Theoretical Background

When production costs are analysed, scale economies and technological learning play an essential role (de Wit et al. 2010). A commonly used concept to meas­ure and quantify effects of technological learning implies fixed percentage cost declines every time when the cumulative production doubles. This concept is called the experience curve approach (Hettinga et al. 2009) and has been imple­mented in numerous energy models. However, thus far, only few authors have developed models with particular focus on biofuels, e. g. de Wit et al. (2010). Just as in studies on biomass-integrated gasification/combined cycle (BIG/CC) plants for the production of electricity (Faaij et al. 1998; Uyterlinde et al. 2007), learning curve effects can be implemented into our model. Therefore, progress ratios for distinct process steps of biofuel production were estimated.

When investigating the cost-efficiency for biofuels, production scale size (scale effects) and technological improvements of the process (learning effects) need to be considered. While the learning effects are very dynamic and will improve over time (typically with decreasing pace), scale effects are rather static. However, the latter may also have a dynamic component in the case that production capaci­ties increase over time. Numerous studies have attempted to differentiate between static scale economies and dynamic learning effects (Stobaugh and Townsend 1975; Sultan 1975; Hollander 1965; Preston and Keachie 1964) and, in general, the studies have discovered static scale economies to be statistically significant but small in magnitude relative to learning-based effects (Lieberman 1984).

Scale effects are based on scale law, which describes an inverse correlation between decreasing production costs resulting from increasing plant sizes (Blok 2006; Haldi and Whitcomb 1967). Up to a point, larger production scales are asso­ciated with decreasing marginal costs per unit and thus decreasing average costs per unit of biofuel outcome. However, transport costs have to be considered, which leads to an optimal production scale for each production facility.

In order to determine the optimum plant size, specific characteristics of differ­ent types of biofuels need to be considered. For bioethanol, Nguyen and Prince (1996) show that capital costs per unit of product can be reduced if mixed crops are used in order to extend the length of the processing season. This leads to lower production costs and results in a smaller optimum plant size.

Further, cost-reduction potential can be realised through technological advance­ments and other learning benefits related to the production process. de Wit et al. (2010) state examples, such as a more efficient organisation of production and transportation processes, the use of advanced materials and lifetime prolongation of catalysts. Various studies have examined and proven the significance of these learn­ing-based cost-reduction components. Hettinga et al. (2009), van den Wall Bake et al. (2009) and Hamelinck et al. (2005) have shown that this type of cost-reduction potential for bioethanol made from corn or sugarcane ranges from 25 to 50 %.

The examples show that a top-down model can be useful for producers, investors and policy makers, because it helps to easily understand production costs for fos­sil fuels and biofuels without a strong focus on technical details. Based on different

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Fig. 1 Investigated biofuels as combinations of raw materials and conversion technologies

feedstocks and conversion technologies, this chapter intends to contribute to the discussion through the development of a simple top-down calculation model which compares the production costs of biofuels to fossil fuels in Europe for 2015 and 2020.

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