AB Ülkelerinin Çevre ve Atık Yönetimi Performanslarının Değerlendirilmesi: Veri Zarflama Analizi ve Yapay Sinir Ağlarının Birlikte Uygulanması

Öz

Sanayinin gelişmesiyle insan ihtiyaçlarındaki artış üretimin ve tüketimin artmasına neden olmuştur. Üretim-tüketim- atık döngü zincirinde atık geri dönüşümünün yeterli olmayışı çevresel, iklimsel, ekonomik ve çoğu sorunları da birlikte getirmektedir. 2015 Döngüsel Ekonomi Eylem Planı çerçevesinde Avrupa Birliği, içerisinde atık yönetiminin de yer aldığı birçok karar almıştır. AB üye ve aday ülkeleri için geri dönüşüm kapasitesi ve atık yönetiminin iyileştirilmesi hedeflenen stratejilerde ilk sıralarda yer almaktadır. Bu çalışmada da 2015 Döngüsel Ekonomi Eylem Planı sonrasında 2017, 2018 ve 2019 yılları için AB ülkelerinin atık yönetimi performansları Veri Zarflama Analizi (VZA) ve Yapay Sinir Ağları yöntemleriyle birlikte incelenmiştir. VZA girdi değişkenleri; kişi başı belediye atık üretimi, geri kazanım için atık ithalatı, çevre korumaya yönelik ulusal harcamalar, kişi başına reel GSYİH, insani gelişmişlik indeksi, kişi başına düşen plastik ambalaj atığı üretimi, çıktı değişkeni; belediye atıklarının geri dönüşüm oranı olarak alınmıştır. VZA ile elde edilen etkinlik skorları yapay sinir ağlarında çıktı değişkeni olarak kullanılmış ve yapay sinir ağları ileri beslemeli ağlarla 2019 yılı ülkelerin etkinlik skorları için öngörüde bulunulmuştur. Elde edilen bulgularda, Slovenya, Letonya, Almanya, İrlanda, Lüksemburg ve Belçika tüm yıllarda etkin olan ve diğer ülkelerin referans kümelerinde sıkça yer alan ülkeler olduğu sonucuna ulaşılmıştır. İleri Beslemeli Yapay Sinir Ağları ile eğitilen veri setinden elde edilen 2019 tahmin değerlerinin gerçek etkinlik değerlerine oldukça yakın değerler aldığı görülmüştür.

Anahtar Kelimeler

• Çevre Kirliliği
• Atık Yönetimi
• Döngüsel Ekonomi
• Yapay Sinir Ağları
• Veri Zarflama Analizi
• Environmental Pollution
• Waste Management
• Circular Economy
• Artificial Neural Networks
• Data Envelopment Analysis

Evaluation of Environmental and Waste Management Performances of EU Countries: A Co-Application of Data Envelopement Analysis and Artificial Neural Networks

Öz

With the development of industry, the increase in human needs has led to an increase in production and consumption. The inadequacy of waste recycling in the production-consumption-waste cycle chain brings environmental, climatic, economic and many problems together. Within the framework of the 2015 Circular Economy Action Plan, the European Union has taken many decisions, including waste management. Improvement of recycling capacity and waste management for EU member and candidate countries is at the top of the targeted strategies. In this study, after the 2015 Circular Economy Action Plan, the waste management performances of EU countries for the years 2017, 2018 and 2019 were examined with the Artificial Neural Networks method based on Data Envelopment Analysis (DEA). DEA input variables; municipal waste generation per capita, import of waste for recycling, national expenditures for environmental protection, real GDP per capita, human development index, plastic packaging waste production per capita, output variable; taken as the recycling rate of municipal waste. Efficiency scores obtained with DEA were used as output variables in artificial neural networks, and predictions were made for the efficiency scores of countries in 2019 with artificial neural networks feed-forward networks. In the findings, it has been concluded that Slovenia, Latvia, Germany, Ireland, Luxembourg and Belgium are the countries that are active in all years and are frequently included in the reference clusters of other countries. It has been observed that the 2019 prediction values obtained from the data set trained with Feed Forward Artificial Neural Networks are very close to the real efficiency values.

Anahtar Kelimeler

• Environmental Pollution
• Waste Management
• Circular Economy
• Artificial Neural Networks
• Data Envelopment Analysis

Kaynakça

1. Acı, A. (2015). Ekonomik İşbirliği ve Kalkınma Teşkilatı (OECD) Ülkelerinin Ar-Ge Etkinliklerinin Veri Zarflama Analizi (VZA) Yöntemi ile Belirlenmesi. Yayınlanmamış Yüksek Lisans Tezi. Gazi Üniversitesi Bilişim Enstitüsü, Ankara.
2. Agarwal, S. (2016). DEA-neural networks approach to assess the performance of public transport sector of India. Opsearch, 53(2), 248-258.
3. Altın, F. G. (2022). A Fuzzy Data Envelopment Analysis-Based Performance Assessment of European Union Countries' Waste Management Practices. In Handbook of Research on Advances and Applications of Fuzzy Sets and Logic (pp. 29-49). IGI Global.
4. Ateş, E. (2021). Döngüsel Ekonomi Kapsamında GSYİH ile Geri Dönüşüm İlişkisi: Avrupa Birliği Ülkeleri Örneği. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (67), 125-137.
5. Banker, R.D., Charnes, A. ve Cooper, W.W., (1984). Some Models For Estimating Technical And Scale Inefficiencies in Data Envelopment Analysis, Management Science, 30(9), 1078-192
6. Barros, C. P. ve Wanke, P. (2014). Insurance companies in Mozambique: a two-stage DEA and neural networks on efficiency and capacity slacks. Applied Economics, 46(29), 3591-3600.
7. Benito, B., Bastida, F., ve García, J. A. (2010). Explaining differences in efficiency: an application to Spanish municipalities. Applied economics, 42(4), 515-528.
8. Bolat, B., Temur, G. T. ve Gürler, H. (2016). Türkiye'deki Havalimanlarının Etkinlik Tahmini: Veri Zarflama Analizi ve Yapay Sinir Ağlarının Birlikte Kullanımı. Ege academic review, 16.

9. Bosch, N., Pedraja, F. ve Suárez‐Pandiello, J. (2000). Measuring the efficiency of Spanish municipal refuse collection services. Local Government Studies, 26(3), 71-90.
10. Callao, C., Martinez-Nuñez, M. ve Latorre, M. P. (2019). European Countries: Does common legislation guarantee better hazardous waste performance for European Union member states?. Waste management, 84, 147-157.
11. Castillo-Giménez, J., Montañés, A., & Picazo-Tadeo, A. J. (2019). Performance and convergence in municipal waste treatment in the European Union. Waste Management, 85, 222-231.
12. Charnes, A., Cooper,W.W. ve Rhodes, E., (1978). Measuring The Efficiency Of Decision Making Units, European Journal of Operational Research, 2, 429–444.
13. Chen, H. W., Chang, N. B., Chen, J. C. ve Tsai, S. J. (2010). Environmental performance evaluation of large-scale municipal solid waste incinerators using data envelopment analysis. Waste Management, 30(7), 1371-1381. CPS (2012). Atık Yönetimi Hakkında AB Müktesebat Rehberi. İstanbul & Brüksel. http://www.mess.org.tr/media/filer_public/6b/58/6b583c70-1daa-4bc5-96b5- 9c988df39db1/mess_atik_yonetimi_ab_mevzuat_rehberi.pdf, Erişim tarihi: 20.10.2017
14. Cristóbal, J., Limleamthong, P., Manfredi, S., & Guillén-Gosálbez, G. (2016). Methodology for combined use of data envelopment analysis and life cycle assessment applied to food waste management. Journal of Cleaner Production, 135, 158-168.
15. Çakın, E., ve Özdemir, A. (2019). Veri Zarflama Analizi Temelli Yapay Sinir Ağları ve Lojistik Regresyon Analizi ile Teknoloji Geliştirme Bölgelerinin Etkinliklerinin Tahminlenmesi. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 37(2), 271-293.
16. Çuhadar, M. ve Kayacan, C. (2005). Yapay Sinir Ağları Kullanılarak Konaklama İşletmelerinde Doluluk Oranı Tahmini: Türkiye'deki Konaklama İşletmeleri Üzerine Bir Deneme. Anatolia: Turizm Araştırmaları Dergisi, 16(1), 24-30.
17. European Environment Agency (EEA). The European Environment- State and Outlook 2015- synthesis report. https://www.eea.europa.eu/soer/2015/synthesis/report/0c-executivesummary Eriş. tarihi: 08.10.2022
18. Europen Comission, (2015b), Proposal for a Directive of the Europen Parliment and of the Council Amending Drective 2008/98/EC.
19. Europen Comission-EC (2015a). EU Action for the Circular Economy. 02.12.2015. EUR-Lex - 52015DC0614 – EN
20. Exposito, A. ve Velasco, F. (2018). Municipal solid-waste recycling market and the European 2020 Horizon Strategy: A regional efficiency analysis in Spain. Journal of Cleaner Production, 172, 938-948.
21. Fura, B., Stec, M. & Mis, T. (2020). Statistical Evaluation of the Level of Development of Circular Economy in European Union Member Countries. Energies, 13, 2-23.
22. Giannakitsidou, O., Giannikos, I., & Chondrou, A. (2020). Ranking European countries on the basis of their environmental and circular economy performance: A DEA application in MSW. Waste management, 109, 181-191.
23. Giannakitsidou, O., Giannikos, I., & Chondrou, A. (2020). Ranking European countries on the basis of their environmental and circular economy performance: A DEA application in MSW. Waste management, 109, 181-191.
24. Halkos, G. ve Petrou, K. N. (2017). Assessing waste generation efficiency in EU regions towards sustainable environmental policies. Sustainable Development, 26(3), 281-301.
25. Halkos, G. ve Petrou, K. N. (2019). Assessing 28 EU member states' environmental efficiency in national waste generation with DEA. Journal of Cleaner Production, 208, 509-521.
26. Ji, Y.B. ve Lee C. (2010), Data Envelopment Analysis in Stata. The Stata Journal, 10(2), 1-13.
27. Kazemi, M. ve Faezirad, M. (2018). Efficiency estimation using nonlinear influences of time lags in DEA Using Artificial Neural Networks. Industrial Management Journal, 10(1), 17-34.
28. Kuznets, S. (1955). “Economic Growth and Income Inequality”, The American Economic Review, 45/1, 1-28.
29. Kwon, H. B. ve Lee, J. (2015). Two-stage production modeling of large US banks: A DEA-neural network approach. Expert Systems with Applications, 42(19), 6758-6766.
30. Lacko, R. ve Hajduová, Z. (2018). Determinants of environmental efficiency of the EU countries using two-step DEA approach. Sustainability, 10(10), 3525.
31. Li, E.Y. (1994), Artificial Neural Networks and Their Business Applications. Information & Management, 27, 303-313.
32. Liu, H. H., Chen, T. Y., Chiu, Y. H. ve Kuo, F. H. (2013). A comparison of three-stage DEA and artificial neural network on the operational efficiency of semi-conductor firms in Taiwan. Modern Economy, 4(1), 20-31.
33. Malinauskaite, J., Jouhara, H., Czajczyńska, D., Stanchev, P., Katsou, E., Rostkowski, P., ... ve Spencer, N. (2017). Municipal solid waste management and waste-to-energy in the context of a circular economy and energy recycling in Europe. Energy, 141, 2013-2044.
34. Marino, A. & Pariso, P. (2020). Comparing European Countries' Performances in the Transition Towards the Circular Economy. Science of the Total Environment, 729, 1-12.
35. Marques, R. C. ve Simões, P. (2009). Incentive regulation and performance measurement of the Portuguese solid waste management services. Waste Management & Research, 27(2), 188-196.
36. Moore, A., Nolan, J. ve Segal, G. F. (2005). Putting out the trash: measuring municipal service efficiency in US cities. Urban Affairs Review, 41(2), 237-259.
37. Mostafa, M. M. (2009). Modeling the efficiency of top Arab banks: A DEA–neural network approach. Expert systems with applications, 36(1), 309-320.
38. Önder, H. (2018). Sürdürülebilir kalkınma anlayışında yeni bir kavram: döngüsel ekonomi. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 57, 196-204.
39. Özden, Ü. (2008). Veri zarflama analizi (VZA) ile Türkiye’deki vakıf üniversitelerinin etkinliğinin ölçülmesi. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 37(2), 167-185.
40. Öztemel, E. (2003). Yapay sinir ağları. PapatyaYayincilik, Istanbul.
41. Pais-Magalhães, V., Moutinho, V. ve Marques, A. C. (2021). Scoring method of eco-efficiency using the DEA approach: Evidence from European waste sectors. Environment, Development and Sustainability, 23(7), 9726-9748.
42. Pelău, C., & Chinie, A. C. (2018). Econometric model for measuring the impact of the education level of the population. Amfiteatru Economic, 20(48), 340-355.
43. Pires, A. ve Martinho, G. (2019). Waste hierarchy index for circular economy in waste management. Waste Management, 95, 298-305.
44. Ríos, A. M., & Picazo-Tadeo, A. J. (2021). Measuring environmental performance in the treatment of municipal solid waste: The case of the European Union-28. Ecological Indicators, 123, 107328.
45. Robaina, M., Murillo, K., Rocha, E. & Villar, J. (2020). Circular Economy in Plastic Waste - Efficiency Analysis of European Countries. Science of the Total Environment, 730, 1-12.
46. Rogge, N. ve De Jaeger, S. (2012). Evaluating the efficiency of municipalities in collecting and processing municipal solid waste: A shared input DEA-model. Waste Management, 32(10), 1968-1978.
47. Sayın, F. (2022). Döngüsel Ekonomi Yaklaşımında İnsani Gelişmenin Atık Yönetimi Üzerindeki Etkilerinin İncelenmesi: Avrupa Birliği Ülkeleri için Dinamik Panel Veri Analiz Bulguları. İzmir İktisat Dergisi, 37(3), 673-685.
48. Shabanpour, H., Yousefi, S. ve Saen, R. F. (2017). Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks. Journal of cleaner production, 142, 1098-1107.
49. Simões, P., De Witte, K. ve Marques, R. C. (2010). Regulatory structures and operational environment in the Portuguese waste sector. Waste Management, 30(6), 1130-1137.
50. Singh, J. ve Ordonez, I. (2016). Resource recovery from post-consumer waste: important lessons for the upcoming circular economy. Journal Of Cleaner Production, 134, 342-353.
51. Skrinjaric, T. (2020). Empirical assessment of the circular economy of selected European Countries. Journal Of Cleaner Production, 255, 1-17.
52. Sreekumar, S. ve Mahapatra, S. S. (2011). Performance modeling of Indian business schools: a DEA‐neural network approach. Benchmarking: An International Journal ,18(2), 221-239.
53. Şen, Z. (2004). Yapay sinir ağları. Su Vakfı.
54. Taboada, G. L., Seruca, I., Sousa, C., & Pereira, Á. (2020). Exploratory data analysis and data envelopment analysis of construction and demolition waste management in the European Economic Area. Sustainability, 12(12), 4995.
55. Vlontzos, G. ve Pardalos, P. M. (2017). Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks. Renewable and Sustainable Energy Reviews, 76, 155-162.
56. Worthington, A. C. ve Dollery, B. E. (2001). Measuring efficiency in local government: an analysis of New South Wales municipalities' domestic waste management function. Policy Studies Journal, 29(2), 232-249.
57. Wu, D. (2009). Supplier selection: A hybrid model using DEA, decision tree and neural network. Expert systems with Applications, 36(5), 9105-9112.
58. Wu, D. D., Yang, Z. ve Liang, L. (2006). Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert systems with applications, 31(1), 108-115.
59. Yılmaz, V. (2022). Avrupa Birliği Ülkelerinin Döngüsel Ekonomi Performansı. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 23(1), 94-114.
60. Yüksel, H. İ. L. M. İ. (2012). Evaluating the success of waste collection programs of municipalities with data envelopment analysis. International journal of environmental protection, 2(5).