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The last years have seen the emergence of the bioeconomy. Assessment of these new technologies is a significant challenge. We develop a unique dynamic programming framework to assess the value of the investment in a multi-stage supply chain with the production of bio-feedstock and its processing into multiple outputs. The system allows for adaptive learning in all supply chain stages, which creates a positive learning effect of co-outputs. We apply the framework to macroalgae (seaweed) farming and biorefinery processing into proteins and sugars for the Philippines and Ireland as representatives of developing and developed economies with emerging supply chains. We run Monte Carlo simulations to analyze the uncertainty of learning and prices. The key results indicate that the macroalgae sector that builds on traditional technologies is quite viable. Developing a new algae industry that generates proteins and other high-value products requires significant investment and depends on the dynamics of learning and prices. Even though the production of high-value chemicals is not yet viable, it gains profitability potential from learning of feedstock farming that is currently produced for the lower value application. The learning is much more valuable in feedstock production and processing into proteins than low-value chemicals currently produced (carrageenan). • Original dynamic, two-stage supply chain model with non-linear cost functions, learning, and heterogenous coproducts. • Analysis of impact of learning effects and prices on investment decision in a supply chain of aquaculture. • The co-production allows financing learning in both stages of the supply chain that may transform the non-viable product into a profitable in the long run. • If the combined impact of learning is greater than the discount effect, than the investment in a technology can be profitable even if prices decline. • Payback period in Asia is short, while in the West long-term investment is vital for high learning rates to reach profitability. [ABSTRACT FROM AUTHOR]