Araştırma Makalesi
BibTex RIS Kaynak Göster

Comparative Analysis of OECD Countries Based on Energy Trilemma Index: A Clustering Approach

Yıl 2025, Cilt: 40 Sayı: 1, 248 - 271
https://doi.org/10.24988/ije.1472312

Öz

This study analyzes OECD countries in the context of the energy trilemma index and clusters countries with similar characteristics. In the study, the k-means clustering technique is used. The optimum number of clusters was determined using the Elbow method in combination with the Silhouette Index. Moreover, all results are visualized to enhance comprehensibility. The results show that countries such as Austria, Canada, Finland, and Denmark are in the high energy trilemma group with index scores of 82.2, 82.3, 82.7, and 83.3, respectively. Countries in the high group have achieved a high level of balance between energy security, energy equity, and environmental sustainability. In addition, countries such as Belgium, Hungary, Australia, the Czech Republic, and Estonia are in the medium energy trilemma group with index scores of 76.4, 76.6, 77.1, 77.6, and 78.7, respectively. Countries in the medium group have made progress in balancing the dimensions of the energy trilemma but have not yet reached excellence. However, countries such as Mexico, Türkiye, Colombia, and Costa Rica are in the low energy trilemma group with index scores of 63.1, 64.1, 64.8, and 69.3, respectively. These low energy trilemma group countries face significant challenges in balancing energy security, energy equity, and environmental sustainability and need to make improvements in these areas.

Kaynakça

  • Aldenderfer, M. S., and Blashfield, R. K. (1984). Cluster analysis. Newberry Park.
  • Alola, A. A., Yalçiner, K., Alola, U. V., and Saint Akadiri, S. (2019). The role of renewable energy, immigration and real income in environmental sustainability target. Evidence from Europe largest states. Science of The Total Environment, 674, 307-315.
  • Amasyali, M. F., and Ersoy, O. (2008, April). The performance factors of clustering ensembles. In 2008 IEEE 16th Signal Processing, Communication and Applications Conference (pp. 1-4). IEEE.
  • Ang, B. W., Xu, X. Y., and Su, B. (2015). Multi-country comparisons of energy performance: the index decomposition analysis approach. Energy Economics, 47, 68-76.
  • Bei, L. T., and Cheng, T. C. (2013). Brand power index–using principal component analysis. Applied Economics, 45(20), 2954-2960.
  • Bholowalia, P., and Kumar, A. (2014). EBK-means: A clustering technique based on elbow method and k-means in WSN. International Journal of Computer Applications, 105(9).
  • Bravo-López, M., Marin, S., Terreros-Barreto, J. R., Garcés, A., Molina, A., Rivera, M., and Wheeler, P. (2022, October). An Overview of the Colombian Power System. In 2022 IEEE International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA) (pp. 1-6). IEEE.
  • Caliński, T., and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1), 1-27.
  • Chamberlain, G., and Rothschild, M. (1982). Arbitrage, factor structure, and mean-variance analysis on large asset markets. NBER Working Paper, 0996. https://www.nber.org/papers/w0996
  • Chi, Y., Esily, R. R., Ibrahiem, D. M., Houssam, N., Chen, Y., Jia, X., and Zhang, X. (2023). Is North Africa region on track to energy trilemma for enhancing economic progress? The role of population growth and energy usage. Energy Strategy Reviews, 50, 101245.
  • Coşkun, E., Gündoğan, E., Kaya, M., and Alhajj, R. (2021). Veri madenciliği yöntemleri kullanarak yoğun bakım ünitesindeki hastaların sınıflandırması. Computer Science, (Special), 319-328.
  • Csereklyei, Z., Thurner, P. W., Langer, J., and Küchenhoff, H. (2017). Energy paths in the European Union: A model-based clustering approach. Energy Economics, 65, 442-457.
  • Davies, D. L., and Bouldin, D. W. (1979). A cluster separation measure. IEEE transactions on pattern analysis and machine intelligence, (2), 224-227.
  • Dzikuć, M., Gorączkowska, J., Piwowar, A., Dzikuć, M., Smoleński, R., and Kułyk, P. (2021). The analysis of the innovative potential of the energy sector and low-carbon development: A case study for Poland. Energy Strategy Reviews, 38, 100769.
  • Evans, R. S., Lloyd, J. F., Stoddard, G. J., Nebeker, J. R., and Samore, M. H. (2005). Risk factors for adverse drug events: a 10-year analysis. Annals of Pharmacotherapy, 39(7-8), 1161-1168.
  • Fu, Y., Lai, K. K., and Yu, L. (2021). Multi-nation comparisons of energy architecture performance: A group decision-making method with preference structure and acceptability analysis. Energy Economics, 96, 105139.
  • Fu, Y., Lu, Y., Yu, C., and Lai, K. K. (2022). Inter-country comparisons of energy system performance with the energy trilemma index: An ensemble ranking methodology based on the half-quadratic theory. Energy, 261, 125048.
  • Fuentes, S., Villafafila-Robles, R., Olivella-Rosell, P., Rull-Duran, J., and Galceran-Arellano, S. (2020). Transition to a greener Power Sector: Four different scopes on energy security. Renewable Energy Focus, 33, 23-36.
  • Garrido, S., Sequeira, T., and Santos, M. (2020). Renewable energy and sustainability from the supply side: A critical review and analysis. Applied Sciences, 10(17), 5755.
  • Gersho, A., and Gray, R. M. (2012). Vector quantization and signal compression (Vol. 159). Springer Science & Business Media.
  • Guo, X., Lu, C. C., Lee, J. H., and Chiu, Y. H. (2017). Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China. Energy, 134, 392-399.
  • Gupta, S. (2023). Nudging international sustainable practices confirmed with renewable energy consumption. International Journal of Energy Economics and Policy, 13(6), 494-503.
  • Hu, Y., Peng, L., Li, X., Yao, X., Lin, H., and Chi, T. (2018). A novel evolution tree for analyzing the global energy consumption structure. Energy, 147, 1177-1187.
  • Iqbal, W., Yumei, H., Abbas, Q., Hafeez, M., Mohsin, M., Fatima, A., Jamali,M.A.,Jamali,M.,Siyal,A., and Sohail, N. (2019). Assessment of wind energy potential for the production of renewable hydrogen in Sindh Province of Pakistan. Processes, 7(4), 196.
  • IRENA, I. (2020). Global renewables outlook: Energy transformation 2050. International Renewable Energy Agency Abu Dhabi. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2020/Apr/IRENA_Global_Renewables_Outlook_2020.pdf?rev=1f416406e50d447cbb2247de30d1d1d0
  • IRENA. (2018). Power System Flexibility for the Energy Transition-Report. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2018/Nov/IRENA_Power_system_ flexibility_1_2018.pdf?rev=472c42dcadb746a7b4f6132d5dbf470e
  • Jalali Sepehr, M., Haeri, A., and Ghousi, R. (2019). A cross-country evaluation of energy efficiency from the sustainable development perspective. International Journal of Energy Sector Management, 13(4), 991-1019.
  • Justus, J. J., and Mannish, S. (2023, May). Descriptive and predictive analytics of energy security in India. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-8). IEEE.
  • Kartsonakis, S., Grigoroudis, E., and Zopounidis, C. (2021). An ELECTRE-TRI model for national energy sustainability assessment. Multiple Criteria Decision Making for Sustainable Development: Pursuing Economic Growth, Environmental Protection and Social Cohesion, 17-37.
  • Karypis, M. S. G., Kumar, V., and Steinbach, M. (2000, August). A comparison of document clustering techniques. In TextMining Workshop at KDD2000 (2000).
  • Ketchen, D. J., and Shook, C. L. (1996). The application of cluster analysis in strategic management research: an analysis and critique. Strategic management journal, 17(6), 441-458.
  • Khan, I., Hou, F., Irfan, M., Zakari, A., and Le, H. P. (2021). Does energy trilemma a driver of economic growth? The roles of energy use, population growth, and financial development. Renewable and Sustainable Energy Reviews, 146, 111157.
  • Khatod, K. J., Katekar, V. P., and Deshmukh, S. S. (2022). Energy security challenges of developing countries: a pragmatic assessment. In Handbook of energy and environmental security (pp. 127-169). Academic Press.
  • Krzanowski, W. J., and Lai, Y. T. (1988). A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics, 23-34.
  • Kumar, L. R., Talan, A., and Tyagi, R. D. (2020). Economic development and sustainability. Sustainability: Fundamentals and Applications, 157-181.
  • Kumar, N. (2020). Environmental Concerns and Sustainable Development. V. Shukla (Ed.). Springer.
  • Lee, K. Y., Tsao, S. H., Tzeng, C. W., and Lin, H. J. (2018). Influence of the vertical wind and wind direction on the power output of a small vertical-axis wind turbine installed on the rooftop of a building. Applied Energy, 209, 383-391.
  • Leguizamon-Perilla, A., Rodriguez-Bernal, J. S., Moralez-Cruz, L., Farfán-Martinez, N. I., Nieto-Londoño, C., Vásquez, R. E., and Escudero-Atehortua, A. (2023). Digitalisation and modernisation of hydropower operating facilities to support the Colombian energy mix flexibility. Energies, 16(7), 3161.
  • Lever, J., Krzywinski, M., and Altman, N. (2016). Points of significance: model selection and overfitting. Nature methods, 13(9), 703-705.
  • Lin, O. Z., Mon, K. K., and Htay, T. T. (2020). Role of solar energy for enhancing sustainable energy and electricity in Myanmar: An outlook. In IOP Conference Series: Earth and Environmental Science,63(1), 012143. IOP Publishing.
  • Linde, Y., Buzo, A., and Gray, R. (1980). An algorithm for vector quantizer design. IEEE Transactions on communications, 28(1), 84-95.
  • Lovell, C. K., Pastor, J. T., and Turner, J. A. (1995). Measuring macroeconomic performance in the OECD: A comparison of European and non-European countries. European journal of operational research, 87(3), 507-518.
  • Mardani, A., Zavadskas, E. K., Streimikiene, D., Jusoh, A., and Khoshnoudi, M. (2017). A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renewable And Sustainable Energy Reviews, 70, 1298-1322.
  • Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica: Journal of the Econometric Society, 867-887.
  • Mohamed, E., and Celik, T. (2022). Early detection of failures from vehicle equipment data using K-means clustering design. Computers and Electrical Engineering, 103, 108351.
  • Mohsin, M., Abbas, Q., Zhang, J., Ikram, M., & Iqbal, N. (2019a). Integrated effect of energy consumption, economic development, and population growth on CO 2 based environmental degradation: a case of transport sector. Environmental Science and Pollution Research, 26, 32824-32835.
  • Mohsin, M., Rasheed, A. K., Sun, H., Zhang, J., Iram, R., Iqbal, N., and Abbas, Q. (2019b). Developing low carbon economies: an aggregated composite index based on carbon emissions. Sustainable Energy Technologies and Assessments, 35, 365-374.
  • Ponomarenko, T., Reshneva, E., and Mosquera Urbano, A. P. (2022). Assessment of energy sustainability issues in the andean community: Additional indicators and their interpretation. Energies, 15(3), 1077.
  • Risheh, A., Tavakolian, P., Melinkov, A., and Mandelis, A. (2022). Infrared computer vision in non-destructive imaging: Sharp delineation of subsurface defect boundaries in enhanced truncated correlation photothermal coherence tomography images using K-means clustering. NDT & E International, 125, 102568.
  • 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.
  • Saint Akadiri, S., Alola, A. A., Akadiri, A. C., and Alola, U. V. (2019). Renewable energy consumption in EU-28 countries: policy toward pollution mitigation and economic sustainability. Energy Policy, 132, 803-810.
  • Sangaiah, A. K., Rezaei, S., Javadpour, A., and Zhang, W. (2023). Explainable AI in big data intelligence of community detection for digitalization e-healthcare services. Applied Soft Computing, 136, 110119.
  • Seiford, L. M., and Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European journal of operational research, 142(1), 16-20.
  • Senol, A., and Karacan, H. (2020). Kd-tree and adaptive radius (KD-AR Stream) based real-time data stream clustering. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(1).
  • Song, L., Fu, Y., Zhou, P., and Lai, K. K. (2017). Measuring national energy performance via energy trilemma index: a stochastic multicriteria acceptability analysis. Energy Economics, 66, 313-319.
  • Šprajc, P., Bjegović, M., and Vasić, B. (2019). Energy security in decision making and governance-Methodological analysis of energy trilemma index. Renewable and Sustainable Energy Reviews, 114, 109341.
  • Steinbach, M., Karypis, G., and Kumar, V. (2000). A comparison of document clustering techniques, Technical Report, 1-22.
  • Sueyoshi, T., and Goto, M. (2013). DEA environmental assessment in a time horizon: Malmquist index on fuel mix, electricity and CO2 of industrial nations. Energy Economics, 40, 370-382.
  • Sueyoshi, T., Mo, F., and Wang, D. D. (2022). Sustainable development of countries all over the world and the impact of renewable energy. Renewable Energy, 184, 320-331.
  • Suranovic, S. (2013). Fossil fuel addiction and the implications for climate change policy. Global Environmental Change, 23(3), 598-608.
  • Syakur, M. A., Khotimah, B. K., Rochman, E. M. S., and Satoto, B. D. (2018, April). Integration k-means clustering method and elbow method for identification of the best customer profile cluster. In IOP conference series: materials science and engineering,336, 012017. IOP Publishing.
  • Taşcı, E., and Onan, A. (2016). K-en yakın komşu algoritması parametrelerinin sınıflandırma performansı üzerine etkisinin incelenmesi. Akademik Bilişim, 1(1), 4-18.
  • Topcu, M., and Payne, J. E. (2017). The financial development–energy consumption nexus revisited. Energy Sources, Part B: Economics, Planning, and Policy, 12(9), 822-830.
  • Verma, R., Patel, M., Shikha, D., and Mishra, S. (2023). Assessment of food safety aspects and socioeconomic status among street food vendors in Lucknow city. Journal of Agriculture and Food Research, 11, 100469.
  • Wang, H., Ang, B. W., Wang, Q. W., and Zhou, P. (2017). Measuring energy performance with sectoral heterogeneity: A non-parametric frontier approach. Energy Economics, 62, 70-78.
  • WEC. (2018). World Energy Trilemma Index. https://www.worldenergy.org/assets/downloads/ World_Energy_Trilemma_Index_2022.pdf?v=1669839605.
  • Woźniak, J., and Pactwa, K. (2019). Possibilities for using mine waters in the context of the construction of heat energy clusters in Poland. Energy, Sustainability and Society, 9, 1-10.
  • Xu, R., and Wunsch II, D. C. (2009). Clustering, A John Wiley & Sons. Inc., Publication.
  • Yılancı, V. (2010). Bulanik kümeleme analizi ile Türkiye’deki illerin sosyoekonomik açidan sınıflandirilmasi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(3), 453-470.
  • Yoon, J., and Klasen, S. (2018). An application of partial least squares to the construction of the Social Institutions and Gender Index (SIGI) and the Corruption Perception Index (CPI). Social Indicators Research, 138, 61-88.
  • Yu, D., and He, X. (2020). A bibliometric study for DEA applied to energy efficiency: Trends and future challenges. Applied Energy, 268, 115048.
  • Zhang, D., Shi, X., and Sheng, Y. (2015). Comprehensive measurement of energy market integration in East Asia: An application of dynamic principal component analysis. Energy economics, 52, 299-305.
  • Zhilkina, Y. (2019). Risks in the energy sector: Management practices analysis in the electricity market. In E3S Web of Conferences,139,01071. EDP Sciences.
  • Zhou, P., and Ang, B. W. (2008). Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 36(8), 2911-2916.
  • Zhou, P., Ang, B. W., and Poh, K. L. (2008). Measuring environmental performance under different environmental DEA technologies. Energy Economics, 30(1), 1-14.
  • Zhou, P., Ang, B. W., and Zhou, D. Q. (2012). Measuring economy-wide energy efficiency performance: A parametric frontier approach. Applied energy, 90(1), 196-200.
  • Zou, X., and Shen, Q. (2023). China's energy security and transition in the post-Paris era. COVID-19 and climate change in BRICS Nations: Beyond the Paris Agreement and Agenda 2030.

OECD Ülkelerinin Enerji Trilemma Endeksine Dayalı Karşılaştırmalı Analizi: Kümeleme Yaklaşımı

Yıl 2025, Cilt: 40 Sayı: 1, 248 - 271
https://doi.org/10.24988/ije.1472312

Öz

Bu çalışma, OECD ülkelerini enerji trilemma endeksi bağlamında analiz etmekte ve benzer özelliklere sahip ülkeleri kümelemektedir. Çalışmada k-ortalamalar kümeleme tekniği kullanılmıştır. Optimum küme sayısı, Siluet Endeksi ile birlikte Dirsek yöntemi kullanılarak belirlenmiştir. Ayrıca, anlaşılabilirliği artırmak için tüm sonuçlar görselleştirilmiştir. Sonuçlar Avusturya, Kanada, Finlandiya ve Danimarka gibi ülkelerin sırasıyla 82.2, 82.3, 82.7 ve 83.3 endeks puanlarıyla yüksek enerji trilemma grubunda yer aldığını göstermektedir. Yüksek grupta yer alan ülkeler enerji güvenliği, enerji eşitliği ve çevresel sürdürülebilirlik arasında yüksek düzeyde denge sağlamışlardır. Ayrıca Belçika, Macaristan, Avustralya, Çek Cumhuriyeti ve Estonya gibi ülkeler sırasıyla 76.4, 76.6, 77.1, 77.6 ve 78.7 endeks puanlarıyla orta enerji trilemma grubunda yer almaktadır. Orta gruptaki ülkeler enerji trilemmasının boyutlarını dengeleme konusunda ilerleme kaydetmiş ancak henüz mükemmelliğe ulaşamamıştır. Ancak Meksika, Türkiye, Kolombiya ve Kosta Rika gibi ülkeler sırasıyla 63,1, 64,1, 64,8 ve 69,3 endeks puanlarıyla düşük enerji trilemma grubunda yer almaktadır. Bu düşük enerji trilemma grubu ülkeleri enerji güvenliği, enerji eşitliği ve çevresel sürdürülebilirliği dengelemede önemli zorluklarla karşı karşıyadır ve bu alanlarda iyileştirmeler yapmaları gerekmektedir.

Kaynakça

  • Aldenderfer, M. S., and Blashfield, R. K. (1984). Cluster analysis. Newberry Park.
  • Alola, A. A., Yalçiner, K., Alola, U. V., and Saint Akadiri, S. (2019). The role of renewable energy, immigration and real income in environmental sustainability target. Evidence from Europe largest states. Science of The Total Environment, 674, 307-315.
  • Amasyali, M. F., and Ersoy, O. (2008, April). The performance factors of clustering ensembles. In 2008 IEEE 16th Signal Processing, Communication and Applications Conference (pp. 1-4). IEEE.
  • Ang, B. W., Xu, X. Y., and Su, B. (2015). Multi-country comparisons of energy performance: the index decomposition analysis approach. Energy Economics, 47, 68-76.
  • Bei, L. T., and Cheng, T. C. (2013). Brand power index–using principal component analysis. Applied Economics, 45(20), 2954-2960.
  • Bholowalia, P., and Kumar, A. (2014). EBK-means: A clustering technique based on elbow method and k-means in WSN. International Journal of Computer Applications, 105(9).
  • Bravo-López, M., Marin, S., Terreros-Barreto, J. R., Garcés, A., Molina, A., Rivera, M., and Wheeler, P. (2022, October). An Overview of the Colombian Power System. In 2022 IEEE International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA) (pp. 1-6). IEEE.
  • Caliński, T., and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1), 1-27.
  • Chamberlain, G., and Rothschild, M. (1982). Arbitrage, factor structure, and mean-variance analysis on large asset markets. NBER Working Paper, 0996. https://www.nber.org/papers/w0996
  • Chi, Y., Esily, R. R., Ibrahiem, D. M., Houssam, N., Chen, Y., Jia, X., and Zhang, X. (2023). Is North Africa region on track to energy trilemma for enhancing economic progress? The role of population growth and energy usage. Energy Strategy Reviews, 50, 101245.
  • Coşkun, E., Gündoğan, E., Kaya, M., and Alhajj, R. (2021). Veri madenciliği yöntemleri kullanarak yoğun bakım ünitesindeki hastaların sınıflandırması. Computer Science, (Special), 319-328.
  • Csereklyei, Z., Thurner, P. W., Langer, J., and Küchenhoff, H. (2017). Energy paths in the European Union: A model-based clustering approach. Energy Economics, 65, 442-457.
  • Davies, D. L., and Bouldin, D. W. (1979). A cluster separation measure. IEEE transactions on pattern analysis and machine intelligence, (2), 224-227.
  • Dzikuć, M., Gorączkowska, J., Piwowar, A., Dzikuć, M., Smoleński, R., and Kułyk, P. (2021). The analysis of the innovative potential of the energy sector and low-carbon development: A case study for Poland. Energy Strategy Reviews, 38, 100769.
  • Evans, R. S., Lloyd, J. F., Stoddard, G. J., Nebeker, J. R., and Samore, M. H. (2005). Risk factors for adverse drug events: a 10-year analysis. Annals of Pharmacotherapy, 39(7-8), 1161-1168.
  • Fu, Y., Lai, K. K., and Yu, L. (2021). Multi-nation comparisons of energy architecture performance: A group decision-making method with preference structure and acceptability analysis. Energy Economics, 96, 105139.
  • Fu, Y., Lu, Y., Yu, C., and Lai, K. K. (2022). Inter-country comparisons of energy system performance with the energy trilemma index: An ensemble ranking methodology based on the half-quadratic theory. Energy, 261, 125048.
  • Fuentes, S., Villafafila-Robles, R., Olivella-Rosell, P., Rull-Duran, J., and Galceran-Arellano, S. (2020). Transition to a greener Power Sector: Four different scopes on energy security. Renewable Energy Focus, 33, 23-36.
  • Garrido, S., Sequeira, T., and Santos, M. (2020). Renewable energy and sustainability from the supply side: A critical review and analysis. Applied Sciences, 10(17), 5755.
  • Gersho, A., and Gray, R. M. (2012). Vector quantization and signal compression (Vol. 159). Springer Science & Business Media.
  • Guo, X., Lu, C. C., Lee, J. H., and Chiu, Y. H. (2017). Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China. Energy, 134, 392-399.
  • Gupta, S. (2023). Nudging international sustainable practices confirmed with renewable energy consumption. International Journal of Energy Economics and Policy, 13(6), 494-503.
  • Hu, Y., Peng, L., Li, X., Yao, X., Lin, H., and Chi, T. (2018). A novel evolution tree for analyzing the global energy consumption structure. Energy, 147, 1177-1187.
  • Iqbal, W., Yumei, H., Abbas, Q., Hafeez, M., Mohsin, M., Fatima, A., Jamali,M.A.,Jamali,M.,Siyal,A., and Sohail, N. (2019). Assessment of wind energy potential for the production of renewable hydrogen in Sindh Province of Pakistan. Processes, 7(4), 196.
  • IRENA, I. (2020). Global renewables outlook: Energy transformation 2050. International Renewable Energy Agency Abu Dhabi. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2020/Apr/IRENA_Global_Renewables_Outlook_2020.pdf?rev=1f416406e50d447cbb2247de30d1d1d0
  • IRENA. (2018). Power System Flexibility for the Energy Transition-Report. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2018/Nov/IRENA_Power_system_ flexibility_1_2018.pdf?rev=472c42dcadb746a7b4f6132d5dbf470e
  • Jalali Sepehr, M., Haeri, A., and Ghousi, R. (2019). A cross-country evaluation of energy efficiency from the sustainable development perspective. International Journal of Energy Sector Management, 13(4), 991-1019.
  • Justus, J. J., and Mannish, S. (2023, May). Descriptive and predictive analytics of energy security in India. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-8). IEEE.
  • Kartsonakis, S., Grigoroudis, E., and Zopounidis, C. (2021). An ELECTRE-TRI model for national energy sustainability assessment. Multiple Criteria Decision Making for Sustainable Development: Pursuing Economic Growth, Environmental Protection and Social Cohesion, 17-37.
  • Karypis, M. S. G., Kumar, V., and Steinbach, M. (2000, August). A comparison of document clustering techniques. In TextMining Workshop at KDD2000 (2000).
  • Ketchen, D. J., and Shook, C. L. (1996). The application of cluster analysis in strategic management research: an analysis and critique. Strategic management journal, 17(6), 441-458.
  • Khan, I., Hou, F., Irfan, M., Zakari, A., and Le, H. P. (2021). Does energy trilemma a driver of economic growth? The roles of energy use, population growth, and financial development. Renewable and Sustainable Energy Reviews, 146, 111157.
  • Khatod, K. J., Katekar, V. P., and Deshmukh, S. S. (2022). Energy security challenges of developing countries: a pragmatic assessment. In Handbook of energy and environmental security (pp. 127-169). Academic Press.
  • Krzanowski, W. J., and Lai, Y. T. (1988). A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics, 23-34.
  • Kumar, L. R., Talan, A., and Tyagi, R. D. (2020). Economic development and sustainability. Sustainability: Fundamentals and Applications, 157-181.
  • Kumar, N. (2020). Environmental Concerns and Sustainable Development. V. Shukla (Ed.). Springer.
  • Lee, K. Y., Tsao, S. H., Tzeng, C. W., and Lin, H. J. (2018). Influence of the vertical wind and wind direction on the power output of a small vertical-axis wind turbine installed on the rooftop of a building. Applied Energy, 209, 383-391.
  • Leguizamon-Perilla, A., Rodriguez-Bernal, J. S., Moralez-Cruz, L., Farfán-Martinez, N. I., Nieto-Londoño, C., Vásquez, R. E., and Escudero-Atehortua, A. (2023). Digitalisation and modernisation of hydropower operating facilities to support the Colombian energy mix flexibility. Energies, 16(7), 3161.
  • Lever, J., Krzywinski, M., and Altman, N. (2016). Points of significance: model selection and overfitting. Nature methods, 13(9), 703-705.
  • Lin, O. Z., Mon, K. K., and Htay, T. T. (2020). Role of solar energy for enhancing sustainable energy and electricity in Myanmar: An outlook. In IOP Conference Series: Earth and Environmental Science,63(1), 012143. IOP Publishing.
  • Linde, Y., Buzo, A., and Gray, R. (1980). An algorithm for vector quantizer design. IEEE Transactions on communications, 28(1), 84-95.
  • Lovell, C. K., Pastor, J. T., and Turner, J. A. (1995). Measuring macroeconomic performance in the OECD: A comparison of European and non-European countries. European journal of operational research, 87(3), 507-518.
  • Mardani, A., Zavadskas, E. K., Streimikiene, D., Jusoh, A., and Khoshnoudi, M. (2017). A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renewable And Sustainable Energy Reviews, 70, 1298-1322.
  • Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica: Journal of the Econometric Society, 867-887.
  • Mohamed, E., and Celik, T. (2022). Early detection of failures from vehicle equipment data using K-means clustering design. Computers and Electrical Engineering, 103, 108351.
  • Mohsin, M., Abbas, Q., Zhang, J., Ikram, M., & Iqbal, N. (2019a). Integrated effect of energy consumption, economic development, and population growth on CO 2 based environmental degradation: a case of transport sector. Environmental Science and Pollution Research, 26, 32824-32835.
  • Mohsin, M., Rasheed, A. K., Sun, H., Zhang, J., Iram, R., Iqbal, N., and Abbas, Q. (2019b). Developing low carbon economies: an aggregated composite index based on carbon emissions. Sustainable Energy Technologies and Assessments, 35, 365-374.
  • Ponomarenko, T., Reshneva, E., and Mosquera Urbano, A. P. (2022). Assessment of energy sustainability issues in the andean community: Additional indicators and their interpretation. Energies, 15(3), 1077.
  • Risheh, A., Tavakolian, P., Melinkov, A., and Mandelis, A. (2022). Infrared computer vision in non-destructive imaging: Sharp delineation of subsurface defect boundaries in enhanced truncated correlation photothermal coherence tomography images using K-means clustering. NDT & E International, 125, 102568.
  • 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.
  • Saint Akadiri, S., Alola, A. A., Akadiri, A. C., and Alola, U. V. (2019). Renewable energy consumption in EU-28 countries: policy toward pollution mitigation and economic sustainability. Energy Policy, 132, 803-810.
  • Sangaiah, A. K., Rezaei, S., Javadpour, A., and Zhang, W. (2023). Explainable AI in big data intelligence of community detection for digitalization e-healthcare services. Applied Soft Computing, 136, 110119.
  • Seiford, L. M., and Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European journal of operational research, 142(1), 16-20.
  • Senol, A., and Karacan, H. (2020). Kd-tree and adaptive radius (KD-AR Stream) based real-time data stream clustering. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(1).
  • Song, L., Fu, Y., Zhou, P., and Lai, K. K. (2017). Measuring national energy performance via energy trilemma index: a stochastic multicriteria acceptability analysis. Energy Economics, 66, 313-319.
  • Šprajc, P., Bjegović, M., and Vasić, B. (2019). Energy security in decision making and governance-Methodological analysis of energy trilemma index. Renewable and Sustainable Energy Reviews, 114, 109341.
  • Steinbach, M., Karypis, G., and Kumar, V. (2000). A comparison of document clustering techniques, Technical Report, 1-22.
  • Sueyoshi, T., and Goto, M. (2013). DEA environmental assessment in a time horizon: Malmquist index on fuel mix, electricity and CO2 of industrial nations. Energy Economics, 40, 370-382.
  • Sueyoshi, T., Mo, F., and Wang, D. D. (2022). Sustainable development of countries all over the world and the impact of renewable energy. Renewable Energy, 184, 320-331.
  • Suranovic, S. (2013). Fossil fuel addiction and the implications for climate change policy. Global Environmental Change, 23(3), 598-608.
  • Syakur, M. A., Khotimah, B. K., Rochman, E. M. S., and Satoto, B. D. (2018, April). Integration k-means clustering method and elbow method for identification of the best customer profile cluster. In IOP conference series: materials science and engineering,336, 012017. IOP Publishing.
  • Taşcı, E., and Onan, A. (2016). K-en yakın komşu algoritması parametrelerinin sınıflandırma performansı üzerine etkisinin incelenmesi. Akademik Bilişim, 1(1), 4-18.
  • Topcu, M., and Payne, J. E. (2017). The financial development–energy consumption nexus revisited. Energy Sources, Part B: Economics, Planning, and Policy, 12(9), 822-830.
  • Verma, R., Patel, M., Shikha, D., and Mishra, S. (2023). Assessment of food safety aspects and socioeconomic status among street food vendors in Lucknow city. Journal of Agriculture and Food Research, 11, 100469.
  • Wang, H., Ang, B. W., Wang, Q. W., and Zhou, P. (2017). Measuring energy performance with sectoral heterogeneity: A non-parametric frontier approach. Energy Economics, 62, 70-78.
  • WEC. (2018). World Energy Trilemma Index. https://www.worldenergy.org/assets/downloads/ World_Energy_Trilemma_Index_2022.pdf?v=1669839605.
  • Woźniak, J., and Pactwa, K. (2019). Possibilities for using mine waters in the context of the construction of heat energy clusters in Poland. Energy, Sustainability and Society, 9, 1-10.
  • Xu, R., and Wunsch II, D. C. (2009). Clustering, A John Wiley & Sons. Inc., Publication.
  • Yılancı, V. (2010). Bulanik kümeleme analizi ile Türkiye’deki illerin sosyoekonomik açidan sınıflandirilmasi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(3), 453-470.
  • Yoon, J., and Klasen, S. (2018). An application of partial least squares to the construction of the Social Institutions and Gender Index (SIGI) and the Corruption Perception Index (CPI). Social Indicators Research, 138, 61-88.
  • Yu, D., and He, X. (2020). A bibliometric study for DEA applied to energy efficiency: Trends and future challenges. Applied Energy, 268, 115048.
  • Zhang, D., Shi, X., and Sheng, Y. (2015). Comprehensive measurement of energy market integration in East Asia: An application of dynamic principal component analysis. Energy economics, 52, 299-305.
  • Zhilkina, Y. (2019). Risks in the energy sector: Management practices analysis in the electricity market. In E3S Web of Conferences,139,01071. EDP Sciences.
  • Zhou, P., and Ang, B. W. (2008). Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 36(8), 2911-2916.
  • Zhou, P., Ang, B. W., and Poh, K. L. (2008). Measuring environmental performance under different environmental DEA technologies. Energy Economics, 30(1), 1-14.
  • Zhou, P., Ang, B. W., and Zhou, D. Q. (2012). Measuring economy-wide energy efficiency performance: A parametric frontier approach. Applied energy, 90(1), 196-200.
  • Zou, X., and Shen, Q. (2023). China's energy security and transition in the post-Paris era. COVID-19 and climate change in BRICS Nations: Beyond the Paris Agreement and Agenda 2030.
Toplam 77 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Kalkınma Ekonomisi - Makro
Bölüm Makaleler
Yazarlar

Emre Akusta 0000-0002-6147-5443

Erken Görünüm Tarihi 24 Şubat 2025
Yayımlanma Tarihi
Gönderilme Tarihi 22 Nisan 2024
Kabul Tarihi 9 Ekim 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 40 Sayı: 1

Kaynak Göster

APA Akusta, E. (2025). Comparative Analysis of OECD Countries Based on Energy Trilemma Index: A Clustering Approach. İzmir İktisat Dergisi, 40(1), 248-271. https://doi.org/10.24988/ije.1472312

İzmir İktisat Dergisi
TR-DİZİN, DOAJ, EBSCO, ERIH PLUS, Index Copernicus, Ulrich’s Periodicals Directory, EconLit, Harvard Hollis, Google Scholar, OAJI, SOBIAD, CiteFactor, OJOP, Araştırmax, WordCat, OpenAIRE, Base, IAD, Academindex
tarafından taranmaktadır.

Dokuz Eylül Üniversitesi Yayınevi Web Sitesi
https://kutuphane.deu.edu.tr/yayinevi/

Dergi İletişim Bilgileri Sayfası
https://dergipark.org.tr/tr/pub/ije/contacts


İZMİR İKTİSAT DERGİSİ 2022 yılı 37. cilt 1. sayı ile birlikte sadece elektronik olarak yayınlanmaya başlamıştır.