Investigating the energy efficiencies of European countries with super efficiency model and super SBM approaches

Gökgöz, Fazıl; Erkul, Ercem

doi:10.1007/s12053-018-9652-6

Cited by 37 publications

(14 citation statements)

References 61 publications

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“…In terms of the study of input and output efficiency between different regions, some scholars used conventional DEA, which likely lead to the situation that the calculated value of many units is 1, and the efficiency value of some units has little difference, so it is not better to compare the efficiency values of different regions. This result is the same as study of some scholars [32,[54][55][56][57][58][59][60][61]. In contrast, the super efficiency SBM used in this paper can solve this problem well, which is also an advantage of efficiency comparison in this paper.…”

Section: Discussionsupporting

confidence: 82%

Evaluation of Forestry Ecological Efficiency: A Spatiotemporal Empirical Study Based on China’s Provinces

Chen

Yao

2021

Forests

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Forests play a very important role in carbon dioxide emissions and climate change, and the development of China’s forestry is of great significance to our citizens. However, it is an arduous task for us to improve forestry output at a high and good level while taking environmental factors into account. In this paper, the non-expected super-efficiency SBM (slacks-based measure) model was used to measure the forestry ecological efficiency (FEE) of 31 provinces in China from 2004 to 2018, and the spatial and temporal evolution of FEE in different regions of China was analysed by using spatial econometric method. Tobit regression and random forest algorithm were selected to analyze the influence on FEE. The results showed that, firstly, the average annual increase of the national total factor productivity change of China’s forestry was 1.2%, and that the average annual increase of the national total factor productivity change in the eastern region was lower than that in the central and western regions. Secondly, the distribution of China’s FEE of the northeast and the south was higher, and FEE of China’s middle regions was relatively lower in 2004, but then the FEE in Northeast China has decreased, and the FEE has increased gradually from north to south in 2018. The agglomeration of high-tech industries in most regions of China had obvious positive spatial correlation characteristics in 2018. Thirdly, there was a negative correlation between forestry fixed assets investment and FEE, environmental regulation was an important factor affecting the ecological efficiency of forestry in China, and the level of economic development and industrial structure also had a certain impact on FEE.

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Section: Discussionsupporting

confidence: 82%

Evaluation of Forestry Ecological Efficiency: A Spatiotemporal Empirical Study Based on China’s Provinces

Chen

Yao

2021

Forests

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“…Jebali et al [23] examined the energy efficiency performance of Mediterranean countries between 2009 and 2012. Gökgöz and Erkul [33] compared the efficiency levels of European countries during the period of 2011-2015 using DEA.…”

Section: Energy Efficiencymentioning

confidence: 99%

Estimating Persistent and Transient Energy Efficiency in Belt and Road Countries: A Stochastic Frontier Analysis

et al. 2020

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In this paper, we examine the energy efficiency performance of the Belt and Road Initiative (BRI) countries using a newly developed panel data stochastic frontier model that allows for estimation of both persistent and transient efficiency while controlling for random country effects and noise. By this, we contribute to the energy economic literature by providing a complete picture of the level of persistent, transient, and total energy efficiency estimates from a cross country perspective for a panel of 48 BRI countries during the period 1990–2015. Adding that there is little evidence to support energy efficiency convergence in the energy economic literature, we went further to check whether energy efficiency converges in the BRI countries. The results show that (1) persistent efficiencies are much lower than transient efficiencies, suggesting that the energy problem in the BRI countries is more of a structural issue; (2) while energy efficiency varies widely across the countries, high-income countries perform better than the lower-income countries; (3) there is evidence of efficiency convergence and it accelerates when trade increases, but decreases when the industrial sector increases. Based on these findings, we propose some policy implications.

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“…Song et al [43] proposed a variant of the super-efficiency DEA model considering undesirable outputs and calculated the super-efficiency values to realize the complete ranking of all DMUs. Gokgoz et al [44] calculated the energy efficiency of European countries and regions for the period of 2011–2015 using super efficiency and super SBM models. In this paper, when studying the TFP of the regional WEF nexus, not only does it produce desirable outputs such as Gross domestic product (GDP), but it also produces undesirable outputs such as waste gas.…”

Section: Introductionmentioning

confidence: 99%

Research on Total Factor Productivity and Influential Factors of the Regional Water–Energy–Food Nexus: A Case Study on Inner Mongolia, China

Chen

Ding

Wang

et al. 2019

IJERPH

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With the supply of water, energy and food facing severe challenges, there has been an increased recognition of the importance of studying the regional water–energy–food nexus. In this paper, Inner Mongolia, including 12 cities in China, was selected as a research case. A super-efficiency slack based measure (SBM) model that considered the undesirable outputs was adopted to calculate the regional total factor productivity (TFP) and the Malmquist–Luenberger index was used to investigate the change trend of the TFP from 2007 to 2016 based on understanding the water–energy–food nexus. Finally, influential factors of the TFP were explored by Tobit regression. The results show that the 12 Inner Mongolia cities are divided into higher, moderate and lower efficiency zones. The higher efficiency zone includes Ordos, Hohhot, Xing’an, and Tongliao, and the lower efficiency zone includes Chifeng, Xilin Gol, Baynnur, Wuhai and Alxa. There is a serious difference in TFP between Inner Mongolia cities. During the study period, the TFP of the water–energy–food nexus in Inner Mongolia cities shows a rising trend, which is mainly driven by the growth of technical progress change. However, the average ML values of the lower and moderate efficiency zones were inferior to the higher efficiency zone in six of the ten years, so the difference between Inner Mongolia cities is growing. According to the Tobit regression, the mechanization level and degree of opening up have positive effects on the TFP, while enterprise scale and the output of the third industry have negative effects on the TFP. Government support does not have any significant impact on the TFP. Finally, suggestions were put forward to improve the TFP of the water–energy–food nexus in Inner Mongolia cities.

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Investigating the energy efficiencies of European countries with super efficiency model and super SBM approaches

Cited by 37 publications

References 61 publications

Evaluation of Forestry Ecological Efficiency: A Spatiotemporal Empirical Study Based on China’s Provinces

Evaluation of Forestry Ecological Efficiency: A Spatiotemporal Empirical Study Based on China’s Provinces

Estimating Persistent and Transient Energy Efficiency in Belt and Road Countries: A Stochastic Frontier Analysis

Research on Total Factor Productivity and Influential Factors of the Regional Water–Energy–Food Nexus: A Case Study on Inner Mongolia, China

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