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A meta-stochastic frontier analysis for energy efficiency of regions in Japan

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  • Satoshi Honma

    (Tokai University)

  • Jin-Li Hu

    (National Chiao Tung University)

Abstract

This paper measures the metafrontier total-factor energy efficiency (TFEE) of 47 regions in Japan for the period 1996–2008, using the stochastic frontier analysis (SFA). The two-step output-oriented SFA approach by Huang et al. (J Prod Anal 42:241–254, 2014) is followed but converted into a two-step input-oriented SFA approach. The metafrontier TFEE is defined as a product of the group TFEE and the technological gap ratio (TGR). The mean group TFEE is smaller than the mean TGR for both the groups, which shows that the energy inefficiency in Japanese regions with respect to the metafrontier comes from primarily operating inefficiency, rather technology gap. The mean metafrontier TFEE of the metropolitan areas is smaller than that of rural areas, implying that the former is energy inefficient than the latter. The mean TGR of the metropolitan areas is also smaller than that of rural areas, implying that many Japanese regions with major cities are far below the metafrontier and still have much room for energy savings.

Suggested Citation

  • Satoshi Honma & Jin-Li Hu, 2018. "A meta-stochastic frontier analysis for energy efficiency of regions in Japan," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 7(1), pages 1-16, December.
    • Handle: RePEc:spr:jecstr:v:7:y:2018:i:1:d:10.1186_s40008-018-0119-x
    DOI: 10.1186/s40008-018-0119-x
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    References listed on IDEAS

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    1. Cliff Huang & Tai-Hsin Huang & Nan-Hung Liu, 2014. "A new approach to estimating the metafrontier production function based on a stochastic frontier framework," Journal of Productivity Analysis, Springer, vol. 42(3), pages 241-254, December.
    2. 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.
    3. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    4. Satoshi Honma & Jin-Li Hu, 2011. "Industry-level Total-factor Energy Efficiency in Developed Countries," Discussion Papers 51, Kyushu Sangyo University, Faculty of Economics.
    5. 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.
    6. Johnstone, Nick & Managi, Shunsuke & Rodríguez, Miguel Cárdenas & Haščič, Ivan & Fujii, Hidemichi & Souchier, Martin, 2017. "Environmental policy design, innovation and efficiency gains in electricity generation," Energy Economics, Elsevier, vol. 63(C), pages 106-115.
    7. Casler, Stephen & Hannon, Bruce, 1989. "Readjustment potentials in industrial energy efficiency and structure," Journal of Environmental Economics and Management, Elsevier, vol. 17(1), pages 93-108, July.
    8. Honma, Satoshi & Hu, Jin-Li, 2014. "A panel data parametric frontier technique for measuring total-factor energy efficiency: An application to Japanese regions," Energy, Elsevier, vol. 78(C), pages 732-739.
    9. 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.
    10. Li, Lan-Bing & Hu, Jin-Li, 2012. "Ecological total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 46(C), pages 216-224.
    11. Yi-Ming Wei & Hua Liao (ed.), 2016. "Energy Economics: Energy Efficiency in China," CEEP-BIT Books, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology, number b5, december.
    12. Honma, Satoshi & Hu, Jin-Li, 2008. "Total-factor energy efficiency of regions in Japan," Energy Policy, Elsevier, vol. 36(2), pages 821-833, February.
    13. Ana Medina & Ángeles Cámara & José-Ramón Monrobel, 2016. "Measuring the Socioeconomic and Environmental Effects of Energy Efficiency Investments for a More Sustainable Spanish Economy," Sustainability, MDPI, vol. 8(10), pages 1-21, October.
    14. Hayami, Yujiro & Ruttan, Vernon W, 1970. "Agricultural Productivity Differences Among Countries," American Economic Review, American Economic Association, vol. 60(5), pages 895-911, December.
    15. Surender Kumar & Hidemichi Fujii & Shunsuke Managi, 2015. "Substitute or complement? Assessing renewable and nonrenewable energy in OECD countries," Applied Economics, Taylor & Francis Journals, vol. 47(14), pages 1438-1459, March.
    16. 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.
    17. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    18. Fang, Chin-Yi & Hu, Jin-Li & Lou, Tze-Kai, 2013. "Environment-adjusted total-factor energy efficiency of Taiwan's service sectors," Energy Policy, Elsevier, vol. 63(C), pages 1160-1168.
    19. Hu, Jin-Li & Kao, Chih-Hung, 2007. "Efficient energy-saving targets for APEC economies," Energy Policy, Elsevier, vol. 35(1), pages 373-382, January.
    20. Tamaki, Tetsuya & Shin, Kong Joo & Nakamura, Hiroki & Fujii, Hidemichi & Managi, Shunsuke, 2018. "Shadow prices and production inefficiency of mineral resources," Economic Analysis and Policy, Elsevier, vol. 57(C), pages 111-121.
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