Comprehensive operating efficiency measurement of 28 Chinese airports using a two-stage DEA-Tobit method

Ming Wei; Shaopeng Zhang; Bo Sun; Ming Wei; Shaopeng Zhang; Bo Sun

doi:10.3934/era.2023078

Electronic Research Archive

2023, Volume 31, Issue 3: 1543-1555. doi: 10.3934/era.2023078

Previous Article Next Article

Research article Special Issues

Comprehensive operating efficiency measurement of 28 Chinese airports using a two-stage DEA-Tobit method

1.
School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China
2.
Beijing Civil Aviation Design and Research Institute of China Design Group, Beijing 100000, China
3.
School of Transportation, Nantong University, Nantong 226019, China

Academic Editor: Hua Wei

Received: 29 September 2022 Revised: 10 December 2022 Accepted: 22 December 2022 Published: 17 January 2023

This paper presents a two-stage method combining data envelopment analysis (DEA) and a Tobit model to analyze the comprehensive operating efficiency of 28 airports in China in 2016. At the first stage, the DEA-BCC (Banker-Charnes-Cooper) model was employed to obtain the comprehensive operating efficiency of the combination of flight departure punctuality, non-cancellations, landing bridge rates from the perspective of airport infrastructure, surrounding airspace, route layouts, flight volume and weather. At the second stage, a Tobit model was used to analyze the influence of nine input variables from four aspects on obtained comprehensive operating efficiency, ultimately providing a clear and straightforward basis for formulating and testing policies. The comprehensive operating efficiency with this combination was further compared with each of the three efficiencies respectively. The important findings included the following: (1) The comprehensive operation efficiencies of most airports were greater than the individual efficiency; (2) These four types of operation efficiencies for most airports did not achieved DEA validity (100% efficiency), except for six airports (i.e., Haikou, Dalian, Jinan, Fuzhou, Nanning and Lanzhou); (3) These factors affecting each of the four types of operation efficiencies were different in that the number of terminals, duration of impact and average daily inbound and outbound flights had a negative impact on airport operational efficiency, while the average number of overnight aircraft per day and peak hour sorties had positive effects.
- airport operation,
- comprehensive efficiency,
- DEA-BCC model,
- Tobit model
Citation: Ming Wei, Shaopeng Zhang, Bo Sun. Comprehensive operating efficiency measurement of 28 Chinese airports using a two-stage DEA-Tobit method[J]. Electronic Research Archive, 2023, 31(3): 1543-1555. doi: 10.3934/era.2023078

Related Papers:

Abstract

This paper presents a two-stage method combining data envelopment analysis (DEA) and a Tobit model to analyze the comprehensive operating efficiency of 28 airports in China in 2016. At the first stage, the DEA-BCC (Banker-Charnes-Cooper) model was employed to obtain the comprehensive operating efficiency of the combination of flight departure punctuality, non-cancellations, landing bridge rates from the perspective of airport infrastructure, surrounding airspace, route layouts, flight volume and weather. At the second stage, a Tobit model was used to analyze the influence of nine input variables from four aspects on obtained comprehensive operating efficiency, ultimately providing a clear and straightforward basis for formulating and testing policies. The comprehensive operating efficiency with this combination was further compared with each of the three efficiencies respectively. The important findings included the following: (1) The comprehensive operation efficiencies of most airports were greater than the individual efficiency; (2) These four types of operation efficiencies for most airports did not achieved DEA validity (100% efficiency), except for six airports (i.e., Haikou, Dalian, Jinan, Fuzhou, Nanning and Lanzhou); (3) These factors affecting each of the four types of operation efficiencies were different in that the number of terminals, duration of impact and average daily inbound and outbound flights had a negative impact on airport operational efficiency, while the average number of overnight aircraft per day and peak hour sorties had positive effects.

References

[1]	L. W. Fan, F. Wu, P. Zhou, Efficiency measurement of Chinese airports with flight delays by directional distance function, J. Air Transp. Manage., 34 (2014), 140–145. https://doi.org/10.1016/j.jairtraman.2013.10.002 doi: 10.1016/j.jairtraman.2013.10.002
[2]	C. N. Wang, N. C. Kristofer, C. C. Huang, T T. Dang, Output targeting and runway utilization of major international airports: A comparative analysis using DEA, Mathematics, 10 (2022), 551–562. https://doi.org/10.3390/math10040551 doi: 10.3390/math10040551
[3]	M. K. Yilmaz, A. O. Kusakci, M. Aksoy, U. Hacioglu, The evaluation of operational efficiencies of Turkish airports: An integrated spherical fuzzy AHP/DEA approach, Appl. Soft Comput., 119 (2020), 108620. https://doi.org/10.1016/j.asoc.2022.108620 doi: 10.1016/j.asoc.2022.108620
[4]	M. G. Tsionas, Z. Chen, P. Wanke, A structural vector autoregressive model of technical efficiency and delays with an application to Chinese airlines, Transp. Res. Part A Policy Pract., 101 (2017), 1–10. https://doi.org/10.1016/j.tra.2017.05.003 doi: 10.1016/j.tra.2017.05.003
[5]	A. Lemetti, T. Polishchuk, R. Sáez, X. Prats, Analysis of weather impact on flight efficiency for Stockholm Arlanda airport arrivals, ENRI Int. Workshop ATM/CNS, 4 (2019), 78–91. https://doi.org/10.1007/978-981-33-4669-7_5 doi: 10.1007/978-981-33-4669-7_5
[6]	M. Schultz, S. Lorenz, R. Schmitz, L. Delgado, Weather impact on airport performance, Aerospace, 5 (2018), 109–121. https://doi.org/10.3390/aerospace5040109 doi: 10.3390/aerospace5040109
[7]	J. N. Sánchez, M. A. P. Eroles, D. Lebbink, Identification and empirical characterisation of flight arrival variation and the impact on departure punctuality, Int. J. Services Oper. Manage., 35 (2020), 307–321. https://doi.org/10.1504/IJSOM.2020.105373 doi: 10.1504/IJSOM.2020.105373
[8]	M. Zhang, L. Li, Z. Cheng, Research on carbon emission efficiency in the Chinese construction industry based on a three-stage DEA-Tobit model, Environ. Sci. Pollut. Res., 28 (2021), 1–17. https://doi.org/10.1007/s11356-021-14298-3 doi: 10.1007/s11356-021-14298-3
[9]	X. Liang, J. Li, G. Guo, S. Li, Q. Gong, Evaluation for water resource system efficiency and influencing factors in western China: A two-stage network DEA-Tobit model, J. Cleaner Prod., 328 (2021), 129674. https://doi.org/10.1016/j.jclepro.2021.129674 doi: 10.1016/j.jclepro.2021.129674
[10]	T. M. Huynh, G. Kim, H. K. Ha, Comparative analysis of efficiency for major Southeast Asia airports: A two-stage approach, J. Air Transp. Manage., 89 (2020), 101898. https://doi.org/10.1016/j.jairtraman.2020.101898 doi: 10.1016/j.jairtraman.2020.101898
[11]	T. Ngo, K. W. H. Tsui, A data-driven approach for estimating airport efficiency under endogeneity: An application to New Zealand airports, Res. Transp. Bus. Manage., 34 (2020), 100412. https://doi.org/10.1016/j.rtbm.2019.100412 doi: 10.1016/j.rtbm.2019.100412
[12]	Z. Zeng, W. Yang, S. Zhang, F. Witlox, Analysing airport efficiency in East China using a three-stage data envelopment analysis, Transport, 35 (2020), 255–272. https://doi.org/10.3846/transport.2020.12869 doi: 10.3846/transport.2020.12869
[13]	S. Pathomsiri, A. Haghani, M. Dresner, R. J. Windle, Impact of undesirable outputs on the productivity of US airports, Transp. Res. Part E Logistics Transp. Rev., 44 (2008), 235–259. https://doi.org/10.1016/j.tre.2007.07.002 doi: 10.1016/j.tre.2007.07.002
[14]	Z. Chen, P. Wanke, J. J. M. Antunes, N. Zhang, Chinese airline efficiency under CO2 emissions and flight delays: A stochastic network DEA model, Energy Econ., 68 (2017), 89–108. https://doi.org/10.1016/j.eneco.2017.09.015 doi: 10.1016/j.eneco.2017.09.015
[15]	S. Suzuki, P. Nijkamp, P. Rietveld, E. Pels, A distance friction minimization approach in data envelopment analysis: A comparative study on airport efficiency, Eur. J. Oper. Res., 207 (2010), 1104–1115. https://doi.org/10.1016/j.ejor.2010.05.049 doi: 10.1016/j.ejor.2010.05.049
[16]	S. Yoo, J. Meng, S. Lim, An analysis of the performance of global major airports using two-stage network DEA model, J. Korean Soc. Qual. Manage., 45 (2017), 65–92. https://doi.org/10.7469/JKSQM.2017.45.1.065 doi: 10.7469/JKSQM.2017.45.1.065
[17]	S. Lozano, E. Gutiérrez, Slacks-based measure of efficiency of airports with airplanes delays as undesirable outputs, Comput. Oper. Res., 38 (2011), 131–139. https://doi.org/10.1016/j.cor.2010.04.007 doi: 10.1016/j.cor.2010.04.007
[18]	R. Paula, The role of delays in the efficiency analysis of air traffic management systems, ICRAT, 3 (2016), 34–51.
[19]	T. Ngo, K. W. H. Tsui, Estimating the confidence intervals for DEA efficiency scores of Asia-Pacific airlines, Oper. Res., 22 (2022), 3411–3434. https://doi.org/10.1007/s12351-021-00667-w doi: 10.1007/s12351-021-00667-w
[20]	J. Tobin, Estimation of relationships for limited dependent variables, Econometrica, 26 (1958), 24–36. https://doi.org/10.2307/1907382 doi: 10.2307/1907382

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)