LMDI Decomposition Analysis of Energy Consumption in the Korean Manufacturing Sector

Kim, Suyi

doi:10.3390/su9020202

Cited by 48 publications

(41 citation statements)

References 26 publications

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“…The decomposition results for seven cities within two stages are illustrated in Figure 5. Industrial output effect was the dominant factor driving the industrial FEU increase in all cities during both stages (with only Shenyang as an exception whose industrial output shrunk during 2010-2015), which is coincidence with the results identified in the previous studies [21,34,71]. During the first stage, the industrial output effect drove a 42.4%~184.0% increase (compared with the value of FEU in 2005) in industrial FEU in all cities, with largest increasing rate appeared in Shenyang.…”

Section: Determinants Of Changes In Aggregate Final Energy Usesupporting

confidence: 90%

“…Studies with focus on individual nations were also conducted, such as decomposition analysis of sectoral energy consumption in Turkey [25], changes in aggregate energy intensity in Australia [26] and Lithuania [27], decomposition of energy efficiency in South Africa [28], analysis of energy efficiency policies launched in Russia [29], and a more recent study focusing on clarifying the contributions of energy efficiency measures through analysis the changes in 11 industrial subsectors' final fuel use in Andalusia of Spain [30]. Further, some studies used LMDI to quantify the contributions of drivers of changes in energy use and efficiency for some specific sectors, including monitoring of European industry' energy efficiency [31], decomposition of Thai industry's energy intensity [32], analyses of manufacturing energy use in Greece and South Korea [33,34], the electricity sector in South Africa [35], and the services sector in France [36]. As climate changes and air pollution become more concerning, analyses of drivers of energy-related emissions using LMDI approach have subsequently emerged, as in the following contributions: comparative analysis of GHGs or CO 2 emissions among nations [37][38][39][40], analysis of driving forces for some developed countries [3,41,42], developing countries [43] or focus on manufacturing sectors [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

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Uncovering Variations, Determinants, and Disparities of Multisector-Level Final Energy Use of Industries Across Cities

et al. 2019

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With continuous industrialization and urbanization, cities have become the dominator of energy consumption, to which industry is making leading contribution among all sectors. Given the insufficiency in comparative study on the drivers of energy use across cities at multisector level, this study selected seven representative cities in China to quantify and analyze the contributions of factors to changes in final energy use (FEU) in industrial aggregate and sectoral levels by using Logarithmic Mean Divisia Index method. Disparities in the drivers of industrial FEU across cities were explicitly revealed within two stages (2005–2010 and 2010–2015). Some key findings are presented as follows. Alongside the increase in industrial output of seven cities within two stages, the variation trends in industrial FEU are different. Industrial output effect (contribution rate 16.7% ~ 184.0%) and energy intensity effect (contribution rate −8.6% ~ −76.5%) contributed to the increase in aggregate FEU positively and negatively, respectively. Beijing had the largest contribution share of industrial structure effect (−24.4% and −12.8%), followed by Shenyang and Xi’an. Contributions of energy intensity effect and industrial output effect for Chemicals, Nonmetals, Metals, and Manufacture of equipment were much larger than those of other sectors. The results revealed that production technological innovations, phase-out of outdated capacities of energy intensive industries, and industrial restructuring are crucial for reduction in industrial FEU of cities. This study also provided reference to reasonable industrial layout among cities and exertion of technological advantages from a national perspective.

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Section: Determinants Of Changes In Aggregate Final Energy Usesupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Uncovering Variations, Determinants, and Disparities of Multisector-Level Final Energy Use of Industries Across Cities

et al. 2019

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“…Past studies using index decomposition analysis on energy consumption often considered only the final energy consumption of one single sector, such as industry, households or transport focussing on one single country (e.g. Holzmann et al (2013) residential in Austria, Chong et al (2017) for residential in the Guangdong Province, Zhang et al (2016) and for the residential energy consumption in the Jiangsu Region and Shandong of China; Achour and Belloumi (2016) transport in Tunisia or China (Zhang et al 2011) and Kim (2017) on manufacturing in Korea). The sectors agriculture, services, transportation and industry in Turkey are analysed by Yilmaz and Atak (2010).…”

Section: Introductionmentioning

confidence: 99%

Applying ex post index decomposition analysis to final energy consumption for evaluating European energy efficiency policies and targets

2019

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This paper aims to unravel the effects of both policies and autonomous developments driving the changes of final energy consumption for the European Union (EU28) and its member states complemented with an in-depth analysis of two EU member states, Germany and Poland, for the period of 2000 to 2015 by the logarithmic mean Divisia index methodology (LMDI). We examine the influences on the changes in final energy consumption for each of the five main demand sectors at aggregated level and at a more detailed level of residential end-uses, transport modes and industrial sub-sectors. With the second level, we provide insights into the effects of policies from a European and national perspective. Our analysis shows that final energy consumption in the EU28 is primarily influenced by an increase in energy efficiency in industry followed by households. For the latter, the energy savings were mainly realised in space heating. The main counteracting drivers were increasing economic activity and the rising demand for higher comfort and social factors, such as a declining household size. Germany shows relatively low energy efficiency improvements in industry, but strong energy efficiency gains in households followed by transport. Poland's transport, however, is responsible for a strong increase in final energy consumption mainly through increased activity, while here the sector industry-despite strong effects of increased activity-in total shows a significant reduction in energy consumption through energy efficiency improvements.

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“…In this study, we used the additive LMDI method because it is easy to examine the number of APE changes influenced by each factor. The LMDI method was usually applied to decompose the factors with respect to different energy types and sectors [50][51][52], but rarely for different regions. As for different regions, APEs can be expressed as Equation (1):…”

Section: Methods and Datamentioning

confidence: 99%

Decomposition Analysis of Factors that Drive the Changes of Major Air Pollutant Emissions in China at a Multi-Regional Level

Yang

Miao

et al. 2019

Sustainability

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The regional emission reduction determines the national emission reduction for one country, and the differences in regional economic characteristics may result in regional differences in air pollutant emissions (APEs). In this regard, this study constructs a regional contribution index of different factors through an extended LMDI (Logarithmic Mean Divisia Index) method and investigates regional differences in factors driving the changes of China's major APEs (SO 2 , NO x , and PM 2.5 ). It reveals that the regional emission efficiency effect was a key inhibitory factor on APEs, which was more obvious in the eastern and northern regions. The regional energy intensity had greater inhibitory effects on SO 2 and NO x than on PM 2.5 , and these inhibitory effects were more obvious in the eastern region. The regional population structure promoted APEs for northern, southern, northeastern, and eastern regions, and inhibited APEs for central, southwestern, and northwestern regions. The regional relative income had a slight effect, which curbed APEs for northern, eastern, southern, and northern regions. The national economic growth effect was the key factor in promoting APEs, which was obvious in eastern and northern regions, followed by southern, central, and southwestern regions. Policy implications are put forward based on empirical results.Sustainability 2019, 11, 7113 2 of 22 reduction target for the 13th Five-Year Plan (FYP) (2016-2020) that by 2020 the energy consumption per 10,000 RMB GDP would be cut by 15% in comparison to 2015, and SO 2 and NO x would be within 15.8 and 15.74 million tons, respectively, which are both 15% lower than the level of 2015. There are regional differences in APEs for China because of its serious imbalances in economic development. As for the air pollutant reduction, the national reduction target should be reasonably allocated to each province and municipality as a regional target, to smoothly realize the national emission reduction target. Therefore, it is urgent and important to study the regional differences in factors that affect APEs in China, which is conductive to offer references for designing policies according to regional differences and effectively reduce air pollutant emissions (APEs).Many previous studies touched on the issues related to air pollutant emission trends and characteristics [6][7][8][9], and the harm to the economy and human health [10][11][12][13][14][15][16][17][18][19]. Recently, more attention has been focused on analyses of factors influencing APEs. Some studies established econometric models to discuss the relationships between APEs and macroeconomic variables [20][21][22][23] and explore the effects of these macroeconomic variables on APEs [24][25][26][27]. Some scholars analyzed sources of APEs from the sectoral perspective and found that the power and transport sectors were the main sources [28][29][30], followed by the agriculture and household sectors [31,32]. The decomposition method has been widely used to decompose factors influencing energy u...

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LMDI Decomposition Analysis of Energy Consumption in the Korean Manufacturing Sector

Cited by 48 publications

References 26 publications

Uncovering Variations, Determinants, and Disparities of Multisector-Level Final Energy Use of Industries Across Cities

Uncovering Variations, Determinants, and Disparities of Multisector-Level Final Energy Use of Industries Across Cities

Applying ex post index decomposition analysis to final energy consumption for evaluating European energy efficiency policies and targets

Decomposition Analysis of Factors that Drive the Changes of Major Air Pollutant Emissions in China at a Multi-Regional Level

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