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DEA with Undesirable Factors

In: Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis

Author

Listed:
  • Zhongsheng Hua

    (University of Science and Technology of China)

  • Yiwen Bian

    (Shanghai University)

Abstract

The standard Data Envelopment Analysis (DEA) models rely on the assumption that inputs are minimized and outputs are maximized. However, when some inputs or outputs are undesirable factors (e.g., pollutants or wastes), these outputs (inputs) should be reduced (increased) to improve inefficiency. This chapter discusses the existing methods of treating undesirable factors in DEA. Under strongly disposable technology and weakly disposable technology, there are at least three approaches of treating undesirable outputs in the DEA literature. The first approach is the hyperbolic output efficiency measure that increases desirable outputs and decreases undesirable outputs simultaneously. Based on the classification invariance property, a linear monotone decreasing transformation is used to treat the undesirable outputs. A directional distance function is used to estimate the efficiency scores based on weak disposability of undesirable outputs. This chapter also presents an extended DEA model in which undesirable outputs and non-discretionary inputs are considered simultaneously.

Suggested Citation

  • Zhongsheng Hua & Yiwen Bian, 2007. "DEA with Undesirable Factors," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 103-121, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-71607-7_6
    DOI: 10.1007/978-0-387-71607-7_6
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    Citations

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    Cited by:

    1. Chen, Po-Chi & Yu, Ming-Miin & Chang, Ching-Cheng & Hsu, Shih-Hsun & Managi, Shunsuke, 2015. "The enhanced Russell-based directional distance measure with undesirable outputs: Numerical example considering CO2 emissions," Omega, Elsevier, vol. 53(C), pages 30-40.
    2. Cordero Ferrera, Jose Manuel & Alonso Morán, Edurne & Nuño Solís, Roberto & Orueta, Juan F. & Souto Arce, Regina, 2013. "Efficiency assessment of primary care providers: A conditional nonparametric approach," MPRA Paper 51926, University Library of Munich, Germany.
    3. Leleu, Hervé, 2013. "Shadow pricing of undesirable outputs in nonparametric analysis," European Journal of Operational Research, Elsevier, vol. 231(2), pages 474-480.
    4. Sartori, Simone & Witjes, Sjors & Campos, Lucila M.S., 2017. "Sustainability performance for Brazilian electricity power industry: An assessment integrating social, economic and environmental issues," Energy Policy, Elsevier, vol. 111(C), pages 41-51.
    5. Benjamin Hampf, 2018. "Measuring inefficiency in the presence of bad outputs: Does the disposability assumption matter?," Empirical Economics, Springer, vol. 54(1), pages 101-127, February.
    6. Murat Bilsel & Nurhan Davutyan, 2014. "Hospital efficiency with risk adjusted mortality as undesirable output: the Turkish case," Annals of Operations Research, Springer, vol. 221(1), pages 73-88, October.
    7. Chien-Ming Chen & Magali A. Delmas, 2012. "Measuring Eco-Inefficiency: A New Frontier Approach," Operations Research, INFORMS, vol. 60(5), pages 1064-1079, October.
    8. Assaf, A. George & Tsionas, Mike G., 2019. "A review of research into performance modeling in tourism research - Launching the Annals of Tourism Research curated collection on performance modeling in tourism research," Annals of Tourism Research, Elsevier, vol. 76(C), pages 266-277.
    9. Gulati, Rachita & Charles, Vincent & Hassan, M. Kabir & Kumar, Sunil, 2023. "COVID-19 crisis and the efficiency of Indian banks: Have they weathered the storm?," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    10. Ngobeni, Victor & Breitenbach, Marthinus C, 2021. "Production and Scale Efficiency of South African Water Utilities: The Case of Water Boards," MPRA Paper 106242, University Library of Munich, Germany.
    11. Djula Borozan, 2021. "Technical Efficiency and Productivity Change in the European Union with Undesirable Output Considered," Energies, MDPI, vol. 14(16), pages 1-15, August.
    12. Cordero, José Manuel & Alonso-Morán, Edurne & Nuño-Solinis, Roberto & Orueta, Juan F. & Arce, Regina Sauto, 2015. "Efficiency assessment of primary care providers: A conditional nonparametric approach," European Journal of Operational Research, Elsevier, vol. 240(1), pages 235-244.
    13. Ahmad, Shabbir & Steen, John & Ali, Saleem & Valenta, Rick, 2023. "Carbon-adjusted efficiency and technology gaps in gold mining," Resources Policy, Elsevier, vol. 81(C).
    14. Breitenbach, Marthinus C & Ngobeni, Victor & Aye, Goodness C, 2020. "Global Healthcare Resource Efficiency in the Management of COVID-19 Death and Infection Prevalence Rates," MPRA Paper 104814, University Library of Munich, Germany.
    15. Aydın Özdemir & Hakan Kitapçı & Mehmet Şahin Gök & Erşan Ciğerim, 2021. "Efficiency Assessment of Operations Strategy Matrix in Healthcare Systems of US States Amid COVID-19: Implications for Sustainable Development Goals," Sustainability, MDPI, vol. 13(21), pages 1-17, October.

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