scholarly journals An Estimation and Factor Decomposition Analysis of Energy-related Carbon Emissions in Beijing

2012 ◽  
Vol 13 ◽  
pp. 1602-1608 ◽  
Author(s):  
J.Y. Zhang ◽  
Y. Zhang ◽  
Z.F. Yang ◽  
S.S. Li
Keyword(s):  
Carbon Emissions ◽  
Decomposition Analysis ◽  
Factor Decomposition

Estimation of energy-related carbon emissions in Beijing and factor decomposition analysis

2013 ◽  
Vol 252 ◽  
pp. 258-265 ◽  
Author(s):  
Jinyun Zhang ◽  
Yan Zhang ◽  
Zhifeng Yang ◽  
Brian D. Fath ◽  
Shengsheng Li
Keyword(s):  
Carbon Emissions ◽  
Decomposition Analysis ◽  
Factor Decomposition

scholarly journals The Changes of Carbon Emission in China’s Industrial Sectors from 2002 to 2010: A Structural Decomposition Analysis and Input-Output Subsystem

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Guoxing Zhang ◽  
Mingxing Liu
Keyword(s):  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Decomposition Analysis ◽  
Final Demand ◽  
Good Effect ◽  
Input Output ◽  
Carbon Emission Intensity ◽  
Industrial Sectors ◽  
Three Components

Based on 2002–2010 comparable price input-output tables, this paper first calculates the carbon emissions of China’s industrial sectors with three components by input-output subsystems; next, we decompose the three components into effect of carbon emission intensity, effect of social technology, and effect of final demand separately by structure decomposition analysis; at last, we analyze the contribution of every effect to the total emissions by sectors, thus finding the key sectors and key factors which induce the changes of carbon emissions in China’s industrial sectors. Our results show that in the latest 8 years five departments have gotten the greatest increase in the changes of carbon emissions compare with other departments and the effect of final demand is the key factor leading to the increase of industrial total carbon emissions. The decomposed effects show a decrease in carbon emission due to the changes of carbon emission intensity between 2002 and 2010 compensated by an increase in carbon emissions caused by the rise in final demand of industrial sectors. And social technological changes on the reduction of carbon emissions did not play a very good effect and need further improvement.

The Decomposition Analysis of Carbon Emissions: Theoretical Basis, Methods and Their Evaluations

2020 ◽  
Vol 08 (04) ◽  
pp. 2050020
Author(s):  
Shenning QU
Keyword(s):  
Environmental Economics ◽  
Carbon Emissions ◽  
Time Series Data ◽  
Decomposition Analysis ◽  
Series Data ◽  
Process Time ◽  
Structural Decomposition Analysis ◽  
Systematic Analysis ◽  
Index Decomposition ◽  
Laspeyres Index

As an analytical framework for studying the characteristics of changes in things and their action mechanisms, the decomposition analysis of greenhouse gas emissions has been increasingly used in environmental economics research. The author introduces several decomposition methods commonly used at present and compares them. The index decomposition analysis (IDA) of carbon emissions usually uses energy identities to express carbon emissions as the product of several factor indexes, and decomposes them according to different weight-determining methods to clarify the incremental share of each index, in which way it is possible to decompose the models that contain less factors, process time series data, and conduct cross-country comparisons. It mainly includes the Laspeyres index decomposition and the Divisia index decomposition. Among them, the LMDI I method has been widely used for its advantages such as generating no residuals and easy to use. The structural decomposition analysis (SDA) can be used to conduct a more systematic analysis, decompose models with more influencing factors, and analyze the impacts of various factors on emissions, but this method has higher requirements for data collection. The biggest difference between the SDA method and the IDA methods of carbon emissions is that the former is based on an input–output system, while the latter only needs to use sectors’ aggregate data.

Decomposition analysis of water utilization in the Beijing-Tianjin-Hebei region between 2003 and 2016

2018 ◽  
Vol 19 (2) ◽  
pp. 626-634
Author(s):  
Hongrui Wang ◽  
Siyang Hong ◽  
Tao Cheng ◽  
Xiayue Wang
Keyword(s):  
Socioeconomic Development ◽  
Industrial Structure ◽  
Decomposition Analysis ◽  
Water Shortage ◽  
Structural Effect ◽  
Industrial Water ◽  
Total Water ◽  
Water Utilization ◽  
Internal Relations ◽  
Factor Decomposition

Abstract Water crisis is prominent in the Beijing-Tianjin-Hebei region, therefore, the internal relations between water utilization changes and socioeconomic development must be urgently analysed. Based on analyses of the spatiotemporal characteristics of total water utilization, the factors that influenced changes in industrial water utilization in the Beijing-Tianjin-Hebei region from 2003 to 2016 were studied using a factor decomposition model. The results show that the scaling effect (SCE) increased water utilization by 31.78 billion m3 over those 13 years and was the only driving effect that caused industrial water utilization to increase. The structural effect (STE) and technological effect (TEE) reduced industrial water utilization by 14.93 and 20.44 billion m3, respectively. The TEE was the main reason for the decrease in industrial water utilization in Beijing, accounting for a reduction of 96.5% in total industrial water utilization. The STE was stronger than TEE in Tianjin, with associated decreases of 94.65% and 90.1% in total industrial water utilization, respectively. In Hebei, the STE and TEE reduced total industrial water utilization by 60.23% and 85.46%, respectively. Adjusting the industrial structure and promoting water-saving technology are efficient methods of alleviating the water shortage in the study area.

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