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Selection of Renewable Energy Alternatives for Green Blockchain Investments: A Hybrid IT2-based Fuzzy Modelling

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Abstract

In this study, it is aimed to determine the most suitable renewable energy alternatives that can be used in blockchain investments. In this context, firstly, a wide literature review is made and 6 different criteria that could have an impact on this decision are determined. The analysis process of the consists of two different stages. Firstly, the significance levels of these criteria are calculated with the help of interval type-2 (IT2F) decision making trial and evaluation laboratory (DEMATEL)-analytical hierarchy process (ANP) (DANP) method. According to the analysis results obtained, it has been determined that continuity in energy supply and legal conditions are the most important criteria. Hence, it is recommended that while choosing among renewable energy alternatives, it is necessary to pay attention to the legal regulations in the country. Another important point is that attention should be paid to this issue in renewable energy sources to be selected in order to have sustainable benefit from energy in blockchain technologies. On the other hand, in the second phase of the analysis process of the study, 5 different renewable energy alternatives are listed according to their suitability in blockchain technology. In this process, the IT2 fuzzy VIšeKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach has been taken into consideration. As a result, it is concluded that wind and solar energy are the most suitable energy alternatives for this technology. Considering the results obtained, it is understood that countries that use blockchain technology should pay particular attention to wind and solar investments. In this regard, companies that use wind and solar energy with individuals or institutions using blockchain technology should be in cooperation. Thanks to these renewable energy alternatives, excess energy consumption resulting from the use of blockchain technology can be achieved with environmentally friendly energy sources. In other words, it will be possible to minimize the carbon emission problem that occurs with the use of this technology.

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References

  1. Bai C, Sarkis J (2020) A supply chain transparency and sustainability technology appraisal model for blockchain technology. Int J Prod Res 58(6):1–21

    Google Scholar 

  2. Dubey R, Gunasekaran A, Bryde DJ, Dwivedi YK, Papadopoulos T (2020) Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. Int J Prod Res 58(11):1–18

    Article  Google Scholar 

  3. Gatteschi, V., Lamberti, F., & Demartini, C. (2020). Blockchain Technology Use Cases. In Advanced Applications of Blockchain Technology (pp. 91–114). Springer, Singapore.

  4. Janssen M, Weerakkody V, Ismagilova E, Sivarajah U, Irani Z (2020) A framework for analysing blockchain technology adoption: Integrating institutional, market and technical factors. Int J Inf Manage 50:302–309

    Article  Google Scholar 

  5. Srivastava, G., Parizi, R. M., & Dehghantanha, A. (2020). The future of blockchain technology in healthcare internet of things security. In Blockchain Cybersecurity, Trust and Privacy (pp. 161–184). Springer, Cham.

  6. Das D, Dutta A (2020) Bitcoin’s energy consumption: Is it the Achilles heel to miner’s revenue? Economics Letters 186:108530

    Article  Google Scholar 

  7. Drljevic N, Aranda DA, Stantchev V (2020) Perspectives on risks and standards that affect the requirements engineering of blockchain technology. Computer Standards & Interfaces 69:103409

    Article  Google Scholar 

  8. Al-Hamamre Z, Saidan M, Hararah M, Rawajfeh K, Alkhasawneh HE, Al-Shannag M (2017) Wastes and biomass materials as sustainable-renewable energy resources for Jordan. Renew Sustain Energy Rev 67:295–314

    Article  Google Scholar 

  9. Warkentin, M., & Orgeron, C. (2020). Using the security triad to assess blockchain technology in public sector applications. International Journal of Information Management, 102090.

  10. Bürer MJ, de Lapparent M, Pallotta V, Capezzali M, Carpita M (2019) Use cases for Blockchain in the Energy Industry Opportunities of emerging business models and related risks. Comput Ind Eng 137:106002

    Article  Google Scholar 

  11. Cole, R., & Cheng, L. (2018, July). Modeling the Energy Consumption of Blockchain Consensus Algorithms. In 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (pp. 1691–1696). IEEE.

  12. Truby J (2018) Decarbonizing Bitcoin: Law and policy choices for reducing the energy consumption of Blockchain technologies and digital currencies. Energy research & social science 44:399–410

    Article  Google Scholar 

  13. Plaza, C., Gil, J., de Chezelles, F., & Strang, K. A. (2018, June). Distributed solar self-consumption and blockchain solar energy exchanges on the public grid within an energy community. In 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (pp. 1–4). IEEE.

  14. Dorfleitner, G., & Braun, D. (2019). Fintech, Digitalization and Blockchain: Possible Applications for Green Finance. In The Rise of Green Finance in Europe (pp. 207–237). Palgrave Macmillan, Cham.

  15. Miglani, A., Kumar, N., Chamola, V., & Zeadally, S. (2020). Blockchain for Internet of Energy management: Review, solutions and challenges. Computer Communications.

  16. Xu, C., Wang, K., Xu, G., Li, P., Guo, S., & Luo, J. (2018, May). Making big data open in collaborative edges: A blockchain-based framework with reduced resource requirements. In 2018 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE.

  17. Imbault, F., Swiatek, M., De Beaufort, R., & Plana, R. (2017, June). The green blockchain: Managing decentralized energy production and consumption. In 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (pp. 1–5). IEEE.

  18. Park, C. H., Barlongo, I. M., & Kim, Y. (2019, October). A Market Place Solution for Energy Transaction on Ethereum Blockchain. In 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 1–5). IEEE.

  19. Fontes CHDO, Freires FGM (2018) Sustainable and renewable energy supply chain: A system dynamics overview. Renew Sustain Energy Rev 82:247–259

    Article  Google Scholar 

  20. Vidadili N, Suleymanov E, Bulut C, Mahmudlu C (2017) Transition to renewable energy and sustainable energy development in Azerbaijan. Renew Sustain Energy Rev 80:1153–1161

    Article  Google Scholar 

  21. Kumar A, Sah B, Singh AR, Deng Y, He X, Kumar P, Bansal RC (2017) A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew Sustain Energy Rev 69:596–609

    Article  Google Scholar 

  22. HassanzadehFard H, Jalilian A (2018) Optimal sizing and location of renewable energy based DG units in distribution systems considering load growth. Int J Electr Power Energy Syst 101:356–370

    Article  Google Scholar 

  23. Bjørnebye H, Hagem C, Lind A (2018) Optimal location of renewable power. Energy 147:1203–1215

    Article  Google Scholar 

  24. Diemuodeke EO, Addo A, Oko COC, Mulugetta Y, Ojapah MM (2019) Optimal mapping of hybrid renewable energy systems for locations using multi-criteria decision-making algorithm. Renewable Energy 134:461–477

    Article  Google Scholar 

  25. Kardooni R, Yusoff SB, Kari FB, Moeenizadeh L (2018) Public opinion on renewable energy technologies and climate change in Peninsular Malaysia. Renewable Energy 116:659–668

    Article  Google Scholar 

  26. Owusu PA, Asumadu-Sarkodie S (2016) A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Engineering 3(1):1167990

    Article  Google Scholar 

  27. Buonocore JJ, Luckow P, Norris G, Spengler JD, Biewald B, Fisher J, Levy JI (2016) Health and climate benefits of different energy-efficiency and renewable energy choices. Nature Climate Change 6(1):100–105

    Article  Google Scholar 

  28. Husein M, Chung IY (2018) Optimal design and financial feasibility of a university campus microgrid considering renewable energy incentives. Appl Energy 225:273–289

    Article  Google Scholar 

  29. Aquila G, de Oliveira Pamplona E, de Queiroz AR, Junior PR, Fonseca MN (2017) An overview of incentive policies for the expansion of renewable energy generation in electricity power systems and the Brazilian experience. Renew Sustain Energy Rev 70:1090–1098

    Article  Google Scholar 

  30. Zhao ZY, Chen YL, Chang RD (2016) How to stimulate renewable energy power generation effectively?–China’s incentive approaches and lessons. Renewable Energy 92:147–156

    Article  Google Scholar 

  31. Scholz, Y. (2019). Cooperative Renewable Energy Expansion in Europe: Cost Savings and Trade Dependencies. In The European Dimension of Germany’s Energy Transition (pp. 353–361). Springer, Cham.

  32. Kuik O, Branger F, Quirion P (2019) Competitive advantage in the renewable energy industry: Evidence from a gravity model. Renewable Energy 131:472–481

    Article  Google Scholar 

  33. Hussain A, Arif SM, Aslam M (2017) Emerging renewable and sustainable energy technologies: State of the art. Renew Sustain Energy Rev 71:12–28

    Article  Google Scholar 

  34. Holdmann GP, Wies RW, Vandermeer JB (2019) Renewable Energy Integration in Alaska’s Remote Islanded Microgrids: Economic Drivers, Technical Strategies, Technological Niche Development and Policy Implications. Proc IEEE 107(9):1820–1837

    Article  Google Scholar 

  35. Chel A, Kaushik G (2018) Renewable energy technologies for sustainable development of energy efficient building. Alexandria Engineering Journal 57(2):655–669

    Article  Google Scholar 

  36. Noailly J, Shestalova V (2017) Knowledge spillovers from renewable energy technologies: Lessons from patent citations. Environmental Innovation and Societal Transitions 22:1–14

    Article  Google Scholar 

  37. Mendel, J. M. (2020). The Interval Weighted Average and Its Importance to Type-2 Fuzzy Sets and Systems. In Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy etc. Methods and Their Applications (pp. 195–211). Springer, Cham.

  38. Shukla AK, Yadav M, Kumar S, Muhuri PK (2020) Veracity handling and instance reduction in big data using interval type-2 fuzzy sets. Eng Appl Artif Intell 88:103315

    Article  Google Scholar 

  39. Yüksel, S., & Dinçer, H. (2020). SERVQUAL-Based Performance Analysis of Agricultural Financing in E-Banking Industry: An Evaluation by IT2 Fuzzy Decision-Making Model. In Tools and Techniques for Implementing International E-Trading Tactics for Competitive Advantage (pp. 21–41). IGI Global.

  40. Jun, Q., Dinçer, H., & Yüksel, S. (2020). Stochastic hybrid decision‐making based on interval type 2 fuzzy sets for measuring the innovation capacities of financial institutions. International Journal of Finance & Economics.

  41. Dinçer, H., Baykal, E., & Yüksel, S. (2019). Innovative capacity-based approach to blue ocean strategies of family firms: An IT2 fuzzy hybrid decision-making analysis for potential investors. Journal of Intelligent & Fuzzy Systems, (Preprint), 1–12.

  42. Bera, A. K., Jana, D. K., Banerjee, D., & Nandy, T. (2019, March). A Group Evaluation Method for Supplier Selection Based on GSCM Practices in an Indian Manufacturing Company. In International Conference on Information Technology and Applied Mathematics (pp. 114–129). Springer, Cham.

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Acknowledgements

This work was sponsored in part by Shanghai Pujiang Program (Grant No. 18PJC031).

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Authors and Affiliations

  1. School of Economic and Management, Shanghai Institute of Technology, Shanghai, 201418, China

    Juan Liu

  2. Faculty of Economics and Management, East China Normal University, Shanghai, 200241, China

    Jun Lv

  3. The School of Business, İstanbul Medipol University, İstanbul, Turkey

    Hasan Dinçer, Serhat Yüksel & Hüsne Karakuş

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  1. Juan Liu
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  2. Jun Lv
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  4. Serhat Yüksel
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Correspondence to Juan Liu or Serhat Yüksel.

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Appendix A

Appendix A

See Tables 9, 10, 11, 12, 13, 14, 15, 16, 17 and 18.

Table 9 Direct relation matrix for dimensions
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Table 10 Normalized direct relation matrix for dimensions
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Table 11 Total relation matrix for dimensions
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Table 12 Defuzzified total relation matrix for dimensions
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Table 13 Unweighted supermatrix for dimensions
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Table 14 Defuzzified total relation matrix for criteria
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Table 15 Unweighted supermatrix for criteria
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Table 16 Weighted supermatrix
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Table 17 Decision matrix
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Table 18 Defuzzified decision matrix
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Liu, J., Lv, J., Dinçer, H. et al. Selection of Renewable Energy Alternatives for Green Blockchain Investments: A Hybrid IT2-based Fuzzy Modelling. Arch Computat Methods Eng 28, 3687–3701 (2021). https://doi.org/10.1007/s11831-020-09521-2

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  • DOI: https://doi.org/10.1007/s11831-020-09521-2

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