Energy saving in China: Analysis on the energy efficiency via bootstrap-DEA approach

Song, Malin; Yang, Li; Wu, Jie; Lv, Wendong

doi:10.1016/j.enpol.2012.11.001

Cited by 73 publications

(27 citation statements)

References 22 publications

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“…x, e, y, b; g = −g x , −g e ,g y , −g b = sup β x−βg x , e−βg e , y+βg y , b−βg b ∈ T B (13) Similar to the definition of above Luenberger productivity index, the biennial Luenberger productivity index has two components:…”

Section: Biennial Luenberger Energy and Environmental Performance Indexmentioning

confidence: 99%

“…Changes in technical efficiency continually declined with the exception of the period 2012-2013, whereas a slower and slower upward trend was observed in technological changes. In other words, EEP change is mainly driven by technological progress rather than technical efficiency improvement [13,48,65]. Government policies targeting the improvement of technical efficiency in the industrial sectors may indeed enhance overall environmental performance.…”

Section: Analysis Of Dynamic Changes In Energy and Environmental Perfmentioning

confidence: 99%

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Regional Differences in Energy and Environmental Performance: An Empirical Study of 283 Cities in China

Sun

Sun³

2018

Sustainability

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This paper proposes a new non-radial biennial Luenberger energy and environmental performance index (EEPI) to measure the energy and environmental performance (EEP) change in various Chinese cities. The sources of EEP change, in terms of technical efficiency change and technological change, are examined by Luenberger EEPI. The contributions from specific undesirable outputs and energy inputs to the EEP change are identified by means of the non-radial efficiency measure. The proposed approach is applied to evaluate the EEP of the industrial sector in 283 cities in China over 2010-2014. Factors influencing the emission abatement potential are investigated by employing geographically weighted regression (GWR) model. We find that (1) changes in EEP can be attributed to technological progress but that technological progress slows down across the study period; (2) the soot emission performance experiences a downtrend among four specific sub-performances (i.e., energy, wastewater, SO 2 and soot performances) in line with the truth that severe haze happened frequently in China; (3) the best performers begin to move from the coastal to inland cities with the less resource consumption and higher ecological quality; (4) cities with the strongest positive effect in regards to pollution intensity on emission abatement potential are located in the areas around the Bohai Gulf, where air pollution is particularly severe.

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Section: Biennial Luenberger Energy and Environmental Performance Indexmentioning

confidence: 99%

Section: Analysis Of Dynamic Changes In Energy and Environmental Perfmentioning

confidence: 99%

Regional Differences in Energy and Environmental Performance: An Empirical Study of 283 Cities in China

Sun

Sun³

2018

Sustainability

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“…Many previous researchers have studied 60 energy efficiency; Hu and Wang [10], for example, first established the total factor energy efficiency 61 (TFEE) concept by using DEA. Song, et al [11] used a bootstrap-DEA approach to find that China's 62 energy efficiency has maintained a slow upward trend from 1992 to 2010. Özkara In the present study, we extended the extant research in two main aspects.…”

Section: Introduction 28mentioning

confidence: 99%

Regional Differences in Energy and Environmental Performance: An Empirical Study of 283 Cities in China

Sun¹,

An²,

Sun³

2018

Preprint

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This paper proposes a new non-radial biennial Luenberger energy and environmental 11 performance index (EEPI) to measure the EEP change in various Chinese cities. The sources of EEP 12 change, in terms of technical efficiency change and technological change, are examined by 13Luenberger EEPI. The contributions from specific undesirable outputs and energy inputs to the EEP 14 change are identified by means of the non-radial efficiency measure. The proposed approach is 15 applied to evaluate the EEP of the industrial sector in 283 cities in China over influencing the emission abatement potential are investigated by employing geographically 17 weighted regression (GWR) model. We find that 1) changes in EEP can be attributed to technological 18 progress but that technological progress slows down across the study period; 2) the soot emission 19 performance experiences a downtrend among four specific sub-performances in line with the truth 20 that severe haze happened frequently in China; 3) the best performers begin to move from the 21 coastal to inland cities with the less resource consumption and higher ecological equality; 4) cities 22 with the strongest positive effect in regards to pollution intensity on emission abatement potential 23 are located in the areas around the Bohai Gulf, where air pollution is particularly severe. 24

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“…Bi et al [35] adopted a slacks-based DEA model to evaluate the energy efficiency of China's thermal power generation, with SO2, NOX and soot considered undesirable outputs. In addition, the super DEA model [36], range-adjusted measure (RAM)-DEA model [37], non-radial DEA model [34,38] and DEA-Malmquist model [29,32] have also been also employed to explore energy efficiency issues in China.…”

Section: Introductionmentioning

confidence: 99%

Estimating Energy Consumption of Transport Modes in China Using DEA

et al. 2015

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Abstract:The rapid growth of transport requirements in China will incur increasing transport energy demands and associated environmental pressures. In this paper, we employ a generalized data envelopment analysis (DEA) to evaluate the relative energy efficiency of rail, road, aviation and water transport from 1971 to 2011 by considering the energy input and passenger-kilometers (PKM) and freight ton-kilometers (TKM) outputs. The results show that the optimal energy efficiencies observed in 2011 are for rail and water transport, with the opposite observed for the energy efficiencies of aviation and road transport. In addition, we extend the DEA model to estimate future transport energy consumption in China. If each transport mode in 2020 is optimized throughout the observed period, the national transport energy consumption in 2020 will reach 497,701 kilotons coal equivalent (ktce), whereas the annual growth rate from 2011 to 2020 will be 5.7%. Assuming that efficiency improvements occur in this period, the estimated national transport energy consumption in 2020 will be 443,126 ktce, whereas the annual growth rate from 2011 to 2020 will be 4.4%, which is still higher than that of the national total energy consumption (3.8%).

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Energy saving in China: Analysis on the energy efficiency via bootstrap-DEA approach

Cited by 73 publications

References 22 publications

Regional Differences in Energy and Environmental Performance: An Empirical Study of 283 Cities in China

Regional Differences in Energy and Environmental Performance: An Empirical Study of 283 Cities in China

Regional Differences in Energy and Environmental Performance: An Empirical Study of 283 Cities in China

Estimating Energy Consumption of Transport Modes in China Using DEA

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