Decomposition of energy intensity in Chinese industries using an extended LMDI method of production element endowment

Over the past six years, the volume of plastics production inprimary forms in Russia increased by 57%, exceeding 7.8 million tons in 2017. During this period, the production capacities were substantially renovated; new plants were put into operation. Increasing the competitiveness of Russian polyvinyl chloride (PVC) is possible on the basis of the integrated use of raw materials, the use of innovative technologies and reduction of production costs. When calculating costs, the following aspects were taken into account: type, availability and cost of raw materials, the number of chemical stages of final product production, its yield, energy intensity and material consumption of the process, the possibility of combining this method of production with other productionp rocesses of a chemical company, i.e. complex use of all obtained products. This is due to the low cost of raw materials, the clustering of coaland chemical enterprises and energy companies located in the Kemerovo region, which makes the production of PVC competitive.

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Using the LMDI Method to Analyze the Change in Greenhouse Gas Emissions in Turkish Sectors

10.20944/preprints202008.0407.v2 ◽

2021 ◽

Author(s):

Abdulkadir BEKTAŞ

Keyword(s):

Co2 Emissions ◽

Heat Production ◽

Economic Activity ◽

Energy Intensity ◽

Decomposition Analysis ◽

Manufacturing Industries ◽

Lmdi Method ◽

Gaseous Fuels ◽

Turkish Economy ◽

Determining Factors

In this study, CO2 emissions of the Turkish economy are decomposed for the 1998–2017 period for four sectors; agriculture, forestry and fishery, manufacturing industries and construction, public electricity and heat production, transport, and residential. The analyses are conducted for five fuel types; liquid, solid, gaseous fuels, biomass, and other fuels. In decomposition analysis, Log Mean Divisia Index (LMDI) method is used. The analysis results point out that energy intensity is one of the determining factors behind the change in CO2 emissions, aside from economic activity. The fuel mix component, especially for the manufacturing industries and construction sector, lowers CO2 emissions during the crisis periods when the economic activity declines. Mainly, it is found that changes in total industrial activity and energy intensity are the primary factors determining the changes in CO2 emissions during the study period. Among GDP sectors, manufacturing industries and construction and public electricity and heat production are the two sectors that dominate the change in CO2 emissions. Additionally, the residential and transport sectors’ contributions have gained importance during recent years. Among the manufacturing industries and construction, the non-metallic minerals sector contributes to CO2 emissions, followed by the chemicals sector.

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Study the Trends in Energy Consumption Change for the Transport Service Sectors

JST: Engineering and Technology for Sustainable Development ◽

10.51316/jst.151.etsd.2021.31.3.16 ◽

2021 ◽

Vol 31 (3) ◽

pp. 89-96

Keyword(s):

Energy Consumption ◽

Service Sector ◽

Energy Intensity ◽

Transport Service ◽

Transport Activity ◽

Economic Sectors ◽

Total Energy Consumption ◽

Lmdi Method ◽

Service Sectors ◽

Consumption Change

This research analyzes the energy consumption of transport service sectors in Vietnam and its changing trend in the past twenty-five years using Input-Output (IO) tables and Logarithmic-mean Divisia index (LMDI) method. IO table of 28 economic sectors in 1996, 2000, 2007, 2012 and 2018 is used to determine energy consumption, in which the transport service sector was always the third or second largest energy consumer, accounting for between 9% and 16% of total energy consumption. LMDI method is used to define influencing factors including transport activity, transport structure, transport intensity, and energy intensity. In these four impacts, the change of transport activity contributes the largest effect (occupied 74.3%), followed by the change of energy intensity (occupied 17.7%) of total increased share for energy consumption. Among the transport service sectors, it is found that Freight transport service by road played the mainstream role in the increasing trends of energy consumption in the period of 2007-2018. In order to improve the energy efficiency of the sector, investments in green transport technologies and modernization of trucks to be more efficient and eco-friendlier will be the key contributors.

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Factors influencing energy intensity in four Chinese industries

The Energy Journal ◽

10.5547/01956574.37.si1.kfis ◽

2016 ◽

Vol 37 (01) ◽

Cited By ~ 9

Author(s):

Karen Fisher-Vanden ◽

Yong Hu ◽

Gary Jefferson ◽

Michael Rock ◽

Michael Toman

Keyword(s):

Energy Intensity ◽

Factors Influencing ◽

Chinese Industries

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Using LMDI Method to Analyze the Energy-Related CO2 Emissions in Guangdong Province, China

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.291-294.1556 ◽

2013 ◽

Vol 291-294 ◽

pp. 1556-1561

Author(s):

Ping Wang ◽

Wan Shui Wu ◽

Bang Zhu Zhu

Keyword(s):

Co2 Emissions ◽

Energy Intensity ◽

Negative Impact ◽

Scale Effects ◽

Energy Structure ◽

Per Capita Gdp ◽

Lmdi Method ◽

Greenhouse Gas Inventories ◽

Per Capita ◽

Population Scale

In recent years, Guangdong has achieved remarkable performance in economic development; meanwhile it is being faced with problems of increasing CO2 emissions. Following the IPCC Guidelines for National Greenhouse Gas Inventories, we estimated the energy-related CO2 emissions in Guangdong during the period of 1980-2010. We employed the logarithmic mean divisia index (LMDI) method to decompose the CO2 emissions into energy intensity, energy structure, per capita GDP and population scale effects. Besides, we deduced the calculation methods for the year by year effects, the accumulated effects and the contribution degrees. Using 1980 as the base year, the empirical results show that the accumulated effects of energy intensity and energy structure in 2010 are negative, while those of per capita GDP and population scale are positive. Per capita GDP is the chief positive influence on the CO2 emissions. Energy intensity is becoming more significant; however, its direction is instability. Population scale has a significant positive effect on the CO2 emissions. Energy structure has a negligible negative impact on the CO2 emissions. Some suggestions on CO2 emissions reduction in Guangdong are given based on the analysis.

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