Hydrogen-based zero-emission transport technologies have reached a level of technical maturity that enables their operational deployment; however, their large-scale uptake remains constrained by the limited availability of refuelling infrastructure. This gap between technological readiness and infrastructure provision represents one of the main bottlenecks for the transition towards hydrogen-powered mobility systems. In this context, stakeholders evaluate hydrogen deployment through a set of key quality objectives, primarily related to emissions, economic performance, and efficiency. The achievement of these objectives is inherently conditioned by the configuration of the hydrogen value chain and, in particular, by the spatial dimension of infrastructure deployment. Despite its relevance, the combined effect of value chain variables and location-specific factors on quality outcomes remains insufficiently characterised in the literature. To address this gap, this study proposes a probabilistic modelling framework based on Bayesian Networks to capture the relationships between value chain variables, location-dependent conditions, and resulting quality indicators. This approach enables the explicit representation and propagation of uncertainty across the system, providing a robust analytical basis for evaluating alternative infrastructure deployment strategies. By integrating technical, economic, and spatial dimensions within a unified modelling structure, the proposed framework supports informed decision-making in the planning and optimisation of hydrogen refuelling infrastructure.
Coomonte et al. (Wed,) studied this question.