Journals / Fiscaoeconomia / 2025 / Volume: 9 Issue: 4

Integration of Global Performance Indicators with Aggregation Techniques

Küresel Performans Göstergelerinin Toplulaştırma Teknikleri ile Entegrasyonu

Erhan Orakçı(HAKKARİ ÜNİVERSİTESİ)0000-0001-8468-5710
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Publication Date
2025-11-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The study emphasizes the importance of policy reforms, investments in education and health, environmental sustainability, and innovation.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Evaluating country performance using a single index is often insufficient because these indices typically specialize in specific areas.
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Key Findings & Data:This study aims to provide a more comprehensive assessment of country performances by using various indices together, including the Human Development Index (HDI), Environmental Performance Index (EPI), Global Innovation Index (GII), World Happiness Report (WHR), and World Justice Project Rule of Law Index (WJP RLI).
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Conclusion & Impact:The study emphasizes the importance of policy reforms, investments in education and health, environmental sustainability, and innovation.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:RAT · Social Choice Functions · Indices

Abstract

Evaluating country performance using a single index is often insufficient because these indices typically specialize in specific areas. This study aims to provide a more comprehensive assessment of country performances by using various indices together, including the Human Development Index (HDI), Environmental Performance Index (EPI), Global Innovation Index (GII), World Happiness Report (WHR), and World Justice Project Rule of Law Index (WJP RLI). Additionally, the results of these indices are aggregated using the Borda, Copeland, Dodgson, and RAT techniques, and the outcomes are compared. The study shows that the RAT technique provides more precise and consistent results compared to the other techniques. The RAT technique was able to rank all countries fully without assigning the same rank to multiple alternatives. In contrast, the Borda, Copeland, and Dodgson techniques faced difficulties in fully ranking the countries, often assigning the same rank to multiple countries. Nevertheless, a high degree of consistency was found among the rankings obtained with the used techniques. Considering country performances, Denmark, Finland, and Sweden demonstrate high performance, while Romania, Hungary, Bulgaria, and Montenegro show lower performance. Turkey generally ranks lower and needs improvements in various areas. The study emphasizes the importance of policy reforms, investments in education and health, environmental sustainability, and innovation.

Özet

Ülke performansını tek bir indeks kullanarak değerlendirmek, bu indekslerin genellikle belirli bir alanda uzmanlaşmış olması nedeniyle yetersiz kalmaktadır. Bu çalışmada, İnsani Gelişme İndeksi (İGE), Çevresel Performans İndeksi (ÇPE), Küresel İnovasyon İndeksi (KİE), Dünya Mutluluk Raporu (DMR) ve Dünya Adalet Projesi Hukukun Üstünlüğü İndeksi (DAP HÜE) gibi farklı indekslerin bir arada kullanılarak ülke performanslarının daha kapsamlı bir şekilde değerlendirilmesi amaçlanmaktadır. Ayrıca, bu indekslerin sonuçlarını toplulaştırmak için kullanılan Borda, Copeland, Dodgson ve RAT tekniklerinin sonuçları karşılaştırılmıştır. Çalışma, RAT tekniğinin diğer tekniklere göre daha kesin ve tutarlı sonuçlar verdiğini göstermektedir. RAT tekniği, tüm ülkeleri tam olarak sıralayabilmiş ve aynı sıraya birden fazla alternatif atamamıştır. Buna karşılık, Borda, Copeland ve Dodgson teknikleri bazı durumlarda aynı sıraya birden fazla ülke atayarak tam sıralama yapmada zorluk yaşamıştır. Bununla beraber kullanılan tekniklerle elde edilen sıralamalar arasında yüksek düzeyde tutarlılık olduğu belirlenmiştir. Ülke performansları dikkate alındığında, Danimarka, Finlandiya ve İsveç gibi ülkeler yüksek performans sergilerken, Romanya, Macaristan, Bulgaristan ve Karadağ daha düşük performans göstermektedir. Türkiye ise genel olarak alt sıralarda yer almakta ve çeşitli alanlarda iyileştirmelere ihtiyaç duymaktadır. Çalışma, politika reformları, eğitim ve sağlık yatırımları, çevresel sürdürülebilirlik ve yenilikçilik alanlarında yapılacak iyileştirmelerin önemini vurgulamaktadır.

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Cite This Article

Orakçı, E. (2025). Integration of Global Performance Indicators with Aggregation Techniques. Fiscaoeconomia, Volume: 9 Issue: 4. https://doi.org/10.25295/fsecon.1543664

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References (93)

  1. Adler, N., Yazhemsky, E., & Tarverdyan, R. (2010). A framework to measure the relative socio-economic performance of developing countries. Socio-Economic Planning Sciences, 44(2), 73-88. https://doi.org/10.1016/j.seps.2009.08.001
  2. Akdemir, D. M., & Şimşek, O. (2023). A financial performance evaluation via hybrid MCDM methods: A case of Amazon. com Inc. Istanbul Business Research, 52(1), 199-232. https://doi.org/10.26650/ibr.2023.52.994729
  3. Alao, M. A., Popoola, O. M., & Ayodele, T. R. (2022). Sustainable prime movers selection for biogas-based combined heat and power for a community microgrid: A hybrid fuzzy multi criteria decision-making approach with consolidated ranking strategies. Energy Conversion and Management: X, 16, 100281. https://doi.org/10.1016/j.ecmx.2022.100281
  4. Almutairi, K., Mostafaeipour, A., Jahanshahi, E., Jooyandeh, E., Himri, Y., Jahangiri, M., Issakhov, A., Chowdhury, S., Dehshiri, S. J. H., Dehshiri, S. S. H., & Techato, K. (2021). Ranking locations for hydrogen production using hybrid wind-solar: A case study. Sustainability, 13(8), 4524. https://doi.org/10.3390/su13084524
  5. Amin, M. M., Sutrisman, A., & Dwitayanti, Y. (2023). Group decision support system model to determine supervisor lecturers for student creativity programs. Bulletin of Electrical Engineering and Informatics, 12(4), 2484-2494. https://doi.org/10.11591/eei.v12i4.4784
  6. Arfanuzzaman, M. (2016). Impact of CO2 emission, per capita income and HDI on Environmental Performance Index: Empirical evidence from Bangladesh. International Journal of Green Economics, 10(3-4), 213-225. https://doi.org/10.1504/IJGE.2016.081900
  7. Arrow, K. J. (2012). Social choice and individual values (Vol. 12). Yale university press.
  8. Aytekin, A., & Orakçı, E. (2020). Spor kulüplerinin performanslarının çok kriterli karar verme ve toplulaştırma teknikleriyle incelenmesi. Ekonomi Politika ve Finans Araştırmaları Dergisi, 5(2), 435-470. https://doi.org/10.30784/epfad.752483
  9. Aytekin, A., Ecer, F., Korucuk, S., & Karamaşa, Ç. (2022). Global innovation efficiency assessment of EU member and candidate countries via DEA-EATWIOS multi-criteria methodology. Technology in Society, 68, 101896. https://doi.org/10.1016/j.techsoc.2022.101896
  10. Bączkiewicz, A., Kizielewicz, B., Shekhovtsov, A., Wątróbski, J., & Sałabun, W. (2021). Methodical aspects of MCDM based E-commerce recommender system. Journal of Theoretical and Applied Electronic Commerce Research, 16(6), 2192-2229. https://doi.org/10.3390/jtaer16060122
  11. Baykul, A. (2022). İnovasyonun belirleyicileri: Küresel inovasyon indeksi üzerinde bir araştırma. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 7(1), 52-66. https://doi.org/10.29106/fesa.1052116
  12. Berkowitz, I. (1979). Social choice and policy formulation: Problems and considerations in the construction of the public interest. J. Soc. & Soc. Welfare, 6, 533.
  13. Biswas, S., Bandyopadhyay, G., & Mukhopadhyaya, J. N. (2022a). A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian FMCG and consumer durable sector. Decision Making: Applications in Management and Engineering, 5(2), 140-175. https://doi.org/10.31181/dmame0306102022b
  14. Biswas, S., Bandyopadhyay, G., & Mukhopadhyaya, J. N. (2022b). A multi-criteria based analytic framework for exploring the impact of Covid-19 on firm performance in emerging market. Decision Analytics Journal, 5, 100143. https://doi.org/10.1016/j.dajour.2022.100143
  15. Block, S., Emerson, J. W., Esty, D. C., de Sherbinin, A., & Wendling, Z. A. (2024). 2024 environmental performance index. New Haven, CT: Yale Center for Environmental Law & Policy. epi.yale.edu. https://epi.yale.edu/measure/2024/EPI
  16. Boyacı, A. Ç. (2021). Which OECD countries are advantageous in fight against COVID-19?. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 37(1), 137-148.
  17. Chen, R., Ji, Y., Jiang, G., Xiao, H., Xie, R., & Zhu, P. (2022). Composite index construction with expert opinion. Journal of Business & Economic Statistics, 41(1), 67-79. https://doi.org/10.1080/07350015.2021.2000418
  18. Chen, Y., Su, X., & Hipel, K. W. (2009). An index aggregation approach to comparing the overall performance of emerging and developed countries. Socio-Economic Planning Sciences, 43(1), 25-39. https://doi.org/10.1016/j.seps.2008.02.004
  19. Copeland, A. H. (1951). A reasonable social welfare function. mimeo, 1951. University of Michigan.
  20. Cornell University, INSEAD ve World Intellectual Property Organization. (2023). Global innovation index 2023. https://www.wipo.int/global_innovation_index/en/2023/
  21. Cracolici, M. F., Cuffaro, M., & Nijkamp, P. (2010). The measurement of economic, social and environmental performance of countries: A novel approach. Social indicators research, 95, 339-356. https://doi.org/10.1007/s11205-009-9464-3
  22. Çubukcu, A., Pervin, Ö. A., Boz, E., & Çalık, A. (2023). An idea evaluation phase in online communities: A case on the COVID-19 innovation platform. Journal of Organisational Studies and Innovation, 10(4), 1-26. https://doi.org/10.51659/josi.22.186
  23. Danış, Y. A. (2022). OECD ülkelerinin TOPSIS, VIKOR ve GRA yöntemleri kullanılarak refah göstergelerine göre sıralanması ve bütünleşik bir çözüm önerisi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 17(2), 433-454. 10.17153/oguiibf.1073257
  24. De Condorcet, N. (2014). Essai sur l'application de l'analyse à la probabilité des décisions rendues à la pluralité des voix. Cambridge University Press.
  25. Donyaii, A., Sarraf, A., & Ahmadi, H. (2020). Using composite ranking to select the most appropriate Multi-Criteria Decision Making (MCDM) method in the optimal operation of the Dam reservoir. Journal of Hydraulic Structures, 6(2), 1-22. https://doi.org/10.22055/jhs.2020.34402.1142
  26. Dortaj, A., Maghsoudy, S., Ardejani, F. D., & Eskandari, Z. (2020). A hybrid multi-criteria decision making method for site selection of subsurface dams in semi-arid region of Iran. Groundwater for Sustainable Development, 10, 100284. https://doi.org/10.1016/j.gsd.2019.100284
  27. Ecer, F. (2021). A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. Renewable and Sustainable Energy Reviews, 143, 110916. https://doi.org/10.1016/j.rser.2021.110916
  28. Elsheikh, Y., Alqasrawi, Y., & Azzeh, M. (2022). On obtaining a stable vote ranking methodology for implementing e-government strategies. Journal of King Saud University-Computer and Information Sciences, 34(6), 3379-3392. https://doi.org/10.1016/j.jksuci.2020.11.035
  29. Figge, L., Oebels, K., & Offermans, A. (2017). The effects of globalization on Ecological Footprints: an empirical analysis. Environment, Development and Sustainability, 19, 863-876. 10.1007/s10668-016-9769-8
  30. Firouzi, S., Allahyari, M. S., Isazadeh, M., Nikkhah, A., & Van Haute, S. (2021). Hybrid multi-criteria decision-making approach to select appropriate biomass resources for biofuel production. Science of the Total Environment, 770, 144449. https://doi.org/10.1016/j.scitotenv.2020.144449
  31. Fishburn, P. C. (1970). Arrow's impossibility theorem: concise proof and infinite voters. Journal of Economic Theory, 2(1), 103-106. https://doi.org/10.1016/0022-0531(70)90015-3
  32. Fishburn, P. C. (1974). Social choice functions. SIAM Review, 16(1), 63-90. https://doi.org/10.1137/1016005
  33. Fratila, A., Gavril, I. A., Nita, S. C., & Hrebenciuc, A. (2021). The importance of maritime transport for economic growth in the european union: A panel data analysis. Sustainability, 13(14), 7961. https://doi.org/10.3390/su13147961
  34. Gamer, M., Lemon, J., Fellows, I., & Singh, P. (2012). irr: Various Coefficients of Interrater Reliability and Agreement. R package version 0.84.1. https://cran.r-project.org/web/packages/irr/irr.pdf
  35. Gasser, P. (2020). A review on energy security indices to compare country performances. Energy Policy, 139, 111339. https://doi.org/10.1016/j.enpol.2020.111339
  36. Ghafari, S., Kaviani, B., Sedaghathoor, S., & Allahyari, M. S. (2020). Ecological potentials of trees, shrubs and hedge species for urban green spaces by multi criteria decision making. Urban Forestry & Urban Greening, 55, 126824. https://doi.org/10.1016/j.ufug.2020.126824
  37. Güçlü, P., & Muzaç, G. (2024). Genişletilmiş Gri MULTIMOORA yöntemi ile çok dönemli çok kriterli karar verme: Demir-çelik sektöründe finansal performans değerlendirmesi örneği. Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 19(1), 267-291. https://doi.org/10.17153/oguiibf.1373450
  38. Heckelman, J. C., & Miller, N. R. (2015). Introduction: issues in social choice and voting. Handbook of Social Choice and Voting (1-12). Edward Elgar Publishing.
  39. Heidary Dahooie, J., Husseinzadeh Kashan, A., Shoaei Naeini, Z., Vanaki, A. S., Zavadskas, E. K., & Turskis, Z. (2022). A hybrid multi-criteria-decision-making aggregation method and geographic information system for selecting optimal solar power plants in Iran. Energies, 15(8), 2801. https://doi.org/10.3390/en15082801
  40. Helden, J. A. (2021). The association between macro-socioeconomic factors and gender-disparities in cancer incidence and mortality. Master's thesis, University of Twente.
  41. Hickel, J. (2020). The sustainable development index: Measuring the ecological efficiency of human development in the anthropocene. Ecological Economics, 167, 106331. https://doi.org/10.1016/j.ecolecon.2019.05.011
  42. Holliday, W. H. (2024). An impossibility theorem concerning positive involvement in voting. Economics Letters, 111589. https://doi.org/10.1016/j.econlet.2024.111589
  43. Ivanová, E., & Masárová, J. (2018). Performance evaluation of the Visegrad Group countries. Economic research-Ekonomska istraživanja, 31(1), 270-289. https://doi.org/10.1080/1331677X.2018.1429944
  44. Jing, D., Imeni, M., Edalatpanah, S. A., Alburaikan, A., & Khalifa, H. A. E. W. (2023). Optimal selection of stock portfolios using multi-criteria decision-making methods. Mathematics, 11(2), 415. https://doi.org/10.3390/math11020415
  45. Kendall, M. G. (1948). Rank correlation methods.
  46. Kiani, M., Bagheri, M., Ebrahimi, A., & Alimohammadlou, M. (2022). A model for prioritizing outsourceable activities in universities through an integrated fuzzy-MCDM method. International Journal of Construction Management, 22(5), 784-800. https://doi.org/10.1080/15623599.2019.1645264
  47. Kılıç, R., & Gökçeli, E. (2024). Gelir eşitsizliğine insani gelişme endeksi yönüyle yeni bir bakiş açisi. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(29), 214-242. https://doi.org/10.36543/kauiibfd.2024.009
  48. Kiseľáková, D., Šofranková, B., Gombár, M., Čabinová, V., & Onuferová, E. (2019). Competitiveness and its impact on sustainability, business environment, and human development of EU (28) countries in terms of global multi-criteria indices. Sustainability, 11(12), 3365. https://doi.org/10.3390/su11123365
  49. Ko, K., & Samajdar, A. (2010). Evaluation of international corruption indexes: Should we believe them or not?. The Social Science Journal, 47(3), 508-540. https://doi.org/10.1016/j.soscij.2010.03.001
  50. Komijan, A. R., Yazdi, A. K., Tan, Y., Ocampo, L., & Nasrollahpourniazi, F. (2024). Spherical fuzzy multicriteria decision making for evaluating healthcare service quality of hospitals during the global pandemic. International Journal of Computational Intelligence Systems, 17(1), 105. https://doi.org/10.1007/s44196-024-00487-8
  51. Legendre, P. (2005). Species associations: the Kendall coefficient of concordance revisited. Journal of agricultural, biological, and environmental statistics, 10(2), 226-245. https://www.jstor.org/stable/27595557
  52. Li, X., Wang, X., & Xiao, G. (2019). A comparative study of rank aggregation methods for partial and top ranked lists in genomic applications. Briefings in bioinformatics, 20(1), 178-189. https://doi.org/10.1093/bib/bbx101
  53. Liu, B., & Matsushima, J. (2019). Annual changes in energy quality and quality of life: A cross-national study of 29 OECD and 37 non-OECD countries. Energy Reports, 5, 1354-1364. https://doi.org/10.1016/j.egyr.2019.09.040
  54. Marchant, T. (2003). Towards a theory of MCDM: stepping away from social choice theory. Mathematical Social Sciences, 45(3), 343-363. https://doi.org/10.1016/S0165-4896(02)00069-0
  55. Marcinkiewicz, E. (2017). Pension systems similarity assessment: An application of Kendall’s W to statistical multivariate analysis. Contemporary Economics, 11(3), 303-314. https://doi.org/10.5709/ce.1897-9254.244
  56. Marques, A. C., Machado, L. C., de Morais Correia, L. M. A., Vieira, M. J. L., da Silva, M. L., de Lima, M. F. M. G., Santo, P. P. P., Morais, D. C., & Frej, E. A. (2021). Support for multicriteria group decision with voting procedures: Selection of electricity generation technologies. Cleaner Environmental Systems, 3, 100060. https://doi.org/10.1016/j.cesys.2021.100060
  57. Mohammedi, W., Mgadmi, N., Abidi, A., & Moussa, W. (2025). The impact of the digital economy on sustainable development in the face of geopolitical risks. Digital Economy and Sustainable Development, 3(1), 1-21. https://doi.org/10.1007/s44265-024-00050-5
  58. Mohseni, S., Baghizadeh, K., & Pahl, J. (2022). Evaluating barriers and drivers to sustainable food supply chains. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/4486132
  59. Mondejar-Jimenez, J. A., Alfaro-Navarro, J. L., & Andrés-Martínez, E. (2014). A review on the main countries’ environmental rankings. International Journal of Environmental Research, 8(4), 1279-1286.
  60. Munda, G., & Nardo, M. (2009). Noncompensatory/nonlinear composite indicators for ranking countries: a defensible setting. Applied Economics, 41(12), 1513-1523. https://doi.org/10.1080/00036840601019364
  61. Musa, M. S., Jelilov, G., Iorember, P. T., & Usman, O. (2021). Effects of tourism, financial development, and renewable energy on environmental performance in EU-28: does institutional quality matter?. Environmental Science and Pollution Research, 28(38), 53328-53339. https://doi.org/10.1007/s11356-021-14450-z
  62. Nuralina, K., Baizholova, R., Aleksandrova, N., Konstantinov, V., & Biryukov, A. (2023). Socio-economic development of countries based on the composite country development index (CCDI). Regional Sustainability, 4(2), 115-128. https://doi.org/10.1016/j.regsus.2023.03.005
  63. Onuferová, E., Čabinová, V., & Matijová, M. (2020). Categorization of the EU member states in the context of selected multicriteria international indices using cluster analysis. Review of Economic Perspectives, 20(3), 379-401. https://doi.org/10.2478/revecp-2020-0018
  64. Orakçı, E. (2022). Çok kriterli karar verme problemleri için toplulaştırma tekniği önerisi. (Tez No. 718295) [Doktora Tezi, Anadolu Üniversitesi]. YÖK Ulusal Tez Merkezi
  65. Orakçı, E. (2024). Çok kriterli karar verme problemleri için toplulaştırma teknikleri. Özgür Yayınları. https://doi.org/10.58830/ozgur.pub623
  66. Orakçı, E., & Özdemir, A. (2017). Telafi edici çok kriterli karar verme yöntemleri ile Türkiye ve AB ülkelerinin insani gelişmişlik düzeylerinin belirlenmesi. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 19(1), 61-74. http://dx.doi.org/10.5578/jeas.49652
  67. Orakçı, E., & Özdemir, A. (2024). Using social choice function for multi criteria decision making problems. Alphanumeric Journal, 12(1), 21-38. https://doi.org/10.17093/alphanumeric.1426694
  68. Özbilen, B. Ş., & Bayazıt, N. (2021). Decision-making method for choosing best alternatives for internal walls based on cost and sound insulation performance. Journal of ITU Faculty of Architecture, 18(3), 735–751. https://doi.org/10.5505/itujfa.2021.65391.
  69. Özdemir, D., & Duman, Ç. N. (2024). İşgücü verimliliğinin insani gelişmişlik endeksi ile ilişkisi: OECD örneği. Erzurum Teknik Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (19), 42-58. https://doi.org/10.29157/etusbed.1462223
  70. Pimonenko, T. V., Liulov, O. V., & Chygryn, O. Y. (2018). Environmental performance index: Relation between social and economic welfare of the countries. http://dx.doi.org/10.21511/ee.09(3).2018.01
  71. Polatgil, M., & Güler, A. (2024). The use of different criteria weighting and multi-criteria decision making methods for university ranking: Two-layer Copeland. Üniversite Araştırmaları Dergisi, 7(1), 60-73. https://doi.org/10.32329/uad.1398302
  72. Poongavanam, G., Sivalingam, V., Prabakaran, R., Salman, M., & Kim, S. C. (2021). Selection of the best refrigerant for replacing R134a in automobile air conditioning system using different MCDM methods: A comparative study. Case Studies in Thermal Engineering, 27, 101344. https://doi.org/10.1016/j.csite.2021.101344
  73. Rao, S. K., & Kopparty, B. R. (2015). A note on Borda method. Economics Bulletin, 35(3), 1969-1975.
  74. Ratliff, T. C. (2001). A comparison of Dodgson's method and Kemeny's rule. Social Choice and Welfare, 18(1), 79-89. https://link.springer.com/article/10.1007/s003550000060
  75. Recknagel, A., & Besold, T. R. (2017). Towards efficiently implementing Dodgson’s formally intractable voting rule. KI-Künstliche Intelligenz, 31(2), 161-167. https://link.springer.com/article/10.1007/s13218-016-0454-8
  76. 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. https://doi.org/10.1016/j.ecolind.2020.107328
  77. Ripley, B., Venables, B., Bates, D. M., Hornik, K., Gebhardt, A., & Firth, D. (2025). MASS: Support functions and datasets for Venables and Ripley's MASS. R package version 7.3-64. https://cran.r-project.org/web/packages/MASS/MASS.pdf
  78. Robaina, M., Rodrigues, S., & Madaleno, M. (2024). Is there a trade-off between human well-being and ecological footprint in European countries?. Ecological Economics, 224, 108296. https://doi.org/10.1016/j.ecolecon.2024.108296
  79. Saari, D. G., & Merlin, V. R. (1996). The copeland method: I.: Relationships and the dictionary. Economic Theory, 8, 51-76. https://link.springer.com/article/10.1007/bf01212012
  80. Schloerke, B., Cook, D., Larmarange, J., Briatte, F., Marbach, M., Thoen, E., Elberg, A., Toomet, O., Crowley, J., Hofmann, H., & Wickham, H. (2024). GGally: Extension to 'ggplot2'. R package version 2.2.1. https://cran.r-project.org/web/packages/GGally/GGally.pdf
  81. Shahabadi, A., Samari, H., & Nemati, M. (2017). The factors affecting environmental performance index (EPI) in selected OPEC countries. Iranian economic review, 21(3), 457-467. https://doi.org/10.22059/ier.2017.62925
  82. Shaker, R. R., & Zubalsky, S. L. (2015). Examining patterns of sustainability across Europe: A multivariate and spatial assessment of 25 composite indices. International Journal of Sustainable Development & World Ecology, 22(1), 1-13. https://doi.org/10.1080/13504509.2014.923058
  83. Sofrankova, B., Kiselakova, D., & Onuferova, E. (2021). An empirical view on the determinants of sustainable economic development: Evidence from EU (28) Member States. SHS Web of Conferences (Vol. 91, p. 01008). EDP Sciences.
  84. Szigeti, C., Tóth, G., Borzán, A., & Farkas, S. (2013). GDP alternatives and their correlations. Journal of Environmental Sustainability, 3(3), 3. http://scholarworks.rit.edu/jes/vol3/iss3/3
  85. Taş, S. (2017). İnovasyon, eğitim ve küresel inovasyon indeksi. Bilge Uluslararası Sosyal Araştırmalar Dergisi, 1(1), 99-123. https://dergipark.org.tr/en/download/article-file/340018
  86. Tunsi, W., & Alidrisi, H. (2023). The innovation-based human development index using Promethee II: The context of G8 countries. Sustainability, 15(14), 11373. https://doi.org/10.3390/su151411373
  87. Ünal, Ç. (2008). İnsani gelişmişlik endeksine göre Türkiye’nin bölgesel farklılıkları. Coğrafi Bilimler Dergisi, 6(2), 89-113. https://doi.org/10.1501/Cogbil_0000000087
  88. United Nation Development Programme. (2023-2024). Human development report 2023-2024: The human development index (HDI). https://hdr.undp.org/data-center/human-development-index#/indicies/HDI
  89. Ūsas, J., Balezentis, T., & Streimikiene, D. (2021). Development and integrated assessment of the circular economy in the European Union: the outranking approach. Journal of enterprise information management. https://doi.org/10.1108/JEIM-11-2020-0440
  90. World Happiness Report. (2023). World Happiness Report 2023. https://happiness-report.s3.amazonaws.com/2023/WHR+23.pdf.
  91. World Intellectual Property Organization (WIPO). (2024). Global Innovation Index 2024: Unlocking the promise of social entrepreneurship. https://doi.org/10.34667/tind.50062
  92. World Justice Project. (2023). WJP rule of law index 2023. https://worldjusticeproject.org/rule-of-law-index/
  93. Wu, J. (2018). votesys: Voting Systems, Instant-Runoff Voting, Borda Method, Various Condorcet Methods. R package version 0.1.1. https://cran.r-roject.org/web/packages/votesys/votesys.pdf