Structural decomposition analysis on China’s energy intensity change for 1987–2005

Xia, Yan; Chen, Yang; Chen, Xikang

doi:10.1007/s11424-012-9061-4

Cited by 23 publications

(7 citation statements)

References 13 publications

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“…Essentially speaking, the changes experienced by the emissions and energy consumption between two periods are explained by the changes in final demand and structural coefficients [33]. In addition, SDA can also be applied to structural change [34], productivity growth [35], consumption of other sources [36], energy intensity [37,38], etc.…”

Section: Introductionmentioning

confidence: 99%

The Driving Forces of Changes in CO2 Emissions in China: A Structural Decomposition Analysis

2016

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Understanding the drivers of changes in CO 2 emissions is vital for a range of stakeholders. Hence, this paper explores the main drivers of CO 2 emissions in China using structural decomposition analysis based on constant price and non-comparative input-output tables. The driving forces at both nationwide and industrial levels are divided into nine effects. To investigate the effects from an energy perspective, all nine effects are further decomposed into three kinds of fossil fuels. Our empirical results show that the energy intensity effect can significantly stimulate emission reduction. Though the energy structure effect is weak, the trend of which over time shows that the energy structure is shifting to low carbon. Additionally, among final demand effect, the urban consumption, investment, and export expansion effects predominantly overwhelm other effects and contribute significantly to CO 2 emissions. Although the short term Leontief effects fluctuate greatly, the total Leontief effect in 1997-2010 reveals that it can significantly contribute to CO 2 emissions. Finally, detailed and concrete policy implications for CO 2 emission reduction are provided.

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Section: Introductionmentioning

confidence: 99%

The Driving Forces of Changes in CO2 Emissions in China: A Structural Decomposition Analysis

2016

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“…References Period Region Indicator [26] 1973-1978 Sweden Energy use [27] 1966-1980 Denmark Energy consumption [28] 1974-1979 Saskatchewan, Canada Energy use [29] 1963-1977 U.S. Energy use [30] 1971-1984 Taiwan Energy demand [34] 1972-1982 U.S. Energy use [35] 1976-1986 Taiwan Industrial electricity [36] 1981-1987 China Energy use [37] 1987-1992 China Energy intensity [38] 1973-1992 India Energy consumption [39] 1966-1992 Denmark Energy demand [40] 1985-1990 Japan Energy consumption [41] 1990 OECD Energy consumption [42] 1985-1990 Japan Energy demand [43] 1995 EU Energy intensity [44] 1996-2000 Vietnam Energy flow [45] 1970-1995 Japan Energy and emission [46] 1980-2000 Korea Household energy [47] 1992-2004 China Energy intensity [48] 1970-1996 Brazil Energy use [49] 1997-2002 U.S. Energy use [50] 1978-2004 China Embodied energy [51] 1992-2005 China Embodied energy [52] 1970-1985 Japan Energy demand [32] 1997-2007 China Energy and emission [31] 2002-2007 China Energy and emission [53] 1987-2007 China Energy consumption [54] 1987-2005 China Energy intensity [55] 1997-2007 China Energy intensity [56] 1987-2007 China Energy consumption [57] 1997-2007 China and U.S. Energy use [58] 2002-2007 China Energy intensity [59] 1995-2007 Austria Raw material use [60] 1990-2010 Global Energy footprint [61] 1995-2010 Thailand Energy intensity [62] 2001-2011 Catalonian, Spain Energy output [6...…”

Section: Yearmentioning

confidence: 99%

Structural Decomposition Analysis of Driving Factors for Energy Use Before and After the Global Financial Crisis: Evidence from Top Energy Consumer Guangdong Province in China

Wang

Wen

et al. 2019

Pol. J. Environ. Stud.

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Understanding drivers for energy consumption is important for economic and environmentally sustainable development. To explore this issue, the SDA (structural decomposition analysis) method based on input-output theory was used to analyze the influencing mechanism of energy consumption in one of the top energy consumers, Guangdong Province in China, during 2002 to 2012. We divided the process into 2 stages: before and after the global financial crisis. The main conclusions are as follows: 1) Economic activity and population size are the main driving factors for the increase in energy consumption, while energy consumption intensity is the main factor restraining the increment, and the effects of final demand structure on energy consumption transformed from positive before the global financial crisis to negative after the global financial crisis. 2) Analysis of allocation of energy consumption changes caused by final demands shows that international and domestic trade had significant effects on changes in energy consumption. Although energy consumption embodied in international exports decreased after the global financial crisis, it is still the most significant important driver for the increments. Guangdong is a net exporter of embodied

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“…Generally, SDA decomposes energy intensity into production effect, structure effect, Leontief effect, intensity effect, and final demand effect [74]. While some use additive decomposition analysis [75][76][77][78], others use multiplicative decomposition analysis [79][80][81][82][83][84].…”

Section: The Relationship Between Asc and Eiap: A Literature Reviewmentioning

confidence: 99%

Structural Change and Its Impact on the Energy Intensity of Agricultural Sector in China

Han

2018

Sustainability

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China’s agricultural structure has undergone significant changes for the past four decades, mainly presenting as the fall of sown proportion of grain crops and the rise of vegetables, as has its energy consumption. Employing the panel data on 30 provinces during 1991–2016, this paper empirically explores the impact of agricultural structure changes (ASC) on the energy intensity of agricultural production (EIAP), direct energy intensity of agricultural production (DEIAP) and indirect energy intensity of agricultural production (IEIAP) in China. Besides, the regional heterogeneity of such impact is examined. The results show that: (1) ASC increases EIAP and IEIAP significantly, while ASC decreases DEIAP, which is explained by the structural effect and different planting modes of different crops; (2) the impact in the three administrative regions is similar to national situation, except the impact of ASC on DEIAP in the West Region, which is explained by regional differences of vegetable mechanization; (3) the result of the six vegetable production regions reveals greater regional heterogeneity, and this is attributed to the scale economy effect and the incremental effect of vegetable mechanization; and (4) fuel price, income, agricultural labor, old dependency ratio, and fiscal expenditure have different but significant impacts on EIAP, DEIAP, and IEIAP. Finally, some policy implications are given.

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Structural decomposition analysis on China’s energy intensity change for 1987–2005

Cited by 23 publications

References 13 publications

The Driving Forces of Changes in CO2 Emissions in China: A Structural Decomposition Analysis

The Driving Forces of Changes in CO2 Emissions in China: A Structural Decomposition Analysis

Structural Decomposition Analysis of Driving Factors for Energy Use Before and After the Global Financial Crisis: Evidence from Top Energy Consumer Guangdong Province in China

Structural Change and Its Impact on the Energy Intensity of Agricultural Sector in China

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