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An epsilon-based measure of efficiency in DEA revisited -A third pole of technical efficiency


  • Kaoru Tone

    (National Graduate Institute for Policy Studies)

  • Miki Tsutsui

    (Central Research Institute of Electric Power Industry)


In DEA, we have two measures of technical efficiency with different characteristics: radial and non-radial. In this paper we compile them into a composite model called “epsilon-based measure (EBM).” For this purpose we introduce two parameters which connect radial and non-radial models. These two parameters are obtained from the newly defined affinity index between inputs or outputs along with principal component analysis on the affinity matrix. Thus, EBM takes into account diversity of input/output data and their relative importance for measuring technical efficiency.

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  • Kaoru Tone & Miki Tsutsui, 2010. "An epsilon-based measure of efficiency in DEA revisited -A third pole of technical efficiency," GRIPS Discussion Papers 09-21, National Graduate Institute for Policy Studies.
  • Handle: RePEc:ngi:dpaper:09-21

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    Cited by:

    1. Wu, Peng & Wang, Yiqing & Chiu, Yung-ho & Li, Ying & Lin, Tai-Yu, 2019. "Production efficiency and geographical location of Chinese coal enterprises - undesirable EBM DEA," Resources Policy, Elsevier, vol. 64(C).
    2. A. M. Aldanondo & V. L. Casasnovas, 2015. "Input aggregation bias in technical efficiency with multiple criteria analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 22(6), pages 430-435, April.
    3. Liangen Zeng, 2021. "China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    4. Ke-Liang Wang & Jianguo Wang & Jianming Wang & Lili Ding & Mingsong Zhao & Qunwei Wang, 2020. "Investigating the spatiotemporal differences and influencing factors of green water use efficiency of Yangtze River Economic Belt in China," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-24, April.
    5. Ren, Fang-rong & Tian, Ze & Liu, Jingjing & Shen, Yu-ting, 2020. "Analysis of CO2 emission reduction contribution and efficiency of China’s solar photovoltaic industry: Based on Input-output perspective," Energy, Elsevier, vol. 199(C).
    6. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    7. Jin, Peizhen & Peng, Chong & Song, Malin, 2019. "Macroeconomic uncertainty, high-level innovation, and urban green development performance in China," China Economic Review, Elsevier, vol. 55(C), pages 1-18.
    8. Xingle Long & Chuanwang Sun & Chao Wu & Bin Chen & Kofi Agyenim Boateng, 2020. "Green innovation efficiency across China’s 30 provinces: estimate, comparison, and convergence," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(7), pages 1243-1260, October.
    9. Cheng, Gang & Qian, Zhenhua, 2011. "An epsilon-based measure of efficiency in DEA - An alternative method for the affinity index," MPRA Paper 41680, University Library of Munich, Germany, revised 06 Nov 2011.
    10. Cui, Qiang & Li, Ye, 2020. "A cross efficiency distinguishing method to explore the cooperation degree in dynamic airline environmental efficiency," Transport Policy, Elsevier, vol. 99(C), pages 31-43.
    11. Cui, Qiang & Li, Ye, 2017. "Airline efficiency measures using a Dynamic Epsilon-Based Measure model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 121-134.
    12. Xu, Zhongwen & Yao, Liming & Liu, Qiaoling & Long, Yin, 2019. "Policy implications for achieving the carbon emission reduction target by 2030 in Japan-Analysis based on a bilevel equilibrium model," Energy Policy, Elsevier, vol. 134(C).
    13. Zhou, Anhua & Li, Jun, 2021. "Investigate the impact of market reforms on the improvement of manufacturing energy efficiency under China’s provincial-level data," Energy, Elsevier, vol. 228(C).
    14. Yu, Yantuan & Huang, Jianhuan & Zhang, Ning, 2019. "Modeling the eco-efficiency of Chinese prefecture-level cities with regional heterogeneities: A comparative perspective," Ecological Modelling, Elsevier, vol. 402(C), pages 1-17.

    More about this item


    Data envelopment analysis; Radial; Non-radial; CCR; SBM; EBM; Principal component analysis;
    All these keywords.

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