A Fuzzy-based model proposal for forecasting greenhouse gas-free supply chain potential

Yıl 2024, Cilt: 9 Sayı: 3, 519 - 542, 18.09.2024

Gürkan Işık , Miraç Tuba Çelik

https://doi.org/10.58559/ijes.1420845

Cited By: 1

Öz

Various greenhouse gas emission control approaches, such as filtration, require costly audits and are not suitable for creating foresight to scale gains across the supply chain. Thus, these practices are not suitable for building effective policies to reduce greenhouse gas emissions. This study proposes an approach to forecast greenhouse gas-free supply chain potential based on the producible renewable energy certificate amount to be able to build consistent, realistic, effective, and applicable policies to reduce emissions and promote renewable energy production. The greenhouse gas-free supply chain potential of countries and states can be measured and tracked through their total energy consumption certified with renewable energy certificates. By proportioning this value to the total energy consumption of the supply chain, the extent to which the green transformation has been achieved can be measured and scaled. The proposed model is built on fuzzy logic since renewable energy certificates contain uncertainties, and there is not enough data to make machine learning-supported forecasts because it is a developing field and an innovative business. The developed model is applied to the example of Türkiye, and the practical greenhouse gas-free supply chain potential of Türkiye is forecasted as 30.9 million megawatts (equivalent to 221 thousand ten-year trees) for 2024. Even in possible adverse events in the market and climatic conditions, it is not expected to decrease below 22.7 million megawatts. By considering these calculations, more realistic and more applicable obligatory energy policies can be produced without bringing additional audit burdens to the industrialists across the country.

Anahtar Kelimeler

Renewable energy certificate, Green supply chain, Greenhouse gas emission, Electricity trading, Fuzzy logic

Kaynakça

• [1] Güzel D, Demirdöğen O. Yeşil Tedarik Zinciri Yönetimi. The International New Issues in Social Sciences 2015; 1(1): 45-70.
• [2] Terzi T. Yeşil lojistik yönetiminde dengelenmiş skorkart ile lojistik performansı ölçümü: İntermodal lojistik sektöründe bir uygulama, Ankara, 2016.
• [3] Sundarakani B, Goh M, Souza RD, Shun C. Measuring carbon footprints across the supply chain, in Proceeding of the 13th International Symposium on Logistics (ISL2008): Integrating the Global Supply Chain, UK, 2008.
• [4] Coşkun S, Bozyiğit S. Yeşil Tedarik Zinciri Uygulamaları Üzerine Kimya Sektöründe Bir Alan Araştırması. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 2019; 21(2): 605-637.
• [5] Ozyoruk B. A literature survey on green supplier selection. The Eurasia Proceedings of Science Technology Engineering and Mathematics 2018; 2: 407-411.
• [6] Srivastava SK. Green supply‐chain management: a state‐of‐the‐art literature review, International journal of management reviews 2007; 9(1): 53-80.
• [7] Saavedra M, Fontes C, Freires G. Sustainable and renewable energy supply chain: A system dynamics overview. Renewable and Sustainable Energy Reviews 2018; 82: 247-259.
• [8] Shereef R, Khaparde S. Current status of REC mechanism in India and possible policy modifications to way forward. Energy Policy 2013; 61: 1443-1451.
• [9] Özcan M, Ergün B, Ocaklı E. Türkiye’de kullanılan yenilenebilir enerji sertifika sistemlerinin değerlendirilmesi. EMO Bilimsel Dergi 2021; 11(21): 29-41.
• [10] Girish GP, Sashikala P, Supra B, Acharya A. Renewable energy certificate trading through power exchanges in India. International Journal of Energy Economics and Policy 2015; 5(3): 805-808.
• [11] Chuang J, Lien HL, Den W, Iskandar L, Liao PH. The relationship between electricity emission factor and renewable energy certificate: The free rider and outsider effect. Sustainable Environment Research 2018; 28(6): 422-429.
• [12] Fazio TD, Delchambre A, Lit PD. Disassembly for recycling of office electronic equipment. European Journal Mechanical and Environmental Engineering 1997; 42(1): 25-31.
• [13] Richter K, Dobos I. Analysis of the EOQ repair and waste disposal problem with integer setup numbers. International Journal of Production Economics 1999; 59(1-3): 463-467.
• [14] Türkay B. Yeşil satınalma ve yeşil tedarikçi seçimi. İstanbul, 2015.
• [15] Peker D. Çevresel Performansın Geliştirilmesinde Yeşil Tedarik Zinciri Yönetimi. Bursa, 2010.
• [16] Atrek B, Özdağoğlu A. Yeşil Tedarik Zinciri Uygulamaları: Alüminyum Doğrama Sektörü İzmir Örneği. Anadolu Üniversitesi Sosyal Bilimler Dergisi 2014; 14(2): 13-25.
• [17] Zhu W, He Y. Green product design in supply chains under competition. European Journal of Operational Research 2017; 258(1): 165-180.
• [18] Arslankaya S, Çelik M. Green supplier selection in steel door industry using fuzzy AHP and fuzzy Moora methods. Emerging Materials Research 2021; 10(4): 357-369.
• [19] Li J, Sarkis J. Product eco-design practice in green supply chain management: a China-global examination of research. Nankai Business Review International 2022; 13(1): 124-153.
• [20] Sheu J, Chou Y, Hu C. An integrated logistics operational model for green-supply chain management. Transportation Research Part E: Logistics and Transportation Review 2005; 41(4): 287-313.
• [21] Kainuma Y, Tawara N. A multiple attribute utility theory approach to lean and green supply chain management. International Journal of Production Economics 2006; 101(1): 99-108.
• [22] Walton S, Handfiels R, Melnyk S. The Green Supply Chain: Integrating Suppliers into Environmental Management Processes. International Journal of Purchasing and Materials Management 1998; 34(1): 2-11.
• [23] Wang F, Lai X, Shi N. A multi-objective optimization for green supply chain network design. Decision Support Systems 2011; 51(2): 262-269.
• [24] Kafa N, Hani Y, Mhamedi AE. Sustainability Performance Measurement for Green Supply Chain Management. IFAC Proceedings Volumes 2013; 46(24): 71-78.
• [25] Rostamzadeh R, Govindan K, Esmaeili A, Sabaghi M. Application of fuzzy VIKOR for evaluation of green supply chain management practices. Ecological Indicators 2015; 49: 188-203.
• [26] Lin RJ. Using fuzzy DEMATEL to evaluate the green supply chain management practices. Journal of Cleaner Production 2013; 40: 32-39.
• [27] Büyüközkan G, Çifçi G. Evaluation of the green supply chain management practices: a fuzzy ANP approach. Production Planning & Control 2012; 23(6): 405-418.
• [28] Pourjavad E, Shahin A. The application of Mamdani fuzzy inference system in evaluating green supply chain management performance. International Journal of Fuzzy Systems 2018; 20: 901-912.
• [29] Uygun Ö, Dede A. Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques. Computers & Industrial Engineering 2016; 102: 502-511.
• [30] Shen L, Olfat L, Govindan K, Khodaverdi R, Diabat A. A fuzzy multi criteria approach for evaluating green supplier's performance in green supply chain with linguistic preferences. Resources, Conservation and Recycling 2013; 74: 170-179.
• [31] Mangla SK, Kumar P, Barua MK. Risk analysis in green supply chain using fuzzy AHP approach: A case study. Resources, Conservation and Recycling 2015; 104: 375-390.
• [32] Köseoğlu A. Intuitionistic multiplicative set approach for green supplier selection problem using TODIM method. Journal of Universal Mathematics 2022; 5(2): 149-158.
• [33] Khan SAR, Yu Z, Farooq K. Green capabilities, green purchasing, and triple bottom line performance: Leading toward environmental sustainability. Business Strategy and the Environment 2022; 32(4): 2022-2034.
• [34] Cucchiella F, D'Adamo I. Issue on supply chain of renewable energy. Energy Conversion Management 2013: 774-780.
• [35] Fernando Y, Bee P, Jabbour C, Thome A. Understanding the effects of energy management practices on renewable energy supply chains: Implications for energy policy in emerging economies. Energy Policy 2018: 418-428.
• [36] Wang Q, Jiang F, Li R. Assessing supply chain greenness from the perspective of embodied renewable energy–A data envelopment analysis using multi-regional input-output analysis. Renewable Energy 2022: 1292-1305.
• [37] Mouraviev N. Renewable energy in Kazakhstan: challenges to policy and governance. Energy Policy 2021: 112051.
• [38] Alizadeh R, Soltanisehat L, Lund P, Zamanisabzi H. Improving renewable energy policy planning and decision-making through a hybrid MCDM method. Energy Policy 2020: 11174.
• [39] Eicke L, Weko S. Does green growth foster green policies? Value chain upgrading and feedback mechanisms on renewable energy policies. Energy Policy 2022: 112948.
• [40] Strunz S, Lehmann P, Gawel E. Analyzing the ambitions of renewable energy policy in the EU and its Member States. Energy Policy 2021: 112447.
• [41] Zhang D, Kong Q. Green energy transition and sustainable development of energy firms: An assessment of renewable energy policy. Energy Economics 2022: 106060.
• [42] Gulagi A, Ram M, Solomon A, Khan M, Breyer C. Current energy policies and possible transition scenarios adopting renewable energy: A case study for Bangladesh. Renewable Energy 2020: 899-920.
• [43] Khan SAR, Yu Z, Ridwan IL, Irshad AUR, Ponce P, Tanveer M. Energy efficiency, carbon neutrality and technological innovation: a strategic move towards green economy. Economic Research-Ekonomska Istraživanja 2023; 36(2): 2140306.
• [44] Xu Y, Liu A, Li Z, Li J, Xiong J, Fan P. Review of green supply-chain management diffusion in the context of energy transformation. Energies 2023; 16(2): 686.
• [45] Gupta SK, Purohit P. Renewable energy certificate mechanism in India: a preliminary assessment. Renewable and Sustainable Energy Reviews 2013; 22: 380-392.
• [46] Irfan M. Integration between electricity and renewable energy certificate (REC) markets: Factors influencing the solar and non-solar REC in India. Renewable Energy 2021: 65-74.
• [47] Hulshof D, Jepma C, Mulder M. Performance of markets for European renewable energy certificates. Energy Policy 2019; 128: 697-710.
• [48] Adamczyk J, Graczyk M. Green certificates as an instrument to support renewable energy in Poland— Strengths and weaknesses. Environmental Science and Pollution Research 2020; 27(6): 6577-6588.
• [49] Çakır S. Renewable energy generation forecasting in Turkey via intuitionistic fuzzy time series approach. Renewable Energy 2023; 214: 194-200.
• [50] Mbarek MB, Feki R. Using fuzzy logic to renewable energy forecasting: a case study of France. International Journal of Energy Technology and Policy 2016; 12(4): 357-37.
• [51] Severiano CA, Silva PCDL, Cohen MW, Guimarães FG. Evolving fuzzy time series for spatio-temporal forecasting in renewable energy systems. Renewable Energy 2021; 171: 764-783.
• [52] Yang H, Jiang P, Wang Y, Li H. A fuzzy intelligent forecasting system based on combined fuzzification strategy and improved optimization algorithm for renewable energy power generation. Applied Energy 2022; 325: 119849.
• [53] Sagir O, Bahadir A. Renewable energy potential and utilization in Turkey. Journal of Engineering Research and Applied Science 2017; 6(1): 577-582.
• [54] Kapluhan E. Evaluation of Turkey’s Renewable Energy Potential in Terms of 2023 Energy Vision. Bulletin of Dnipropetrovsk National University Series Chemistry 2021; 2(135): 71-87.
• [55] Republic of Türkiye Ministry of Energy and Natural Resources. Energy. 2022. [Online]. https://enerji.gov.tr/bilgi-merkezi-enerji.
• [56] EXIST.Injectionquantity,2024.[Online]. https://seffaflik.epias.com.tr/electricity/electricity- generation/ex-post-generation/injection-quantity.
• [57] EXIST, Transparency Platform, 2024. [Online]. Available: https://seffaflik.epias.com.tr/transparency/index.xhtml.
• [58] Foton, 2023 Yıl Sonu Raporu, 2023. [Online]. Available: https://tr.foton.energy/tr/news-article/15.
• [59] United States Environmental Protection Agency (EPA). Greenhouse Gas Equivalencies Calculator, 03 2022. [Online]. Available: https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator.