2012

Korean government is enforcing 'Greenhouse gas target management' in order to achieve Greenhouse gas reduction target. To attain Greenhouse gas reduction target, companies in Korea must establish their GHG inventory system and analysis their GHG emissions characteristics for deduction of mitigation measures. LMDI(Log Mean Divisia Index) decomposition analysis is widely used to understand characteristics of GHG emission and energy consumption. In this paper, the characteristics of GHG emission from the line of railroad in Korea is respectively analyzed in terms of conversion effect, intensity effect, production effect and distance effect. Data of railroad GHG emission from 2000 to 2007 are used. As a result, total effect of railroad's GHG emission is $96,813tCO_2eq$. Production effect ($39,865tCO_2eq$) and distance effect ($327,923tCO_2eq$) affect increase of railroad GHG emissions while Conversion effect ($-158,161tCO_2eq$) and intensity effect ($-112,814tCO_2eq$) influence decrease of the emissions.

• Environmental and Resource Economics Review
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• v.31 no.4
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• pp.825-848
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• 2022

This paper analyzed the factors for increasing energy consumption in the domestic manufacturing sector using the LMDI (Log mean division index) decomposition method for the period from 1999 to 2019. Among the LMDI decomposition analysis methods, both additive and multiplicative factor decomposition methods were used. in this analysis. According to the result of the analysis, the factor that increased energy consumption in the domestic manufacturing industry was the production effect, and the structure effect and intensity effect were found to be the factors that decreased energy consumption. In particular, the reduction of energy consumption due to the structure effect was greater than that of energy consumption effect due to the intensity effect. By period, it can be seen that energy consumption increased rapidly due to the production effect until 2011, but after that, the increase in energy consumption due to the production effect slowed down. On the other hand, after that, the energy reduction effect due to the structure effect and the intensity effect became prominent. In order to save energy in the manufacturing sector in the future, energy diagnosis and management through EMS (Energy management system) and FEMS (Factory energy management system) are more necessary. In addition, restructuring into a low-energy consumption industry seems more necessary.

• Journal of Energy Engineering
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• v.27 no.3
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• pp.10-20
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• 2018

Due to cheap electricity tariff on agricultural use, electricity consumption in agricultural sector has grown dramatically. We evaluated the negative effects of the cheap electricity tariff such as electricity over-consumption, increased dependency on electricity, decreased electricity productivity, and unequal distribution of the benefit. We also estimated the effects of agricultral output growth, structural change, and electricity intensity change on sharp electricity consumption increase in agricultural sector between 1998 and 2016 using the Log Mean Divisia Index decomposition method. The findings reinforce the necessity of raising the electricity tariff for agricultural use.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.407-421
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• 2018

In this paper, I analyze the GHG (greenhouse gas) emission factor of the domestic railway transportation sector using the LMDI (Log Mean Divisia Index) methodology. These GHG factors are the emission factor effect, energy intensity effect, transportation intensity effect, and economic activity effect. The analysis period was from 2011 to 2016, and the analysis objects were an intercity railway, wide area railway, and urban railway. The results show that the GHG emission of railway transportation sector decreased during these 6 years. The factors decreasing the GHG emission are the emission factor effect, energy intensity effect, and transportation intensity effect, while the factor increasing the GHG emission is the economic activity effect.

• 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.

• Environmental and Resource Economics Review
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• v.19 no.1
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• pp.129-157
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• 2010

This paper is intended to evaluate energy efficiency policy in demand side, to assess the residential sector's energy efficiency policy and to analyze the system of energy efficiency practices. We examined residential energy consumption over the period 1990~2006. The decomposition method in the analysis was a logarithmic mean Divisia index (LMDI) techniques to decompose changes in energy intensity. First of all, the energy use in residential sector was adjusted to correct weather-induced variations in energy consumption, because adjustments for normal weather patterns facilitated inter-temporal comparison of intensity. The analysis on the residential sector shows that the overall energy intensity of the residential sector declined at an average 1.0% per year, while the structure effect increased by 1.8% per year, and the activity effect increased by 0.7% per year. In other words, the decline of floor space, number of household, and appliance ownership per capita has an effect on increase in residential consumption. The improvement in energy efficiency had strong contribution on the decrease of energy consumption. We find that the general results of analysis on residential energy are similar to those of IEA. The energy efficiency policy in residential sector is assessed to obtain some results during 1990~2006. In residential sector, structural variables such population per household, diffusion of appliance and activity factor such as population contributed to the increase of energy consumption while energy intensity effect induced the decrease of energy consumption. These findings are consistent with international trend as well as our prior expectation.

• 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.

• Journal of Korean Society on Water Environment
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• v.38 no.5
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• pp.240-246
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• 2022

Water and energy are inextricably linked and referred to as 'Water-Energy Nexus'. Recently, this topic has been drawing a lot of attention from various studies due to the exacerbated water availability. Korea's water and energy consumption has been increasing consistently, which calls for better management. This paper aims to identify changes in electricity consumption in relation to water intake and purification processes. Using Log Mean Divisia Index (LMDI) Decomposition Analysis method, this study attributes the changes to major factors such as; Total population (population effect), household/population (structure effect), GDP/household (economic effect), and water-related energy use/GDP (unit effect). The population effect, structure effect, and economic effect contributed to an increase in water-related electricity consumption, while the unit effect contributed to a decrease. As of 2019, the economic effect increased the water supply sector's electricity consumption by 534 GWh, the population effect increased by 73 GWh, and the structure effect increased by 243 GWh. In contrast, the unit effect decreased the electricity consumption by -461 GWh. We would like to make the following suggestions based on the findings of this study; first, the unit effect must be improved by increasing the energy efficiency of water intake and purification plants and installing renewable energy power generation facilities. Second, the structure effect is expected to increase over time, and to mitigate it, water consumption must be reduced through water conservation policies and the improvement of water facilities. Finally, the findings of this study are expected to be used as foundational data for integrated water and energy management.