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Exploring energy efficiency in China׳s iron and steel industry: A stochastic frontier approach

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  • Lin, Boqiang
  • Wang, Xiaolei

Abstract

The iron and steel industry is one of the major energy-consuming industries in China. Given the limited research on effective energy conservation in China׳s industrial sectors, this paper analyzes the total factor energy efficiency and the corresponding energy conservation potential of China׳s iron and steel industry using the excessive energy-input stochastic frontier model. The results show that there was an increasing trend in energy efficiency between 2005 and 2011 with an average energy efficiency of 0.699 and a cumulative energy conservation potential of 723.44 million tons of coal equivalent (Mtce). We further analyze the regional differences in energy efficiency and find that energy efficiency of Northeastern China is high while that of Central and Western China is low. Therefore, there is a concentration of energy conservation potential for the iron and steel industry in the Central and Western areas. In addition, we discover that inefficient factors are important for improving energy conservation. We find that the structural defect in the economic system is an important impediment to energy efficiency and economic restructuring is the key to improving energy efficiency.

Suggested Citation

  • Lin, Boqiang & Wang, Xiaolei, 2014. "Exploring energy efficiency in China׳s iron and steel industry: A stochastic frontier approach," Energy Policy, Elsevier, vol. 72(C), pages 87-96.
  • Handle: RePEc:eee:enepol:v:72:y:2014:i:c:p:87-96
    DOI: 10.1016/j.enpol.2014.04.043
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    References listed on IDEAS

    as
    1. Lin, Boqiang & Zhang, Guoliang, 2013. "Estimates of electricity saving potential in Chinese nonferrous metals industry," Energy Policy, Elsevier, vol. 60(C), pages 558-568.
    2. Lin, Boqiang & Wu, Ya & Zhang, Li, 2011. "Estimates of the potential for energy conservation in the Chinese steel industry," Energy Policy, Elsevier, vol. 39(6), pages 3680-3689, June.
    3. Gale A. Boyd, 2008. "Estimating Plant Level Energy Efficiency with a Stochastic Frontier," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 23-44.
    4. Chang, Young-Tae & Zhang, Ning & Danao, Denise & Zhang, Nan, 2013. "Environmental efficiency analysis of transportation system in China: A non-radial DEA approach," Energy Policy, Elsevier, vol. 58(C), pages 277-283.
    5. Olanrewaju, O.A. & Jimoh, A.A. & Kholopane, P.A., 2012. "Integrated IDA–ANN–DEA for assessment and optimization of energy consumption in industrial sectors," Energy, Elsevier, vol. 46(1), pages 629-635.
    6. Zhou, P. & Ang, B.W. & Zhou, D.Q., 2012. "Measuring economy-wide energy efficiency performance: A parametric frontier approach," Applied Energy, Elsevier, vol. 90(1), pages 196-200.
    7. Buck, J. & Young, D., 2007. "The potential for energy efficiency gains in the Canadian commercial building sector: A stochastic frontier study," Energy, Elsevier, vol. 32(9), pages 1769-1780.
    8. Raymond W. Goldsmith, 1951. "A Perpetual Inventory of National Wealth," NBER Chapters, in: Studies in Income and Wealth, Volume 14, pages 5-73, National Bureau of Economic Research, Inc.
    9. Azadeh, A. & Amalnick, M.S. & Ghaderi, S.F. & Asadzadeh, S.M., 2007. "An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors," Energy Policy, Elsevier, vol. 35(7), pages 3792-3806, July.
    10. Gregory C. Chow, 1993. "Capital Formation and Economic Growth in China," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(3), pages 809-842.
    11. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    12. Lin, Boqiang & Du, Kerui, 2013. "Technology gap and China's regional energy efficiency: A parametric metafrontier approach," Energy Economics, Elsevier, vol. 40(C), pages 529-536.
    13. Lin, Boqiang & Yang, Lisha, 2013. "The potential estimation and factor analysis of China′s energy conservation on thermal power industry," Energy Policy, Elsevier, vol. 62(C), pages 354-362.
    14. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    15. Lin, Boqiang & Zhang, Li & Wu, Ya, 2012. "Evaluation of electricity saving potential in China's chemical industry based on cointegration," Energy Policy, Elsevier, vol. 44(C), pages 320-330.
    16. Subal Kumbhakar & M. Denny & M. Fuss, 2000. "Estimation and decomposition of productivity change when production is not efficient: a paneldata approach," Econometric Reviews, Taylor & Francis Journals, vol. 19(4), pages 312-320.
    17. Coelli, Tim & Perelman, Sergio, 1999. "A comparison of parametric and non-parametric distance functions: With application to European railways," European Journal of Operational Research, Elsevier, vol. 117(2), pages 326-339, September.
    18. Shi, Guang-Ming & Bi, Jun & Wang, Jin-Nan, 2010. "Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs," Energy Policy, Elsevier, vol. 38(10), pages 6172-6179, October.
    19. Aranda-Usón, Alfonso & Ferreira, Germán & Mainar-Toledo, M.D. & Scarpellini, Sabina & Llera Sastresa, Eva, 2012. "Energy consumption analysis of Spanish food and drink, textile, chemical and non-metallic mineral products sectors," Energy, Elsevier, vol. 42(1), pages 477-485.
    20. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    21. Rulon D. Pope & Jean-Paul Chavas, 1994. "Cost Functions Under Production Uncertainty," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 76(2), pages 196-204.
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