A Novel Fuzzy Model for Knowledge-Driven Process Optimization in Renewable Energy Projects
-
Published:2024-06-24
Issue:
Volume:
Page:
-
ISSN:1868-7873
-
Container-title:Journal of the Knowledge Economy
-
language:en
-
Short-container-title:J Knowl Econ
Author:
Huang Chicheng, Yüksel Serhat, Dinçer HasanORCID
Abstract
AbstractThis study is aimed at identifying key indicators to increase knowledge-based process optimization for renewable energy projects. Within this context, a novel fuzzy decision-making model is introduced that has two different stages. The first stage is related to the weighting of the knowledge-based determinants of process optimization in investment decisions by using quantum picture fuzzy rough sets (QPFR)-based multi-step wise weight assessment ratio analysis (M-SWARA). On the other side, the second stage consists of ranking the investment alternatives for process optimization in renewable energy projects via the QPFR-based technique for order preference by similarity (TOPSIS) methodology. The main contribution of this study is that a priority analysis is conducted for information-based factors affecting the performance of renewable energy projects. This situation provides an opportunity for the investments to implement appropriate strategies to increase the optimization of these investments. It is concluded that quality is the most essential indicator with respect to the process optimization of these projects. It can be possible to increase the efficiency of these projects by using better quality products. Innovation has an important role in ensuring the use of quality products in environmental sustainability. Owing to new technologies, it is easier to use more effective and innovative products. This condition also contributes to increasing the efficiency of the energy production process. Furthermore, the findings also denote that the most appropriate energy innovation alternative is the variety of clean energy sources. By focusing on different clean energy alternatives, the risk of interruptions in energy generation can be minimized. In other words, the negative impact of climatic conditions on energy production can be lowered significantly with the help of this situation.
Funder
Istanbul Medipol University
Publisher
Springer Science and Business Media LLC
Reference86 articles.
1. Abbas, J., Wang, L., Belgacem, S. B., Pawar, P. S., Najam, H., & Abbas, J. (2023). Investment in renewable energy and electricity output: Role of green finance, environmental tax, and geopolitical risk: Empirical evidence from China. Energy, 269, 126683. 2. Abdulkader, R., Ghanimi, H. M., Dadheech, P., Alharbi, M., El-Shafai, W., Fouda, M. M. ,..., & Sengan, S. (2023). Soft computing in smart grid with decentralized generation and renewable energy storage system planning. Energies, 16(6), 2655. 3. Abid, L., Kacem, S., & Saadaoui, H. (2024). Addressing the environmental Kuznets curve in the West African countries: Exploring the roles of FDI, corruption, and renewable energy. Journal of the Knowledge Economy, 1–25. 4. Adebayo, T. S., Ullah, S., Kartal, M. T., Ali, K., Pata, U. K., & Ağa, M. (2023). Endorsing sustainable development in BRICS: The role of technological innovation, renewable energy consumption, and natural resources in limiting carbon emission. Science of the Total Environment, 859, 160181. 5. Ai, R., Zheng, Y., Yüksel, S., & Dinçer, H. (2023). Investigating the components of fintech ecosystem for distributed energy investments with an integrated quantum spherical decision support system. Financial Innovation, 9(1), 27.
|
|