Abstract Amid the rapid global emergence of the low-altitude economy (LAE), electric vertical take-off and landing (eVTOL) aircraft, recognized as a key technology for urban air mobility (UAM), face multifaceted challenges in terms of usability and risk evaluation. Existing assessment approaches are generally limited by their inadequate capacity to handle uncertainty, insufficient identification of causal relationships, and fragmented indicator systems, thus hindering comprehensive decision support for eVTOL deployment. To address these issues, this study proposes a multi-stage hybrid multi-criteria decision-making (MCDM) framework that integrates neutrosophic fuzzy sets (NFS), the weighted influence non-linear gauge system (WINGS), and interpretive structural modeling (ISM). The framework first employs NFS to quantitatively represent experts’ uncertain cognitive evaluations, then applies the WINGS model to capture the non-linear interaction intensity and centrality among factors, and finally incorporates ISM to uncover multi-level causal pathways. This integrated approach enables a closed-loop decision support process encompassing evaluation, diagnosis, and optimization. An empirical analysis focusing on eVTOL applications in public service scenarios identifies critical structural barriers such as infrastructure development gaps, policy lag, and market adaptation challenges. Corresponding systematic optimization strategies are proposed. The results demonstrate that the proposed model exhibits strong robustness and interpretability in managing fuzzy information and modeling causal structures, thereby providing effective strategic planning support for governments and enterprises in the LAE sector.
Li et al. (Wed,) studied this question.
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