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Evaluating energy efficiency for airlines: An application of Virtual Frontier Dynamic Slacks Based Measure

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  • Cui, Qiang
  • Li, Ye
  • Yu, Chen-lu
  • Wei, Yi-Ming

Abstract

The fast growing Revenue Passenger Kilometers and the relatively lagged energy supply of aviation industry impels the airlines to improve energy efficiency. In this paper, we focus on evaluating and analyzing influencing factors for airline energy efficiency. Number of employees and aviation kerosene are chosen as the inputs. Revenue Ton Kilometers, Revenue Passenger Kilometers and total business income are the outputs. Capital stock is selected as the dynamic factor. A new model, Virtual Frontier Dynamic Slacks Based Measure, is proposed to calculate the energy efficiencies of 21 airlines from 2008 to 2012. We verify two important properties to manifest the advantages of the new model. Then a regression is run to analyze the influencing factors of airline energy efficiency. The main findings are: 1. The overall energy efficiency of Malaysia Airlines is the highest during 2008–2012.2. Per capita Gross Domestic Product, the average service age of fleet size and average haul distance have significant impacts on the efficiency score. 3. The difference between full-service carriers and low-cost carriers has no significant effects on airline energy efficiency.

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  • Cui, Qiang & Li, Ye & Yu, Chen-lu & Wei, Yi-Ming, 2016. "Evaluating energy efficiency for airlines: An application of Virtual Frontier Dynamic Slacks Based Measure," Energy, Elsevier, vol. 113(C), pages 1231-1240.
  • Handle: RePEc:eee:energy:v:113:y:2016:i:c:p:1231-1240
    DOI: 10.1016/j.energy.2016.07.141
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    Cited by:

    1. Seufert, Juergen Heinz & Arjomandi, Amir & Dakpo, K. Hervé, 2017. "Evaluating airline operational performance: A Luenberger-Hicks-Moorsteen productivity indicator," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 104(C), pages 52-68.
    2. Hashem Omrani & Khatereh Shafaat & Arash Alizadeh, 2019. "Integrated data envelopment analysis and cooperative game for evaluating energy efficiency of transportation sector: a case of Iran," Annals of Operations Research, Springer, vol. 274(1), pages 471-499, March.
    3. Barak, Sasan & Dahooei, Jalil Heidary, 2018. "A novel hybrid fuzzy DEA-Fuzzy MADM method for airlines safety evaluation," Journal of Air Transport Management, Elsevier, vol. 73(C), pages 134-149.
    4. Miao, Zhuang & Chen, Xiaodong & Baležentis, Tomas & Sun, Chuanwang, 2019. "Atmospheric environmental productivity across the provinces of China: Joint decomposition of range adjusted measure and Luenberger productivity indicator," Energy Policy, Elsevier, vol. 132(C), pages 665-677.
    5. Wang, Chia-Nan & Nguyen, Xuan-Tho & Le, Thi-Dao & Hsueh, Ming-Hsien, 2018. "A partner selection approach for strategic alliance in the global aerospace and defense industry," Journal of Air Transport Management, Elsevier, vol. 69(C), pages 190-204.
    6. Yu, Hang & Zhang, Yahua & Zhang, Anming & Wang, Kun & Cui, Qiang, 2019. "A comparative study of airline efficiency in China and India: A dynamic network DEA approach," Research in Transportation Economics, Elsevier, vol. 76(C).
    7. Arjomandi, Amir & Dakpo, K. Hervé & Seufert, Juergen Heinz, 2018. "Have Asian airlines caught up with European Airlines? A by-production efficiency analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 389-403.
    8. Cui, Qiang & Wei, Yi-Ming & Li, Ye, 2016. "Exploring the impacts of the EU ETS emission limits on airline performance via the Dynamic Environmental DEA approach," Applied Energy, Elsevier, vol. 183(C), pages 984-994.
    9. Li, Ye & Cui, Qiang, 2018. "Airline efficiency with optimal employee allocation: An Input-shared Network Range Adjusted Measure," Journal of Air Transport Management, Elsevier, vol. 73(C), pages 150-162.
    10. Boban Djordjević & Evelin Krmac, 2019. "Evaluation of Energy-Environment Efficiency of European Transport Sectors: Non-Radial DEA and TOPSIS Approach," Energies, MDPI, Open Access Journal, vol. 12(15), pages 1-27, July.
    11. Cui, Qiang & Li, Ye, 2017. "Airline efficiency measures under CNG2020 strategy: An application of a Dynamic By-production model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 130-143.
    12. Liu, Xiao & Zhou, Dequn & Zhou, Peng & Wang, Qunwei, 2017. "What drives CO2 emissions from China’s civil aviation? An exploration using a new generalized PDA method," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 30-45.
    13. Li, Ye & Cui, Qiang, 2017. "Carbon neutral growth from 2020 strategy and airline environmental inefficiency: A Network Range Adjusted Environmental Data Envelopment Analysis," Applied Energy, Elsevier, vol. 199(C), pages 13-24.
    14. Abbas Mardani & Dalia Streimikiene & Tomas Balezentis & Muhamad Zameri Mat Saman & Khalil Md Nor & Seyed Meysam Khoshnava, 2018. "Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends," Energies, MDPI, Open Access Journal, vol. 11(8), pages 1-21, August.
    15. Xu, Xin & Cui, Qiang, 2017. "Evaluating airline energy efficiency: An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure," Energy, Elsevier, vol. 122(C), pages 274-286.
    16. Cui, Qiang & Li, Ye, 2017. "Airline efficiency measures using a Dynamic Epsilon-Based Measure model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 121-134.
    17. Cui, Qiang & Li, Ye & Wei, Yi-Ming, 2017. "Exploring the impacts of EU ETS on the pollution abatement costs of European airlines: An application of Network Environmental Production Function," Transport Policy, Elsevier, vol. 60(C), pages 131-142.
    18. Cui, Qiang, 2019. "Investigating the airlines emission reduction through carbon trading under CNG2020 strategy via a Network Weak Disposability DEA," Energy, Elsevier, vol. 180(C), pages 763-771.
    19. Li, Ye & Cui, Qiang, 2018. "Investigating the role of cooperation in the GHG abatement costs of airlines under CNG2020 strategy via a DEA cross PAC model," Energy, Elsevier, vol. 161(C), pages 725-736.
    20. Cui, Qiang & Li, Ye & Lin, Jing-ling, 2018. "Pollution abatement costs change decomposition for airlines: An analysis from a dynamic perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 96-107.
    21. Chen, Zhongfei & Wanke, Peter & Antunes, Jorge Junio Moreira & Zhang, Ning, 2017. "Chinese airline efficiency under CO2 emissions and flight delays: A stochastic network DEA model," Energy Economics, Elsevier, vol. 68(C), pages 89-108.
    22. Cui, Qiang & Li, Ye, 2018. "Airline dynamic efficiency measures with a Dynamic RAM with unified natural & managerial disposability," Energy Economics, Elsevier, vol. 75(C), pages 534-546.

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