Total-factor energy efficiency with congestion

Total-factor energy efficiency (TFEE) assessment has received increasing attention in both operations research and energy economics communities. Earlier TFEE studies implicitly assume that production activity lies in the economic area, which precludes the possibility that the production activity lies in the non-economic area and thus suffers from congestion. This paper contributes to develop TFEE index by taking into account congestion effect. It starts with the definition of congested production technology, based on which several data envelopment analysis models are proposed to construct TFEE index with congestion. The models for quantifying energy inefficiency caused by congestion effect are also developed. We apply the proposed index to evaluate the energy efficiency performance of Chinese industrial sectors at province level in 2010–2012. It is found that TFEE with congestion can yield useful insights about the choice of proper ways to achieve energy efficiency improvement. A comparison with the empirical results under congestion-free production technology indicates that ignoring congestion effect may lead to significantly different TFEE scores when congestion effect does exist.

[1]  Gale A. Boyd,et al.  Estimating the changes in the distribution of energy efficiency in the U.S. automobile assembly industry , 2014 .

[2]  Kenneth Løvold Rødseth,et al.  A note on input congestion , 2013 .

[3]  Dequn Zhou,et al.  Optimal path for controlling CO2 emissions in China: A perspective of efficiency analysis , 2014 .

[4]  Dequn Zhou,et al.  Does Congestion Affect Total-factor Energy Efficiency? A Theoretical Study☆ , 2014 .

[5]  L. Liang,et al.  Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model , 2014 .

[6]  G. Boyd,et al.  Estimating the linkage between energy efficiency and productivity , 2000 .

[7]  B. W. Ang,et al.  Linear programming models for measuring economy-wide energy efficiency performance , 2008 .

[8]  Peng Zhou,et al.  A non-radial DEA approach to measuring environmental performance , 2007, Eur. J. Oper. Res..

[9]  Timo Kuosmanen,et al.  Alternative treatments of congestion in DEA: A rejoinder to Cooper, Gu, and Li , 2001, Eur. J. Oper. Res..

[10]  David O. Allen,et al.  Congestion in the Chinese automobile and textile industries revisited , 2009 .

[11]  Yemane Wolde-Rufael,et al.  CO2 emissions, nuclear energy, renewable energy and economic growth in the US , 2010 .

[12]  M. Filippini.  US Residential Energy Demand and Energy Efficiency: A Stochastic Demand Frontier Approach , 2012 .

[13]  P. Zhou,et al.  Measuring Energy Congestion in Chinese Industrial Sectors: A Slacks-Based DEA Approach , 2015 .

[14]  Yung‐ho Chiu,et al.  Energy and emission reduction efficiency of China’s industry sector: a non-radial directional distance function analysis , 2019, Carbon Management.

[15]  R. Färe,et al.  Measuring congestion in production , 1983 .

[16]  P. He,et al.  Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach , 2013 .

[17]  B. W. Ang,et al.  Accounting frameworks for tracking energy efficiency trends , 2010 .

[18]  Shahiduzzaman,et al.  Changes in energy efficiency in Australia: A decomposition of aggregate energy intensity using logarithmic mean Divisia approach , 2013 .

[19]  Chien-Ming Chen,et al.  Evaluating eco-efficiency with data envelopment analysis: an analytical reexamination , 2014, Ann. Oper. Res..

[20]  Guanghui Zhou,et al.  A study of energy efficiency of transport sector in China from 2003 to 2009 , 2013 .

[21]  Zhaohua Wang,et al.  An empirical analysis of China's energy efficiency from both static and dynamic perspectives , 2014 .

[22]  Kankana Mukherjee,et al.  Measuring energy efficiency in the context of an emerging economy: The case of indian manufacturing , 2010, Eur. J. Oper. Res..

[23]  Peng Zhou,et al.  Index Decomposition Analysis for Tracking Energy Efficiency Trends , 2015 .

[24]  Boqiang Lin,et al.  Technology gap and China's regional energy efficiency: A parametric metafrontier approach , 2013 .

[25]  F. Wua,et al.  Does congestion affect total-factor energy efficiency ? A theoretical study , 2015 .

[26]  R. Samuelson,et al.  The unexpected challenges of using energy intensity as a policy objective: Examining the debate over the APEC energy intensity goal $ , 2014 .

[27]  Satoshi Honma,et al.  Panel Data Parametric Frontier Technique for Measuring Total-factor Energy Efficiency: Application to Japanese Regions , 2014 .

[28]  Constantin Zopounidis,et al.  Empirical research in the EU banking sector and the financial crisis , 2010 .

[29]  Jie Wu,et al.  Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs , 2017, Ann. Oper. Res..

[30]  B. W. Ang,et al.  Multi-country comparisons of energy performance: The index decomposition analysis approach , 2015 .

[31]  Ali Emrouznejad,et al.  Advances in data envelopment analysis , 2014, Ann. Oper. Res..

[32]  Jin-Li Hu,et al.  Industry-level Total-factor Energy Efficiency in Developed Countries , 2011 .

[33]  Peng Zhou,et al.  A survey of data envelopment analysis in energy and environmental studies , 2008, Eur. J. Oper. Res..

[34]  Rolf Färe,et al.  Directional distance functions and slacks-based measures of efficiency , 2010, Eur. J. Oper. Res..

[35]  William L. Weber,et al.  A slacks-based inefficiency measure for a two-stage system with bad outputs , 2010 .

[36]  Yi-Ming Wei,et al.  Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis , 2013 .

[37]  B. W. Ang,et al.  Tracking industrial energy efficiency trends using index decomposition analysis , 2013 .

[38]  Jin-Li Hu,et al.  Total-factor energy efficiency of regions in China , 2006 .

[39]  L. Hunt,et al.  Impact of energy policy instruments on the estimated level of underlying energy efficiency in the EU residential sector , 2013 .

[40]  Dequn Zhou,et al.  Scenario-based energy efficiency and productivity in China: A non-radial directional distance function analysis , 2013 .

[41]  Sabuj Kumar Mandal,et al.  Do undesirable output and environmental regulation matter in energy efficiency analysis? Evidence from Indian Cement Industry , 2010 .

[42]  Hui Wang,et al.  Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach , 2012, Eur. J. Oper. Res..

[43]  W. Cooper,et al.  Using DEA to improve the management of congestion in Chinese industries (1981-1997) , 2001 .

[44]  Yi-Ming Wei,et al.  An empirical analysis of energy efficiency in China's iron and steel sector , 2007 .

[45]  Yi-Ming Wei,et al.  China’s regional industrial energy efficiency and carbon emissions abatement costs , 2014 .

[46]  Ning Zhang,et al.  Energy efficiency, CO2 emission performance and technology gaps in fossil fuel electricity generation in Korea: A meta-frontier non-radial directional distance functionanalysis , 2013 .

[47]  Dequn Zhou,et al.  Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis , 2012 .

[48]  B. W. Ang,et al.  Measuring economy-wide energy efficiency performance: A parametric frontier approach , 2012 .

[49]  P. Zhou,et al.  Does there exist energy congestion? Empirical evidence from Chinese industrial sectors , 2016 .