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Tytuł artykułu

Clustering analysis of energy consumption data in EU regions and other countries of the war ld

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study aims to analyze energy consumption patterns across selected nations from Africa, America, Asia/Middle East, and Europe, with a focus on the types of energy sources used. Covering 46 countries, the research spans the years 2000 to 2018 and examines the distribution and changes in energy consumption by source and type. The regions studied include diverse countries such as Austria, Sweden, Czechia, and Croatia in Europe; Algeria, Egypt, and South Africa in Africa; China, India, and Saudi Arabia in Asia/Middle East; and Brazil, Canada, and the United States in the Americas, with Australia and New Zealand representing Oceania. Utilizing data from the BP Statistical Review of World Energy and the SHIFT Data Portal, along with key indicators maintained by Our World in Data, the study employs methods such as descriptive statistics, cluster analysis using k-means, and time-series clustering with dynamic time warping (DTW). The analysis highlights regional similarities and variances in energy use, providing new insights into the complex relationship between energy consumption patterns and factors such as economic growth, national policies, and geopolitical contexts. This research addresses a significant gap in the existing literature by offering a detailed comparative analysis of how different nations manage and consume energy. It contributes to the broader discourse on sustainable energy policies and economic development in the face of global energy challenges.
Rocznik
Strony
209--226
Opis fizyczny
Bibliogr. 44 poz., rys., wykr.
Twórcy
autor
  • Warsaw University of Technology, Faculty of Power and Aeronautical Engineering, Institute of Heat Engineering, 21 Nowowiejska street, 00-665 Warsaw, Poland
  • Warsaw University of Technology, Faculty of Power and Aeronautical Engineering, Institute of Heat Engineering, 21 Nowowiejska street, 00-665 Warsaw, Poland
Bibliografia
  • [1] Alcantara, V., Duro, J.A. Inequality of energy intensities across OECD countries: A note. Energy Policy 2004;32:1257-1260.
  • [2] Rokicki, T., Perkowska, A. Diversity and Changes in the Energy Balance in EU Countries. Energies 2021;14:1098.
  • [3] Rokicki, T., Perkowska, A. Changes in Energy Supplies in the Countries of the Visegrad Group. Sustainability 2020;12:7916.
  • [4] Le Pen, Y., Sevi, B. On the non-convergence of energy intensities: Evidence from a pair-wise econometric approach. Ecol. Econ. 2010;69:641-650.
  • [5] Markandya, A., Pedroso-Galinato, S., Streimikiene, D. Energy intensity in transition economies: Is there convergence towards the EU average? Energy Econ. 2006;28:121-145.
  • [6] Liddle, B. Revisiting world energy intensity convergence for regional differences. Appl. Energy 2010;87:3218-3225.
  • [7] Jakob, M., Haller, M., Marschinski, R. Will history repeat itself? Economic convergence and convergence in energy use patterns. Energy Econ. 2012;34:9 5-104.
  • [8] Mulder, P., De Groot, H.L. Structural change and convergence of energy intensity across OECD countries, 1970-2005. Energy Econ. 2012;34:1910-1921.
  • [9] Mishra, V., Smyth, R. Convergence in energy consumption per capita among ASEAN countries. Energy Policy 2014;73:180-185.
  • [10] Payne, J.E., Vizek, M., Lee, J. Stochastic convergence in per capita fossil fuel consumption in US states. Energy Econ. 2017;62:382-395.
  • [11] Le, T.H., Chang, Y., Park, D. Energy demand convergence in APEC: An empirical analysis. Energy Econ. 2017;65:32-41.
  • [12] Chen, Y., Lee, C.C. Does technological innovation reduce CO2 emissions? Cross-country evidence. J. Clean. Prod. 2020;263:121550.
  • [13] Herrerias, M.J. World energy intensity convergence revisited: A weighted distribution dynamics approach. Energy Policy 2012;49:383-399.
  • [14] Cheong, T.S., Li, V.J., Shi, X. Regional disparity and convergence of electricity consumption in China: A distribution dynamics approach. China Econ. Rev. 2019;58:101154. [15] Parker, S., Liddle, B. Economy-wide and manufacturing energy productivity transition paths and club convergence for OECD and non-OECD countries. Energy Econ. 2017;62:338-346.
  • [16] Rokicki, T., Perkowska, A., Klepacki, B., Szczepaniuk, H., Szczepaniuk, E.K., Berezinski, S., Ziółkowska, P. The Importance of Higher Education in the EU Countries in Achieving the Objectives of the Circular Economy in the Energy Sector. Energies 2020;13:4407.
  • [17] Lee, C.C., Chang, C.P. Energy consumption and economic growth in Asian economies: A more comprehensive analysis using panel data. Resour. Energy Econ. 2008;30:50-65.
  • [18] Wen, H., Lee, C.C. Impact of environmental labeling certification on firm performance: Empirical evidence from China. J. Clean. Prod. 2020;255:120201.
  • [19] Zhao, C.P., Gukasyan, G., Bezpalov, V., Prasolov, V. Development of modern standards for energy efficiency of industrial enterprises within the European Union policy. Int. J. Energy Econ. Policy 2020;10:451.
  • [20] Hassan, Y.A., Isiaka, M.A. Convergence in electricity consumption among selected West African Countries. Colombo Bus. J. 2019;10:l-18.
  • [21] Steckel, J.C., Brecha, R.J., Jakob, M., Strefler, J., Luderer, G. Development without energy? Assessing future scenarios of energy consumption in developing countries. Ecol. Econ. 2013;90:53-67.
  • [22] Arto, I., Capellan-Perez, I., Lago, R., Bueno, G., Bermejo, R. The energy requirements of a developed world. Energy Sustain. Dev. 2016;33:l-13.
  • [23] Hao, Y., Liao, H., Wei, Y.M. Is China's carbon reduction target allocation reasonable? An analysis based on carbon intensity convergence. Appl. Energy 2015;142:229-239.
  • [24] Kim, Y.S. Electricity consumption and economic development: Are countries converging to a common trend? Energy Econ. 2015;49:192-202.
  • [25] Cheshmehzangi, A. Low carbon transition at the township level: Feasibility study of environmental pollutants and sustainable energy planning. Int. J. Sustain. Energy 2021;40:670-696.
  • [26] Mahmood, T., Ahmad, E. The relationship of energy intensity with economic growth: Evidence for European economies. Energy Strategy Rev. 2018;20:90-98.
  • [27] Acaravci, A., Ozturk, I. On the relationship between energy consumption, CO2 emissions and economic growth in Europe. Energy 2010;35:5412-5420.
  • [28] Judson, R.A., Schmalensee, R., Stoker, T.M. Economic development and the structure of the demand for commercial energy. Energy J. 1999;20:29-57.
  • [29] Medlock III, K.B., Soligo, R. Economic development and end-use energy demand. Energy J. 2001;22:77-105.
  • [30] Ma, C., Stern, D.I. China's changing energy intensity trend: A decomposition analysis. Energy Econ. 2008;30:1037-1053.
  • [31] Voigt, S., De Cian, E., Schymura, M., Verdolini, E. Energy intensity developments in 40 major economies: Structural change or technology improvement? Energy Econ. 2014;41:47-62.
  • [32] Gostkowski, M., Gajowniczek, K. Weighted Quantile Regression Forests for Bimodal Distribution Modeling: A Loss Given Default Case. Entropy 2020;22:545. [33] Nafkha, R., Gajowniczek, K., Zabkowski, T. Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques. Energies 2018;11:514.
  • [34] Tzortzis, G., Likas, A. The MinMax K-means clustering algorithm. Pattern Recognition 2014;47:2505-2516.
  • [35] Gan, G., Ng, K.P. K-means clustering with outlier removal. Pattern Recognition Letters 2017;90:8-14.
  • [36] Luo, Y.L., Yu, Q.Y., et al. Outlier-eliminated k-means clustering algorithm based on differential privacy preservation. Applied Intelligence the International Journal of Artificial Intelligence, Neural Networks & Complex Problem-Solving Technologies 2016.
  • [37] Samrin, R., Vasumathi, D. Hybrid Weighted K-Means Clustering and Artificial Neural Network for an Anomaly-Based Network Intrusion Detection System. Journal of Intelligent Systems 2016;27.
  • [38] Meng, J.L., Liu, D.C. A new method for identifying bad data in power system based on Spark and cluster analysis. Power System Protection and Control 2016;44(03):85-91.
  • [39] Raport IJ: Inwestycje w Energetyce Mogq Zasilic Polski PKB o 200-300 Mld zl {IJ Report: Investments in the Energy Sector Can Boost Polish GDP by PLN 200-300 Billion). Available online: https:/ /www.teraz--srodowisko.pl/aktualnosci/instytut--jagiellonski--raport-transformacja--energetyczna--koszty--9002.html (accessed on 31 August 2021).
  • [40] Wurster, S., Hagemann, C. Expansion of Renewable Energy in Federal Settings: Austria, Belgium, and Germany in Comparison. J. Environ. Dev. 2020;29:147-168.
  • [41] Mlynarski, T. Polityka i Bezpieczenstwo Energetyczne Francji (Energy Policy and Energy Security of France). TEKA Political Sci. Int. Re lat. 2014;9:51-62.
  • [42] Karanfil, F. How many times again will we examine the energy-income nexus using a limited range of traditional econometric tools? Energy Policy 2009;37:1191-1194.
  • [43] Hmaidan, W. Oil in a Low-Carbon Economy, Un Chronicale; United Nations: New York, NY, USA. Available online: https://www.un.org/ en/ chronicle/ article/ oil--low--carbon-economy (accessed on 20 August 2021).
  • [44] Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the Promotion of the Use of Energy from Renewable Sources and Amending and Subsequently Repealing DIRECTIVES 2001/77 /EC and 2003/30/EC. Off. J. Eur. Union 2009, L 140/16, art. 2a. Available online: https:// eur-lex.europa.eu/legalcontent/EN/TXT /HTML/?uri=CELEX:32009L0028
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-70dd4e02-810a-42da-8893-9e27199f25e2
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