An analysis of the decoupling relationship between CO2 emission in power industry and GDP in China based on LMDI method

Xie, Pinjie; Gao, Shuangshuang; Sun, Feihu

doi:10.1016/j.jclepro.2018.11.212

Cited by 168 publications

(59 citation statements)

References 29 publications

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“…The results showed that the factors restricting decoupling include the carbon coefficient effect, economic structure effect, labor force input effect and investment effect. Xie et al [17] also used the Tapio and LMDI models to analyze the factors influencing the decoupling of CO 2 emissions from GDP in China's power industry, and they found that economic scale and electrification were the two main factors inhibiting it. Li et al [18] predicted that the decoupling relationship between China's CO 2 emissions and economic development would become stronger between 2015 and 2030.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Engo et al [15] Xie et al [17] Etem et al [29] Li et al [30] Yan et al [32] LMDI and Tapio index Energy structure, energy intensity, economic structure, population economic activity [15,30], transmission and distribution loss [17] electrification [17], carbon intensity [29], fossil fuel intensity [29], conversion efficiency [29], carbon coefficient [32], fossil energy share [32], energy efficiency [32], transport activity [32] Wang et al [16] C-D production function, LMDI and Tapio [26], investment structure [26], investment dependence [26], emission reduction [28], energy saving [28], transport share [28] Shi et al [24] Decoupling index Per capita GDP, energy intensity, per capita carbon Zheng et al [27] LMDI and OECD decoupling model Carbon emission coefficient, population energy structure, industrial structure, energy intensity, economic growth…”

Section: National Level Co 2 Emissionsmentioning

confidence: 99%

See 1 more Smart Citation

Decoupling Elasticity and Driving Factors of Energy Consumption and Economic Development in the Qinghai-Tibet Plateau

Fan

Meng

et al. 2020

Sustainability

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Decoupling of energy consumption and economic development is a key factor in achieving sustainable regional development. The decoupling relationship between energy consumption and economic development in the Qinghai-Tibet Plateau region is still unclear. This paper uses the logarithmic mean Divisia index (LMDI) decomposition method and Tapio elastic index model to analyze the decoupling degree and driving factors of energy consumption and economic development, and evaluates the decoupling effort level in Qinghai-Tibet Plateau from 2006 to 2016. The results indicate that the Qinghai-Tibet Plateau region showed a weak decoupling as a whole, and that only Tibet experienced expanding negative decoupling in 2006–2007 and an expansion link in 2007–2008. Economic scale is a primary factor that hinders the decoupling of energy consumption, followed by investment intensity and industrial energy structure. The cumulative promotion effect of research and development (R&D) efficiency and intensity and the inhibition effect of investment intensity cancel each other out. With the exception of Tibet and Xinjiang, all provinces in the Qinghai-Tibet plateau have made decoupling efforts. Decoupling efforts made by R&D efficiency contributed the most, followed by energy intensity and R&D intensity. This paper provides policy recommendations for the decoupling of energy consumption experience for underdeveloped regions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: National Level Co 2 Emissionsmentioning

confidence: 99%

Decoupling Elasticity and Driving Factors of Energy Consumption and Economic Development in the Qinghai-Tibet Plateau

Fan

Meng

et al. 2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…Deepening the penetration of renewable energy and international cooperation would help to reduce ACI in developing countries. Xie et al (2019) analyzed CO 2 emissions from the process of electric transmission. Gu et al (2015) predicted the emission reduction under different scenarios, and suggested policies of low carbon development of China's electric power industry.…”

Section: Literature Reviewmentioning

confidence: 99%

A LMDI decomposition analysis of carbon dioxide emissions from the electric power sector in Northwest China

Mai

Ran

2020

Natural Resource Modeling

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Taking advantage of the electrification strategy, Northwest China has made full use of its natural resources endowment, to develop renewable energy as the substitution of thermal power. To evaluate carbon dioxide (CO2) emissions from electric power sector, an extended Kaya identity equation and the Logarithmic mean Divisia index decomposition method are applied to Northwest China from 1998 to 2017. Six explaining factors are analyzed, including carbon intensity, energy mixes, generating efficiency, electrification, economy and population. The results show that driving forces of CO2 emissions from electricity system varied greatly among provinces. Generally, economic growth has mainly contributed to increase CO2 emission, while the improvement in the power‐generating efficiency has crucially decreased CO2 emission. In 2017, Promoting electrification directly increased CO2 emissions from electric system, but indirectly reduced CO2 emissions from the whole region by 5.10% through the estimation of a clean development mechanism method. Therefore, local governments are suggested continuing to promote electrification to guide future emission reduction, while enterprises and individuals need to make their own contributions to low‐carbon development. Recommendations for Resource Managers: Variations of carbon dioxide (CO2) emissions of all five provinces in Northwest China are analyzed. Logarithmic mean Divisia index analysis is used to study the main drivers of CO2 emission change. Improvements in the generating efficiency significantly reduced CO2 emissions. Due to electrification effects, CO2 emissions from electric power increased, but CO2 emissions from the region decreased. Economy effects were still the biggest drivers affecting CO2 emission.

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“…In 2005, Tapio [30] introduced the decoupling index to specify eight logical possibilities, divided into three categories (i.e., decoupling, coupling, and negative decoupling). The Tapio decoupling model has been adopted in several studies of various industries [31][32]. Most of the existing literature applying the decoupling model conducted a general analysis, referring to the delink states between economic growth and energy-related CO 2 emissions from a general national perspective [15,[33][34].…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring Driving Factors and Decoupling Effect of Transportation CO<sub>2</sub> Emissions in Low-Carbon Regions: A Case Study from Liaoning, China

Ma¹,

Chen²,

Yang³

2020

Pol. J. Environ. Stud.

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Climate scientists have confirmed that the atmospheric concentrations of carbon dioxide (CO 2) have been increasing significantly over the past century, resulting in a negative influence on the global climate system [1]. Due mainly to the CO 2 emissions produced by fossil fuels consumption, climate change is considered an unprecedented global challenge [2]. The transportation sector remains the largest consumer of petroleum, and its use for freight services is increasing at a staggering rate, faster than any other applications [3]. Because of its sustained high energy consumption, the transportation industry generates approximately

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An analysis of the decoupling relationship between CO2 emission in power industry and GDP in China based on LMDI method

Cited by 168 publications

References 29 publications

Decoupling Elasticity and Driving Factors of Energy Consumption and Economic Development in the Qinghai-Tibet Plateau

Decoupling Elasticity and Driving Factors of Energy Consumption and Economic Development in the Qinghai-Tibet Plateau

A LMDI decomposition analysis of carbon dioxide emissions from the electric power sector in Northwest China

Measuring Driving Factors and Decoupling Effect of Transportation CO<sub>2</sub> Emissions in Low-Carbon Regions: A Case Study from Liaoning, China

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