A long-term hydrogen-based scenario of the global energy system is described in qualitative and quantitative terms here, illustrating the key role of hydrogen in a long-term transition toward a clean and sustainable energy future. In an affluent, low-population-growth, equity and sustainability-oriented B l-H2 world, hydrogen technologies experience substantial but plausible performance and costs improvements and are able to diffuse extensively. Corresponding production and distribution infrastructures emerge. The global hydrogen production system, initially fossil based, progressively shifts toward renewable sources. Fuel cells and other hydrogen-using technologies play a major role in a substantial transformation toward a more flexible, less vulnerable, distributed energy system which meets energy needs in a cleaner, more efficient and cost-effective way. This profound structural transformation of the global energy system brings substantial improvements in energy intensity and security of supply and results in an accelerated decarbonization of the energy mix, with subsequent relatively low climate impacts. Such energy-system path might still not be sufficient to protect against the risk of high climate sensitivities, but hydrogen-based technologies emerge as flexible options for the energy system and, thus, would be prime candidates for a risk management strategy against an uncertain climate future.

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ERIS, an energy-systems optimization model that endogenizes learning curves, is modified in order to incorporate the effects of R&D investments, an important contributing factor to the technological progress of a given technology. For such purpose a modified version of the standard learning curve formulation is applied, where the investment costs of the technologies depend both on cumulative capacity and the so-called knowledge stock. The knowledge stock is a function of R&D expenditures that takes into account depreciation and lags in the knowledge accumulated through R&D. An endogenous specification of the R&D expenditures per technology allows the model to perform an optimal allocation of R&D funds among competing technologies. The formulation is described, illustrative results presented, some insights are derived, and further research needs are identified.

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