The global energy transition necessitates the rapid decarbonisation of industrial clusters, with hydrogen emerging as a key solution (kim2023). However, ongoing global crises have led to a shift in policy priorities, potentially delaying decarbonisation efforts and increasing the urgency for strategic and efficient resource planning (Stemerding2025). As climate targets approach, minimising delays becomes essential. This underscores the need for models that can capture the complex behaviour of firms and their mutual influence under uncertainty. Yet, most existing models do not account for how firm level behaviour and interdependencies shape infrastructure adoption and investment decisions in uncertain environments.

This study investigates how different firm characteristics, interdependencies, and scenario conditions influence the development of hydrogen infrastructure over time. The main objective is to understand how early investment decisions affect network formation and spatial outcomes in industrial clusters. To achieve this, a dynamic modelling framework was developed that combines a threshold based adoption model with the Optimal Network Layout Tool (ONLT). This approach incorporates firm level attributes such as hydrogen trade volume, grid connection capacity, plot size, and company type, and uses scenario analyses that vary hydrogen demand, import volumes, and early adopter configurations to simulate firm behaviour.

The results show that network development is highly sensitive to firm interdependencies, adoption behaviour, and external conditions. The timing of adoption depends on each firm's characteristics, with emerging strategic hubs such as Air Liquide, Eneco, and BP accelerating the rollout. In contrast, scenarios with high hydrogen demand might promote more integrated networks, whereas low demand scenarios often lead to fragmentation. Furthermore, the delayed adoption by Air Products, driven by relatively unfavourable characteristics, resulted in inefficient connections that were both long and costly.

The findings inform infrastructure planners and project developers on where to prioritise early incentives. The model provides guidance on investment priorities, supporting a more coordinated and cost effective infrastructure planning process, while also contributing to risk mitigation. By analysing different network layouts, robust segments can be identified that perform consistently across a range of scenario configurations, thereby reducing the risk of stranded assets. This study focuses on the Rotterdam Industrial Cluster as an illustrative case, but the approach could be adapted for application in other clusters beyond Rotterdam.