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DEA Models with Undesirable Inputs, Intermediates, and Outputs

In: Data Envelopment Analysis

Author

Listed:
  • Zhongbao Zhou

    (Hunan University)

  • Wenbin Liu

    (Hunan University
    University of Kent)

Abstract

In real applications involving the use of Data Envelopment Analysis (DEA) models, undesirable inputs and outputs have been frequently encountered and addressed, e.g., via data transformation. These studies were scattered in the literature and often confined to some particular applications. In this paper, we present a systematic investigation concerning the building of DEA models. First, we describe the desirability of inputs and outputs, as well as the disposability assumptions in the presence of undesirable inputs and outputs. Next we construct a number of DEA models with different disposability assumptions and performance measures for the case of single-stage DEA. Next, we try to systematically investigate two-stage DEA models with undesirable inputs, intermediates and outputs. Particularly, we utilize the free-disposal axioms to construct the production possibility sets and the corresponding DEA models with undesirable inputs, intermediates, and outputs.

Suggested Citation

  • Zhongbao Zhou & Wenbin Liu, 2015. "DEA Models with Undesirable Inputs, Intermediates, and Outputs," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 15, pages 415-446, Springer.
    • Handle: RePEc:spr:isochp:978-1-4899-7553-9_15
    DOI: 10.1007/978-1-4899-7553-9_15
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    Citations

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    Cited by:

    1. Harald Dyckhoff, 2018. "Multi-criteria production theory: foundation of non-financial and sustainability performance evaluation," Journal of Business Economics, Springer, vol. 88(7), pages 851-882, September.
    2. Dyckhoff, Harald & Souren, Rainer, 2022. "Integrating multiple criteria decision analysis and production theory for performance evaluation: Framework and review," European Journal of Operational Research, Elsevier, vol. 297(3), pages 795-816.
    3. Sebastian Cuadros & Yeny E. Rodríguez & Javier Contreras, 2020. "Dynamic Data Envelopment Analysis Model Involving Undesirable Outputs in the Electricity Power Generation Sector: The Case of Latin America and the Caribbean Countries," Energies, MDPI, vol. 13(24), pages 1-20, December.
    4. Victoria Wojcik & Harald Dyckhoff & Sebastian Gutgesell, 2017. "The desirable input of undesirable factors in data envelopment analysis," Annals of Operations Research, Springer, vol. 259(1), pages 461-484, December.
    5. Pooja Bansal & Aparna Mehra, 2018. "Multi-period additive efficiency measurement in data envelopment analysis with non-positive and undesirable data," OPSEARCH, Springer;Operational Research Society of India, vol. 55(3), pages 642-661, November.
    6. Fenfen Li & Bo Dai & Qifan Wu, 2021. "Dynamic Green Growth Assessment of China’s Industrial System with an Improved SBM Model and Global Malmquist Index," Mathematics, MDPI, vol. 9(20), pages 1-26, October.
    7. Malte L. Peters & Stephan Zelewski, 2021. "Upper and lower satisficing levels in efficiency analysis: a corporate social responsibility perspective," Sustainability Nexus Forum, Springer, vol. 29(3), pages 187-195, December.

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