scholarly journals Driving forces of Chinese primary air pollution emissions: an index decomposition analysis

2016 ◽  
Vol 133 ◽  
pp. 136-144 ◽  
Author(s):  
Wanning Lyu ◽  
Yuan Li ◽  
Dabo Guan ◽  
Hongyan Zhao ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Driving Forces ◽  
Decomposition Analysis ◽  
Index Decomposition Analysis ◽  
Index Decomposition ◽  
Pollution Emissions ◽  
Air Pollution Emissions

scholarly journals Driving Forces of Air Pollution in Ulaanbaatar City Between 2005 and 2015: An Index Decomposition Analysis

2020 ◽  
Vol 12 (8) ◽  
pp. 3185 ◽  
Author(s):  
Enkhjargal Enkhbat ◽  
Yong Geng ◽  
Xi Zhang ◽  
Huijuan Jiang ◽  
Jingyu Liu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Driving Forces ◽  
Decomposition Analysis ◽  
Air Pollutant ◽  
Capital City ◽  
Energy Structure ◽  
Index Decomposition Analysis ◽  
Socioeconomic Drivers ◽  
Index Decomposition ◽  
Concentration Changes

Ulaanbaatar, the capital city of Mongolia, is facing serious air pollution challenges—especially during the cold and long winter months—mainly due to fossil fuel combustion. This study investigates the socioeconomic drivers of the sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter (PM2.5) concentration changes in Ulaanbaatar between 2005 and 2015 by applying the index decomposition analysis (IDA) method. Five socio-economic driving forces are considered in the decomposition analysis. All the driving forces contributed to more air pollution concentration changes in 2015 than in 2005, despite the decreasing trends of decomposition results for the period of 2010–2015. In general, economic growth, pollution intensity, and energy intensity significantly contributed to the changes of air pollutant concentrations, while energy structure and population growth had marginal effects. Finally, appropriate policy recommendations are proposed to the local government so that they can initiate feasible policies to effectively reduce air pollution, protect human health, and respond to climate change in Ulaanbaatar.

scholarly journals The factors of milk revenue change in Lithuania: index decomposition analysis based on the Shapley value

2015 ◽  
Vol 37 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Tomas Baležentis ◽  
Irena Kriščiukaitienė
Keyword(s):  
Milk Yield ◽  
Shapley Value ◽  
Market Integration ◽  
Driving Forces ◽  
Decomposition Analysis ◽  
Milk Composition ◽  
Country Level ◽  
Index Decomposition Analysis ◽  
Level Data ◽  
Index Decomposition

The paper aims to analyse the main driving forces behind the changes in milk revenue in Lithuania during 1993–2013 by the virtue of the index decomposition analysis. Specifically, an index decomposition model based upon Shapley value is introduced to account for dynamics in endogenous and exogenous factors underlying dynamics of the revenue. Particularly, the number of cows, average milk yield, market integration, milk composition, and milk price are considered as the main factors causing variation in milk revenue. The country level data are used for the analysis. Results show that such practices as animal recording and application of improved fodder have increased milk revenue by 67 million EUR and 75 million EUR during 1993–2004 and 2004–2013, respectively, due to increase in milk yield and fat.

Driving factors of energy-relevant CO2 emissions in China’s fixed asset investment

2018 ◽  
Vol 12 (4) ◽  
pp. 601-616
Author(s):  
Hongtao Liu ◽  
Jin Shang
Keyword(s):  
Intensity Factor ◽  
Carbon Emissions ◽  
Driving Forces ◽  
Decomposition Analysis ◽  
Carbon Intensity ◽  
Policy Recommendations ◽  
Fixed Asset ◽  
Content Type ◽  
Index Decomposition Analysis ◽  
Index Decomposition

Purpose The purpose of this paper is to use an index decomposition analysis to investigate the driving forces of China’s CO2 emissions related to fixed asset investments from 2003 to 2015. Design/methodology/approach This paper uses an index decomposition analysis to investigate the driving forces of China’s CO2 emissions related to fixed asset investments from 2003 to 2015. To make policy recommendations, this paper identifies three effects. An approach to calculating energy-relevant CO2 emissions is also presented. Findings The results suggest that the amount of CO2 emissions related to fixed asset investments increased during the entire period. The social and economic effect played a major role in promoting carbon emissions, followed by the fixed asset effect. Therefore, the activity factor was the dominant positive factor, followed by the construction factor. The negative element was the energy effect, in which the energy intensity factor played an important role in reducing emissions, followed by the structural factor. Moreover, the carbon intensity factor might be a potential inhibitory force in reducing carbon emissions. Research/limitations/implications A steady financial policy, relaxed family planning, sustainable urbanization, strategy of innovation-driven development, reform of scientific and technological structures, development of science and technology and exploration of new energy sources are proposed to mitigate carbon emissions from fixed asset investments. The conclusion also provides a reference for developing countries in similar situations. Originality/value This paper uses an index decomposition analysis to investigate the driving forces of China’s CO2 emissions related to fixed asset investments from 2003 to 2015. To make policy recommendations, this paper identifies three effects. An approach to calculating energy-relevant CO2 emissions is also presented.

The driving forces of material use in China: An index decomposition analysis

2017 ◽  
Vol 52 ◽  
pp. 336-348 ◽  
Author(s):  
Zhiping Wang ◽  
Chao Feng ◽  
Jinyu Chen ◽  
Jianbai Huang
Keyword(s):  
Driving Forces ◽  
Decomposition Analysis ◽  
Index Decomposition Analysis ◽  
Index Decomposition

scholarly journals A Decomposition Analysis of the Korean Manufacturing Sector: Monetary vs. Physical Outputs

2021 ◽  
Vol 13 (11) ◽  
pp. 6192
Author(s):  
Junghwan Lee ◽  
Jinsoo Kim
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Driving Forces ◽  
Manufacturing Sector ◽  
Decomposition Analysis ◽  
Primary Energy ◽  
Intensity Effect ◽  
Activity Effect ◽  
Index Decomposition ◽  
Output Indicator

This study analyzes the changes in energy consumption of the Korean manufacturing sector using the index decomposition analysis (IDA) method. To capture the production effect based on actual physical activities, we applied the activity revaluation (AR) approach in the analysis. We also developed energy consumption data in terms of primary energy supply to consider conversion loss in the energy sector to avoid any distortions in the intensity effect. The analysis covers every manufacturing subsector in Korea over the period between 2006 and 2018. Combining two distinctive approaches from the previous literature, the AR approach and primary energy-based analysis gives us helpful findings for a climate policy. First, the overall activity effect estimated from the physical output indicator is lower than that from the monetary output indicator. The monetary indicator shows that the share of energy-intensive industries decreases, whereas the physical indicator shows the opposite. Second, in terms of energy efficiency, the intensity effect is estimated as an increasing factor of energy use, whereas inversed results are shown when we use the monetary indicator. Lastly, unlike the previous studies, the AR approach results indicate that Korean manufacturing sectors have been shifting toward an energy-intensive, so it is hard to anticipate positive intensity effects, which means decreasing energy consumption factor, for a while. These results support why analyzing the driving forces of energy consumption through the AR approach and primary energy base is highly recommended.

scholarly journals COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 290
Author(s):  
Akvilė Feiferytė Skirienė ◽  
Žaneta Stasiškienė
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Social Services ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Rapid Spread ◽  
Air Quality Improvement ◽  
Environment Agency ◽  
Pollution Emissions ◽  
Air Pollution Emissions

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

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