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Analysing airport efficiency in East China using a three-stage data envelopment analysis

    Zhuxuan Zeng Affiliation
    ; Wendong Yang Affiliation
    ; Shengrun Zhang Affiliation
    ; Frank Witlox Affiliation

Abstract

This paper evaluates the Technical Efficiencies (TEs) of a group of airports in East China by applying a three-stage Data Envelopment Analysis (DEA) method. The merit of this method allows us to consider the impact of the environmental factors on measuring airport efficiencies. Three variables, i.e. per capita Gross Domestic Product (GDP), the proportion of the tertiary industry, and the number of tourists, are used to represent the environmental factors. The results show that the environmental factors have airport-specific impacts on the value of the efficiencies. Additionally, airport TE are dominated by both Pure Technical Efficiency (PTE) and Scale Efficiency (SE). Based on empirical results, airport specific strategies can be provided to enhance airport efficiency, such as taking the effects of environmental variables and the statistical noise into consideration when analysing the airport efficiency, improving airport efficiencies according to their own conditions and improving the PTE or SE according to their categorizations.

Keyword : air transport, airport, technical efficiency, scale efficiency, three-stage DEA, East China

How to Cite
Zeng, Z., Yang, W., Zhang, S., & Witlox, F. (2020). Analysing airport efficiency in East China using a three-stage data envelopment analysis. Transport, 35(3), 255-272. https://doi.org/10.3846/transport.2020.12869
Published in Issue
Jun 25, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Adler, N.; Liebert, V. 2014. Joint impact of competition, ownership form and economic regulation on airport performance and pricing, Transportation Research Part A: Policy and Practice 64: 92–109. https://doi.org/10.1016/j.tra.2014.03.008

Banker, R. D.; Charnes, A.; Cooper, W. W. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science 30: 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078

Barros, C. P.; Weber, W. L. 2009. Productivity growth and biased technological change in UK airports, Transportation Research Part E: Logistics and Transportation Review 45(4): 642–653. https://doi.org/10.1016/j.tre.2009.01.004

Button, K.; Kramberger, T.; Grobin, K.; Rosi, B. 2018. A note on the effects of the number of low-cost airlines on small tourist airports’ efficiencies, Journal of Air Transport Management 72: 92–97. https://doi.org/10.1016/j.jairtraman.2017.12.003

CAAC. 2017a. Issues the Statistics Bulletin of Civil Airports in China 2017. Available from Internet: http://www.caac.gov.cn/en

CAAC. 2017b. CAAC Specifies Five Main Tasks in the 13th Five-year Plan for Civil Aviation Development. Civil Aviation Administration of China (CAAC). Available from Internet: http://www.caac.gov.cn/en

CAAC. 2018. Statistical Bulletin of Civil Aviation Industry Development in 2017. Civil Aviation Administration of China (CAAC). Available from Internet: http://www.caac.gov.cn/en

CAAC. 2014. Statistical Bulletin of Civil Aviation Industry Development in 2013. Civil Aviation Administration of China (CAAC). Available from Internet: http://www.caac.gov.cn/en

Charles, V.; Zegarra, L. F. 2014. Measuring regional competitiveness through data envelopment analysis: a Peruvian case, Expert Systems with Applications 41(11): 5371–5381. https://doi.org/10.1016/j.eswa.2014.03.003

Charnes, A.; Cooper, W. W.; Rhodes, E. 1978. Measuring the efficiency of decision making units, European Journal of Operational Research 2(6): 429–444. https://doi.org/10.1016/0377-2217(78)90138-8

Chen, Y.-H.; Lai, P.-L.; Piboonrungroj, P. 2017. The relationship between airport performance and privatisation policy: a nonparametric metafrontier approach, Journal of Transport Geography 62: 229–235. https://doi.org/10.1016/j.jtrangeo.2017.06.005

Cui, Q.; Li, Y. 2014. The evaluation of transportation energy efficiency: an application of three-stage virtual frontier DEA, Transportation Research Part D: Transport and Environment 29: 1–11. https://doi.org/10.1016/j.trd.2014.03.007

Cui, Y.; Huang, G.; Yin, Z. 2015. Estimating regional coal resource efficiency in China using three-stage DEA and bootstrap DEA models, International Journal of Mining Science and Technology 25(5): 861–864. https://doi.org/10.1016/j.ijmst.2015.07.024

Curi, C.; Gitto, S.; Mancuso, P. 2011. New evidence on the efficiency of Italian airports: a bootstrapped DEA analysis, Socio-Economic Planning Sciences 45(2): 84–93. https://doi.org/10.1016/j.seps.2010.11.002

D’Alfonso, T.; Daraio, C.; Nastasi, A. 2015. Competition and efficiency in the Italian airport system: new insights from a conditional nonparametric frontier analysis, Transportation Research Part E: Logistics and Transportation Review 80: 20–38. https://doi.org/10.1016/j.tre.2015.05.003

Fernandes, E.; Pacheco, R. R. 2018. Managerial performance of airports in Brazil before and after concessions, Transportation Research Part A: Policy and Practice 118: 245–257. https://doi.org/10.1016/j.tra.2018.09.003

Ferreira, D. C.; Marques, R. C.; Pedro, M. I. 2016. Comparing efficiency of holding business model and individual management model of airports, Journal of Air Transport Management 57: 168–183. https://doi.org/10.1016/j.jairtraman.2016.07.020

Fragoudaki, A.; Giokas, D.; Glyptou, K. 2016. Efficiency and productivity changes in Greek airports during the crisis years 2010–2014, Journal of Air Transport Management 57: 306–315. https://doi.org/10.1016/j.jairtraman.2016.09.003

Fried, H. O.; Lovell, C. A. K.; Schmidt, S. S.; Yaisawarng, S. 2002. Accounting for environmental effects and statistical noise in data envelopment analysis, Journal of Productivity Analysis 17(1–2): 157–174. https://doi.org/10.1023/a:1013548723393

Fuentes, R.; Fuster, B.; Lillo-Bañuls, A. 2016. A three-stage DEA model to evaluate learning-teaching technical efficiency: key performance indicators and contextual variables, Expert Systems with Applications 48: 89–99. https://doi.org/10.1016/j.eswa.2015.11.022

Gillen, D.; Lall, A. 1997. Developing measures of airport productivity and performance: an application of data envelopment analysis, Transportation Research Part E: Logistics and Transportation Review 33(4): 261–273. https://doi.org/10.1016/S1366-5545(97)00028-8

Gitto, S.; Mancuso, P. 2012. Two faces of airport business: a non-parametric analysis of the Italian airport industry, Journal of Air Transport Management 20: 39–42. https://doi.org/10.1016/j.jairtraman.2011.11.003

Graham, A. 2005. Airport benchmarking: a review of the current situation, Benchmarking: an International Journal 12(2): 99–111. https://doi.org/10.1108/14635770510593059

Jaržemskienė, I. 2012. Applying the method of measuring airport productivity in the Baltic region, Transport 27(2): 178–186. https://doi.org/10.3846/16484142.2012.694079

Lai, P.-L.; Potter, A.; Beynon, M.; Beresford, A. 2015. Evaluating the efficiency performance of airports using an integrated AHP/DEA-AR technique, Transport Policy 42: 75–85. https://doi.org/10.1016/j.tranpol.2015.04.008

Li, K.; Lin, B. 2016. Impact of energy conservation policies on the green productivity in China’s manufacturing sector: evidence from a three-stage DEA model, Applied Energy 168: 351–363. https://doi.org/10.1016/j.apenergy.2016.01.104

Liu, D. 2017. Evaluating the multi-period efficiency of East Asia airport companies, Journal of Air Transport Management 59: 71–82. https://doi.org/10.1016/j.jairtraman.2016.11.009

Lozano, S. 2015. A joint-inputs network DEA approach to production and pollution-generating technologies, Expert Systems with Applications 42(21): 7960–7968. https://doi.org/10.1016/j.eswa.2015.06.023

Lozano, S.; Gutiérrez, E.; Moreno, P. 2013. Network DEA approach to airports performance assessment considering undesirable outputs, Applied Mathematical Modelling 37(4): 1665–1676. https://doi.org/10.1016/j.apm.2012.04.041

Luo, D. 2012. A note on estimating managerial inefficiency of three-stage DEA model, Statistical Research 29(4): 104–107. https://doi.org/10.3969/j.issn.1002-4565.2012.04.017 (in Chinese).

Merkert, R.; Mangia, L. 2014. Efficiency of Italian and Norwegian airports: A matter of management or of the level of competition in remote regions?, Transportation Research Part A: Policy and Practice 62: 30–38. https://doi.org/10.1016/j.tra.2014.02.007

OAG. 2018. OAG Database. Official Aviation Guide of the Airways, OAG, Luton, UK. Available from Internet: https://www.oag.com

Örkcü, H. H.; Balıkçı, C.; Doğan, M. I.; Genç, A. 2016. An evaluation of the operational efficiency of Turkish airports using data envelopment analysis and the Malmquist productivity index: 2009–2014 case, Transport Policy 48: 92–104. https://doi.org/10.1016/j.tranpol.2016.02.008

Radonjić, A.; Pjevčević, D.; Hrle, Z.; Čolić, V. 2011. Application of DEA method to intermodal container transport, Transport 26(3): 233–239. https://doi.org/10.3846/16484142.2011.622127

Wanke, P. F. 2012. Capacity shortfall and efficiency determinants in Brazilian airports: Evidence from bootstrapped DEA estimates, Socio-Economic Planning Sciences 46(3): 216–229. https://doi.org/10.1016/j.seps.2012.01.003

Wanke, P. F. 2013. Physical infrastructure and shipment consolidation efficiency drivers in Brazilian ports: A two-stage network-DEA approach, Transport Policy 29: 145–153. https://doi.org/10.1016/j.tranpol.2013.05.004

Wanke, P.; Barros, C. P. 2017. Efficiency thresholds and cost structure in Senegal airports, Journal of Air Transport Management 58: 100–112. https://doi.org/10.1016/j.jairtraman.2016.10.005

Wanke, P.; Barros, C. P. 2014. Two-stage DEA: an application to major Brazilian banks, Expert Systems with Applications 41(5): 2337–2344. https://doi.org/10.1016/j.eswa.2013.09.031

Yoshida, Y.; Fujimoto, H. 2004. Japanese-airport benchmarking with the DEA and endogenous-weight TFP methods: testing the criticism of overinvestment in Japanese regional airports, Transportation Research Part E: Logistics and Transportation Review 40(6): 533–546. https://doi.org/10.1016/j.tre.2004.08.003