The structure of technological learning: insights from water electrolysis for cost forecasting, policy, and strategy

TL;DR

This paper examines how different structural assumptions in learning curves affect cost forecasts for water electrolysis, emphasizing the importance of multiple models for robust policy and strategy decisions.

Contribution

It evaluates the impact of various learning structures on cost trajectories, highlighting the need for multiple frameworks to account for uncertainty in emerging technology forecasting.

Findings

Different learning structures lead to significantly different cost forecasts.

Model assumptions about competition and supply chains greatly influence projections.

Applying multiple frameworks improves robustness of policy and strategy planning.

Abstract

Forecasting the cost evolution of emerging clean technologies is crucial for informed policy, investment, and decarbonization decisions, yet it remains deeply uncertain. Learning curves, which link cost declines to cumulative deployment, are widely used for technological cost forecasting. However, applying them to emerging technologies is challenging due to parametric uncertainty in learning rates, which are scarce and highly uncertain, and structural uncertainty stemming from multiple plausible learning frameworks. Using water electrolysis as a case study, we evaluate how different learning structures, from shared to fragmented learning across technology variants and regions, alter expected cost paths. We interrogate model assumptions that represent contrasting industrial realities, including competition among electrolyzer variants and supply chain fragmentation associated with protectionism and industrial policy. We find that plausible modeling choices generate widely different trajectories, with materially different implications for policy design and technology strategy. We argue for routinely applying multiple learning frameworks to explore decision spaces and stress-test conclusions for scale-up planning, national industrial strategy, and energy-systems modeling.