Skip to main content

Advertisement

Log in

An application of Dynamic Range Adjusted Measure with weak-G disposability in evaluating airline energy efficiency

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

Abstract

The dynamic energy efficiencies of airlines are measured in this paper. Greenhouse gas emissions are selected as the undesirable output, and the dynamic factor is defined as fleet size. Weak-G disposability is considered to reflect the material balance principle. A new model, the Dynamic Range Adjusted Measure (RAM) with weak-G disposability, is built to evaluate the dynamic energy efficiency of 29 international airlines during the year of 2011–2017. We find that Air Berlin, Scandinavian Airlines and Norwegian are efficient airlines and most airlines have an efficiency change of less than or equal to 1. Then we performed a sensitivity analysis for the annual weights and found that the results of equal weight are more reasonable.

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.

    Article  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.

    Article  Google Scholar 

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

    Article  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.

    Article  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.

    Article  Google Scholar 

  • Cui, Q. (2020). Airline energy efficiency measures using a network range-adjusted measure with unified natural and managerial disposability. Energy Efficiency, 13, 1195–1211.

    Article  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.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Cui, Q., & Li, Y. (2018). 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, 2298–2318.

    Article  Google Scholar 

  • Cui, Q., Li, Y., Yu, C. L., & Wei, Y. M. (2016a). Evaluating energy efficiency for airlines: An application of Virtual Frontier Dynamic Slacks Based Measure. Energy, 113, 1231–1240.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Cui, Q., Lin, J. L., & Jin, Z. Y. (2020). Evaluating airline efficiency under “Carbon Neutral Growth from 2020” strategy through a Network Interval Slack-Based Measure. Energy, 193, 5–15.

    Article  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.

    Article  MathSciNet  Google Scholar 

  • Distexhe, V., & Perelman, S. (1994). Technical efficiency and productivity growth in an era of deregulation: The case of airlines. Swiss Journal of Economics and Statistics, 130(4), 669–689.

    Google Scholar 

  • Färe, R., & Grosskopf, S. (1996). Intertemporal production frontiers: with dynamic DEA. Kluwer.

  • Färe, R., Grosskopf, S., Norris, S., & Zhang, Z. (1994). Productivity growth, technical progress and efficiency change in industrialized countries. The American Economic Review, 84(1), 66–83.

    Google Scholar 

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

    Article  Google Scholar 

  • Fethi M D, Jackson P M, Weyman-Jones T G., 2000. Measuring the efficiency of European airlines: An application of DEA and Tobit Analysis. Annual Meeting of the European Public Choice Society, Siena, Italy.

  • 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.

    Article  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.

    Article  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.

    Article  Google Scholar 

  • Hoang, V. N., & Coelli, T. (2011). Measurement of agricultural total factor productivity growth incorporating environmental factors: A nutrients balance approach. Journal of Environmental Economics and Management, 62, 462–474.

    Article  Google Scholar 

  • IATA, 2021. https://www.iata.org/en/publications/annual-review/.

  • ICAO, 2021. http://www.icao.int/environmental-protection/Pages/market-based-measures.aspx.

  • Kenneth, L. R. (2016). Environmental efficiency measurement and the materials balance condition reconsidered. European Journal of Operational Research, 250, 342–346.

    Article  MathSciNet  Google Scholar 

  • Klopp G A, 1985. The analysis of the efficiency of production system with multiple inputs and outputs. PhD dissertation, University of Illinois, Industrial and System Engineering College, Chicago.

  • 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.

    Article  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.

    Article  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.

    Article  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), 207–232.

    Article  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.

    Article  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.

    Article  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 Econometrics Journal, 106(439), 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.

    Article  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.

    Article  Google Scholar 

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

    Article  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.

    Article  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.

    Article  Google Scholar 

  • Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3), 145–156.

    Article  Google Scholar 

  • Wang, K., Wei, Y. M., & Huang, Z. M. (2018). Environmental efficiency and abatement efficiency measurements of China’s thermal power industry: A data envelopment analysis based materials balance approach. European Journal of Operational Research, 269, 35–50.

    Article  MathSciNet  Google Scholar 

  • Wanke, P., & Barros, C. P. (2016). Efficiency in Latin American airlines: A two-stage approach combining virtual frontier dynamic DEA and simplex regression. Journal of Air Transport Management, 54, 93–103.

    Article  Google Scholar 

  • Xu, Y., Park, Y. S., Park, J. D., & Cho, W. (2020). Evaluation the environmental efficiency of the U.S. airline industry using a directional distance function DEA approach. Journal of Management Analytics, 8, 1–18. https://doi.org/10.1080/23270012.2020.1832925.

    Article  Google Scholar 

Download references

Funding

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qiang Cui.

Ethics declarations

Conflict of interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cui, Q., Yu, Lt. An application of Dynamic Range Adjusted Measure with weak-G disposability in evaluating airline energy efficiency. Energy Efficiency 14, 44 (2021). https://doi.org/10.1007/s12053-021-09961-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12053-021-09961-0

Keywords

Navigation