Energy and CO 2 emissions efficiency of major economies: A non-parametric analysis

Iftikhar, Yaser; He, Weijun; Wang, Zhaohua

doi:10.1016/j.jclepro.2016.08.072

Cited by 119 publications

(48 citation statements)

References 35 publications

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“…The concept of carbon efficiency in this paper is similar with the definition of energy efficiency in some literatures [1,2]. But carbon efficiency focus on less emissions in the process of generating outputs while the energy efficiency refers to use less energy to get more outputs.…”

Section: Introductionmentioning

confidence: 92%

“…But carbon efficiency focus on less emissions in the process of generating outputs while the energy efficiency refers to use less energy to get more outputs. Current measurements on the carbon efficiency or energy efficiency can be divided into two categories: one is single-factor method represented by carbon intensity, which is defined as the energy consumption divided by the economic output [2]. It is easy to calculate by these two methods but they ignored the substitution effect of capital, labor force and other factors.…”

Section: Introductionmentioning

confidence: 99%

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Regional Carbon Efficiency in China: A Super-SBM Model with Undesirable Outputs

Xiong¹,

Ma²

2017

dteees

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Abstract. In this paper, we use the Super-SBM model with undesirable output (Super-SBM-UN) to measure the carbon efficiency of thirteen regions and seven sub-areas in China from 2002 to 2014. Firstly, the results indicate that the proposed Super-SBM-UN model can effectively rank the SBM-efficient regions. Secondly, China's overall carbon efficiency is low which urgently needs to be improved. Thirdly, China's regional carbon efficiency is extremely unbalanced that Beijing, Shanghai mostly appear at the efficient frontier among 30 regions and the south area rank first among seven regions. And different areas present different development trends of carbon efficiency. Finally, we provide relevant suggestions to improve the carbon efficiency based on their carbon emission characteristics.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Regional Carbon Efficiency in China: A Super-SBM Model with Undesirable Outputs

Xiong¹,

Ma²

2017

dteees

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“…In 2008, McKinsey's report "Carbon Productivity Challenges: Containing Global Change and Sustaining Economic Growth" clearly stated that any successful climate change mitigation technology must support the two goals of both stabilizing the greenhouse gas content in the atmosphere and maintaining economic growth, and the combination of the two goals is "carbon productivity". China's carbon productivity has excellent potential for improvement [6]. Therefore, improving carbon productivity is a realistic requirement for China to control greenhouse gas emissions, improve energy efficiency, balance economic growth, and achieve low-carbon development.…”

Section: Introductionmentioning

confidence: 99%

Application of Threshold Regression Analysis to Study the Impact of Clean Energy Development on China’s Carbon Productivity

Han

Feng

et al. 2020

IJERPH

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Facing the pressures of international carbon emission reduction, the transformation into a low-carbon economy has become a common issue of all countries. The core of developing a low-carbon economy is to increase carbon productivity, which can be measured as the economic benefits of unit carbon emissions. Therefore, using province-level panel data in China from 2009 to 2017, we analyze the carbon productivity level of each region, and empirically investigate the threshold effect of clean energy development on carbon productivity under different technological innovation levels. The results show that the carbon productivity is rising, and China’s economic development pattern has been shifting towards low-carbon and sustainable development. Furthermore, the driving force of clean energy development on carbon productivity is not monotonously increasing (decreasing) but is a “double threshold effect” of technological innovation capability. Finally, based on the research conclusions and realistic requirements of China’s low-carbon economic transformation, this paper proposes improving carbon productivity from the aspects of innovation capability improvement and institutional guarantee.

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“…The results of a survey conducted by the International Energy Agency in 2010 indicated that urban areas were responsible for 71% of global energy-related carbon dioxide (CO 2 ) emissions and that this percentage was likely to increase with the continuation of the trend of accelerated urbanization [22,23]. Because urbanization has a significant impact on CO 2 emissions [24][25][26], there is an urgent need to discuss the measurement and spatial patterns of urbanization efficiency in cities like Beijing.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics of Beijing’s Urbanization Efficiency from 2005 to 2014

Gao

Zhang

2017

Sustainability

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Abstract:In the context of Beijing's accelerated economic growth, a high urbanization rate and associated urban problems pose challenges. We collected panel data for the period 2005-2014 to examine the relationship between Beijing's urbanization efficiency and economic growth rate as well as its spatial patterns of dynamic and static urbanization efficiency. Specifically, we developed a comprehensive index system for assessing Beijing's economic growth rate and urbanization efficiency at the district (county) level. Economic level was selected as an indicator of the economic growth rate. Economic urbanization and consumption levels were selected as indicators of urbanization efficiency. We applied a sequential Malmquist total factor productivity index to estimate the dynamic urbanization efficiency and economic growth rate at the district/country level from 2005 to 2014. We measured Beijing's static urbanization efficiency in 2014 using a data envelopment analysis model and assessed its spatiotemporal dynamics and urbanization efficiency pattern using a Getis-Ord General G i index. The results indicated an overall average increase of 1.07% in the total factor urbanization efficiency (TFUE), with an average value of 0.91, while the total factor economic growth rate (TFEE) remained stable at an average value of 0.979. The low TFUE level evidently continues to significantly constrain TFEE. Both TFUE and TFEE levels in the Capital Function Core (CFC) area were significant, exhibiting high inputs and outputs, while these levels in the Urban Function Development (UFD), City Development Zone (CDZ), and Ecological Conservation Development (ECD) areas were below 1 for most periods, strongly indicating inefficient factor allocation. In view of this spatial pattern, TFUE's regional spatial distribution appears remarkable, showing a decreasing trend from north to south in Beijing, excluding CFC areas. During the period 2005-2014, the CFC area and northeastern Beijing gradually developed into high urbanization efficiency cluster regions. The dominant factors accounting for the difference in total factor productivity indices between TFUE and TFEE were technical change (TC) and scale efficiency change (SEC), and the main factors driving the regional spatial distribution pattern for urbanization efficiency were TC and technical efficiency change (TEC). Accordingly, local governments should promote TC, SEC, and TEC to improve urbanization levels, with optimal strategies entailing strengthening policy support and encouraging investments in technology in UFD, CDZ, and ECD areas. Within Beijing, Dongcheng, Xicheng, Shijingshan, Mentougou, and Yanqing demonstrated effectively balanced static urbanization efficiency levels in 2014, whereas these levels in the city's remaining 11 districts were not optimal, with extensive development. County governments should therefore promote efforts to reduce input redundancy and improve pure technical efficiency to maintain sustainable and steady development.

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Energy and CO 2 emissions efficiency of major economies: A non-parametric analysis

Cited by 119 publications

References 35 publications

Regional Carbon Efficiency in China: A Super-SBM Model with Undesirable Outputs

Regional Carbon Efficiency in China: A Super-SBM Model with Undesirable Outputs

Application of Threshold Regression Analysis to Study the Impact of Clean Energy Development on China’s Carbon Productivity

Spatiotemporal Dynamics of Beijing’s Urbanization Efficiency from 2005 to 2014

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