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Evaluating the environmental efficiency of the U.S. airline industry using a directional distance function DEA approach

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  • Yuan Xu
  • Yong Shin Park
  • Ju Dong Park
  • Wonjoo Cho

Abstract

This study applies a directional distance function (DDF) data envelopment analysis (DEA) model to measure the environmental efficiency of 12 U.S. airlines 2013–2016 by considering flight delay and greenhouse gas (GHG) emissions as joint undesirable outputs. First, the environmental efficiency of airlines is compared using the CCR DEA (without flight delay) and DDF DEA (with flight delay). We find that several airlines experienced substantial changes in environmental efficiency scores when flight delay is considered. Secondly, a tobit regression is used to explore whether the environmental factors of fleet age, ownership type, freight traffic, market share, and carrier type affect airlines’ environmental efficiency. The results demonstrate that all of these factors significantly influence airline performance.

Suggested Citation

  • Yuan Xu & Yong Shin Park & Ju Dong Park & Wonjoo Cho, 2021. "Evaluating the environmental efficiency of the U.S. airline industry using a directional distance function DEA approach," Journal of Management Analytics, Taylor & Francis Journals, vol. 8(1), pages 1-18, January.
    • Handle: RePEc:taf:tjmaxx:v:8:y:2021:i:1:p:1-18
    DOI: 10.1080/23270012.2020.1832925
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    Cited by:

    1. Montacer Ben Cheikh Larbi & Sina Belkhiria, 2024. "Absolute Technical Efficiency Indices," Papers 2404.04590, arXiv.org.
    2. Yu, Ming-Miin & Rakshit, Ipsita, 2023. "Target setting for airlines incorporating CO2 emissions: The DEA bargaining approach," Journal of Air Transport Management, Elsevier, vol. 108(C).
    3. Hashem Omrani & Meisam Shamsi & Ali Emrouznejad, 2023. "Evaluating sustainable efficiency of decision-making units considering undesirable outputs: an application to airline using integrated multi-objective DEA-TOPSIS," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 5899-5930, July.
    4. Zhishuo Zhang & Yao Xiao & Huayong Niu, 2022. "DEA and Machine Learning for Performance Prediction," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    5. Vera Amicarelli & Giovanni Lagioia & Antonio Patruno & Raluca Mariana Grosu & Christian Bux, 2021. "Enhancing the Sustainability of the Aviation Industry: Airlines’ Commitment to “Green” Practices," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(Special15), pages 934-934, November.

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