Türk havalimanlarının operasyonel verimliliği ve toparlanma örüntüleri: Dinamik VZA ve Malmquist endeksi analizi

Öz

Havalimanları, yolcu hareketliliğini, turizmi, bölgesel kalkınmayı ve ekonomik bağlantısallığı destekleyen stratejik ulaşım altyapılarıdır. Bu nedenle havalimanı etkinliği, havacılık yönetimi açısından kritik öneme sahiptir. COVID-19 pandemisi hava taşımacılığı sektörünü derinden etkilemiş ve yalnızca havalimanı etkinliğinin değil, aynı zamanda toparlanma örüntülerinin de değerlendirilmesini gerekli kılmıştır. Bu çalışma, Türkiye’deki 10 büyük havalimanının 2019–2024 yılları arasındaki operasyonel etkinliğini ve toparlanma örüntüsünü analiz etmektedir. Araştırmada, değişken getiriye göre ölçek varsayımı altında çıktı odaklı Veri Zarflama Analizi (VZA) ile Malmquist Toplam Faktör Verimliliği Endeksi uygulanmıştır. Pist, terminal sayısı, terminal alanı ve apron park pozisyonu girdi; uçak hareketleri, yolcu trafiği ve kargo trafiği ise çıktı değişkenleri olarak kullanılmıştır. Bulgular, havalimanları arasında etkinlik farklılıkları bulunduğunu göstermektedir. İstanbul Havalimanı, İstanbul Sabiha Gökçen, Ankara Esenboğa, Trabzon ve Muğla Milas-Bodrum havalimanları analiz dönemi boyunca etkinlik sınırında yer alırken, İzmir Adnan Menderes ve Kayseri havalimanları etkinlik sınırının altında kalmıştır. Ayrıca, Malmquist endeksi pandeminin ilk aşamasında verimlilikte önemli bir düşüş yaşandığını, ancak toparlanma örüntülerinin havalimanı türüne göre farklılaştığını göstermektedir. Bu araştırma, havalimanı etkinliğini ve verimlilik ile toparlanma açısından dinamik örüntülerini inceleyerek havacılık yönetimi literatürüne katkı sağlamaktadır. Uygulama açısından ise, havalimanı planlaması ve politikalarının yalnızca trafik artışına değil, aynı zamanda altyapı kullanımına ve her havalimanı türüne özgü stratejilere odaklanması gerektiğini önermektedir.

Anahtar Kelimeler

• Havalimanı verimliliği
• Veri zarflama analizi
• Malmquist verimlilik endeksi
• Havalimanı toparlanması
• Türk havalimanları

Operational efficiency and recovery patterns of Turkish airports: A dynamic DEA and Malmquist index analysis

Öz

Airports are of strategic importance to national transport systems, facilitating passenger mobility, tourism, regional development, and economic connectivity. Therefore, airport efficiency is a critical issue for aviation management. The COVID-19 pandemic profoundly affected the air transport industry, highlighting the need to evaluate not only airport efficiency but also airport recovery patterns. This study analyzes the operational efficiency and recovery patterns of 10 major airports in Turkey between 2019 and 2024. Output-oriented Data Envelopment Analysis (DEA) with variable returns to scale and the Malmquist Productivity Index were applied to evaluate airport efficiency. Runway, terminal count, terminal area, and apron stand count were used as input variables, whereas aircraft movements, passenger traffic, and cargo traffic were used as output variables. The findings indicate variations in airport efficiency across the sample. İstanbul Airport, İstanbul Sabiha Gökçen, Ankara Esenboğa, Trabzon, and Muğla Milas-Bodrum airports remained on the efficiency frontier throughout the analysis period, whereas İzmir Adnan Menderes and Kayseri airports remained below the efficiency frontier. Furthermore, the Malmquist index indicates a significant decline in productivity during the initial phase of the pandemic; however, recovery patterns vary across airport types. This research contributes to the aviation management literature by examining airport efficiency and its dynamic patterns of productivity and recovery. From a practical perspective, it suggests that airport planning and policy should focus not only on traffic growth but also on infrastructure utilization and strategies tailored to each airport type.

Anahtar Kelimeler

• Airport efficiency
• Data envelopment analysis
• Malmquist productivity index
• Turkish airports
• Airport recovery

Etik Beyan

This article meets the standards of research and publication ethics.

Kaynakça

1. Airports Council International. (n.d.). A guide to airport performance measures. Retrieved from https://store.aci.aero/product/a-guide-to-airport-performance-measures/
2. Airports Council International. (n.d.). Data center. Retrieved from https://aci.aero/resources/data-center/
3. Akın, N. G. (2025). Measuring the Performance of Airport Operations in Türkiye with The MPSI Based MABAC Method. Verimlilik Dergisi, 59(4), 803-826. https://doi.org/10.51551/verimlilik.1647363
4. Ateş, S. S., Ulufer, S., & Altınbaş, Ş. (2018). Efficiency Evaluation of Turkish airports with dea and malmquist-TFP index approach. İstanbul Aydın Üniversitesi Dergisi, 10(3), 83-94. https://dx.doi.org/10.17932/IAU.IAUD.m.13091352.2018.3/39.83-94
5. Baltazar, M. E., Rosa, T., & Silva, J. (2018). Global decision support for airport performance and efficiency assessment. Journal of Air Transport Management, 71, 220-242. https://doi.org/10.1016/j.jairtraman.2018.04.009
6. Bansal, P., & Mehra, A. (2022). Malmquist-Luenberger productivity indexes for dynamic network DEA with undesirable outputs and negative data. RAIRO-operations Research, 56(2), 649-687. https://doi.org/10.1051/ro/2022023
7. Bugayko, D., Ierkovska, Y., & Bugayko, D. (2022). Strategic management of airports efficiency and safety in the process of air and multimodal logistics transportations development. Electronic scientific and practical publication in economic sciences. https://doi.org/10.46783/smart-scm/2022-12-1
8. Chaoqun, H., Shen, W., Huizhen, J., & Wei, L. (2024). Evaluating the impact of uncertainty and risk on the operational efficiency of credit business of commercial banks in China based on dynamic network DEA and Malmquist Index Model. Heliyon, 10(1). https://doi.org/10.1016/j.heliyon.2023.e22850

9. Choi, Y. S., Bernal, M. L., Tsymzhitov, S., & Yeo, G. T. (2024). Assessing airport efficiency in the Sakha Republic: A DEA-SBM analysis. The Asian Journal of Shipping and Logistics, 40(2), 103-108. https://doi.org/10.1016/j.ajsl.2024.02.002
10. Deng, L., Fu, W., Hu, M., Hu, S., & Liu, T. (2025). Based on the New Dynamic Network DEA and Malmquist Index of High-Quality Innovation in the New Energy Vehicle Economy Development Research of the Internet of Things. International Journal of High Speed Electronics and Systems, 34(04). https://doi.org/10.1142/S0129156425403249
11. Ekici, F., & Orhan, İ. (2026). Determinants of Unit Costs at Airports in Türkiye: Economies of Scale and Revenue Effects. Journal of Aviation, 10(1), 131-138. https://doi.org/10.30518/jav.1769326
12. Eren, M., & Doğan, M. A. (2023). Measurement of process-performances of Turkish airports using network data envelopment analysis. Journal of Aviation, 7(2), 272-283. https://doi.org/10.30518/jav.1296416
13. EUROCONTROL. (n.d.-a). Airport capacity and performance (ACAP). Retrieved from https://www.eurocontrol.int/service/airport-capacity-and-performance
14. EUROCONTROL. (n.d.-b). Total airport management (TAM). Retrieved from https://www.eurocontrol.int/project/total-airport-management,
15. Ganji, S. S., Mardani, A., Xu, D. L., & Heidarighadikolaei, S. (2023). A new evaluation technique based on DEA, prospect theory and ER approach: assessment of airports. Annals of Operations Research, 1-39. https://dx.doi.org/10.1007/s10479-023-05593-y
16. 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://dx.doi.org/10.1016/S1366-5545(97)00028-8
17. Guven, T. B., Degirmenci, H., Gunerhan, A., & Altuntas, O. (2024). Energy efficiency and sustainability: Implementing circular economy principles for cabin waste management in aviation. Energy for Sustainable Development, 81. https://doi.org/10.1016/j.esd.2024.101515
18. H Ulutas, B. (2018). Efficiency analysis of airports in Turkey considering environmental effects. The Central European Review of Economics and Management (CEREM), 2(1), 65-78. https://doi.org/10.29015/cerem.583
19. Hiney, N., Efthymiou, M., & Morgenroth, E. (2025). Adapting to uncertainty: Black swans, VUCA challenges and airport resilience strategies. Journal of the Air Transport Research Society, 4. https://dx.doi.org/10.1016/j.jatrs.2025.100068
20. Horton, R., Trump, B. D., Trump, J., Knowles, H. S., Linkov, I., Jones, P., & Kiker, G. (2025). Performance metrics for resilience of airport infrastructure. Transportation Research Part D: Transport and Environment, 142. https://doi.org/10.1016/j.trd.2025.104676
21. International Air Transport Association. (n.d.). Operational readiness and airport transfer (ORAT). Retrieved from https://www.iata.org/en/services/consulting/airports/operational-readiness-and-airport-transfer-orat/
22. International Civil Aviation Organization. (n.d.-a). Air navigation capacity and efficiency. Retrieved from https://www.icao.int/airnavigation
23. International Civil Aviation Organization. (n.d.-b). Strategic objective — Air navigation capacity and efficiency global air navigation priorities. Retrieved from https://www.icao.int/strategic-objective-air-navigation-capacity-and-efficiency-global-air-navigation-priorities
24. Iyer, K. C., & Jain, S. (2019). Performance measurement of airports using data envelopment analysis: A review of methods and findings. Journal of Air Transport Management, Elsevier, 81 (C). https://dx.doi.org/10.1016/j.jairtraman.2019.101707
25. Jain, R., Chandan, Kumar Ojha, M., & Mishra, V. (2025). Investigating the determinants of productivity dynamics in Indian commercial banks: a DEA based Malmquist Index approach. Cogent Business & Management, 12(1). https://doi.org/10.1080/23311975.2025.2530031
26. Janić, M. (2021). Analysing and modelling airport resilience: Robustness, vulnerability and recoverability. Journal of Airport Management, 15(4), 361-389. https://doi.org/10.69554/XVVB7195
27. Karanki, F., & Bilotkach, V. (2023). Capacity utilization of US airports: a data envelopment analysis. Transportation Research Part A: Policy and Practice, 177. https://dx.doi.org/10.1016/j.tra.2023.103840
28. Keskin, B. (2021). Airport regulations in Turkey: An efficiency review. In M. Eroğlu & M. Finger (Eds.), The regulation of Turkish network industries (pp. 177–200). Springer. https://doi.org/10.1007/978-3-030-81720-6_9
29. Liu, Q., & Qian, Q. (2024). Research on the Efficiency of Major Airports in China’s Six Major Airport Cluster: Based on Common Frontier Super-Efficiency DEA and Malmquist Method. Promet-Traffic&Transportation, 36(5), 885-901. https://dx.doi.org/10.7307/ptt.v36i5.609
30. Liu, X., Huang, J., Zhou, H., Sun, J., Wang, Q., & Cheng, X. (2023). Dynamic analysis of provincial forest carbon storage efficiency in china based on DEA malmquist index. Forests, 14(8), 1629. https://doi.org/10.3390/f14081629
31. Lodiagin, B. A., Nazarova, V. V., & Rincon, C. J. (2026). Airport resilience to large-scale events: the case of Pulkovo Airport. Urban, Planning and Transport Research, 14(1). https://doi.org/10.1080/21650020.2026.2629123
32. MoghadasNian, S. A. H., & Mojavezi, S. (2024). KPI-driven decision making in airport services: Enhancing operational efficiency, customer satisfaction, and sustainability [Conference paper]. Zenodo. https://doi.org/10.5281/zenodo.19966515
33. Ngo, T. T., Huynh, N. T., & Yang, C. C. (2026). A sequential approach to airport efficiency evaluation: Integrating two-stage DEA and truncated regression. Journal of Air Transport Management, 131(C). http://dx.doi.org/10.1016/j.jairtraman.2025.102922
34. Pehlivanli, E. A., & Özsoy, V. S. (2025, May). Assessment of Turkish airport performances via two-phase models based on common weights. In Proceedings of the Institution of Civil Engineers-Transport, 178(3), 168-183, Emerald Publishing Limited. https://doi.org/10.1680/jtran.23.00111
35. Singh, D. P., Pandey, M. M., & Sharma, V. (2024). Evaluating the efficiency of the Indian airports using fuzzy DEA method. International Journal of Public Sector Performance Management, 13(4), 453-470. https://doi.org/10.1504/IJPSPM.2024.138763
36. Štimac, I., Vidović, A., Mihetec, T., & Drljača, M. (2020). Optimization of airport capacity efficiency by selecting optimal aircraft and airline business model. Sustainability, 12(10). https://doi.org/10.3390/su12103988
37. Wandelt, S., Zhang, A., & Sun, X. (2025). Global Airport Resilience Index: Towards a comprehensive understanding of air transportation resilience. Transportation Research Part D: Transport and Environment, 138. https://doi.org/10.1016/j.trd.2024.104522
38. Wang, C. N., Imperial, K. N. C., Huang, C. C., & Dang, T. T. (2022). Output targeting and runway utilization of major international airports: A comparative analysis using DEA. Mathematics, 10(4), 551. https://doi.org/10.3390/math10040551
39. Wang, L. F., & Zhang, X. F. (2025). Analyze agricultural efficiency and influencing factors base on the three-stage DEA model and Malmquist index. Frontiers in Earth Science, 13. http://dx.doi.org/10.3389/feart.2025.1633859
40. Yüksel, F. Ş., & Pırıl, T. (2026). An Integrated FAHP–IF-COPRAS Approach for Evaluating Airport Sustainability Performance in Türkiye. Sustainability, 18(2), 661. https://doi.org/10.3390/su18020661
41. Zhang, G., Ye, Y., & Sun, M. (2023). Assessing the static and dynamic efficiency of digital economy in China: Three stage DEA–Malmquist index based approach. Sustainability, 15(6), 5270. https://doi.org/10.3390/su15065270
42. Zhou, L., & Chen, Z. (2020). Measuring the performance of airport resilience to severe weather events. Transportation research part D: transport and environment, 83. https://doi.org/10.1016/j.trd.2020.102362