The structure of infrastructure adoption: competition of hydrogen and electric for long-haul freight decarbonization
Long-haul freight decarbonization requires deploying hydrogen refueling stations (HRSs) across networks designed around incumbent diesel supply. Existing planning tools treat node-level demand as uniform, ignoring how it emerges from the coupling between freight flows and technology adoption dynamics. Understanding this coupling is essential for capital-constrained deployment decisions. This paper applies a network-coupled deployment framework—integrating Bass diffusion with reinforcement-learning-based hydrogen refueling station (HRS) sequencing under capital constraints—to a southeastern U.S. freight network derived from Freight Analysis Framework data, systematically varying hydrogen cost trajectory and deployment pace. Three findings emerge. First, deployment pace yields non-linear benefits: the adoption midpoint decreases sharply with the deployment rate but saturates quickly. Second, network position drives order-of-magnitude demand differences—a high-centrality hub generates over ten times the refueling volume of peripheral nodes at saturation—challenging uniform sizing assumptions. Third, the long-run hydrogen fuel-cell electric truck (FCET) versus battery-electric truck (BET) market split is well approximated by a linear function of the fraction of tonne-hour capacity on corridors exceeding BET range; this relation is largely insensitive to fuel-cost dynamics once hydrogen reaches diesel parity. These results show that the competition between fuel-cell electric trucks (FCETs) and battery-electric trucks (BETs) is governed by two mechanisms—the pace at which stations are deployed and the heterogeneity of demand across network positions—yielding transferable scaling relationships for infrastructure planning under capital constraints.