Bazı Avrupa ülkeleri ve Türkiye'nin tarımsal çevre ve ekonomi göstergelerine dayalı performanslarının ÇKKV yöntemleri ile değerlendirmesi

Öz

Amaç: Bu çalışma, bazı Avrupa Birliği ülkeleri ile Türkiye’nin tarımsal faaliyetlerinin çevre ve ekonomi üzerindeki etkilerini karşılaştırmalı olarak değerlendirmeyi amaçlamaktadır. Tarımsal çevre göstergeleri ile ekonomik üretim gücünü birlikte ele alan analiz, ülkelerin tarımsal sürdürülebilirlik düzeylerini bütüncül bir bakış açısıyla ortaya koymaktadır. Böylece çalışma, çevresel baskılar ile üretim yoğunluğu arasındaki dengenin ülkeler arasında nasıl farklılaştığını inceleyerek sürdürülebilir tarım politikalarının geliştirilmesine önemli ölçüde katkı sağlamaktadır. Tasarım/Metodoloji/Yaklaşım: Araştırmada, EUROSTAT veri tabanından elde edilen 2000–2023 dönemi yıllık ortalama değerlere dayanan on çevresel gösterge (K1–K10) ile FAOSTAT veri tabanından alınan 2019–2023 dönemi ortalama Brüt Üretim Değeri (K11) olmak üzere toplam on bir kriter kullanılmıştır. Yirmi sekiz ülke iki aşamalı olarak değerlendirilmiştir: İlk aşamada yalnızca çevresel kriterler dikkate alınmış; ikinci aşamada ekonomik boyutun analize dahil edilmesiyle sıralamalar yeniden oluşturulmuştur. Analiz sürecinde MOOSRA, MABAC, ROV, TOPSIS, PSI, WASPAS, REF-I ve REF-II yöntemleri uygulanmış; elde edilen sıralamalar BORDA yöntemi aracılığıyla bütünleştirilmiştir. Tüm hesaplamalar Python tabanlı Ka-Decision yazılımı kullanılarak kapsamlı şekilde gerçekleştirilmiştir. Bulgular: Çevresel göstergeler değerlendirildiğinde Estonya, Letonya, İsveç, Finlandiya ve Slovakya en yüksek performansa sahip ülkeler olarak belirlenmiştir. Ancak ekonomik kriterin dahil edilmesiyle bu ülkelerin genel sıralamada gerilediği; buna karşılık Fransa, Türkiye ve Almanya gibi yüksek üretim kapasitesine sahip ülkelerin öne çıktığı görülmüştür. Bu sonuç, çevresel performans ile üretim yoğunluğu arasında belirgin ters yönlü bir ilişki bulunduğunu göstermektedir. Radar grafiklerinde çevresel açıdan zayıf ülkelerin yüksek üretim kapasitelerine rağmen çevresel etkilerinin daha olumsuz olduğu görülmüştür. Özgünlük/Değer: Çalışma, çevresel ve ekonomik göstergelerin birlikte değerlendirilmesiyle tarımsal sürdürülebilirliğe çok boyutlu bir yaklaşım sunarak politika yapıcılar için son derece önemli stratejik çıktılar oluşturmaktadır.

Anahtar Kelimeler

• Avrupa ülkeleri
• Çok Kriterli Karar Verme (ÇKKV)
• Ka-Decision
• tarımsal çevre göstergeleri

Evaluation of the performance of some European countries and Turkey based on agricultural environment and economic indicators using MCDM methods

Öz

Purpose: This study aims to comparatively assess the environmental and economic impacts of agricultural activities in some European Union countries and Turkey. By considering agricultural environmental indicators and economic production capacity together, the analysis reveals the agricultural sustainability levels of countries from a holistic perspective. Thus, by examining how the balance between environmental pressures and production intensity varies across countries, the study significantly contributes to the development of sustainable agricultural policies. Design/Methodology/Approach: The study employed a total of eleven criteria: ten environmental indicators (K1–K10) based on annual average values from the EUROSTAT database for the period 2000–2023, and the average Gross Production Value (K11) from the FAOSTAT database for the period 2019–2023. Twenty-eight countries were evaluated in two stages: In the first stage, only environmental criteria were considered; in the second stage, the rankings were re-established by incorporating the economic dimension into the analysis. MOOSRA, MABAC, ROV, TOPSIS, PSI, WASPAS, REF-I, and REF-II methods were applied during the analysis process, and the resulting rankings were integrated using the BORDA method. All calculations were comprehensively performed using the Python-based Ka-Decision software. Findings: When environmental indicators were evaluated, Estonia, Latvia, Sweden, Finland, and Slovakia were identified as the highest-performing countries. However, with the inclusion of the economic criterion, these countries were found to have a decline in the overall ranking, while countries with high production capacity, such as France, Turkey, and Germany, emerged. This result demonstrates a significant inverse relationship between environmental performance and production intensity. Radar charts reveal that environmentally weak countries, despite their high production capacity, have more negative environmental impacts. Originality/Value: By evaluating environmental and economic indicators together, the study provides a multidimensional approach to agricultural sustainability, generating crucial strategic outcomes for policymakers.

Anahtar Kelimeler

• European Countries
• Multi-Criteria Decision Making (MCDM)
• Ka-Decision
• Agricultural environment indicators

Kaynakça

1. Aytekin, A. (2020). Çok Kriterli Karar Problemine Uzaklık ve Referans Temelli Çözüm Yaklaşımı. Yayımlanmamış Doktora Tezi, Anadolu Üniversitesi.
2. Borda, J. C. D. (1781), Mémoire sur les élections au scrutin. Histoire de l’Academie Royale Des Sciences, 102, 657–665.
3. Brauers, W. K. (2003), Optimization methods for a stakeholder society: a revolution in economic thinking by multi-objective optimization (Vol. 73), Springer Science & Business Media.
4. Çakır, S., & Perçin, S. (2013), Çok Kriterli Karar Verme Teknikleriyle Lojistik Firmalarında Performans Ölçümü. Ege Akademik Bakış Dergisi, 449-459.
5. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı, (2025), https://cevreselgostergeler.csb.gov.tr/erozyon-tehlikesi-altindaki-alanlar-i-85769.
6. Çukur, T., & Işın, F. (2024), Bazı Avrupa Birliği ülkelerinin organik tarım performanslarının TOPSIS yöntemiyle değerlendirilmesi. Tarım Ekonomisi Dergisi, 30(2), 99-109.
7. Das, M. C., Sarkar, B., & Ray, S. (2012), Decision making under conflicting environment: A new MCDM method. International Journal of Applied Decision Sciences, 5(2), 142–162.
8. European Commission. (2021), The common agricultural policy: 2023–2027. Directorate-General for Agriculture and Rural Development. https://agriculture.ec.europa.eu/common-agricultural-policy/cap-overview/cap-2023-27_en.

9. European Commission. (2022a), Commission Implementing Decision C(2022) 8657 final approving the CAP Strategic Plan 2023-2027 of Estonia.
10. European Commission. (2022b), Commission Implementing Decision approving the CAP Strategic Plan 2023–2027 of the Slovak Republic. Brussels: C(2022) 7345 final.
11. European Commission. (2022c), CAP Strategic Plan 2023–2027 of Finland. Retrieved July 13, 2025, from https://agriculture.ec.europa.eu/cap-my-country/cap-strategic-plans/finland_en.
12. European Commission. (2023), EU Agricultural Outlook for Markets, Income and Environment, 2023-2035. Brussels: DG AGRI.
13. European Commission. (2024), Draft regulation on the safe use of RENURE products derived from livestock manure (COM(2024) 182 final),
14. European Court of Auditors. (2024), Special Report 20/2024: Eco-schemes—More ambition needed to reduce nutrient pollution in EU agriculture. Luxembourg: Publications Office of the European Union.
15. European Environment Agency (2022), National emissions reported to the UNFCCC and to the EU Greenhouse Gas Monitoring Mechanism. https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc.
16. FAO. (2021), The state of the world’s land and water resources for food and agriculture 2021 Systems at breaking point (SOLAW 2021), Food and Agriculture Organization of the United Nations.
17. FiBL & IFOAM. (2023), The World of Organic Agriculture: Statistics and Emerging Trends. Frick & Bonn: FiBL & IFOAM – Organics International. https://www.organic-world.net/yearbook/yearbook-2023.html.
18. FiBL & IFOAM. (2025), The World of Organic Agriculture – Statistics and Emerging Trends 2025. Frick & Bonn.
19. Financial Times. (2025, January 18), Farmers step up protests in Italy and Netherlands over green rules. London.
20. Gabbrielli, E. (2016), Impact of Agro-environmental measures in the Tuscany Region. Geographic Multi-Criteria Analysis. Italian Review of Agricultural Economics (REA), 71(1), 607-634. https://doi.org/10.13128/REA-18676.
21. Gómez-Limón, J. A., Arriaza, M., & Guerrero-Baena, M. D. (2020), Building a composite indicator to measure environmental sustainability using alternative weighting methods. Sustainability, 12(11), 4398. https://doi.org/10.3390/su12114398.
22. Gürlük, S., & Uzel, H. B. (2016), An evaluation of agri-environmental indicators through a multi-criteria decision-making tool in Germany, France, the Netherlands, and Turkey. Polish Journal of Environmental Studies, 25(6), 2407–2414. https://doi.org/10.15244/pjoes/62127.
23. HELCOM. (2021), Baltic Sea Action Plan 2021–2030. Helsinki: Baltic Marine Environment Protection Commission. https://helcom.fi/baltic-sea-action-plan.
24. Ka-decision, (2025), https://github.com/kadirkirda/ka-decision.
25. Kırda, K., & Aytekin, A. (2024), Assessing industrialized countries’ environmental sustainability performances using an integrated multi-criteria model and software. Environment, Development and Sustainability, 26(7), 17505-17550. https://doi.org/10.1007/s10668-023-03349-z.
26. Latvia Ministry of Agriculture. (2020), Code of Good Agricultural Practice for the Reduction of Nitrate Pollution. Riga: Ministry of Agriculture. https://www.zm.gov.lv.
27. Madiyoh, A. (2020), Güneydoğu Asya ÜLkeler Birliği’ne ÜYe ÜLkelerin (ASEAN) Tarim Sektörlerinin ÇOk Kriterli Karar Verme Yöntemleri İle İncelenmesi (Doctoral dissertation, Bursa Uludag University (Turkey)).
28. Maniya, K., & Bhatt, M. G. (2010), A selection of material using a novel type decision-making method: Preference selection index method. Materials and Design, 31(4), 1785–1789. https://doi.org/10.1016/j.matdes.2009.11.020.
29. Marković, M., Stanković, J., Marjanović, I., Krstić, B., & Papathanasiou, J. (2024), Multi-criteria measurement of agri-environmental performance in European Union countries. Ekonomika Poljoprivrede, 71(3), 835-851. https://doi.org/10.59267/ekoPolj2403835M.
30. Mulliner, E., Malys, N., & Maliene, V. (2016), Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega, 59, 146–156. https://doi.org/10.1016/J.OMEGA.2015.05.013.
31. Müller Karulis, B., McCrackin, M. L., Dessirier, B., Gustafsson, B. G., & Humborg, C. (2024), Legacy nutrients in the Baltic Sea drainage basin: How past practices affect eutrophication management. Journal of Environmental Management, 370, 122478.
32. Namiotko, V., Galnaityte, A., Krisciukaitiene, I., & Balezentis, T. (2022), Assessment of agri-environmental situation in selected EU countries: A multi-criteria decision-making approach for sustainable agricultural development. Environmental Science and Pollution Research, 29(17), 25556-25567. https://doi.org/10.1007/s11356-021-17655-4.
33. OECD. (2023), Measuring the environmental performance of agriculture across OECD countries. OECD Publishing. https://doi.org/10.1787/4edcd747-en.
34. Özkan, O., Destek, M. A., & Erdem, A. (2024), Assessing the environmental impact of fertilizer consumption in Turkey. The Science of the total environment, 955, 177107. https://doi.org/10.1016/j.scitotenv.2024.177107.
35. Özkaya, G. (2022), Country comparisons on the concept of economic freedom: A multi-criteria decision-making approach. Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 20(03), 245-268. https://doi.org/10.18026/cbayarsos.1098468.
36. Pamučar, D., & Ćirović, G. (2015), The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC), Expert Systems with Applications, 42(6), 3016–3028. https://doi.org/10.1016/J. ESWA. 2014.11.057.
37. Shin, E., Shin, Y., Lee, S. W., & An, K. (2024), Evaluating the environmental factors of organic farming areas using the Analytic Hierarchy Process. Sustainability, 16(6), 2395.https://doi.org/10.3390/su16062395.
38. Streimikiene, D., Remeikiene, R., & Lapinskienė, G. (2024), Evaluating agri-environmental indicators for land use impact in Baltic countries using multi-criteria decision-making and EUROSTAT data. Land, 13(12), 2238. https://doi.org/10.3390/land13122238.
39. Triantaphyllou, E. (2000), Multi-criteria decision making methods. Boston, MA: Springer. https://doi.org/10.1007/978-1- 4757- 3157-6_2.
40. Yakowitz, D. S., Lane, L. J., & Szidarovszky, F. (1993), Multi-attribute decision making: Dominance with respect to an importance order of the attributes. Applied Mathematics and Computation, 54(2–3), 167–181. https://doi.org/10.1016/0096- 3003(93) 90057-L.
41. Yıldırır, M. (2020), Water quality and two-way effects in terms of animal production. Toprak Su Dergisi, 9(2), 122-129. https://doi.org/10.21657/ topraksu.780468.
42. Yoon, K., & Hwang, C. L. (1980), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)- A Multiple Attribute Decision Making. In A state-of-the-at survey.
43. Zafeiriou, E., Mallidis, I., Galanopoulos, K., & Arabatzis, G. (2018), Greenhouse Gas Emissions and Economic Performance in EU Agriculture: An Empirical Study in a Non-Linear Framework. Sustainability, 10(11), 3837. https://doi.org/10.3390/su10113837.
44. Zafeiriou, E., Sofios, S., & Partalidou, X. (2017), Environmental Kuznets curve for EU agriculture: Empirical evidence from new entrant EU countries. Environmental Science and Pollution Research, 24, 15510-15520.
45. Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012), Optimization of weighted aggregated sum product assessment, Elektronika ir elektrotechnika. 122(6), 3-6. https://doi:10.5755/j01.eee.122.6.1810.