Veri Zarflama Analizinde istenmeyen girdi/çıktıya sahip tüm birimler için çevresel etkinlik değerlendirilmesine yeni bir model önerisi

Yıl 2024, Cilt: 39 Sayı: 4, 2435 - 2446, 20.05.2024

Melike Kübra Ekiz Bozdemir , Selen Avcı , Gulsen Akman

https://doi.org/10.17341/gazimmfd.1283649

Öz

Sanayi devrimi sonrasında hızlanan endüstriyel süreçler, artan nüfus, kentleşme ve ormansızlaşma gibi etkenler atmosferdeki sera gazı miktarını arttırarak başta küresel ısınma olmak üzere çeşitli çevresel sorunlara yol açmıştır. Bu sorunlara uluslararası platformlarda çözüm arayışları başlamış ve ülkeler arasında yaptırım gücüne sahip ilk belge olan Kyoto Protokolü imzalanmıştır. Çalışmada, Kyoto Protokolü’nü imzalamış gelişmiş ve gelişmekte olan ülkelerin çevresel etkinliklerinin değerlendirilmesi için yeni bir Veri Zarflama Analizi (VZA) modeli önerilmiştir. Önerilen model ile benzer performansa sahip Karar Verici Birim (KVB)’ler etkin sınırlar oluşturularak kümelenmiş ve her kümede yer alan etkin KVB’lerin birbirleri üzerindeki etkinlik iyileşmesi ve tüm veri setinde yer alan etkin olmayan KVB’ler üzerindeki etkinlik iyileşmesi analiz edilmiştir. Önerilen modelin nihai etkinlik skoru KVB’lerin başlangıç pozisyonunu ifade eden başlangıç etkinlik skoru ile bütün KVB’ler üzerinde etkinlik iyileşmesinin toplamı ile elde edilmektedir. Son olarak kümelerin küçükten büyüğe sıralanması ve küme içerisinde yer alan KVB’lerin nihai etkinlik skorlarının büyükten küçüğe sıralanması ile tam sıralama elde edilmektedir. Çalışmanın uygulama aşamasında ise Kyoto Protokolünü imzalamış ve belirlenen girdi/çıktı bazında verilerine ulaşılabilen 38 ülkenin çevresel etkinliği önerilen model ile değerlendirilmiştir. Çevresel etkinlik değerlendirmesinde karşılaşılan istenmeyen girdi/çıktı sorunu için monoton azalan fonksiyon belirlenerek veri dönüşümü yapılmıştır. Önerilen model ile elde edilen sıralama literatürde var olan üç farklı model ile karşılaştırılmıştır.

Anahtar Kelimeler

Veri Zarflama Analizi, çevresel etkinlik, istenmeyen girdi/çıktı, çapraz bağımlılık, bağlam bağımlılık

Kaynakça

• 1. Koçoğlu E., Gökalp L., Türkiye'de küresel ısınma alanında yapılan lisansüstü tezlerin analizi: Bir meta sentez çalışması, Doğu Coğrafya Dergisi, 26 (56), 129-142, 2021.
• 2. Aizebeokhai A. P., Global warming and climate change: Realities, uncertainties and measures, International Journal of Physical Sciences, 4 (13), 868-879, 2009.
• 3. Gülsoy E., Korkmaz M., Üniversite öğrencilerinin sosyo-ekonomik özelliklerinin küresel ısınma ve iklim değişikliği algıları üzerine etkileri, Turkish Journal of Forestry, 21 (4), 428-437, 2020.
• 4. World Meteorological Organization, World Climate Conference-3, Bulletin, 58 (3), 2009.
• 5. MacKenzie S., Stenport A. W., Visualizing climate change in the Arctic and beyond: Participatory media and the United Nations Conference of the Parties (COP), and interactive Indigenous Arctic media, Journal of Environmental Media, 1 (1), 79-99, 2020.
• 6. Barrett S., Political economy of the Kyoto protcol, Oxford Review of Economic Policy, 14(4), 20-39, 1998.
• 7. Erhan B., Çerçioğlu H., The productive efficiency of the Turkish health care sector based on provincial panel data, Journal of the Faculty of Engineering and Architecture of Gazi University, 34 (2), 929-944, 2018.
• 8. Çakır S., Efficiency measurement in caykur factories with fuzzy data envelopment analysis, Journal of the Faculty of Engineering and Architecture of Gazi University, 31 (2), 369-381, 2016.
• 9. Jia T., Yuan H., The application of DEA (Data Envelopment Analysis) window analysis in the assessment of influence on operational efficiencies after the establishment of branched hospitals, BMC Health Services Research, 17 (265), 1-8, 2017.
• 10. López-Torres L., Prior D., Centralized allocation of human resources. An application to public schools, Computers & Operations Research, 73 (1), 104–114, 2016.
• 11. Gattoufi S., Amin G. R., Emrouznejad A., A new inverse DEA method for merging banks, IMA Journal of Management Mathematics, 25 (1), 73–87, 2014.
• 12. Hsiao B., Chern C.-C., Chiu Y. H., Chiu C.R., Using fuzzy super-efficiency slack-based measure data envelopment analysis to evaluate Taiwan’s commercial bank efficiency, Expert Systems with Applications, 38 (8), 9147–9156, 2011.
• 13. Ekiz M.K, Tuncer Şakar C., A new DEA approach to fully rank DMUs with an application to MBA programs, International Transactions in Operational Research, 27, 1886-1910, 2020.
• 14. Ekiz Bozdemir, M. K., Avcı S., Aladağ, Z., Assessment of the Turkish Health Transformation Program with Data Envelopment Analysis, IEEE Transactions on Engineering Management, 70 (8), 2800 - 2808, 2023.
• 15. Matsumoto K. I., Makridou G., Doumpos M., Evaluating environmental performance using data envelopment analysis: The case of European countries, Journal of Cleaner Production, 272, 122637, 2020.
• 16. Seiford L. M., Zhu J., Modeling undesirable factors in efficiency evaluation, European Journal of Operational Research, 142 (1), 16-20, 2002.
• 17. Kazancıoğlu F., Performance assessment of Turkish state railways enterprise, Journal of the Faculty of Engineering and Architecture of Gazi University, 27 (1), 219-227, 2012.
• 18. Tone K., Toloo M., Izadikhah M., A modified slacks-based measure of efficiency in data envelopment analysis, European Journal of Operational Research, 287 (2), 560-571, 2020.
• 19. Charnes A., Cooper W., Rhodes E., Measuring the efficiency of decision making units, European Journal of Operational Research, 2, 429-444, 1978.
• 20. Kaffash S., Azizi R., Huang Y., Zhu J., A survey of data envelopment analysis applications in the insurance industry, European Journal of Operational Research, 284 (3), 801-813, 2020.
• 21. Banker R., Charnes A., Cooper W., Some models for estimating techical and scale inefficiencies in Data Envelopment Analysis, Management Science, 30, 1078-1092, 1984.
• 22. Aldamak A., Zolfaghari S., Review of efficiency ranking methods in data envelopment analysis, Measurement, 106, 161–172, 2017.
• 23. Pelone F., Kringos D. S., Romaniello A., Archibugi M., Salsiri C., Ricciardi W., Primary care efficiency measurement using data envelopment analysis: a systematic review, Journal of Medical Systems, 39, 1-14, 2015.
• 24. Mardani A., Zavadskas E. K., Streimikiene D., Jusoh A., Khoshnoudi, A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency, Renewable and Sustainable Energy Reviews, 70, 1298–1322, 2017.
• 25. Mariano E. B., Sobreiro V. A., Rebelatto D. A. D. N., Human development and data envelopment analysis: A structured literature review, Omega, 54, p. 33–49, 2015.
• 26. Sinuany-Stern Z., Mehrez A., Barboy A., Academic departments efficiency via DEA, Comput. Oper. Res., 21 (5), 543–556, 1994.
• 27. Köksalan M., Tuncer C., A dea-based approach to ranking multi-criteria alternatives, International Journal of Information Technology & Decision Making, 8, 29–54, 2009.
• 28. Ekiz M.K., Tuncer Şakar C., A new approach to rank all alternatives in data envelopment analysis: ASES, A new approach to rank all alternatives in data envelopment analysis: ASES, Journal of the Faculty of Engineering and Architecture of Gazi University, 35 (2), 683-695, 2020.
• 29. Doyle J., Green R., Strategic choice and data envelopment analysis: comparing computers across many attributes, Journal of Information Technology, 9 (1), 61–69, 1994.
• 30. Andersen P., Petersen N. C., A procedure for ranking efficient units in Data Envelopment Analysis, Management Science, 39, 1261-1264, 1993.
• 31. Jahanshahloo G. R., Junior H. V., Lotfi F. H., Akbarian D., A new DEA ranking system based on changing the reference set, European Journal of Operational Research, 181, 331–337, 2007.
• 32. Seiford L. M., Zhu J., Context-dependent data envelopment analysis-Measuring attractiveness and progress, Omega, 31 (5), 397-408, 2003.
• 33. Yang H., Pollitt M., The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: Environmental performance of Chinese coal-fired power plants, Energy Policy, 38 (8), 4440–4444, 2010.
• 34. Li H., Shi J. F., Energy efficiency analysis on Chinese industrial sectors: an improved Super-SBM model with undesirable outputs, Journal of Cleaner Production, 65, 97-107, 2014.
• 35. Chen J., Song M., Xu L., Evaluation of environmental efficiency in China using data envelopment analysis, Ecological Indicators, 52, 577–583, 2015.
• 36. Apergis N., Aye G. C., Barros C. P., Gupta R., Wanke P., Energy efficiency of selected OECD countries: A slacks based model with undesirable outputs, Energy Economics, 51, 45–53, 2015.
• 37. X. Liu, Chu J., Yin P., Sun J., DEA cross-efficiency evaluation considering undesirable output and ranking priority: a case study of eco-efficiency analysis of coal-fired power plants, Journal of Cleaner Production, 142 (2), 877-885, 2017.
• 38. Kuang B., Lu X., Zhou M., Chen D., Provincial cultivated land use efficiency in China: Empirical analysis based on the SBM-DEA model with carbon emissions considered, Technological Forecasting and Social Change, 151, 119874, 2020.
• 39. X. Lin, X. Zhu, Y. Han, Z. Geng, L. Liu, Economy and carbon dioxide emissions effects of energy structures in the world: Evidence based on SBM-DEA model, Science of The Total Environment, 729, 138947, 2020.
• 40. Zhao X., Ma X., Shang Y., Yang Z., Shahzad U., Green economic growth and its inherent driving factors in Chinese cities: Based on the Metafrontier-global-SBM super-efficiency DEA model, Gondwana Research, 106, 315-328, 2022.
• 41. Liu Y., Qu Y., Cang Y., Ding X., Ecological security assessment for megacities in the Yangtze River basin: Applying improved emergy-ecological footprint and DEA-SBM model, Ecological Indicators, 134, 108481, 2022.
• 42. M. Taleb, R. Khalid, A. Emrouznejad, R. Ramli, Environmental efficiency under weak disposability: an improved super efficiency data envelopment analysis model with application for assessment of port operations considering NetZero, Environment, Development and Sustainability, 25, 6627–6656, 2023.
• 43. S. Shuai, Z. Fan, Modeling the role of environmental regulations in regional green economy efficiency of China: Empirical evidence from super efficiency DEA-Tobit model, Journal of Environmental Management, 261,110227, 2020.
• 44. S.-R. Piao, J. Li, C.-J. Ting, Assessing regional environmental efficiency in China with distinguishing weak and strong disposability of undesirable outputs, Journal of Cleaner Production, 227, 748-759, 2019.
• 45. Y. Zhang, L. Shen, C. Shuai, J. Bian, M. Zhu, Y. Tan, G. Ye, How is the environmental efficiency in the process of dramatic economic development in the Chinese cities?, Ecological Indicators, 98, 349–362, 2019.
• 46. X. Wang, H. Ding, L. Liu, Eco-efficiency measurement of industrial sectors in China: A hybrid super-efficiency DEA analysis, Journal of Cleaner Production, 229, 53-64, 2019.
• 47. M. Song, Q. Xie, Z. Shen, Impact of green credit on high-efficiency utilization of energy in China considering environmental constraints, Energy Policy, 153, 112267, 2021.
• 48. E. Koçak, H. Kınacı, K. Shehzad, Environmental efficiency of disaggregated energy R&D expenditures in OECD: a bootstrap DEA approach, Environmental Science and Pollution Research, 28, 19381–19390, 2021.
• 49. Wu J., Q. Zhu, X. Ji, J. Chu, L. Liang, Two-stage network processes with shared resources and resources recovered from undesirable outputs, European Journal of Operational Research, 251, 1, 82–197, 2016.
• 50. Du Y. W., Jiang J., Li C. H., Ecological efficiency evaluation of marine ranching based on the Super-SBM model: A case study of Shandong, Ecological Indicators, 131, 108174, 2021.
• 51. Sueyoshi T., Yuan Y., China's regional sustainability and diversified resource allocation: DEA environmental assessment on economic development and air pollution, Energy Economics, 49 (1), 239–256, 2015.
• 52. Ozkan N. F., Haktanirlar Ulutas B., Efficiency analysis of cement manufacturing facilities in Turkey considering undesirable outputs, Journal of Cleaner Production, 156 (1), 932-938, 2017.
• 53. Du J., Liang L., Yang F., Bi G., Yu X., A new DEA‐based method for fully ranking all decision‐making units, Expert Systems, 27 (5), 363-373, 2010.
• 54. Liu W., Sharp J., DEA Models Via Goal Programming, Data Envelopment Analysis in the Service Sector, Springer, Wiesbaden, 79-101, 1999.
• 55. Koopmans T., An Analysis of Production as an Efficient Combination Of Activities, Activity Analysis of Production and Allocation, Wiley, Newyork, 33-97,1951.
• 56. Ali A. I., Seiford L. M., Translation invariance in data envelopment analysis, Operations Research Letters, 9 (6), 403-405, 1990.
• 57. Lovell C., Pastor J. T., Turner J. A., Measuring macroeconomic performance in the OECD: A comparison of European and non-European countries, European Journal of Operational Research, 87 (3), 507-518, 1995.
• 58. World Bank Open Data. World Bank Open Data.https://data.worldbank.org/. Erişim tarihi Mayıs 4, 2021.