Total-factor energy efficiency with congestion

Zhou, P.; Wu, Fei; Zhou, Dequn

doi:10.1007/s10479-015-2053-8

Cited by 27 publications

(6 citation statements)

References 44 publications

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“…15 Similar studies using DEA to study total factor energy efficiency include Borozan, 16 Ouyang et al., 17 and Zhou et al. 18 However, the studies above did not take undesired outputs into account; Mandal 19 pointed out that the DEA model that ignores undesired outputs will cause deviation of energy efficiency measurement. Wang et al.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal pattern evolution and influencing factors of energy–environmental efficiency: A case study of Yangtze River urban agglomeration in China

Zhong

Peng

Elahi

2020

Energy & Environment

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Improving energy–environmental efficiency is prerequisite for sustainable development. In order to explore ways to improve energy–environmental efficiency, this paper uses the undesired output slack-based model to measure the energy–environmental efficiency of the Yangtze River urban agglomeration based on the input and output index data from 2008 to 2017, and its spatial and temporal pattern evolution is analyzed by using kernel density estimation, Gini coefficient, and coefficient of variation. Moreover, the Tobit regression model is used to analyze the influencing factors of the energy–environmental efficiency of the Yangtze River urban agglomeration. The results indicate that the energy–environmental efficiency of each city is increased continuously, and the regional differences are gradually narrowed. The spatial pattern is changed from polar nucleus type to valley type, and finally the distribution characteristics of “overall high” are formed. Overall, the energy–environmental efficiency presents a spatial layout of “high in the east and low in the west.” The regression results show that the level of economic development and energy–environmental efficiency are “U-type” associated characteristics, and government regulation and population density have significant positive effects on it. Industrial structure and technological progress have negative effects on it, and the effect of opening degree is not significant.

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Section: Introductionmentioning

confidence: 99%

Spatial and temporal pattern evolution and influencing factors of energy–environmental efficiency: A case study of Yangtze River urban agglomeration in China

Zhong

Peng

Elahi

2020

Energy & Environment

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“…Academic researchers have been active in research in the energy sector from different perspectives, one stream of the studies investigated energy efficiency and its determinants (Bashir et al, 2020;Bian et al, 2017;Lv et al, 2015;Omrani et al, 2019;Zhou et al, 2017), the second stream of studies examined the issue of energy demand and energy consumption (Li et al, 2017;Shadzad, 2020;Shahzad et al, 2021;Talbi et al, 2020;Yang & Yang, 2015); while there is a growing awareness and concern during recent decades about climate change derived from carbon emission, therefore, relevant efforts have been made by academic research in the area of renewable energy and carbon emissions (Dogan et al, 2021;Farooq et al, 2019;Fatima et al, 2021;Ghazouani et al, 2020;Sarwar et al, 2019;Shahzad et al, 2020). Although relevant studies during recent couple of years focused on the evaluation of energy demand as illustrated before, however, none of the above-mentioned studies provided a detailed analysis about the energy spot market and futures market, let alone the inter-relationships among the price and demand in these two markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Unveiling endogeneity and temporal dependence in energy prices and demand in Iberian countries: a stochastic hidden Markov model approach

et al. 2021

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In this paper, we analyze the temporal dependence in energy prices and demand using daily data of Portugal and Spain over the period 2007–2017. The methodology used is based on a stochastic Hidden Markov Model and the results indicate first that all significant relationships between energy prices and demands were found to be positive; second, spot prices are only time dependent on future prices and spot energy, while future energy is solely time dependent on spot energy behavior; third, future prices are not only autocorrelated but also time-dependent with spot energy and future energy demands level; and finally, spot energy is autocorrelated and time-dependent with future prices and future energy. Policy implications of the results obtained are presented at the end of the article.

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“…In the existing related research, previous studies on green total factor productivity have focused on one industry or one resource for efficiency evaluation, and then make a comparative analysis to find out the causes of inefficiency and put forward corresponding suggestions [10][11][12][13][14]. At the same time, we can see that most of the previous studies regard the economic production process as an encapsulated "black box", ignoring the impact of its internal factors and related structure on efficiency [15,16].…”

Section: Literature Reviewmentioning

confidence: 99%

Green Total Factor Productivity during the 12th Five-Year Plan Period in China: A Network Malmquist Productivity Index Analysis

Deng¹,

Zhu²,

Lu³

2019

dteees

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Because of the existence of the "black box" hypothesis, the internal operation mechanism of the economic development process is neglected. The paper describes the internal components and operation mechanism of the economic development process by constructing a network, consisting of economic production stage and environmental governance stage. The network Malmquist productivity index is employed to evaluate the green total factor productivity of China during the 12th Five-Year Plan period. The results show that: The overall environmental governance of China is in good condition. The change of network Malmquist productivity index is more closely related to the network technical change. The Malmquist productivity index of the whole network of economic development system shows an inverted "V" change, which is the same as that of two stages. The performance of the eastern region is the best, followed by the northeast region, the central region and the western region are below average.

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Total-factor energy efficiency with congestion

Cited by 27 publications

References 44 publications

Spatial and temporal pattern evolution and influencing factors of energy–environmental efficiency: A case study of Yangtze River urban agglomeration in China

Spatial and temporal pattern evolution and influencing factors of energy–environmental efficiency: A case study of Yangtze River urban agglomeration in China

Unveiling endogeneity and temporal dependence in energy prices and demand in Iberian countries: a stochastic hidden Markov model approach

Green Total Factor Productivity during the 12th Five-Year Plan Period in China: A Network Malmquist Productivity Index Analysis

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