Integrating hydrogen into a computable general equilibrium model

This study presents a comprehensive framework for incorporating hydrogen technologies into a Computable General Equilibrium (CGE) model ex-ante. Four hydrogen-related activities (green hydrogen production, blue hydrogen production, underground storage, and transportation pipeline) are introduced into the GEM-E3-NLD model. Each activity is parameterized using bottom-up techno-economic data to capture its input structure, capital requirements, and intersectoral linkages. Using this approach, we calculate the full input-output (IO) transactions of each hydrogen activity and the investment shares across relevant sectors. We then soft-link GEM-E3-NLD with an energy system model to better account for shifts in the energy mix, changes in technology deployment, and investment patterns during the energy transition. Building on two key scenarios from the energy system model — a business as usual (BAU) that maintains the current fuel and technology mix with only modest improvements, and an Energy Transition (ET) pathway that reaches carbon neutrality by 2050 — we conduct scenario analysis to explore how learning rates for green hydrogen influence the macroeconomic impacts of the energy transition. Our results show that GDP in 2050 rises from +1.2% (without learning) to +2.2% when the highest learning rate is applied (25%), both relative to the corresponding BAU scenario. This additional gain signals the prospective weight of hydrogen in the broader economy, as its lower production costs boost competitiveness across downstream sectors. Our framework for integrating hydrogen activities into CGE models is replicable for other emerging technologies and can thus be used more broadly to support the macroeconomic analysis of the energy transition.