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Total-factor energy efficiency with congestion

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Abstract

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.

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Notes

  1. DEA as a mainstream methodology for efficiency and productivity analysis has been employed in a wide range of areas, and this journal has reported some recent developments in the theory and applications of DEA (Emrouznejad 2014). An excellent example is the study by Chen (2014) which examined several key theoretical issues in measuring carbon performance.

  2. One recent study by Wu et al. (2015c) offered interesting insights into total-factor energy efficiency evaluation by dividing the industrial systems into energy utilization stage and a pollution treatment stage.

  3. Agriculture sector provides a typical example of input congestion in production area. Land is a ‘limiting input’ on which additional unit of labor on the same plot of land cause congestion, which also holds for other inputs such as water and fertilizer.

  4. Cooper et al. (2001) and Flegg and Allen (2009) have identified labor congestion and capital congestion in the textile industry and automobile industry. Different from their work which only considered labor and capital inputs, a recent study by Wu et al. (2015b) examined the issue of energy congestion and found that the overuse of energy may also cause congestion in industrial sectors.

  5. According to Färe and Grosskopf (1983), the assumption of weak disposability of inputs would not preclude congestion and could be well characterized within a nonparametric DEA framework.

  6. For the case of variable returns to scale (VRS), an additional constraint \(\sum \limits _{i=1}^I {\lambda _i =1} \) is required to be added.

  7. Model (3) can be converted to a weighted slacks-based DEA model (Fukuyama and Weber 2010) or a special form of non-radial directional distance function that does not consider output objective (Färe and Grosskopf 2010; Zhou et al. 2012a). However, it should be pointed out that a crucial difference between our TFEE measure and others is that our DEA models are conducted under the congested production technology.

  8. The recent study by Wu et al. (2015b) proposed a decomposition model to examine the energy inefficiency caused by energy congestion. However, their overall energy inefficiency is based on the standard strong disposability assumption. Different from their method, we construct our models under the congested production technology. Besides, we propose a more general method by considering the effect of non-energy input congestion, such as labor congestion and capital congestion.

  9. In addition to the two measures, the non-radial directional distance function approach has also been used to measure TFEE performance, e.g. Wang et al. (2013a). However, under a specific setting of direction vector, the method used by Wang et al. (2013a) will collapse to our energy-oriented measure.

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Acknowledgments

The authors are grateful to the financial support provided by the National Natural Science Foundation of China (Nos. 71273005, 71573119), the Jiangsu Natural Science Foundation for Distinguished Young Scholar (No. BK20140038), the Funding of Jiangsu Innovation Program for Graduate Education (CXLX13_170) and the NUAA research funding (No. NE2013104).

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Authors and Affiliations

  1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    P. Zhou, F. Wu & D. Q. Zhou

  2. Research Centre for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    P. Zhou, F. Wu & D. Q. Zhou

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  1. P. Zhou
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  2. F. Wu
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  3. D. Q. Zhou
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Correspondence to P. Zhou or F. Wu.

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Zhou, P., Wu, F. & Zhou, D.Q. Total-factor energy efficiency with congestion. Ann Oper Res 255, 241–256 (2017). https://doi.org/10.1007/s10479-015-2053-8

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