ÜLKELERİN SÜRDÜRÜLEBİLİR KALKINMA ENDEKSLERİNİN KÜMELEME ANALİZİ İLE DEĞERLENDİRİLMESİ: KÜRESEL SÜRDÜRÜLEBİLİRLİK HARİTASI

Abstract

Bu çalışma, seçili 14 Sürdürülebilir Kalkınma Hedefi Endeksini, Sürdürülebilir Kalkınma Hedeflerinin çevresel, sosyal ve ekonomik olmak üzere üç temel boyutuna hitap edecek şekilde ayırarak, sürdürülebilir kalkınma performansları bakımından ülkeler arasındaki benzerlikleri ve farklılıkları ortaya koymayı amaçlamaktadır. Bu amaca yönelik olarak, her bir boyut için belirlenen endeksler, alt göstergeler arası bağlılıktan doğan korelasyonlarından arındırılmak adına Temel Bileşenler Analizi ile bileşenlerine ayrılmış, ardından ele alınan ülkeler hiyerarşik kümeleme ve k-means kümeleme algoritmaları ile kümelerine ayrılmıştır. Bulgular, ekonomik boyutun tek bir bileşenle temsil edilebileceğini, çevresel ve sosyal boyutların ise temsil için iki bileşen gerektirdiğini göstermektedir. Beş temel bileşenin belirlenmesiyle, 86 ülke kümeleme analizi yoluyla dört kümeye ayrılmıştır. Analizin sonuçları, Avrupa ülkelerinden oluşan kümenin beş temel bileşenden üçünde en iyi performansı sergilediğini ortaya koymaktadır. Buna karşılık, en az olumlu performansa sahip ülkeler kümesi, farklı yapılara sahip ekonomilerden oluşmaktadır. Bu sonuç, bazı ülke kümelerinin sürdürülebilir kalkınmanın üç ana boyutunu da ele alan kapsamlı politikalar benimsediğini, diğerlerinin ise belirli alt boyutları hedefleyen politikalara odaklandığını göstermektedir.

Keywords

• Sürdürülebilir Kalkınma Endeksleri
• Kümeleme Analizi
• K-Ortalamalar
• Temel Bileşenler Analizi

EVALUATION OF SUSTAINABLE DEVELOPMENT INDICES OF COUNTRIES: GLOBAL SUSTAINABILITY MAP

Abstract

This study disaggregates the selected 14 Sustainable Development Goal Indices into the three main dimensions of the Sustainable Development Goals: environmental, social, and economic, and aims to reveal the similarities and differences among countries in terms of their sustainable development performance. To achieve this aim, the indices determined for each dimension were separated into their components by Principal Component Analysis to eliminate any correlations resulting from the interdependence among sub-indicators. Subsequently, the countries under consideration were grouped into clusters using Hierarchical Clustering and K-means clustering algorithms. The results show that a single component can adequately represent the economic dimension, but two components are necessary to adequately represent the environmental and social dimensions. With the identification of five principal components, 86 countries were divided into four clusters through clustering analysis. The outcomes of the analysis reveal that the cluster comprising European countries exhibits the best performance in three out of the five principal components. Conversely, the cluster of countries with the lowest performance consists of economies with diverse structures. The results imply that each cluster is undergoing an achievement or difficulty in a specific aspect of sustainable development. By comprehending the strengths and weaknesses of each cluster group, this information can be used to effectively develop sustainable development strategies and policies that address economic growth, social welfare, and environmental protection in a balanced manner.

Keywords

• Sustainable Development Indices
• Cluster Analysis
• K-means
• Principal Component Analysis

References

1. Aziz, S. A., Amin, R. M., Yusof, S. A., Haneef, M. A., Mohamed, M. O. and Oziev, G. (2015) “A Critical Analysis of Development Indices”, Australian Journal of Sustainable Business and Society, 01(01), 37-53.
2. Boggia, A. and Cortina, C. (2010) “Measuring Sustainable Development Using A Multi-Criteria Model: A Case Study”, Journal of Environmental Management, 91(11), 2301-2306.
3. Bro, R. and Smilde, A. K. (2014) “Principal Component Analysis”, Analytical Methods, 6, 2812-2831.
4. Dong, F., Mitchell, P.D., Knuteson, D., Wyman, J., Bussan, A.J. and Conley, S. (2016) “Assessing Sustainability and Improvements in US Midwestern Soybean Production Systems Using A PCA-DEA Approach”, Renewable Agriculture Food Systems, 31 (6), 524-539. https://doi.org/10.1017/S1742170515000460.
5. Drastichova, M. and Filzmoser, P. (2019) “Assessment of Sustainable Development Using Cluster Analysis and Principal Component Analysis”, Problems of Sustainable Development, 14(2), 7-24.
6. EPI. (2023) “Welcome | Environmental Performance Index”, Retrieved 21.12.2023, from https://epi.yale.edu/ Ersungur, Ş. M., Kızıltan, A. and Polat, Ö. (2007) “Türkiye’de Bölgelerin Sosyo-Ekonomik Gelişmişlik Sıralaması: Temel Bileşenler Analizi”, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 21(2), 55-66.
7. Esmaili Vardanjani, M., Marsousi, N., Mokhtari Malekabadi, R. and Aliakbari, I. (2023) “Evaluation of The Principal Component and Spatial Patterns of Social Sustainability in Cities of Chaharmahal And Bakhtiari Province”, Regional Planning, 13(51), 73-90.
8. Gilbert, R., Stevenson, D., Girardet, H. and Stren, R. (1996) “Making Cities Work: The Role of Local Authorities in The Urban Environment”, London: Earthscan.

9. Goodland, R. and Daly, H. (1996) “Environmental Sustainability: Universal and Non-Negotiable”, Ecological Applications, 6(4), 1002–1017.
10. Howley, T., Madden, M.G., O’Connell, ML. and Ryder, A.G. (2006) “The Effect of Principal Component Analysis on Machine Learning Accuracy with High Dimensional Spectral Data”, In Macintosh, A., Ellis, R., Allen, T. (Ed) Applications and Innovations in Intelligent Systems XIII, SGAI 2005. Springer, London. https://doi.org/10.1007/1-84628-224-1_16.
11. Kanchan, B. D. and Kishor, M. M. (2016) “Study of Machine Learning Algorithms for Special Disease Prediction Using Principal of Component Analysis”, 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, India, 5-10, doi 10.1109/ICGTSPICC.2016.7955260.
12. Kanmani, A., Obringer, R., Rachunok, B. and Nateghi, R. (2020) “Assessing Global Environmental Sustainability via an Unsupervised Clustering Framework”, Sustainability, 12(2), 1-12.
13. Karangoda, R. C. and Nanayakkara, K. G. N. (2023) “Use of The Water Quality Index and Multivariate Analysis to Assess Groundwater Quality for Drinking Purpose in Ratnapura District, Sri Lanka”, Groundwater for Sustainable Development, 21, 1-12.
14. Kwatra, S., Kumar, A. and Sharma, P. (2020) “A Critical Review of Studies Related to The Construction and Computation of Sustainable Development Indices”, Ecological Indicators, 112, 1-15.
15. Lamichhane, S., Eğilmez, G., Gedik, R., Bhutta, M. K. S. and Erenay, B. (2021) “Benchmarking OECD Countries’ Sustainable Development Performance: A Goal-Specific Principal Component Analysis Approach”, Journal of Cleaner Production, 287, 1-15.
16. Lasisi, A. and Attoh-Okine, N. (2018) “Principal Components Analysis and Track Quality Index: A Machine Learning Approach”, Transportation Research, Part C, 230-248.
17. Liberatore, A. (2009) “International Cooperation and Sustainable Development”, In R. C. Elliot (Ed.) Institutional Issues Involving Ethics and Justice, Oxford: Eolss Publishers, Vol II, 24-35.
18. Mair, S., Jones, A., Ward, J., Christie, I., Druckman, A. and Lyon, F. (2017) “A Critical Review of The Role of Indicators in Implementing the Sustainable Development Goals”, In W. Leal (Ed.) Handbook of Sustainable Science, Springer, 41-56.
19. Mathrani, A., Wang, J., Li, D. and Zhang, X. (2023) “Clustering Analysis on Sustainable Development Goal Indicators for Forty-Five Asian Countries”, Sci, 5(2), 1-22.
20. Megyesiova, S. and Lieskovska, V. (2018) “Analysis of The Sustainable Development Indicators in The OECD Countries”, Sustainability, 10, 1-22.
21. Mekonnen, G. B., Muchangos, L.S., Ito, L. and Tokai, A. (2023) “Reducing Waste Management Scenario Space for Developing Countries: A Hierarchical Clustering on Principal Components Approach”, Waste Management & Research, 41(11), 1622-1631.
22. Odera, J. A. and Mulusa, J. (2020) “SDGs, Gender Equality and Women’s Empowerment: What Prospects for Delivery?”, In M. Kaltenborn, M. Krajewski and H. Kuhn (Ed.) Sustainable Development Goals and Human Rights, Springer Open, 95-119.
23. Park, Y.S., Eğilmez, G. and Küçükvar, M. (2015) “A Novel Lifecycle-Based Principal Component Analysis Framework for Eco-Efficiency Analysis: The Case of The United States Manufacturing and Transportation Nexus”, Journal of Cleaner Production. 92, 327-342.
24. Parris, T. M. and Kates, R. W. (2003) “Characterizing and Measuring Sustainable Development”, Annual Review of Environment and Resources, 28(1), 559–586.
25. Pearce, D. W. (2014) “Blueprint 3: Measuring Sustainable Development”, Routledge.
26. Roshan, G., Moghbel, M. and Taleghani, M. (2022) “Spatial Analysis of Bioclimatic Patterns Over Iranian Cities as An Important Step in Sustainable Development”, Sustainable Cities and Society, 83, 103939.
27. Ruggerio, C. A. (2021) “Sustainability and Sustainable Development: A Review of Principles and Definitions”, Science of the Total Environment, 786, 147481.
28. Sachs, J.D., Lafortune, G., Fuller, G. and Drumm, E. (2023) “Implementing the SDG Stimulus”, Sustainable Development Report 2023, Paris: SDSN, Dublin: Dublin University Press, 2023. 10.25546/102924
29. Sebestyén, V., Domokos, E. and Abonyi, J. (2020) “Focal Points for Sustainable Development Strategies—Text Mining-Based Comparative Analysis of Voluntary National Reviews”, Journal of Environmental Management, 263, 110414.
30. Strezov, V. and Eeg, A. (2016) “Assessment of The Economic, Social and Environmental Dimensions of The Indicators for Sustainable Development”, Sustainable Development, 25(3), 242-253.
31. Tatlıdil, H. and Ünal, E. (2010) “Türkiye İçin Sürdürülebilir Kalkınma Skoru Geliştirilmesi”, TİSK Akademi, 5(9), 44-63.
32. Tuazona, D., Corder, G. and McLellan, B. (2013) “Sustainable Development: A Review of Theoretical Contributions”, International Journal Sustainable Future for Human Security, 1(1), 40-48.
33. UN (1992) “Agenda 21, UN Conference on Environment and Development”, Rio de Janeiro, Brazil: United Nations.
34. UN (2023) “THE 17 GOALS | Sustainable Development”, Retrieved 19.11.2023, from https://sdgs.un.org/goals. Vyas, S. and Kumaranayake, L. (2006) “Constructing Socio-Economic Status Indices: How to Use Principal Components Analysis”, Health Policy and Planning, 21(6), 459-468.
35. WIPO (2023) “Global Innovation Index 2023: Innovation in The Face of Uncertainty”, Retrieved 18.11.2023, from https://www.wipo.int.
36. Zhang, Y., Chen, N., Wang, S., Wen, M. and Chen, Z. (2023) “Will Carbon Trading Reduce Spatial Inequality? A Spatial Analysis Of 200 Cities in China”, Journal of Environmental Management, 325, 116402.
37. Zhao, G. (2015) “A Sustainability Classification for A Country Based on PCA”, International Symposium on Social Science (ISSS 2015), 1st International Symposium on Social Science Bildiri Kitabı (ISSS-15) 100-103.