Perfect decomposition techniques in energy and environmental analysis

Ang, B.W.; Liu, F.L.; Chew, Ek Peng

doi:10.1016/s0301-4215(02)00206-9

Cited by 314 publications

(149 citation statements)

References 9 publications

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“…The result of the LMDI method does not produce residuals. Therefore, LMDI is a relatively superior decomposition method [72][73][74][75][76][77][78][79]. This paper uses the LMDI method to analyze the influencing factors of carbon emissions in the transportation sector of North China.…”

Section: The Logarithmic Mean Divisia Index (Lmdi) Modelmentioning

confidence: 99%

An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China

Zhu

2017

Sustainability

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Abstract:The transport sector is the major green-house gas emitter and most rapidly growing sector in terms of consuming energy in China. Understanding the driving forces behind carbon emission is a prerequisite for reducing carbon emissions and finding a balance between economic growth and carbon emissions. The purpose of this paper is to identify the impact of the factors which influence the level of carbon emissions from the transportation sector in the Beijing-Tianjin-Hebei (BTH) area, China, using decomposition model, combined with a decoupling elasticity index. The results of our study indicate that: (1) changes in the level of carbon emissions from the transportation sector are not always synchronized with changes in economic growth. (2) The decoupling state between the carbon emissions and economic growth of Tianjin and Beijing can be roughly divided into two phases. The first phase was during the 2005 to 2009 period, when the decoupling state was pessimistic. The second phase was from 2009 to 2013, when the decoupling state became better overall and was mainly dominated by weak decoupling. Conversely, the decoupling state of Hebei was mainly weak during this period. (3) Economic growth and population size play positive roles in increasing the levels of transportation-related carbon emissions in BTH. However, the energy structure is a negative force. The effect of energy intensity always plays a negative role in Tianjin and Hebei, but positive in Beijing. The industrial structure effect shows a fluctuating trend, but the cumulative effect value is negative, and negative interaction is prominent. Finally, this paper gives some suggestions on how to develop low-carbon transport in BTH area.

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Section: The Logarithmic Mean Divisia Index (Lmdi) Modelmentioning

confidence: 99%

An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China

Zhu

2017

Sustainability

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“…In general, studies have found that the LMDI is the most appropriate method to decompose energy consumption and emission changes [62][63][64][65]. Recently, the Logarithmic Mean Divisia Index (LMDI) approach to energy decomposition has emerged as a preferred decoupling model [66].…”

Section: Logarithmic Mean Divisia Index (Lmdi)mentioning

confidence: 99%

Decoupling and Decomposition Analysis of Carbon Emissions from Industry: A Case Study from China

Wang

Jiang

2016

Sustainability

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China has overtaken the United States as the world's largest producer of carbon dioxide, with industrial carbon emissions (ICE) accounting for approximately 65% of the country's total emissions. Understanding the ICE decoupling patterns and factors influencing the decoupling status is a prerequisite for balancing economic growth and carbon emissions. This paper provides an overview of ICE based on decoupling elasticity and the Tapio decoupling model. Furthermore, the study identifies the factors contributing to ICE changes in China, using the Kaya identity and Log Mean Divisia Index (LMDI) techniques. Based on the effects and contributions of ICE, we close with a number of recommendations. The results revealed a significant upward trend of ICE during the study period 1994 to 2013, with a total amount of 11,147 million tons. Analyzing the decoupling relationship indicates that "weak decoupling" and "expansive decoupling" were the main states during the study period. The decomposition analysis showed that per capita wealth associated with industrial outputs and energy intensity are the main driving force of ICE, while energy intensity of industrial output and energy structure are major determinants for ICE reduction. The largest contributing cumulative effect to ICE is per capita wealth, at 1.23 in 2013. This factor is followed by energy intensity, with a contributing cumulative effect of −0.32. The cumulative effects of energy structure and population are relatively small, at 0.01 and 0.08, respectively.

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“…Since both the structure effect and the efficiency effect change over time, one has to establish appropriate weights in order to measure the contribution of each effect. Decomposition analysis in the field of energy studies have used a variety of weights, which translates into a range of slightly different applied decomposition approaches (see Ang et al 2003, Ang 2004, Boyd and Roop 2004, and Zhang and Ang 2001, for reviews and details). In this study we use the so-called log mean Divisia index method (LMDI I), as introduced by Ang and Liu (2001), which in its additive form decomposes a change in aggregate energy intensity (∆I tot ) between period 0 and T into an efficiency effect (∆I eff ) and a structure effect (∆I str ) according to:…”

Section: Decomposition Analysismentioning

confidence: 99%

Dynamics and determinants of energy intensity in the service sector: A cross-country analysis, 1980–2005

Mulder

Groot

Pfeiffer

2014

Ecological Economics

111

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Perfect decomposition techniques in energy and environmental analysis

Cited by 314 publications

References 9 publications

An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China

An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China

Decoupling and Decomposition Analysis of Carbon Emissions from Industry: A Case Study from China

Dynamics and determinants of energy intensity in the service sector: A cross-country analysis, 1980–2005

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