Fuzzy Decision-Making Models in Renewable Energy Investments
Summary
Fuzzy decision-making models have become indispensable tools for evaluating the multifaceted uncertainties inherent in renewable energy investments. By accommodating imprecise data and subjective expert judgements, these models go beyond traditional crisp methods to capture the nuances of market volatility, policy shifts and technological innovation. At their core, fuzzy approaches represent criteria and stakeholder preferences as membership functions, allowing analysts to articulate degrees of truth rather than binary assessments. Hybrid frameworks often combine fuzzy sets with multi-criteria decision-making techniques—such as DEMATEL, TOPSIS and VIKOR—to structure complex interactions among technical, economic and environmental factors. Interval type-2 and Pythagorean fuzzy sets extend classical fuzzy logic by introducing additional layers of uncertainty, while more recent spherical fuzzy numbers bolster the capacity to handle group decision-making and consensus building. These methodologies have been applied across solar, wind and emerging microgeneration technologies to rank investment alternatives, prioritise risk-mitigation strategies and optimise levelised cost metrics. The global significance of this work lies in its ability to guide policymakers, financiers and project developers towards robust, transparent and adaptable investment decisions under deep uncertainty.
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Fuzzy Decision-Making Models in Renewable Energy Investments publication trend
The graph below shows the total number of articles in fuzzy decision-making models in renewable energy investments across all publications each year (not limited to Nature Index journals).
Technical terms
Fuzzy decision-making model: A framework that represents uncertain or imprecise information using degrees of membership rather than binary values.
Interval Type-2 Fuzzy Set: An extension of fuzzy sets in which membership grades are themselves fuzzy, allowing for an additional uncertainty layer.
DEMATEL: Decision-Making Trial and Evaluation Laboratory, a method for mapping and analysing causal relationships among criteria.
TOPSIS: Technique for Order Preference by Similarity to Ideal Solution, a ranking approach based on distances to ideal and anti-ideal solutions.
VIKOR: A compromise ranking method (Vlse Kriterijumska Optimizacija I Kompromisno Resenje) that focuses on achieving a balance among conflicting criteria.
Spherical Fuzzy Numbers: A fuzzy set extension characterised by three membership functions (truth, indeterminacy, falsity) to better accommodate group consensus.
TRIZ: A systematic problem-solving theory that identifies inventive principles and technical contradictions to guide innovation in engineering contexts.
References
- Inventive problem-solving map of innovative carbon emission strategies for solar energy-based transportation investment projects. Applied Energy (2022).
- Multi-Faceted Analysis of Systematic Risk-Based Wind Energy Investment Decisions in E7 Economies Using Modified Hybrid Modeling with IT2 Fuzzy Sets. Energies (2020).
- Application of M-SWARA and TOPSIS Methods in the Evaluation of Investment Alternatives of Microgeneration Energy Technologies. Sustainability (2022).
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