ArticleScientific reports2025
Investment strategies for renewable energy technologies and harvesting systems in airport operations using spherical fuzzy MCDM models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- An innovative fuzzy Revan index with CRITIC integration in multi-criterion decision-making.Scientific reports · 2026Article
- Integrated life cycle assessment and multicriteria decision making framework for evaluating renewable energy technologies at Istanbul Airport.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents a novel evaluation framework for prioritizing investment strategies in sustainable airport energy systems by integrating advanced fuzzy decision-making techniques with artificial intelligence-based expert weighting. Specifically, it employs a hybrid Spherical Fuzzy CRITIC-RATGOS model to rank renewable energy alternatives based on economic feasibility, environmental impact, technological efficiency, scalability, and operational reliability. To address limitations associated with equal expert weighting, a Principal Component Analysis-driven dimension reduction technique is applied to calibrate expert influence based on professional background and consistency of evaluation. The model is applied to a real-world case study at Istanbul Airport, demonstrating that AI-optimized energy management, solar microgrids, and waste-to-energy conversion are the most promising investment alternatives. In contrast, although technologies such as piezoelectric harvesting show future potential, their current limitations reduce their immediate feasibility. Sensitivity analysis affirms the robustness and stability of the results across various weighting configurations. The proposed framework contributes to both theory and practice by offering a scalable, transparent, and replicable decision-support tool for airport authorities, policymakers, and energy planners aiming to align infrastructure development with global sustainability and decarbonization goals.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.