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Panel Data Parametric Frontier Technique for Measuring Total-factor Energy Efficiency: Application to Japanese Regions


  • Honma, Satoshi
  • Hu, Jin-Li


Using the stochastic frontier analysis (SFA) model, we estimate total-factor energy efficiency (TFEE) scores for 47 regions across Japan during 1996–2008. We extend the cross-sectional SFA model proposed by Zhou et al. (Applied Energy, 2012) to panel data models and add environmental variables. The results provide not only the TFEE scores, in which statistical noise is taken into account, but also the determinants of inefficiency. The three SFA TFEEs are compared with a TFEE derived from data envelopment analysis (DEA). The four TFEEs are highly correlated with one another. For the inefficiency estimates, the higher the manufacturing industry share and wholesale and retail trade share, the lower the TFEE score.

Suggested Citation

  • Honma, Satoshi & Hu, Jin-Li, 2014. "Panel Data Parametric Frontier Technique for Measuring Total-factor Energy Efficiency: Application to Japanese Regions," MPRA Paper 54304, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:54304

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    References listed on IDEAS

    1. Stern, David I., 2012. "Modeling international trends in energy efficiency," Energy Economics, Elsevier, vol. 34(6), pages 2200-2208.
    2. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 59-80.
    3. Miketa, Asami & Mulder, Peter, 2005. "Energy productivity across developed and developing countries in 10 manufacturing sectors: Patterns of growth and convergence," Energy Economics, Elsevier, vol. 27(3), pages 429-453, May.
    4. repec:dau:papers:123456789/6801 is not listed on IDEAS
    5. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    6. Le Pen, Yannick & Sévi, Benoît, 2010. "On the non-convergence of energy intensities: Evidence from a pair-wise econometric approach," Ecological Economics, Elsevier, vol. 69(3), pages 641-650, January.
    7. Liddle, Brantley, 2010. "Revisiting world energy intensity convergence for regional differences," Applied Energy, Elsevier, vol. 87(10), pages 3218-3225, October.
    8. Honma, Satoshi & Hu, Jin-Li, 2008. "Total-factor energy efficiency of regions in Japan," Energy Policy, Elsevier, vol. 36(2), pages 821-833, February.
    9. 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.
    10. Mukherjee, Kankana, 2008. "Energy use efficiency in U.S. manufacturing: A nonparametric analysis," Energy Economics, Elsevier, vol. 30(1), pages 76-96, January.
    11. Herrala, Risto & Goel, Rajeev K., 2012. "Global CO2 efficiency: Country-wise estimates using a stochastic cost frontier," Energy Policy, Elsevier, vol. 45(C), pages 762-770.
    12. O. Olesen & N. Petersen, 2009. "Target and technical efficiency in DEA: controlling for environmental characteristics," Journal of Productivity Analysis, Springer, vol. 32(1), pages 27-40, August.
    13. Nilsson, Lars J., 1993. "Energy intensity trends in 31 industrial and developing countries 1950–1988," Energy, Elsevier, vol. 18(4), pages 309-322.
    14. Satoshi Honma & Jin-Li Hu, 2011. "Industry-level Total-factor Energy Efficiency in Developed Countries," Discussion Papers 51, Kyushu Sangyo University, Faculty of Economics.
    15. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    16. 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.
    17. Vivoda, Vlado, 2012. "Japan’s energy security predicament post-Fukushima," Energy Policy, Elsevier, vol. 46(C), pages 135-143.
    18. Hu, Jin-Li & Kao, Chih-Hung, 2007. "Efficient energy-saving targets for APEC economies," Energy Policy, Elsevier, vol. 35(1), pages 373-382, January.
    19. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    20. Lozano, Sebastián & Gutiérrez, Ester, 2008. "Non-parametric frontier approach to modelling the relationships among population, GDP, energy consumption and CO2 emissions," Ecological Economics, Elsevier, vol. 66(4), pages 687-699, July.
    21. Sözen, Adnan & Alp, Ihsan, 2009. "Comparison of Turkey's performance of greenhouse gas emissions and local/regional pollutants with EU countries," Energy Policy, Elsevier, vol. 37(12), pages 5007-5018, December.
    22. Patterson, Murray G, 1996. "What is energy efficiency? : Concepts, indicators and methodological issues," Energy Policy, Elsevier, vol. 24(5), pages 377-390, May.
    23. Iglesias, Guillermo & Castellanos, Pablo & Seijas, Amparo, 2010. "Measurement of productive efficiency with frontier methods: A case study for wind farms," Energy Economics, Elsevier, vol. 32(5), pages 1199-1208, September.
    24. MORIKAWA Masayuki, 2012. "The Effects of the Great East Japan Earthquake and Policy Priorities for Restoring Economic Growth: Evidence from a survey of Japanese firms (Japanese)," Policy Discussion Papers (Japanese) 12010, Research Institute of Economy, Trade and Industry (RIETI).
    25. 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.
    26. 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.
    27. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    28. Peter Schmidt, 2011. "One-step and two-step estimation in SFA models," Journal of Productivity Analysis, Springer, vol. 36(2), pages 201-203, October.
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    Cited by:

    1. Wang, Jian & Lv, Kangjuan & Bian, Yiwen & Cheng, Yu, 2017. "Energy efficiency and marginal carbon dioxide emission abatement cost in urban China," Energy Policy, Elsevier, vol. 105(C), pages 246-255.
    2. repec:eee:energy:v:128:y:2017:i:c:p:575-585 is not listed on IDEAS
    3. Lin, Boqiang & Du, Kerui, 2015. "Modeling the dynamics of carbon emission performance in China: A parametric Malmquist index approach," Energy Economics, Elsevier, vol. 49(C), pages 550-557.
    4. Makridou, Georgia & Andriosopoulos, Kostas & Doumpos, Michael & Zopounidis, Constantin, 2016. "Measuring the efficiency of energy-intensive industries across European countries," Energy Policy, Elsevier, vol. 88(C), pages 573-583.
    5. Du, Kerui & Lin, Boqiang, 2017. "International comparison of total-factor energy productivity growth: A parametric Malmquist index approach," Energy, Elsevier, vol. 118(C), pages 481-488.
    6. repec:spr:annopr:v:255:y:2017:i:1:d:10.1007_s10479-015-2053-8 is not listed on IDEAS
    7. Jianglong Li & Boqiang Lin, 2016. "Green Economy Performance and Green Productivity Growth in China’s Cities: Measures and Policy Implication," Sustainability, MDPI, Open Access Journal, vol. 8(9), pages 1-21, September.

    More about this item


    Stochastic frontier analysis (SFA); Data envelopment analysis (DEA); Total-factor energy efficiency (TFEE); Panel data; Shephard distance functions;

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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