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Measuring inefficiency in the presence of bad outputs: Does the disposability assumption matter?

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  • Benjamin Hampf

    (Darmstadt University of Technology)

Abstract

Multiple approaches to incorporate environmental pollution in production technologies have been proposed. In this paper, we analyze the consequences of these theoretical models for applied research. Our comparison focuses on alternative disposability assumptions imposed on bad outputs and their effects on the measurement of productive efficiency. Using empirical data on electricity generation in the US, we quantify and analyze differences among the theoretical models. Moreover, Monte Carlo simulations are conducted to evaluate the ability of the models to correctly estimate inefficiency given varying specifications of the pollution-generating process.

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  • 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.
    • Handle: RePEc:spr:empeco:v:54:y:2018:i:1:d:10.1007_s00181-016-1204-3
    DOI: 10.1007/s00181-016-1204-3
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    Cited by:

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    2. Niu, Yiran & Boussemart, Jean-Philippe & Shen, Zhiyang & Vardanyan, Michael, 2024. "Performance evaluation using multi-stage production frameworks: Assessing the tradeoffs among the economic, environmental, and social well-being," European Journal of Operational Research, Elsevier, vol. 318(3), pages 1000-1013.
    3. Andreas Eder, 2022. "Environmental efficiency measurement when producers control pollutants under heterogeneous conditions: a generalization of the materials balance approach," Journal of Productivity Analysis, Springer, vol. 57(2), pages 157-176, April.
    4. repec:zbw:inwedp:752021 is not listed on IDEAS
    5. Fang, Lei, 2020. "Opening the “black box” of environmental production technology in a nonparametric analysis," European Journal of Operational Research, Elsevier, vol. 286(2), pages 769-780.
    6. Jean-Philippe Boussemart & Hervé Leleu & Zhiyang Shen & Vivian Valdmanis, 2020. "Performance analysis for three pillars of sustainability," Journal of Productivity Analysis, Springer, vol. 53(3), pages 305-320, June.
    7. Andreas Eder, 2021. "Environmental efficiency measurement when producers control pollutants under heterogeneous conditions: a generalization of the materials balance approach," Working Papers 752021, University of Natural Resources and Life Sciences, Vienna, Department of Economics and Social Sciences, Institute for Sustainable Economic Development.
    8. Zhongfei Chen & Stavros Kourtzidis & Panayiotis Tzeremes & Nickolaos Tzeremes, 2022. "A robust network DEA model for sustainability assessment: an application to Chinese Provinces," Operational Research, Springer, vol. 22(1), pages 235-262, March.
    9. Wang, Derek D. & Ren, Yaoyao, 2024. "Accuracy of Deterministic Nonparametric Frontier Models with Undesirable Outputs," European Journal of Operational Research, Elsevier, vol. 315(2), pages 596-612.
    10. N. Torabi Golsefid & M. Salahi, 2023. "An SOCP approach to a two-stage network DEA with feedbacks and shared resources," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1153-1178, September.
    11. Ahmad, Shabbir & Steen, John & Ali, Saleem & Valenta, Rick, 2023. "Carbon-adjusted efficiency and technology gaps in gold mining," Resources Policy, Elsevier, vol. 81(C).
    12. Hampf, Benjamin & Rødseth, Kenneth Løvold, 2019. "Environmental efficiency measurement with heterogeneous input quality: A nonparametric analysis of U.S. power plants," Energy Economics, Elsevier, vol. 81(C), pages 610-625.

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    More about this item

    Keywords

    Environmental pollution; Nonparametric efficiency analysis; Technology modeling; Disposability assumptions;
    All these keywords.

    JEL classification:

    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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