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Sources of airline productivity from carbon emissions: an analysis of operational performance under good and bad outputs

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Abstract

This study incorporates carbon dioxide emissions in productivity measurement in the airline industry and examines the determinants of productivity change. For this purpose a two-stage analysis under joint production of good and bad outputs is employed to compare the operational performance of airlines. In the first stage, productivity index are derived using the Luenberger productivity indicator. In the second stage, productivity change scores derived therefrom are regressed using the random-effects Generalized Least Squares to quantify determinants of productivity change. The paper finds low cost carriers and average number of hours flown per aircraft having a positive impact on productivity under joint production model while demand variable negatively impacts on productivity under market model.

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Notes

  1. The above selection is only a modest list solely to limit the number of pages of this article for brevity purpose.

  2. The term ‘productivity’ used in this article does not refer to total-factor productivity but simply a general term. TFP is not referred to in this article because the Malmquist index of Caves et al. (1982) and other derivatives (e.g. Luenberger) are not TFP indices as argued in O’Donnell (2010, 2012) and Peyrache (2014).

  3. A complete list of the axioms imposed on the technologies specified in this paper is found in Färe et al. (2007).

  4. The rationale for constant returns to scale is that it is consistent with the vast majority of airline literature such as White (1979), Cornwell et al (1990), Good et al. (1995) and Sickles et al. (2002). As empirically demonstrated in Caves et al. (1984) that U.S. large and small carriers could compete with one another over extended periods of time, an observation that is consistent with constant returns to scale.

  5. According to the U.S. Department of Transportation, Centre for Climate Change and Environmental Forecasting, CO2 constitutes roughly 70% of aircraft engine emissions. Although other pollutants such as NOx are produced, we only consider CO2 as this is the main pollutant emitted by airlines (Mendes and Santos 2008).

  6. These include Barros and Peypoch (2009) and Assaf and Josiassen (2011).

  7. WLF includes tonnage of passengers, freight and mail. Hence, we do not consider passenger load factor since this is already included in WLF.

  8. We also estimate each indicator applying 1000 times bootstrap approach. Bootstrap approach in nonparametric frontier analysis allows us to calculate confidence intervals and statistical significance level (Simar and Wilson 2000; Jeon and Sickles 2004). The bootstrap estimation is widely applied in nonparametric frontier analysis with undesirable output (Yagi et al. 2015). We described the results of bootstrap estimation in Appendix Tables 9, 10, 11, 12, 13 and 14. From the bootstrap estimation results, we confirm the consistent trend between the results in Tables 3 and 4, and confidence intervals in Appendix Tables 9, 10, 11, 12, 13 and 14.

  9. As alternative specifications, we run a generalised method of moments (GMM) (see Managi and Jena 2008). The results do not pass a Sargan test. In general, the estimated results are similar but less significant. This reflects smaller sample because we apply two year lag as instruments. These results are presented in Appendix Tables 15 and 16

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Acknowledgments

Any errors, opinions, or conclusions are the authors’ and should not be attributed to the U.S. Environmental Protection Agency. The authors would like to thank the two anonymous referees for providing useful comments and improving the paper.

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Authors and Affiliations

  1. School of Economics and Finance, Queensland University of Technology, Brisbane, QLD, Australia

    Boon Liat Lee, Clevo Wilson & Shunsuke Managi

  2. U.S. Environmental Protection Agency (1809T), Office of Policy, 1200 Pennsylvania Ave., N.W., Washington, DC, 20460, USA

    Carl A. Pasurka Jr.

  3. Graduate School of Fisheries Science and Environmental Studies, Nagasaki University, Nagasaki, Japan

    Hidemichi Fujii

  4. Departments of Urban and Environmental Engineering, School of Engineering, Urban Institute and Engineering, Kyushu University, Fukuoka, Japan

    Shunsuke Managi

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  1. Boon Liat Lee
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  2. Clevo Wilson
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  4. Hidemichi Fujii
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  5. Shunsuke Managi
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Correspondence to Boon Liat Lee.

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Appendix

Appendix

See Tables 9, 10, 11, 12, 13, 14, 15 and 16

Table 9 Luenberger indicator estimated from Joint model with 1000 times bootstrap estimation
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Table 10 Efficiency change indicator estimated from Joint model with 1000 times bootstrap estimation
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Table 11 Technical change indicator estimated from Joint model with 1000 times bootstrap estimation
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Table 12 Luenberger indicator estimated from Market model with 1000 times bootstrap estimation
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Table 13 Efficiency change indicator estimated from Market model with 1000 times bootstrap estimation
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Table 14 Technical change indicator estimated from Market model with 1000 times bootstrap estimation
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Table 15 System GMM estimation results (dependent variables are estimated by joint model)
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Table 16 System GMM estimation results (Dependent variables are estimated by market model)
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Lee, B.L., Wilson, C., Pasurka, C.A. et al. Sources of airline productivity from carbon emissions: an analysis of operational performance under good and bad outputs. J Prod Anal 47, 223–246 (2017). https://doi.org/10.1007/s11123-016-0480-4

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