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Japanese fuel mix strategy after disaster of Fukushima Daiichi nuclear power plant: Lessons from international comparison among industrial nations measured by DEA environmental assessment in time horizon

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  • Sueyoshi, Toshiyuki
  • Goto, Mika

Abstract

On June 1, 2015, the Japanese government has announced that the fuel mix will consist of nuclear generation with a range between 20% and 22% and renewable generation with a range between 22% and 24% of the total electricity generation by 2030. A difficulty in understanding the governmental future energy plan is that it does not contain any scientific evidence concerning why Japan needs to maintain the amount of nuclear generation and that of renewable at the level between 20% and 22% and between 22% and 24%, respectively. To investigate the future energy plan, this study uses Data Envelopment Analysis (DEA) as a methodology. The proposed DEA approach incorporates the property of “translation invariance”. The property indicates that an efficiency measure should not be influenced even if inputs and/or outputs are shifted toward a same direction by adding or subtracting a specific real number. The property makes it possible that we can evaluate the performance of organizations, whose production factors (i.e., inputs, desirable and undesirable outputs) contain many zeros and negative values in a data set. Such an occurrence of zero and negative in production factors is widely observed in many DEA performance evaluations. This study first uses the desirable property for DEA environmental assessment in a time horizon. The proposed DEA environmental assessment identifies that the reasonable ranges for Japanese future fuel mix are (a) combustible fuel generation within the range between 34.5% and 56.1%, (b) hydro generation within the range between 22.4% and 40.5%, (c) nuclear generation within the range between 10.4% and 13.7%, (d) pumped hydro generation within the range between 3.9% and 6.9% and (e) renewable generation within the range between 3.7% and 8.4%, all of which are measured by these generation capacities. It is clearly identified that the future fuel mix proposed by the Japanese government is inconsistent with the estimated one, which we determine by relatively comparing it with the energy structures of 33 industrial nations. The comparison indicates that the Japanese future energy plan will be too ambitious to implement the future goal. It is easily envisioned that the energy plan will have a considerable difficulty in future. It is hoped that the electric power industry can access drastic technological advancements, including eco-technology innovation, and then Japan will be able to change the future direction on fuel mix as the government expects.

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  • Sueyoshi, Toshiyuki & Goto, Mika, 2015. "Japanese fuel mix strategy after disaster of Fukushima Daiichi nuclear power plant: Lessons from international comparison among industrial nations measured by DEA environmental assessment in time hori," Energy Economics, Elsevier, vol. 52(PA), pages 87-103.
  • Handle: RePEc:eee:eneeco:v:52:y:2015:i:pa:p:87-103
    DOI: 10.1016/j.eneco.2015.09.001
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    Cited by:

    1. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    2. Toshiyuki Sueyoshi & Yan Yuan & Aijun Li & Daoping Wang, 2017. "Social Sustainability of Provinces in China: A Data Envelopment Analysis (DEA) Window Analysis under the Concepts of Natural and Managerial Disposability," Sustainability, MDPI, vol. 9(11), pages 1-18, November.
    3. Ying Li & Yung-ho Chiu & Tai-Yu Lin, 2019. "Research on New and Traditional Energy Sources in OECD Countries," IJERPH, MDPI, vol. 16(7), pages 1-21, March.
    4. Jie Liu & Chunhui Yuan & Xiaolong Li, 2019. "The Environmental Assessment on Chinese Logistics Enterprises Based on Non-Radial DEA," Energies, MDPI, vol. 12(24), pages 1-18, December.
    5. Sueyoshi, Toshiyuki & Yuan, Yan, 2016. "Returns to damage under undesirable congestion and damages to return under desirable congestion measured by DEA environmental assessment with multiplier restriction: Economic and energy planning for s," Energy Economics, Elsevier, vol. 56(C), pages 288-309.
    6. Sueyoshi, Toshiyuki & Goto, Mika, 2018. "Resource utilization for sustainability enhancement in Japanese industries," Applied Energy, Elsevier, vol. 228(C), pages 2308-2320.
    7. Sueyoshi, Toshiyuki & Goto, Mika & Wang, Derek, 2017. "Malmquist index measurement for sustainability enhancement in Chinese municipalities and provinces," Energy Economics, Elsevier, vol. 67(C), pages 554-571.
    8. Sueyoshi, Toshiyuki & Yuan, Yan, 2017. "Social sustainability measured by intermediate approach for DEA environmental assessment: Chinese regional planning for economic development and pollution prevention," Energy Economics, Elsevier, vol. 66(C), pages 154-166.
    9. Sueyoshi, Toshiyuki & Wang, Derek, 2018. "DEA environmental assessment on US petroleum industry: Non-radial approach with translation invariance in time horizon," Energy Economics, Elsevier, vol. 72(C), pages 276-289.
    10. Sun, Chuanwang & Liu, Xiaohong & Li, Aijun, 2018. "Measuring unified efficiency of Chinese fossil fuel power plants: Intermediate approach combined with group heterogeneity and window analysis," Energy Policy, Elsevier, vol. 123(C), pages 8-18.
    11. Aizhen Zhang & Aijun Li & Yaping Gao, 2018. "Social Sustainability Assessment across Provinces in China: An Analysis of Combining Intermediate Approach with Data Envelopment Analysis (DEA) Window Analysis," Sustainability, MDPI, vol. 10(3), pages 1-24, March.
    12. Sueyoshi, Toshiyuki & Goto, Mika, 2016. "Undesirable congestion under natural disposability and desirable congestion under managerial disposability in U.S. electric power industry measured by DEA environmental assessment," Energy Economics, Elsevier, vol. 55(C), pages 173-188.
    13. Zhang, Haoran & Song, Xuan & Xia, Tianqi & Yuan, Meng & Fan, Zipei & Shibasaki, Ryosuke & Liang, Yongtu, 2018. "Battery electric vehicles in Japan: Human mobile behavior based adoption potential analysis and policy target response," Applied Energy, Elsevier, vol. 220(C), pages 527-535.
    14. Sueyoshi, Toshiyuki & Goto, Mika, 2017. "Measurement of returns to scale on large photovoltaic power stations in the United States and Germany," Energy Economics, Elsevier, vol. 64(C), pages 306-320.
    15. Sueyoshi, Toshiyuki & Yuan, Yan, 2015. "Comparison among U.S. industrial sectors by DEA environmental assessment: Equipped with analytical capability to handle zero or negative in production factors," Energy Economics, Elsevier, vol. 52(PA), pages 69-86.
    16. Sueyoshi, Toshiyuki & Yuan, Yan & Li, Aijun & Wang, Daoping, 2017. "Methodological comparison among radial, non-radial and intermediate approaches for DEA environmental assessment," Energy Economics, Elsevier, vol. 67(C), pages 439-453.
    17. Sueyoshi, Toshiyuki & Wang, Derek, 2017. "Measuring scale efficiency and returns to scale on large commercial rooftop photovoltaic systems in California," Energy Economics, Elsevier, vol. 65(C), pages 389-398.
    18. Sueyoshi, Toshiyuki & Yuan, Yan, 2016. "Marginal Rate of Transformation and Rate of Substitution measured by DEA environmental assessment: Comparison among European and North American nations," Energy Economics, Elsevier, vol. 56(C), pages 270-287.
    19. Toshiyuki Sueyoshi & Youngbok Ryu & Mika Goto, 2020. "Operational Performance of Electric Power Firms: Comparison between Japan and South Korea by Non-Radial Measures," Energies, MDPI, vol. 13(15), pages 1-23, August.
    20. Zhou, Haibo & Yang, Yi & Chen, Yao & Zhu, Joe, 2018. "Data envelopment analysis application in sustainability: The origins, development and future directions," European Journal of Operational Research, Elsevier, vol. 264(1), pages 1-16.

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    More about this item

    Keywords

    Fuel mix; DEA; Environmental assessment; Fukushima Daiichi;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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