A key aspect in modeling the (future) competition between biofuels is the way in which production cost developments are computed. The objective of this study was threefold: (i) to construct a (endogenous) relation between cost development and cumulative production (ii) to implement technological learning based on both engineering study insights and an experience curve approach, and (iii) to investigate the impact of different technological learning assumptions on the market diffusion patterns of different biofuels. The analysis was executed with the European biofuel model BioTrans, which computes the least cost biofuel route. The model meets an increasing demand, reaching a 25% share of biofuels of the overall European transport fuel demand by 2030. Results show that 1st generation biodiesel is the most cost competitive fuel, dominating the early market. With increasing demand, modestly productive oilseed crops become more expensive rapidly, providing opportunities for advanced biofuels to enter the market. While biodiesel supply typically remains steady until 2030, almost all additional yearly demands are delivered by advanced biofuels, supplying up to 60% of the market by 2030. Sensitivity analysis shows that (i) overall increasing investment costs favour biodiesel production, (ii) separate gasoline and diesel subtargets may diversify feedstock production and technology implementation, thus limiting the risk of failure and preventing lock-in and (iii) the moment of an advanced technology's commercial market introduction determines, to a large degree, its future chances for increasing market share.

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Marc de Wit
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Marc de Wit
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