| تعداد نشریات | 24 |
| تعداد شمارهها | 917 |
| تعداد مقالات | 8,230 |
| تعداد مشاهده مقاله | 16,274,285 |
| تعداد دریافت فایل اصل مقاله | 13,749,866 |
A Machine-Learning-Based Typology of Sustainable Development Pathways: Evidence from 19 Countries with a Special Focus on Iran | ||
| Iranian Journal of Economic Studies | ||
| مقاله 3، دوره 14، شماره 2 - شماره پیاپی 28، 2025، صفحه 399-420 اصل مقاله (611.22 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22099/ijes.2025.54083.2081 | ||
| نویسندگان | ||
| Forough Esmaeily Sadrabadi* 1؛ Samineh Ghasemifar2 | ||
| 1Department of Economics, Ardakan University, Ardakan, Iran. | ||
| 2Department of Economics and Accounting, University of Hormozgan, Bandarabbas, Iran. | ||
| چکیده | ||
| This study introduces a novel, fully unsupervised, data-driven typology of national sustainability pathways, aiming to identify distinct development trajectories across countries and to precisely locate Iran within a global context. The innovative methodological approach integrates K-means clustering optimized with the silhouette score, t-SNE visualization for high-dimensional data representation, and hierarchical clustering for robustness validation, providing a comprehensive and objective framework for cross-country comparison. Five core indicators—GDP per capita, CO₂ emissions per capita, renewable energy share, energy intensity, and the Human Development Index (HDI)—are used for 19 selected countries over 2021–2025. This multidimensional approach captures economic, environmental, and social dimensions of sustainability, allowing a detailed assessment of each country’s performance and trajectory. The results reveal four distinct pathways. Iran clusters with fossil fuel-dependent emerging economies, including China, India, Turkey, Malaysia, and Thailand. Among these peers, Iran exhibits the lowest renewable energy share (1.1%) and the highest energy intensity, positioning it on the most extreme fossil-fuel-locked trajectory worldwide. In contrast, leading countries such as Sweden achieve 62.9% renewable energy share, emphasizing a significant sustainability gap. These findings provide policymakers with a clear benchmarking tool and identify three urgent priorities for Iran: accelerating renewable energy investment, gradually reforming fossil-fuel subsidies, and modernizing energy infrastructure. Implementing targeted reforms along these lines could enable Iran to transition toward a more resilient, competitive, and low-carbon development path, aligning economic growth with environmental sustainability. | ||
| کلیدواژهها | ||
| Sustainable economic development؛ K-means clustering؛ machine learning؛ renewable energy؛ Iran’s position | ||
| مراجع | ||
|
Acemoglu, D., Johnson, S., & Robinson, J. A. (2005). Institutions as a fundamental cause of long-run growth. In P. Aghion & S. Durlauf (Eds.), Handbook of Economic Growth (Vol. 1A, pp. 385–472). Elsevier.
Ahani, A., & Afshar-Kazemi, M. (2021). Cluster analysis of Iran’s position in global sustainability indices. Iranian Journal of Development Studies, 9(3), 45–67. [In Persian].
Bradshaw, M. (2014). The global political economy of energy. Oxford University Press.
Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S., & Rickne, A. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37(3), 407–429.
Brundtland, G., et al. (1987). Our common future. United Nations World Commission on Environment and Development.
Çelik, S., Öztürk, Ö. F., Akkuçuk, U., & Şaşmaz, M. Ü. (2025). Global sustainability performance and regional disparities: A machine learning approach based on the 2025 SDG Index. Sustainability, 17(16), Article 7411. https://doi.org/10.3390/su17167411
Chen, Y., Li, Z., & Wang, X. (2025). Integrating clustering algorithms and machine learning to optimize regional snapshot municipal solid waste management for achieving sustainable development goals. Environmental Advances, 19, Article 100597. https://doi.org/10.1016/j.envadv.2024.100597
García-Rodríguez, A., Núñez, M., Robles Pérez, M., Govezensky, T., Barrio, R. A., Gershenson, C., Kaski, K. K., & Tagüeña, J. (2025). Sustainable visions: Unsupervised machine learning insights on global development goals. PLOS ONE, 20(3), Article e0317412. https://doi.org/10.1371/journal.pone.0317412
Gholamia, M., Sadeghi, Z., Jalaee Esfandabadi, S. A. M., & Nejati, M. (2023). Economic and environmental effects of energy transition in Iran by 2050: A dynamic multiregional computable general equilibrium model. Iranian Journal of Economic Studies, 12(2), 551–584. https://doi.org/10.22099/ijes.2025.52176.1992.
Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. Quarterly Journal of Economics, 110(2), 353–377. https://doi.org/10.2307/2118443
Gohr, C., Rodríguez, G., Belomestnykh, S., Berg-Moelleken, D., Chauhan, N., Engler, J.-O., Heydebreck, L. V., Hintz, M. J., Kretschmer, M., Krügermeier, C., Meinberg, J., Rau, A.-L., Schwenck, C., Aoulkadi, I., Poll, S., Frank, E., Creutzig, F., Lemke, O., Maushart, M., … von Wehrden, H. (2025). Artificial intelligence in sustainable development research. Nature Sustainability, 8, 970–978.
Kian Poor, S , & Hajian, M. (2025). Development of digital economy, human development solution in Iran. International Journal of New Political Economy, 6(1),47-82.
Heidary, B., Kiani, M. A., & Golzar, F. (2025). Toward sustainable development: Energy transition scenarios for oil-dependent countries, with Iran as a case study. Energies, 18(10), 2651. https://doi.org/10.3390/en18102651
International Energy Agency. (2025a). Global Energy Review 2025. IEA. https://www.iea.org/reports/global-energy-review-2025
International Energy Agency. (2025b). Middle East energy outlook. IEA.
International Energy Agency. (2025c). World Energy Outlook 2025. IEA. https://www.iea.org/reports/world-energy-outlook-2025
International Energy Agency. (2025d). Change management in Iran's energy policies: Analyzing the impacts of sanctions and climate change. Energy Strategy Reviews, 45, Article 101115. https://doi.org/10.1016/j.esr.2025.101115
International Renewable Energy Agency (IRENA). (2025). Global landscape of energy transition finance 2025. IRENA.
International Renewable Energy Agency (IRENA). (2025). Renewable energy statistics 2025. IRENA. https://www.irena.org/Publications/2025/Jul/Renewable-energy-statistics-2025.
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666.
Khalili, A., Bahmani, M., & Nejati, M. (2025). A dynamic analysis of the environmental impacts and energy consumption of the industrial sector on Iran’s economic growth. Iranian Journal of Economic Studies, 13(2), 343–368.
Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability , 1, 281–297.
Malthus, T. R. (1798). An Essay on the Principle of Population, as It Affects the Future Improvement of Society. London: J. Johnson.
Meadows, D. H., Meadows, D. L., Randers, J., & Behrens III, W. W. (1972). The Limits to Growth. New York: Universe Books.
Moradi, Z., Ziyari, K., & Rajaei, S. A. (2024). Analysis of livability in deteriorated urban fabrics of Tehran’s District 7 using machine learning algorithms. Sustainable City, 8(2), 1–23. https://doi.org/10.22034/jsc.2024.427202.1748
Najati, M., Sadeghi, Z., & Arabpour, M. A. (2022). Prediction of CO₂ emissions in Iran using key economic indicators and deep learning models. Environment and Interdisciplinary Sustainable Development, 7(75), 1–17. https://doi.org/10.22034/envj.2022.149127
North, D. C. (1990). Institutions, Institutional Change and Economic Performance. Cambridge University Press.
OECD. (2024). Energy Transition Indicators: Policy frameworks and international benchmarks. OECD Publishing.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.
Sadabadi, A. A., Shahi, N., & Borhani, F. (2025). Change management in Iran’s energy policies: Analyzing the transition process from fossil fuels to renewable energies. Energy Conversion and Management: X, 28, Article 101269. https://doi.org/10.1016/j.ecmx.2025.101269
Sachs, J. D. (2015). The age of sustainable development. Columbia University Press.
Sayardoost Tabrizi, S., Yakideh, K., Moradi, M., & Ebrahimpour, M. (2024). Clustering with machine learning and using NDEA in development planning: A case study in the petrochemical two-stage SSC. Research in English Journal, 5(2), 192–212. https://doi.org/10.22105/riej.2024.455576.1443
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.
United Nations. (1992). Agenda 21: Programme of action for sustainable development. https://sustainabledevelopment.un.org/outcomedocuments/agenda21
United Nations. (2000). United Nations Millennium Declaration. https://www.un.org/millennium/declaration/ares552e.htm
United Nations Development Programme (UNDP). (2025). Human Development Report 2025. UNDP. https://hdr.undp.org/content/human-development-report-2025
Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., … Nerini, F. F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), Article 233. https://doi.org/10.1038/s41467-019-14108-y
World Bank. (2024). World Development Indicators. https://data.worldbank.org/indicator
World Bank. (2025). Global Economic Prospects, June 2025. World Bank Group. https://www.worldbank.org/en/publication/global-economic-prospects
World Economic Forum. (2025). Fostering effective energy transition 2025. World Economic Forum. | ||
|
آمار تعداد مشاهده مقاله: 220 تعداد دریافت فایل اصل مقاله: 289 |
||