Sürdürülebilirlik Bağlamında G-20 Ülkelerinin Enerji Üretim Kaynaklarına Göre TOPSIS Yöntemiyle Sıralanması
Öz
Bu çalışmada temel olarak, Çok Kriterli Karar Analizi (ÇKKA) teknikleri uygulanarak ülkeler düzeyinde farklı enerji üretim türlerine göre G20’ye üye ülkelerinin sıralanması amaçlanmaktadır. Bu çalışmada, literatürde sıkça kullanılan ÇKKA yaklaşımlarından biri olan TOPSIS yöntemi ile G20 ülkeleri enerji üretimlerine göre sıralanmıştır. G20’ye üye ülkeler alternatifleri; fosil yakıtlardan (petrol, doğalgaz ve kömür) elde edilen elektrik enerjisi, yenilenebilir enerji, nükleer enerji ve CO2 salınımı kriterleri oluşturmaktadır. 2020-2022 yılları arasında her yıl için G20 ülkeleri farklı enerji üretim tiplerine göre iki farklı senaryo altında değerlendirilmiştir. Tüm kriterlerin eşit ağırlığa sahip olduğu ilk senaryoda değerlendirme altındaki yıllarda her yıl için sırasıyla Amerika Birleşik Devletleri (ABD), Avrupa Birliği (AB) ve Çin en üst sırada yer almıştır. Kriter ağırlıklandırılmasına yönelik çevresel perspektife sahip ikinci senaryoda ise, ilk senaryoya benzer şekilde AB ve ABD ilk iki sırada yer alırken, Fransa sıralamada üçüncü ülke konumundadır. Çalışma kapsamında elde edilen en dikkat çekici bulgu, ilk senaryoda üst sıralarda yer alan Çin ve Suudi Arabistan’ın, çevresel bakış açısı ile değerlendirilen ikinci senaryoda son sıralarda yer almasıdır. Literatürde yer alan çalışmalardan farklı olarak, bu çalışmada yenilenebilir enerji üretimi, nükleer enerji üretimi ve emisyon değerlerinden oluşan kriterlere daha yüksek ağırlıklar atanarak çevreci bir bakış açısı ile değerlendirmelerde bulunulmuştur.
Anahtar Kelimeler
Çok Kriterli Karar Analizi Enerji Kaynakları G20 Ülkeleri TOPSIS Yenilenebilir Enerji Sürdürülebilirlik
Kaynakça
- Alizadeh, R. et al. (2020), “Improving renewable energy policy planning and decision-making through a hybrid MCDM method”, Energy Policy, 137, 111174.
- Bagočius, V. et al. (2014), “Selecting a location for a liquefied natural gas terminal in the Eastern Baltic Sea”, Transport, 29(1), 69-74.
- Baležentis, T. & D. Streimikiene (2017), “Multi-criteria ranking of energy generation scenarios with Monte Carlo simulation”, Applied Energy, 185, 862-871.
- Boran, F.E. et al. (2013), “Is nuclear power an optimal option for electricity generation in Turkey?”, Energy Sources, Part B: Economics, Planning, and Policy, 8(4), 382-390.
- Brand, B. & R. Missaoui (2014), “Multi-criteria analysis of electricity generation mix scenarios in Tunisia”, Renewable and Sustainable Energy Reviews, 39, 251-261.
- Brodny, J. & M. Tutak (2023), “Assessing the Energy and Climate Sustainability of European Union Member States: An MCDM-Based Approach”, Smart Cities, 6(1), 339-367.
- Carlsson, C. & R. Fullér (1996), “Fuzzy multiple criteria decision making: Recent developments”, Fuzzy Sets and Systems, 78(2), 139-153.
- Chen, S.J. & C.L. Hwang (1992), “Fuzzy multiple attribute decision making methods”, in: Fuzzy multiple attribute decision making (289-486), Springer, Berlin, Heidelberg.
- G20 (2022) About the G20, <https://g20.org/about-the-g20/#about>, 27.01.2022.
- Georgiou, D. et al. (2015), “Multi-criteria decision making on the energy supply configuration of autonomous desalination units”, Renewable Energy, 75, 459-467.
- Goswami, S.S. et al. (2022), “Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool”, Materials Today: Proceedings, 52, 1153-1160.
- Gökgöz, F. & E. Yalçın (2023), “Investigating the energy security performance, productivity, and economic growth for the EU”, Environmental Progress & Sustainable Energy, e14139.
- Hasheminasab, H. et al. (2023), “A novel energy poverty evaluation: Study of the European Union countries”, Energy, 264, 126157.
- Hwang, C.L. & K. Yoon (1981), “Methods for multiple attribute decision making”, in: Multiple attribute decision making (58-191), Springer, Berlin, Heidelberg.
- Ishfaq, S. et al. (2018), “Selection of optimum renewable energy source for energy sector in Pakistan by using MCDM approach”, Process Integration and Optimization for Sustainability, 2(1), 61-71.
- Jahanshahloo, G.R. et al. (2006), “An algorithmic method to extend TOPSIS for decision-making problems with interval data”, Applied Mathematics and Computation, 175(2), 1375-1384.
- Kablan, M.M. (2004), “Decision support for energy conservation promotion: an analytic hierarchy process approach”, Energy Policy, 32(10), 1151-1158.
- Karimi, A.R. et al. (2011), “Selection of wastewater treatment process based on the analytical hierarchy process and fuzzy analytical hierarchy process methods”, International Journal of Environmental Science & Technology, 8(2), 267-280.
- Kaya, M. et al. (2023), “Electricity price estimation using deep learning approaches: An empirical study on Turkish markets in normal and Covid-19 periods”, Expert Systems with Applications, 224, 120026.
- Kim, P.O. et al. (1999), “Selection of an optimal nuclear fuel cycle scenario by goal programming and the analytic hierarchy process”, Annals of Nuclear Energy, 26(5), 449-460.
- Krysiak, M. & A. Kluczek (2021), “A Multifaceted Challenge to Enhance Multicriteria Decision Support for Energy Policy”, Energies, 14(14), 4128.
- Lee, H.C. & C.T. Chang (2018), “Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan”, Renewable and Sustainable Energy Reviews, 92, 883-896.
- Li, T. et al. (2020), “The sustainable development-oriented development and utilization of renewable energy industry - A comprehensive analysis of MCDM methods”, Energy, 212, 118694.
- Lootsma, F.A. (ed.) (1999), Multi-criteria decision analysis via ratio and difference judgement, Boston, MA: Springer US.
- Oberschmidt, J. et al. (2010), “Modified PROMETHEE approach for assessing energy technologies”, International Journal of Energy Sector Management, 4(2), 183-212.
- Önüt, S. et al. (2008), “Multiple criteria evaluation of current energy resources for Turkish manufacturing industry”, Energy Conversion and Management, 49(6), 1480-1492.
- Ren, H. et al. (2009), “Multi-criteria evaluation for the optimal adoption of distributed residential energy systems in Japan”, Energy Policy, 37(12), 5484-5493.
- Roy, B. (2005), “Paradigms and challenges”, in: Multiple criteria decision analysis: state of the art surveys (3-24), Springer, New York, NY.
- San Cristóbal, J.R. (2012), “A goal programming model for the optimal mix and location of renewable energy plants in the north of Spain”, Renewable and Sustainable Energy Reviews, 16(7), 4461-4464.
- Sánchez-Lozano, J.M. et al. (2016), “Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain”, Journal of Cleaner Production, 127, 387-398.
- Sarkodie, W.O. et al. (2022), “Decision optimization techniques for evaluating renewable energy resources for power generation in Ghana: MCDM approach”, Energy Reports, 8, 13504-13513.
- Seddiki, M. & A. Bennadji (2019), “Multi-criteria evaluation of renewable energy alternatives for electricity generation in a residential building”, Renewable and Sustainable Energy Reviews, 110, 101-117.
- Wang, J.J. et al. (2009), “Review on multi-criteria decision analysis aid in sustainable energy decision-making”, Renewable and Sustainable Energy Reviews, 13(9), 2263-2278.
- Wang, C.N. et al. (2021), A multicriteria decision-making model for the selection of suitable renewable energy sources”, Mathematics, 9(12), 1318.
- Yoon, K.P. & C.L. Hwang (1995), Multiple attribute decision making: an introduction, Sage Publications.
- Zhang, C. et al. (2019), “Probabilistic multi-criteria assessment of renewable micro-generation technologies in households”, Journal of Cleaner Production, 212, 582-592.
- Zulqarnain, R.M. et al. (2020), “Application of TOPSIS method for decision making”, IJSRMSS International Journal of Scientific Research in Mathematical and Statistical Sciences, 7(2), 76-81.
Ranking of G-20 Countries According to Energy Production Sources in the Context of Sustainability by TOPSIS Method
Öz
The primary purpose of this study is to rank G20 member countries according to different types of energy production at the country level using Multi-Criteria Decision Analysis (MCDA) techniques. In this study, G20 countries are ranked according to their energy production using the TOPSIS method, one of the most widely used approaches in the MCDA literature. The alternatives are G20 members, and the criteria consist of electricity generation from fossil fuels (oil, natural gas, and coal), renewable energy, nuclear energy, and CO2 emissions. In the 2020-2022 period, G20 countries are evaluated under two scenarios according to different types of energy production. In the first scenario, where all criteria are equally weighted, the United States (USA), European Union (EU), and China ranked highest for each year in the years under evaluation, respectively. In the second scenario, which has an environmental perspective on the weighting of the criteria, similar to the first scenario, the EU and the USA are in the first two places, while France is the third country in the ranking. The most remarkable finding obtained within the scope of the study is that China and Saudi Arabia, which rank highly in the first scenario, are ranked last in the second scenario evaluated from an environmental perspective. Different from the studies in the literature, in this study, evaluations are made with an environmental view by assigning higher weights to the criteria of renewable energy production, nuclear energy production, and emission values.
Anahtar Kelimeler
Multi-Criteria Decision Analysis Energy Sources G20 Countries TOPSIS Renewable Energy Sustainability
Kaynakça
- Alizadeh, R. et al. (2020), “Improving renewable energy policy planning and decision-making through a hybrid MCDM method”, Energy Policy, 137, 111174.
- Bagočius, V. et al. (2014), “Selecting a location for a liquefied natural gas terminal in the Eastern Baltic Sea”, Transport, 29(1), 69-74.
- Baležentis, T. & D. Streimikiene (2017), “Multi-criteria ranking of energy generation scenarios with Monte Carlo simulation”, Applied Energy, 185, 862-871.
- Boran, F.E. et al. (2013), “Is nuclear power an optimal option for electricity generation in Turkey?”, Energy Sources, Part B: Economics, Planning, and Policy, 8(4), 382-390.
- Brand, B. & R. Missaoui (2014), “Multi-criteria analysis of electricity generation mix scenarios in Tunisia”, Renewable and Sustainable Energy Reviews, 39, 251-261.
- Brodny, J. & M. Tutak (2023), “Assessing the Energy and Climate Sustainability of European Union Member States: An MCDM-Based Approach”, Smart Cities, 6(1), 339-367.
- Carlsson, C. & R. Fullér (1996), “Fuzzy multiple criteria decision making: Recent developments”, Fuzzy Sets and Systems, 78(2), 139-153.
- Chen, S.J. & C.L. Hwang (1992), “Fuzzy multiple attribute decision making methods”, in: Fuzzy multiple attribute decision making (289-486), Springer, Berlin, Heidelberg.
- G20 (2022) About the G20, <https://g20.org/about-the-g20/#about>, 27.01.2022.
- Georgiou, D. et al. (2015), “Multi-criteria decision making on the energy supply configuration of autonomous desalination units”, Renewable Energy, 75, 459-467.
- Goswami, S.S. et al. (2022), “Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool”, Materials Today: Proceedings, 52, 1153-1160.
- Gökgöz, F. & E. Yalçın (2023), “Investigating the energy security performance, productivity, and economic growth for the EU”, Environmental Progress & Sustainable Energy, e14139.
- Hasheminasab, H. et al. (2023), “A novel energy poverty evaluation: Study of the European Union countries”, Energy, 264, 126157.
- Hwang, C.L. & K. Yoon (1981), “Methods for multiple attribute decision making”, in: Multiple attribute decision making (58-191), Springer, Berlin, Heidelberg.
- Ishfaq, S. et al. (2018), “Selection of optimum renewable energy source for energy sector in Pakistan by using MCDM approach”, Process Integration and Optimization for Sustainability, 2(1), 61-71.
- Jahanshahloo, G.R. et al. (2006), “An algorithmic method to extend TOPSIS for decision-making problems with interval data”, Applied Mathematics and Computation, 175(2), 1375-1384.
- Kablan, M.M. (2004), “Decision support for energy conservation promotion: an analytic hierarchy process approach”, Energy Policy, 32(10), 1151-1158.
- Karimi, A.R. et al. (2011), “Selection of wastewater treatment process based on the analytical hierarchy process and fuzzy analytical hierarchy process methods”, International Journal of Environmental Science & Technology, 8(2), 267-280.
- Kaya, M. et al. (2023), “Electricity price estimation using deep learning approaches: An empirical study on Turkish markets in normal and Covid-19 periods”, Expert Systems with Applications, 224, 120026.
- Kim, P.O. et al. (1999), “Selection of an optimal nuclear fuel cycle scenario by goal programming and the analytic hierarchy process”, Annals of Nuclear Energy, 26(5), 449-460.
- Krysiak, M. & A. Kluczek (2021), “A Multifaceted Challenge to Enhance Multicriteria Decision Support for Energy Policy”, Energies, 14(14), 4128.
- Lee, H.C. & C.T. Chang (2018), “Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan”, Renewable and Sustainable Energy Reviews, 92, 883-896.
- Li, T. et al. (2020), “The sustainable development-oriented development and utilization of renewable energy industry - A comprehensive analysis of MCDM methods”, Energy, 212, 118694.
- Lootsma, F.A. (ed.) (1999), Multi-criteria decision analysis via ratio and difference judgement, Boston, MA: Springer US.
- Oberschmidt, J. et al. (2010), “Modified PROMETHEE approach for assessing energy technologies”, International Journal of Energy Sector Management, 4(2), 183-212.
- Önüt, S. et al. (2008), “Multiple criteria evaluation of current energy resources for Turkish manufacturing industry”, Energy Conversion and Management, 49(6), 1480-1492.
- Ren, H. et al. (2009), “Multi-criteria evaluation for the optimal adoption of distributed residential energy systems in Japan”, Energy Policy, 37(12), 5484-5493.
- Roy, B. (2005), “Paradigms and challenges”, in: Multiple criteria decision analysis: state of the art surveys (3-24), Springer, New York, NY.
- San Cristóbal, J.R. (2012), “A goal programming model for the optimal mix and location of renewable energy plants in the north of Spain”, Renewable and Sustainable Energy Reviews, 16(7), 4461-4464.
- Sánchez-Lozano, J.M. et al. (2016), “Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain”, Journal of Cleaner Production, 127, 387-398.
- Sarkodie, W.O. et al. (2022), “Decision optimization techniques for evaluating renewable energy resources for power generation in Ghana: MCDM approach”, Energy Reports, 8, 13504-13513.
- Seddiki, M. & A. Bennadji (2019), “Multi-criteria evaluation of renewable energy alternatives for electricity generation in a residential building”, Renewable and Sustainable Energy Reviews, 110, 101-117.
- Wang, J.J. et al. (2009), “Review on multi-criteria decision analysis aid in sustainable energy decision-making”, Renewable and Sustainable Energy Reviews, 13(9), 2263-2278.
- Wang, C.N. et al. (2021), A multicriteria decision-making model for the selection of suitable renewable energy sources”, Mathematics, 9(12), 1318.
- Yoon, K.P. & C.L. Hwang (1995), Multiple attribute decision making: an introduction, Sage Publications.
- Zhang, C. et al. (2019), “Probabilistic multi-criteria assessment of renewable micro-generation technologies in households”, Journal of Cleaner Production, 212, 582-592.
- Zulqarnain, R.M. et al. (2020), “Application of TOPSIS method for decision making”, IJSRMSS International Journal of Scientific Research in Mathematical and Statistical Sciences, 7(2), 76-81.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Sürdürülebilir Kalkınma |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 26 Ekim 2023 |
| Gönderilme Tarihi | 16 Kasım 2022 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 31 Sayı: 58 |
