Abstract
The ongoing transition towards more sustainable and renewable energy sources represents a multi-factorial process posing to socio-technological systems novel hazards, characterized by both epistemic and aleatory uncertainty. In the realm data data-driven approaches, historical data can provide knowledge by deriving from them low-dimensional features. Building on this concept, this paper proposes a comparative analysis of two methodologies for identifying influential factors in the assessment of emerging risk. The former approach is based on the Analytic Hierarchy Process (AHP) integrated with a fishbone diagram, while the latter relies on gradient-boosted decision trees and feature importance, integrated into the fishbone diagram framework. The study focuses on accelerating and mitigating factors associated with accidents and near misses within the context of the energy transition, elucidating insights gained from post-accidental investigations, and covering both the technical and organizational aspects. A set of accident data was collected and utilized, covering both industrial settings and urban industrial port data. The last investigated context is of peculiar interest, considering the significant responsibility to assess emerging operational risks and mitigate potential catastrophic events, mainly connected to hazardous material handling. The paper aims to offer a nuanced understanding of the strengths and limitations of the explored approaches, contributing to attaining enhanced decision-making processes in sustainable energy transitions.
