Skip to main content

Advertisement

Log in

Airline energy efficiency measures using a network range-adjusted measure with unified natural and managerial disposability

  • Original Article
  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

In recent years, the issue of aviation carbon emissions has caused wide public concern. How to measure airline performance under the premise of considering aviation carbon emissions has become a hot issue. In this paper, airline energy efficiency is divided into three stages as follows: operations, services, and sales. A network range-adjusted measure with unified natural and managerial disposability model is proposed to calculate the environmental efficiencies of 29 airlines from 2008 to 2015. We get some interesting results. (1) Scandinavian has the highest overall efficiency among these 29 airlines during 2008–2015. (2) For the other 28 airlines, the improvement of the undesirable outputs is not the most urgent work to improve overall efficiency. (3) The overall efficiency has no obvious fluctuation in this period.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Aida, K., Cooper, W. W., Pastor, J. T., & Sueyoshid, T. (1998). Evaluating water supply services in Japan with RAM: A range-adjusted measure of inefficiency. Omega, 26(2), 207–232.

    Google Scholar 

  • Alam, I. M. S., & Sickles, R. C. (1998). The relationship between stock market returns and technical efficiency innovations: Evidence from the US airline industry. Journal of Productivity Analysis, 9(1), 35–51.

    Google Scholar 

  • Avkiran, N. K., & McCrystal, A. (2012). Sensitivity analysis of network DEA: NSBM versus NRAM. Applied Mathematics and Computation, 218(22), 11226–11239.

    Google Scholar 

  • Bhadra, D. (2009). Race to the bottom or swimming upstream: Performance analysis of US airlines. Journal of Air Transport Management, 15, 227–235.

    Google Scholar 

  • Chang, Y. T., Park, H. S., Jeong, J. B., & Lee, J. W. (2014). Evaluating economic and environmental efficiency of global airlines: A SBM-DEA approach. Transportation Research Part D, 27, 46–50.

    Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    MathSciNet  MATH  Google Scholar 

  • Chiou, Y. C., & Chen, Y. H. (2006). Route-based performance evaluation of Taiwanese domestic airlines using data envelopment analysis. Transportation Research Part E, 42(2), 116–127.

    Google Scholar 

  • Cooper, W. W., Park, K. S., & Pastor, J. T. (1999). RAM: A range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. Journal of Productivity Analysis, 11(1), 5–42.

    Google Scholar 

  • Cui, Q., & Li, Y. (2015a). Evaluating energy efficiency for airlines: An application of VFB-DEA. Journal of Air Transport Management, 44-45, 34–41.

    Google Scholar 

  • Cui, Q., & Li, Y. (2015b). The change trend and influencing factors of civil aviation safety efficiency: The case of Chinese airline companies. Safety Science, 75, 56–63.

    Google Scholar 

  • Cui, Q., & Li, Y. (2016). Airline energy efficiency measures considering carbon abatement: A new strategic framework. Transportation Research Part D, 49, 246–258.

    Google Scholar 

  • Cui, Q., & Li, Y. (2017a). Will airline efficiency be affected by “carbon neutral growth from 2020” strategy? Evidences from 29 international airlines. Journal of Cleaner Production, 164, 1289–1300.

    Google Scholar 

  • Cui, Q., & Li, Y. (2017b). CNG2020 strategy and airline efficiency: A network epsilon-based measure with managerial disposability. International Journal of Sustainable Transportation, 1–11.

  • Cui, Q., & Li, Y. (2017c). Airline efficiency measures under CNG2020 strategy: An application of a dynamic by-production model. Transportation Research Part A, 106, 130–143.

    Google Scholar 

  • Cui, Q., & Li, Y. (2018a). Airline dynamic efficiency measures with a dynamic RAM with unified natural & managerial disposability. Energy Economics, 75, 534–546.

    Google Scholar 

  • Cui, Q., & Li, Y. (2018b). Airline environmental efficiency measures considering materials balance principles: An application of a network range-adjusted measure with weak-G disposability. Journal of Environmental Planning and Management, 61(13), 2298–2318.

    Google Scholar 

  • Cui, Q., Kuang, H. B., Wu, C. Y., & Li, Y. (2013). Dynamic formation mechanism of airport competitiveness: The case of China. Transportation Research Part A, 47(1), 10–18.

    Google Scholar 

  • Cui, Q., Wei, Y. M., & Li, Y. (2016a). Exploring the impacts of the EU ETS emission limits on airline performance via the dynamic environmental DEA approach. Applied Energy, 183, 984–994.

    Google Scholar 

  • Cui, Q., Li, Y., Yu, C. L., & Wei, Y. M. (2016b). Evaluating energy efficiency for airlines: An application of virtual frontier dynamic slacks based measure. Energy, 113, 1231–1240.

    Google Scholar 

  • Dakpo, K. H., Jeanneaux, P., & Latruffe, L. (2016). Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric framework. European Journal of Operational Research, 250(2), 347–359.

    MathSciNet  MATH  Google Scholar 

  • Färe, R., Grosskopf, S., & Pasurka Jr., C. A. (2007). Environmental production functions and environmental directional distance functions. Energy, 32, 1055–1066.

    Google Scholar 

  • Good, D. H., Röller, L. H., & Sickles, R. C. (1995). Airline efficiency differences between Europe and the US: Implications for the pace of EC integration and domestic regulation. European Journal of Operational Research, 80(3), 508–518.

    MATH  Google Scholar 

  • Gramani, M. C. N. (2012). Efficiency decomposition approach: A cross-country airline analysis. Expert Systems with Applications, 39(5), 5815–5819.

    Google Scholar 

  • Hailu, A., & Veeman, T. S. (2001). Non-parametric productivity analysis with undesirable outputs: An application to the Canadian pulp and paper industry. American Journal of Agricultural Economics, 83(3), 605–616.

    Google Scholar 

  • Hampf, B., & Rødseth, K. L. (2015). Carbon dioxide emission standards for U.S. power plants: An efficiency analysis perspective. Energy Economics, 50, 140–153.

    Google Scholar 

  • Li, Y., & Cui, Q. (2017). Carbon neutral growth from 2020 strategy and airline environmental inefficiency: A network range adjusted environmental data envelopment analysis. Applied Energy, 199, 13–24.

    Google Scholar 

  • Li, Y., Wang, Y., & Cui, Q. (2015). Evaluating airline efficiency: An application of virtual frontier network SBM. Transportation Research Part E, 81, 1–17.

    Google Scholar 

  • Li, Y., Wang, Y., & Cui, Q. (2016a). Has airline efficiency affected by the inclusion of aviation into European Union emission trading scheme? Evidences from 22 airlines during 2008-2012. Energy, 96c, 8–22.

    Google Scholar 

  • Li, Y., Wang, Y., & Cui, Q. (2016b). Energy efficiency measures for airlines: An application of virtual frontier dynamic range adjusted measure. Journal of Renewable and Sustainable Energy, 8(1), 1–13.

    Google Scholar 

  • Lozano, S., & Gutiérrez, E. (2014). A slacks-based network DEA efficiency analysis of European airlines. Transportation Planning and Technology, 37(7), 623–637.

    Google Scholar 

  • Lu, W. M., Wang, W. K., Hung, S. W., & Lu, E. T. (2012). The effects of corporate governance on airline performance: Production and marketing efficiency perspectives. Transportation Research Part E, 48(2), 529–544.

    Google Scholar 

  • Mallikarjun, S. (2015). Efficiency of US airlines: A strategic operating model. Journal of Air Transport Management, 43, 46–56.

    Google Scholar 

  • Murty, S., Russell, R. R., & Levkoff, S. B. (2012). On modeling pollution-generating technologies. Journal of Environmental Economics and Management, 64(1), 117–135.

    Google Scholar 

  • Ray, S. C., & Mukherjee, K. (1996). Decomposition of the fisher ideal index of productivity: A non-parametric dual analysis of US airlines data. The Economic Journal, 106, 1659–1678.

    Google Scholar 

  • Rødseth, K. L., & Romstad, E. (2013). Environmental regulations, producer responses, and secondary benefits: Carbon dioxide reductions under the acid rain program. Environmental and Resource Economics, 59, 111–135.

    Google Scholar 

  • Sausen, R., Isaksen, I., Grewe, V., Hauglustaine, D., Lee, S. D., Myhre, G., Kohler, M. O., Pitan, G., Shumann, U., Stordal, F., & Zerefos, C. (2005). Aviation radiative forcing in 2000: An update on IPCC (1999). Meteorologische Zeitschrift, 14, 555–561.

    Google Scholar 

  • Schefczyk, M. (1993). Operational performance of airlines: An extension of traditional measurement paradigms. Strategic Management Journal, 14(4), 301–317.

    Google Scholar 

  • Seufert, J., Arjomandi, A., & Dakpo, K. H. (2017). Evaluating airline operational performance: A Luenberger-Hicks-Moorsteen productivity indicator. Transportation Research Part E, 104, 52–68.

    Google Scholar 

  • Sueyoshi, T., & Goto, M. (2012). Weak and strong disposability vs. natural and managerial disposability in DEA environmental assessment: Comparison between Japanese electric power industry and manufacturing industries. Energy Economics, 34(3), 686–699.

    Google Scholar 

  • Tavassoli, M., Faramarzi, G. R., & Saen, R. F. (2014). Efficiency and effectiveness in airline performance using a SBM-NDEA model in the presence of shared input. Journal of Air Transport Management, 34, 146–153.

    Google Scholar 

  • Wang, W. K., Lu, W. M., & Tsai, C. J. (2011). The relationship between airline performance and corporate governance amongst US listed companies. Journal of Air Transport Management, 17(2), 148–152.

    Google Scholar 

  • Xu, X., & Cui, Q. (2017). Evaluating airline energy efficiency: An integrated approach with network epsilon-based measure and network slacks-based measure. Energy, 122, 274–286.

    Google Scholar 

  • Yu, M. M. (2010). Assessment of airport performance using the SBM-NDEA model. Omega, 38(6), 440–452.

    Google Scholar 

  • Zhu, J. (2011). Airlines performance via two-stage network DEA approach. Journal of CENTRUM Cathedra: The Business and Economics Research Journal, 4, 260–269.

    Google Scholar 

Download references

Acknowledgments

We are grateful to the anonymous reviewers for their constructive comments that improved this paper significantly.

Funding

This research is funded by National Natural Science Foundation of China (Nos.71403034 and 71701088) and National Social Science Foundation of China (Nos. 19AJY011).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qiang Cui.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Glossary of terms

Glossary of terms

Abbreviation

Full name

Meaning

NE

Number of Employees

The number of employees that the airline employs and the part-time employees have been converted into full-time ones.

AK

Aviation Kerosene

The yearly consumption amount of aviation kerosene.

ASK

Available Seat Kilometers

The sum of fly kilometers multiplied by the number of seats available for sale.

FS

Fleet Size

The total amount of the aircraft in service containing the rented ones.

RPK

Revenue Passenger Kilometers

The sum of flight kilometers multiplied by the number of passengers charged.

GHG

Greenhouse gases emission

The total carbon dioxide equivalent of GHG and the other GHG (except CO2) have been converted.

SC

Sales Costs

The total expense on sales marketing and commissions.

TR

Total Revenue

The total revenue containing passenger income freight income and non-aviation income.

DEA

Data Envelopment Analysis

A mathematic programming model to evaluate efficiency which was proposed by Charnes et al. (1978).

DMU

Decision-Making Units

The basic evaluation objects of DEA models.

RAM

Range-Adjusted Measure

A kind of DEA model which is linear and can show the slacks of inputs and outputs.

SBM

Slacks-Based Measure

A kind of DEA model which is nonlinear and can show the slacks of inputs and outputs.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cui, Q. Airline energy efficiency measures using a network range-adjusted measure with unified natural and managerial disposability. Energy Efficiency 13, 1195–1211 (2020). https://doi.org/10.1007/s12053-020-09868-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12053-020-09868-2

Keywords

Navigation