Energy Consumption Trends Before and During the Covid-19 Pandemic: An Entropy-Based PROMETHEE Analysis

Year 2022, Volume: 8 Issue: 1, 1 - 28, 31.05.2022

Gökhan Özkaya

https://doi.org/10.30613/curesosc.1071005

Cited By: 1

Abstract

Renewable energy sources have a crucial role in decreasing reliance on fossil fuels. In the literature, there is a need for new studies on the evaluation of energy use preferences and policies of countries before and during the Covid-19 pandemic. In order to fill this gap, the study aimed to compare and evaluate 36 countries through Entropy-based PROMETHEE methods using TheGlobalEconomy.com indicators. As a result of Entropy, while the criterion with the highest importance is the gasoline consumption criterion with a value of 14.12%, the criterion that follows these criteria in order are the fossil fuels electricity generation (12.25%); coal consumption (11.8%); coal imports (10.52%); renewable power generation (10.29%). According to the PROMETHEE results, Sweden ranked first and is a good performer in almost all the indicators. In addition, the first 10 countries in the ranking are the European Union countries. The novelty of the study is that it provides an important review of the current literature on energy and that 36 countries with significant economic power from almost all continents are evaluated in terms of energy consumption and preferences at both pre-pandemic and pandemic conditions.

Keywords

Renewable Energy, Environmental Management, Entropy, PROMETHEE, Covid-19.

Covid-19 Pandemisi Öncesi ve Sırasındaki Enerji Tüketim Trendleri: Entropi Tabanlı PROMETHEE Analizi

Abstract

Yenilenebilir enerji kaynakları, fosil yakıtlara olan bağımlılığın azaltılmasında önemli bir role sahiptir. Literatürde Covid-19 pandemisi öncesi ve sırasında ülkelerin enerji kullanım tercihleri ve politikalarının değerlendirilmesine yönelik yeni çalışmalara ihtiyaç duyulmaktadır. Bu boşluğu doldurmak için çalışma, TheGlobalEconomy.com göstergelerini kullanarak Entropi tabanlı PROMETHEE yöntemleriyle 36 ülkeyi karşılaştırmayı ve değerlendirmeyi amaçlamıştır. Entropi sonucunda en yüksek öneme sahip kriter %14,12 değeri ile benzin tüketim kriteri iken, bu kriterleri sırasıyla takip eden kriter fosil yakıtlardan elektrik üretimi (%12,25); kömür tüketimi (%11,8); kömür ithalatı (%10.52); yenilenebilir enerji üretimi (%10,29). PROMETHEE sonuçlarına göre İsveç ilk sırada yer almaktadır ve hemen hemen tüm göstergelerde iyi bir performans sergilmektedir. Ayrıca, sıralamada ilk 10 ülke Avrupa Birliği ülkelerinden oluşmaktadır. Çalışmanın literatüre sunduğu yenilik, güncel enerji literatürüne önemli bir inceleme sunması ve hemen hemen tüm kıtalardan önemli ekonomik güce sahip 36 ülkenin hem pandemi öncesi hem de pandemi koşullarında enerji tüketimi ve tercihleri açısından değerlendirilmesidir.

Keywords

Yenilenebilir Enerji, Çevre Yönetimi, Entropi, PROMETHEE, Covid-19

References

• aenerji. (2021). Renewable Energy Projects. Retrieved 02.07.2021, from https://aenerji.com/yenilenebilir-enerji-projeleri/
• Alizadeh, R., Soltanisehat, L., Lund, P. D., & Zamanisabzi, H. (2020). Improving renewable energy policy planning and decision-making through a hybrid MCDM method. Energy Policy, 137, 111174.
• Apan, M., Öztel, A., & İslamoğlu, M. (2015). Teknoloji Sektörünün Entropi Ağırlıklı Uzlaşık Programlama (CP) ile Finansal Performans Analizi: BİST’de Bir Uygulama. Paper presented at the 19th Finance Symposium, Hitit University Çorum, Turkey [online] https://www. researchgate. net/publication/283299704 (accessed 7 December 2017).
• Behzadian, M., Kazemzadeh, R. B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European journal of operational research, 200(1), 198-215.
• Brans, J.-P., & Mareschal, B. (2005). PROMETHEE methods, in ‘Multiple Criteria Decision Analysis: State of the Art Surveys’ Edited by J. Figueira, S. Greco, M. Ehrgott: Springer.
• Brans, J.-P., & Vincke, P. (1985). Note—A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision-Making). Management science, 31(6), 647-656.
• Burg, J. P. (1974). Maximum entropy spectral analysis. Astronomy and Astrophysics Supplement, 15, 383.
• Canedo, M. M. L., & de Almeida, A. T. (2008). Electronic government: a multi-criterion approach to prioritizing projects by integrating balanced scorecard methodology indicators. Brazilian Journal of Operations & Production Management, 5(2), 49-72.
• Dağdeviren, M., & Erarslan, E. (2008). PROMETHEE siralama yöntemi ile tedarikçi seçimi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 23(1).
• Digkoglou, P., & Papathanasiou, J. (2018). Ranking the EU countries according to the environmental performance index using PROMETHEE. International Journal of Sustainable Agricultural Management and Informatics, 4(3-4), 290-305.
• Economy, G. (2021). Compare countries with annual data from official sources: TheGlobalEconomy. Com.
• Enerdata. (2020). Global Energy Statistical Yearbook 2020: Enerdata Grenoble, France.
• Energiewende, E. a. A. (2020). EU Power Sector in 2020. Retrieved 25.01.2021, from https://ember-climate.org/project/eu-power-sector-2020/
• Ergen, E., & NAZLIGÜL, H. (2021). Energy criticism of Turkey and comparison with the world: energy harvesting, development, challenges, and opportunities. Artıbilim: Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi Fen Bilimleri Dergisi, 4(1), 43-54.
• Ernst&Young. (2020). Recovery in the renewable energy sector. Retrieved 12.07.2021, from https://www.ey.com/tr_tr/news/2020/ey-yenilenebilir-enerji-ulke-cekiciligi-endeksi-basin-bulteni
• Golan, A., Judge, G., & Miller, D. (1997). Maximum entropy econometrics: Robust estimation with limited data.
• Guler, E., Yerel Kandemir, S., Acikkalp, E., & Ahmadi, M. H. (2021). Evaluation of sustainable energy performance for OECD countries. Energy Sources, Part B: Economics, Planning, and Policy, 16(6), 491-514.
• Güneş, İ. (2018). Türkiye’nin Enerji Kaynaklari: Güneş. Retrieved 18.07.2021, from http://ankaenstitusu.com/turkiyenin-enerji-kaynaklari-gunes/
• Hoang, A. T., Nižetić, S., Olcer, A. I., Ong, H. C., Chen, W.-H., Chong, C. T., . . . Nguyen, X. P. (2021). Impacts of COVID-19 pandemic on the global energy system and the shift progress to renewable energy: Opportunities, challenges, and policy implications. Energy Policy, 154, 112322.
• Hosseini, S. E. (2020). An outlook on the global development of renewable and sustainable energy at the time of COVID-19. Energy Research & Social Science, 68, 101633.
• IEA, I. (2020). World energy balances: Overview.
• IRENA. (2021). Renewable Capacity Statistics 2021 Abu Dhabi.
• Ishizaka, A., & Nemery, P. (2011). Selecting the best statistical distribution with PROMETHEE and GAIA. Computers & Industrial Engineering, 61(4), 958-969.
• Kahraman, Ç., Abdulhamit, E., & Özevin, O. (2017). Futbol Takımlarının Finansal Ve Sportif Etkinliklerinin Entropi ve TOPSIS Yöntemiyle Analiz Edilmesi: Avrupa’nın 5 Büyük Ligi ve Süper Lig Üzerine Bir Uygulama. Uluslararası Yönetim İktisat ve İşletme Dergisi, 13(1), 199-222.
• Kaya, S. K. (2020). Evaluation of the Effect of COVID-19 on Countries’ Sustainable Development Level: A comparative MCDM framework. Operational Research in Engineering Sciences: Theory and Applications, 3(3), 101-122.
• Lee, H.-C., & Chang, C.-T. (2018). Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renewable and sustainable energy reviews, 92, 883-896.
• Lihong, M., Yanping, Z., & Zhiwei, Z. (2008). Improved VIKOR algorithm based on AHP and Shannon entropy in the selection of thermal power enterprise's coal suppliers. Paper presented at the 2008 International Conference on Information Management, Innovation Management and Industrial Engineering.
• Looney, B. (2020). BP statistical review of world energy. BP Statistical Review, London UK.
• Mareschal, B., & Brans, J.-P. (1988). Geometrical representations for MCDA. European journal of operational research, 34(1), 69-77.
• Mareschal, B., Brans, J. P., & Vincke, P. (1984). PROMETHEE: A new family of outranking methods in multicriteria analysis: ULB--Universite Libre de Bruxelles.
• Naderipour, A., Abdul-Malek, Z., Ahmad, N. A., Kamyab, H., Ashokkumar, V., Ngamcharussrivichai, C., & Chelliapan, S. (2020). Effect of COVID-19 virus on reducing GHG emission and increasing energy generated by renewable energy sources: A brief study in Malaysian context. Environmental technology & innovation, 20, 101151.
• Outlook, C. M. (2020). World Bank Group.
• Phillis, A., Grigoroudis, E., & Kouikoglou, V. S. (2021). Assessing national energy sustainability using multiple criteria decision analysis. International Journal of Sustainable Development & World Ecology, 28(1), 18-35.
• Projects, G. D. o. N. E. a. I. (2021). Nuclear Power Plants in Our Country and in the World. Retrieved 13.09.2021, from https://enerji.gov.tr/nukleer-enerji-ve-uluslararasi-projeler-genel-mudurlugu-ulkemizde-ve-dunyada-nukleer-santraller#:~:text=%C5%9Eu%20an%20d%C3%BCnyada%2054%20n%C3%BCkleer,1%20n%C3%BCkleer%20reakt%C3%B6r%20in%C5%9Fa%20halindedir.
• Remeikienė, R., Gasparėnienė, L., Fedajev, A., Szarucki, M., Đekić, M., & Razumienė, J. (2021). Evaluation of sustainable energy development progress in EU member states in the context of building renovation. Energies, 14(14), 4209.
• Rita, E., Chizoo, E., & Cyril, U. S. (2021). Sustaining COVID-19 pandemic lockdown era air pollution impact through utilization of more renewable energy resources. Heliyon, 7(7), e07455.
• Rosenfeld, R. (1994). Adaptive statistical language modeling. PhD Thesis, Carnegie Mellon University.
• Shannon, C. E., & Weaver, W. (1949). A mathematical model of communication. Urbana, IL: University of Illinois Press, 11.
• Sharif, A., Baris-Tuzemen, O., Uzuner, G., Ozturk, I., & Sinha, A. (2020). Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: Evidence from Quantile ARDL approach. Sustainable cities and society, 57, 102138.
• Shemshadi, A., Shirazi, H., Toreihi, M., & Tarokh, M. J. (2011). A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert systems with applications, 38(10), 12160-12167.
• TENMAK. (2021). What is the effect of nuclear power plants on global warming? Retrieved 15.09.2021, from https://www.tenmak.gov.tr/sik-sorulan-sorular/136-nukleer-enerji-ve-nukleer-reaktorler-sss/845-nukleer-santrallerin-kuresel-isinmaya-etkisi-ne-kadardir.html#:~:text=N%C3%BCkleer%20enerjinin%20iklim%20de%C4%9Fi%C5%9Fikli%C4%9Fine%20sebep,olarak%20yakla%C5%9F%C4%B1k%20%17%20tasarruf%20sa%C4%9Flamaktad%C4%B1r.
• Tolga, G. (2013). PROMETHEE yöntemi ve GAIA düzlemi. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(1), 133-154.
• Tsiaras, S., & Andreopoulou, Z. (2020). Forest Policy Evaluation in European Countries Using the PROMETHEE Method Advances in Operational Research in the Balkans (pp. 95-109): Springer.
• Wang, C.-N., Dang, T.-T., Tibo, H., & Duong, D.-H. (2021). Assessing renewable energy production capabilities using DEA window and fuzzy TOPSIS model. Symmetry, 13(2), 334.
• Wang, T.-C., & Lee, H.-D. (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert systems with applications, 36(5), 8980-8985.
• Yaralıoğlu, K. (2010). Karar verme yöntemleri: Detay Yayıncılık.
• Zeleny, M. (2012). Multiple criteria decision making Kyoto 1975 (Vol. 123): Springer Science & Business Media.