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Assessing the Electricity Production Capacities of Emerging Markets for the Sustainable Investments
- Source :
- IEEE Access, Vol 11, Pp 24574-24590 (2023)
- Publication Year :
- 2023
- Publisher :
- IEEE, 2023.
-
Abstract
- It is essential to supply the necessary electricity for both the increase in the quality of life of the citizens and the stable growth of the country’s economy. For countries to have energy independence, they need to increase their electricity generation capacity. However, all alternatives required to increase electrical capacity have both advantages and disadvantages. Within this scope, it is not easy for countries to make the right investment decisions. Therefore, a comprehensive analysis is needed to determine the right investment policy. The purpose of this study is to evaluate the electricity production capacities of emerging markets. A new fuzzy decision-making model has been constructed to find a solution for this situation. The groups for the electricity production capacities are examined by the extension of DEMATEL with Quantum Spherical fuzzy sets and golden ratio. In the following stage, emerging seven economies are ranked by using QSF TOPSIS technique. This situation helps to understand which of these countries are more successful in generating electricity capacity effectively. The main novelty is to define the most significant electricity generation alternatives by a novel model that integrates DEMATEL and TOPSIS with QSFSs and golden ratio. The results demonstrate that solar photovoltaic is the most optimal way to increase electricity capacity of the countries. Additionally, China is the most successful emerging country to generate electricity in an efficient way. Countries should take some actions to increase their solar energy investments. First, it would be appropriate to provide tax exemptions to solar energy investors so that the costs of these projects can be decreased. Additionally, investments in solar energy technologies need to be further increased.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 11
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.59dcc9ab084b4884be76731725473148
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2023.3255175