scholarly journals What Influences the Cross-Border Air Pollutant Transfer in China–United States Trade: A Comparative Analysis Using the Extended IO-SDA Method

2019 ◽  
Vol 11 (22) ◽  
pp. 6252 ◽  
Author(s):  
Shichun Xu ◽  
Chang Gao ◽  
Yunfan Li ◽  
Xiaoxue Ma ◽  
Yifeng Zhou ◽  
...  
Keyword(s):  
Energy Intensity ◽  
Scale Effects ◽  
Decomposition Analysis ◽  
Air Pollutant ◽  
Emission Coefficient ◽  
Structural Decomposition Analysis ◽  
Export Structure ◽  
Cross Border ◽  
The Us ◽  
Pollutant Transfer

This paper extends the IO-SDA (input–output and structural decomposition analysis) method to decompose the CBAPT (cross-border air pollutant transfer) into different effects, and reveals the status of CBAPT and analyzes influencing factors affecting the CBAPT in China–US trade by comparing China with the US in these factors. This study found that China was a net air pollutant exporter, and this indicates the air pollutants were transferred from the US into China through China–US trade. On the whole, the China energy intensity, China emission coefficient, and import scale effects decreased the CBAPT, whereas the export scale and US emission coefficient effects increased the CBAPT; the influences of export structure, US energy intensity, and import structure on CBAPT were uncertain. The sectoral distribution of effects on the CBAPT in China–US trade was unbalanced, which was mainly concentrated in heavy industry and transportation. The China energy intensity, China emission coefficient, and import scale effects inhibited sectoral CBAPT, and the export scale effect promoted this sectoral transfer. Other effects on the sectoral transfer were negligible. This paper provides some policy suggestions based on empirical results.

scholarly journals Factorizing the Changes in CO2 Emissions from Indian Road Passenger Transport: A Decomposition Analysis

2016 ◽  
Vol 11 (3) ◽  
pp. 67-83 ◽  
Author(s):  
Monika Gupta ◽  
Sanjay Singh
Keyword(s):  
Economic Growth ◽  
Energy Intensity ◽  
Policy Design ◽  
Decomposition Analysis ◽  
Emission Coefficient ◽  
Passenger Transport ◽  
Transport Activity ◽  
Positive Role ◽  
Negative Role ◽  
Major Factors

AbstractThe main aim of this paper is to analyse the role of different factors responsible for CO2 emission from Indian road passenger transport with the help of Logarithmic Mean Divisia Index over the period of 1971-2011. CO2 emission increase is decomposed into five major factors - emission coefficient, transport energy intensity, transport activity, economic growth, and population. Findings suggest that economic growth, transport activity and population have a significant positive role in increasing CO2 emission from road passenger transport, whereas energy intensity plays a negative role in CO2 emission increase. Emission coefficient has also a negative role in CO2 emission increase during all the periods except during 1971-81. Therefore, emission coefficient and energy intensity are the two most important factors for policy design and implementation to reduce CO2 emission from the sector.

Factors Leading to Increased Carbon Dioxide Emissions of the APEC Countries: the LMDI Decomposition Analysis

2020 ◽  
pp. 1-20
Author(s):  
Yu-Fang Chang ◽  
Bwo-Nung Huang
Keyword(s):  
Carbon Dioxide ◽  
Carbon Dioxide Emissions ◽  
Energy Intensity ◽  
Decomposition Analysis ◽  
Substitution Effect ◽  
The Philippines ◽  
Population Increase ◽  
Intensity Effect ◽  
Energy Substitution ◽  
The Us

This paper explores the factors that lead to increased carbon dioxide emissions in the 18 countries of the APEC. We apply the LMDI multiplicative decomposing method to 18 countries between 1971 and 2012. We summarize these factors that are as follows: (1) population increase and economic growth play a key role in increased carbon dioxide emissions. (2) All the 18 countries of the APEC have improved their energy efficiency as manifested in the change of energy intensity ([Formula: see text]), which is less than 1 in the 42 years; (3) In terms of energy substitution effect ([Formula: see text]) and fuel coefficient effect ([Formula: see text]), the decomposition results point out that Hong Kong, Indonesia, and Malaysia witnessed growth in [Formula: see text] and [Formula: see text], indicating the only factor to reduce the emissions for these three countries is intensity effect, which gives rise to relatively higher emission for these three countries during the period. In the case of Peru, the Philippines, Singapore, Thailand, and Vietnam, we witnessed increases in [Formula: see text], but decreases in [Formula: see text]; In the case of Australia, Canada, Chile, China, Japan, South Korea, Mexico, New Zealand, Taiwan, and the US, there seem to decrease in [Formula: see text], but increases in [Formula: see text] during the 42-year period.

scholarly journals Coupling Structural Decomposition Analysis and Sensitivity Analysis to Investigate CO2 Emission Intensity in China

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2396 ◽  
Author(s):  
Ling Li ◽  
Ling Tang ◽  
Junrong Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Emission Intensity ◽  
Co2 Emission ◽  
Decomposition Analysis ◽  
Emission Coefficient ◽  
Structural Decomposition Analysis ◽  
Structural Decomposition ◽  
Direct Emission ◽  
Future Emission ◽  
Co2 Emission Intensity

A coupled structural decomposition analysis (SDA) and sensitivity analysis approach is developed to explore the drivers of China’s CO2 emission intensity at both general and sectoral levels and from both ex-post and ex-ante perspectives. Two steps are involved—structural decomposition and sensitivity analysis. First, the popular factor decomposition method, SDA, is implemented to identify which drivers “have” made the largest contribution to emission intensity changes. Second, an emerging ex-ante approach, sensitivity analysis, is introduced to answer how and to what extent such drivers “will” influence future emission intensity at a sectoral level. Based on China’s input-output tables for 1997–2012, the empirical study provides a hotspot map of China’s energy system. (1) Direct-emission coefficient and technology coefficient are observed as the top two overall drivers. (2) For the former, reducing direct-emission coefficient in an emission-intensity sector (e.g., electricity and heat sectors) by 1% will mitigate China’s total emission intensity by at least 0.05%. (3) For the latter, future emission intensity is super-sensitive to direct transactions in emission-intensity sectors (particularly the chemical industry with elasticities up to 0.82%).

scholarly journals Multiplicative Structural Decomposition Analysis of Spatial Differences in Energy Intensity among G20 Countries

2020 ◽  
Vol 10 (8) ◽  
pp. 2832
Author(s):  
Yang Wang ◽  
Meng Sun ◽  
Rui Xie ◽  
Xiangjie Chen
Keyword(s):  
Energy Consumption ◽  
South Korea ◽  
Energy Intensity ◽  
Decomposition Analysis ◽  
Embodied Energy ◽  
Structure Effect ◽  
Structural Decomposition Analysis ◽  
Intensity Effect ◽  
Input Structure ◽  
Spatial Differences

Comparing the spatial differences in the energy intensity of the Group of Twenty (G20) countries and identifying the factors that influence these differences can help the G20 countries formulate targeted policies to achieve energy conservation goals. This study analyzes the spatial differences in the G20 countries’ energy intensity at the aggregate and sectoral levels based on an input–output framework and reveals its driving factors by employing multiplicative structural decomposition analysis, obtaining the sectoral energy intensity, input structure, and final demand structure effects. The results show that: (1) the gap in aggregate energy intensity among the G20 countries tended to converge from 2000 to 2014 with the reducing energy intensity in Russia, India, China, and South Korea having great potential to reduce global energy consumption and improve global energy efficiency; (2) in 2014, the main driving forces for above-average energy intensity was the sectoral energy intensity effect in India, South Korea, and Canada, the input structure effect in Russia and China, and the final demand structure effect in Indonesia; (3) using the average of the G20 countries as a reference, the energy reduction potential of China, Russia, India, South Korea, Indonesia, and Canada is 62.75, 31.94, 21.24, 7.67, 1.47, and 0.81 exajoules (EJ), respectively. The embodied energy consumption decline in these countries was equivalent to 21.78% of the G20’s total energy consumption in 2014; and (4) the most important factor of the high embodied energy intensity of key sectors in India and South Korea is the sectoral energy intensity effect, while for Russia and China, it is the input structure effect.

scholarly journals The Drivers of China’s Regional Carbon Emission Change—A Structural Decomposition Analysis from 1997 to 2007

2019 ◽  
Vol 11 (12) ◽  
pp. 3254 ◽  
Author(s):  
Ling Yang ◽  
Michael L. Lahr
Keyword(s):  
Carbon Emission ◽  
Energy Intensity ◽  
Energy Savings ◽  
Decomposition Analysis ◽  
Structural Decomposition Analysis ◽  
Emission Data ◽  
Intermediate Inputs ◽  
Emission Change ◽  
End Use ◽  
Partial Effects

Using three official multiregional input–output tables and carbon emission data, we decompose the change in carbon emission for eight regions of China between 1997 and 2007. We do so according to the following seven partial effects: (i) Changes in energy end-use structure, (ii) effect of energy intensity, (iii) the added value’s share of gross output, (iv) changes in sub-industry structure, (v) changes in the substitution of import for intermediate inputs, and changes in (vi) structure and (vii) level of final demand. We find energy intensity contributes most to CO2 abatement throughout China, while other factors vary widely across the different regions. We suggest that governments consider regional disparity and CO2 flows when formulating policies; structural change with an eye toward energy-savings and general efficiency improvements, like better insulated buildings, are among measures we deem effective.

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