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Measuring Energy Congestion in Chinese Industrial Sectors: A Slacks-Based DEA Approach

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Abstract

Measuring the magnitude of energy congestion provides useful information for determining the optimal level of energy input with reference to other inputs. This paper clarifies the concept of energy congestion and adapts a slacks-based DEA method to examine the energy congestion effect in Chinese industrial sectors over time. Our empirical results show that Chinese industrial sectors showed an increasing trend in energy congestion. The size of energy congestion effect varied across different provinces and regions. The central area had a significantly higher amount of energy congestion than that in west area, while the east area registered for the lowest energy congestion inefficiency. On average, 32 % of the energy consumption in Chinese industry was excessively used. A multiple regression analysis within a panel data analysis framework shows that the total energy consumption and industrial value added per capita have a positive while total-factor energy efficiency has a negative effect on energy congestion.

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Notes

  1. In most cases, the amounts of energy congestion are the same as the total slack values obtained in Model (1), indicating that there is no plateau-like phenomenon as portrayed between points B and C in Fig. 1. This is probably because of the increasing number of input variables. Cooper et al. (2001) gave the same finding in their work. Flegg and Allen (2009) also pointed out that the use of Model (2) made little practical difference to the results.

  2. The total-factor energy efficiency index is calculated using a non-radial directional distance function approach (Zhou et al. 2012), which seek to reduce energy input and expand output non-proportionally while keeping non-energy inputs constant.

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Acknowledgments

The authors are grateful to the financial support provided by the National Natural Science foundation of China (Nos. 71273005 & 71203055), the Jiangsu Natural Science Foundation for Distinguished Young Scholar (No. BK20140038), the Funding of Jiangsu Innovation Program for Graduate Education (CXLX13_170), the Humanities and Social Science Foundation of the Ministry of Education (No. 12YJCZH039), the Ph.D. Programs Foundation of Ministry of Education of China (No. 20123218110028) 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, Jiangsu province, China

    F. Wu, P. Zhou & D. Q. Zhou

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

    F. Wu, P. Zhou & D. Q. Zhou

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

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Wu, F., Zhou, P. & Zhou, D.Q. Measuring Energy Congestion in Chinese Industrial Sectors: A Slacks-Based DEA Approach. Comput Econ 46, 479–494 (2015). https://doi.org/10.1007/s10614-015-9499-2

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