Publication | Open Access
A Fuzzy Model of Risk Assessment for Environmental Start-Up Projects in the Air Transport Sector
72
Citations
22
References
2019
Year
EngineeringBusiness IntelligenceEnvironmental Impact AssessmentRisk MetricEnvironmental EconomicsFuzzy Risk AnalysisAir Transport SystemAir Transport SectorFuzzy Multi-criteria Decision-makingRisk ManagementManagementData-driven Decision SupportLogisticsSystems EngineeringBusiness ExpansionTransportation EngineeringFuzzy LogicIndustrial RiskEnvironmental Risk AssessmentGeneralized AlgorithmRisk AssessmentExpert KnowledgeFuzzy ModelRisk Analysis (Business)Risk Decisions
The paper develops a fuzzy risk‑assessment model for environmental start‑up projects in the air transport sector during business expansion. The model is built on 21 expert‑derived criteria, uses linguistic variables to capture uncertainty, and is implemented as a generalized algorithm that outputs risk assessments in linguistic terms. The resulting tool improves investment decision resilience and was validated through a case study of environmental start‑up projects in the sector.
The purpose of this paper is to develop a fuzzy model of the risk assessment for environmental start-up projects in the air transport sector at the stage of business expansion. The model developed for the following software will be a useful tool for the risk decision support system of investment funds in financing environmental start-up projects at the stage of market conquest. Developing a quantitative risk assessment for environmental start-up projects for the air transport sector will increase the resilience of making risk decisions about their financing by the investors. In this paper, a set of 21 criteria for assessing the risk of launching environmental start-up projects in the air transport sector were formulated for the first time by presenting inputs in the form of a linguistic risk assessment and the number of credible expert considerations. The fuzzy risk assessment model, based on expert knowledge, uses linguistic variables, reveals the uncertainty of the input data, and displays a risk assessment with linguistic interpretation. The result of the paper is a fuzzy model that is embedded in a generalized algorithm and tested in an example risk assessment of environmental start-up projects in the air transport sector.
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