Norway's Position on Global Hydrogen Value Chains: A Dual Model Approach

Europe has ambitious hydrogen-related goals. Strategies have been developed at both the union and individual country level to maximize the benefits of this clean energy source. Besides Europe, many countries globally have implemented their own strategies, ambitions, and programs to promote hydrogen development and use. Norway is uniquely positioned in this global context. It has abundant clean energy sources, substantial natural gas reserves, and is geographically close to Europe. These factors could boost hydrogen production and use. Furthermore, these factors could drive the development and application of the necessary technology, potentially positioning Norway as a global leader in the hydrogen landscape. Although the details of the economics and logistics are not clear yet, understanding them is crucial. The Hydorgen-i project aims to thoroughly investigate the hydrogen value chain, in Norway and also internationally. This examination will include both economic factors and social perspectives. This comprehensive understanding will help us develop effective strategies for the future. We will use the BROMo optimisation model, which focuses on logistics, to gain insights into localisation, transport, and investment aspects of the hydrogen value chain. Depending on prices, distances, and the availability of raw materials, BROMo could help determine Norway's optimal role in the future of hydrogen. For the second analysis, we use a macroeconomic model called SUMSNorway. This model operates on a national scale, analysing national industrial structures and determining how investments, imports, and production factors impact value chains. SUMSNorway helps us understand which industries will need more labor and likely education to enhance value creation. Although these models are from entirely different research areas, we have successfully combined their results in the past. We've used macroeconomic tables, like those in SUMS, to guide our optimisation models’ demand, supply, and industrial conversion parameters. We plan to use the results of the optimisation simulations to inform the macroeconomic models.