Understanding energy efficiency and its drivers: An empirical analysis of China’s 14 coal intensive industries

Qi, Xiaoyan; Guo, Pibin; Guo, Yanshan; Liu, Xiuli; Zhou, Xijun

doi:10.1016/j.energy.2019.116354

Cited by 51 publications

(12 citation statements)

References 37 publications

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“…That is, the TFP index is decomposed into technical efficiency changes (EC) and technological progress changes (TECH), and technical efficiency changes can be further decomposed into pure technical efficiency changes (BPC) and scale efficiency changes (SECH). In the output results of maxdea software, whether it is input-oriented or output oriented, the meaning of Malmquist index is that greater than 1 means higher productivity and less than 1 means lower productivity [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Research on grain production efficiency in China’s main grain producing areas from the perspective of financial support

et al. 2021

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Grain production is vital to the national economy and people’s livelihood, and improving grain production efficiency is of great significance to the sustainable development of China’s economy and society. From the perspective of financial support, using the DEA global Malmquist productivity index model and based on the data of 13 main grain producing areas in China from 2001 to 2017, this paper discusses the evolution characteristics and regional distribution differences of the total factor productivity index of grain production in China’s main grain producing areas. The results show that from 2001 to 2017, the total factor productivity index of grain production in China’s main grain producing areas showed an overall fluctuation trend of gradual decline, with an average annual decline of 7.3%. From the perspective of spatial analysis, the grain production efficiency in China’s main grain producing areas is characterized by uneven spatial development, which is generally manifested as the decreasing trend from the central region to the eastern and western regions. Meanwhile, it can be seen from the decomposition index that the change of total factor productivity of grain production in China’s main grain producing areas mainly depends on the change of technical efficiency.

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Section: Methodsmentioning

confidence: 99%

Research on grain production efficiency in China’s main grain producing areas from the perspective of financial support

et al. 2021

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“…Choosing single pollutant to express the undesirable output cannot thoroughly reflect the level of pollutant emissions. Therefore, recently a few researchers used CO 2 , SO 2 and industrial waste gas as the undesirable output index [ 41 , 42 ]. Although these studies consider different indicators of undesirable output, they all emphasize that undesirable output is an important factor in determining the energy efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Qi et al. [ 42 ] analyzed energy efficiency of China’s 14 coal intensive industries. Feng and Wang [ 49 ] and Huo et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Revisiting China’s provincial energy efficiency and its influencing factors

Liu

Zhang

Tao

et al. 2020

Energy

112

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Improving the energy efficiency is a fundamental way to ensure energy security and sustainable development, and is also the requirement of supply-side structural reform of China’s energy. This paper uses the DEA-BCC model to estimate China’s energy efficiency at the provincial level, analyzes its regional differences from 2006 to 2016, and applies a panel data model to analyze the influencing factors of energy efficiency. It selects labor, capital stock and total energy consumption as inputs and takes real GDP and comprehensive index of environmental pollution as desirable and undesirable outputs, respectively. The results show that (1) energy efficiency when undesirable output is included is generally lower than when undesirable output is excluded; (2) There is a considerable difference in energy efficiency among provinces, and China’s energy efficiency, by and large, shows a trend of declining. The energy efficiency of four major regions demonstrates obvious regional differences: coastal region>northeastern region> middle region >western region; (3) The economic development level, technological progress, energy price and urbanization level are positively associated with energy efficiency, while the proportion of secondary industry and the energy consumption structure dominated by coal and oil are negatively correlated with energy efficiency.

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“…Since 2007, China has been number one in the world in regards to the amount of carbon emissions generated (Du et al 2019b), which puts the government under intense pressure to reduce carbon emissions. In an effort to deal with the issue, the Chinese government submitted an enhanced action report, which is aimed at decreasing the carbon intensity to 60%-65% based on the 2005 level and stop the growth of carbon dioxide emissions by 2030 (Li and Li 2020;Qi et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Spatial spillover effect of carbon emission efficiency in the construction industry of China

Deng

Zhou

et al. 2021

Environ Sci Pollut Res

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The construction industry plays an important role in energy saving and carbon emissions mitigation of China. Promoting carbon emission efficiency is seen as an efficient way to abate carbon emissions. Using 2005-2016 data, the carbon emission efficiency of the construction sector in 30 provinces is estimated, and the spatial distribution characteristics of the carbon emission efficiency of the construction industry is explored. The spatial Markov transition probability matrix is employed to investigate the influence of the spatial spillover effect on the regional distribution pattern of carbon emission efficiency. The results demonstrate that the carbon emission efficiency of the construction industry exhibits an unbalanced regional distribution, which is high in the east and low in the west. The spatial autocorrelation indicates that the carbon emission efficiency has a spatial dependence and is characterized by spatial agglomeration. Markov Chain results show a significant spatial spillover effect in carbon emission efficiency. The provinces with higher carbon emission efficiency have a positive effect on their neighbors, while the provinces with lower efficiency have a negative effect on neighbors. The findings are of great importance to understand the differences in and interactions of carbon emission efficiency between regions.

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Understanding energy efficiency and its drivers: An empirical analysis of China’s 14 coal intensive industries

Cited by 51 publications

References 37 publications

Research on grain production efficiency in China’s main grain producing areas from the perspective of financial support

Research on grain production efficiency in China’s main grain producing areas from the perspective of financial support

Revisiting China’s provincial energy efficiency and its influencing factors

Spatial spillover effect of carbon emission efficiency in the construction industry of China

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