The structure of technological learning: Insights from water electrolysis for cost forecasting, policy, and strategy
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.
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BibTeX
@article{atouife2026structure,
title = {{The structure of technological learning: Insights from water electrolysis for cost forecasting, policy, and strategy}},
author = {Mohamed Atouife and Jesse D. Jenkins},
journal = {iScience},
year = {2026},
month = oct,
doi = {10.1016/j.isci.2026.118027},
url = {https://www.cell.com/iscience/fulltext/S2589-0042(26)03410-3},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - The structure of technological learning: Insights from water electrolysis for cost forecasting, policy, and strategy
AU - Mohamed Atouife
AU - Jesse D. Jenkins
PY - 2026
DA - 2026/10/27
JO - iScience
DO - 10.1016/j.isci.2026.118027
UR - https://www.cell.com/iscience/fulltext/S2589-0042(26)03410-3
AB - 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.
SN - 2589-0042
ER -