Frequency indicates the degree of recurrence a transaction is performed (Williamson 1985), which according to Azevedo (2000) has a twofold role. First, when it is very frequent, the average fixed costs reduce, which are related to infor­mation collection and the preparation of a complex contract that sets restrictions to opportunism. Second, the higher the frequency, the less reasons for agents to impose losses on their partners, since an opportunistic attitude could lead to a disruption of the transaction and result in future earning losses from the transac­tion. In other words, for recurring transactions, the parties can create a reputation, which limits their interest in opportunistic attitudes for short-term gains, since according to the agents’ interpretation, gains tend to be higher in the long term (Azevedo 2000).

Repeating a transaction results in the parties getting to know each other through a reliable agreement stipulated around common interests. Even negotiations in the spot market have a cost reduction with recurring transactions due to a higher repu­tation (Farina et al. 1997). By establishing a reputation, trust on that agent also increases, which can lead to reducing safeguard clauses, hence reducing contrac­tual and monitoring costs (Bonfim 2011).

The governance structure regulated by the market itself is recommended for occasional or recurrent non-specific transactions, but in both cases, they are subject to standardization. Thus, the market can coordinate the relationships between the agents in a particular chain. The second one is characterized by a multilateral governance structure intended for occasional transactions, but it is characterized by mixed or highly specific investments. Therefore, this structure will inevitably be coordinated by contracts, that is, companies will try to elabo­rate individual or collective contracts for each type of transaction and for each type of agent. The third case is the one with a vertical governance structure, related to different types of recurring transactions and characterized by their high investment specificity, in other words, requiring more specific investments. Thus, this structure is characterized by incorporating a specific activity by the contracting party or even by all activities associated with the final product. This incorporation can be identified by a full or partial verticalization (Garcia and Romeiro 2009).

3.1 Uncertainty

The second key attribute discussed by Williamson (1996) is uncertainty. The impor­tance of considering this attribute results from the safeguards not addressed in the contracts. In an environment of uncertainty, agents are unable to predict all the events. Thus, the lower this prediction, the greater the gaps in the contracts and therefore the higher the chances of losses arising from the agents’ opportunistic behavior: In agri­culture, uncertainty may stem from various forms, such as natural disasters or unan­ticipated interventions in the food markets. Given this situation, contract renegotiation conflicts are plausible, which adds costs to the system as a whole (Azevedo 2000).

The most widely used analytical technologies for bioenergy chains are described

below:

• Titrimetry or volumetry determination of ions, especially by means of compl- exation reactions, neutralization or oxidation-reduction, resulting in the color change of the solution; this is the case of cation determination for feedstock and biofuels quality control (Artiga et al. 2005);

• Gravimetry determination of ions through complexation reactions, redox and precipitation, by means of drying and weighing the compound formed/ solid; this is the case of anion determination in effluent. For suspended sol­ids, it proceeds only to water evaporation and subsequent weighing of the solid

obtained. Gravimetry can be applied for feedstock and biofuels quality control (Seixo et al. 2004);

• Thermal analysis determining the water content and ash, loss of mass for con­stituents versus temperature, thermal stability, among other parameters asso­ciated with temperature effects on the material: thermal gravimetric analysis (TGA) and differential scanning calorimetry (DSC)—can be applied for pro­cesses, feedstock, and biofuels quality control (Kanaujia et al. 2013);

• Electrochemical the determination of metal oxidation states, quantification of organic and inorganic compounds, polar contaminants in effluents or products: potentiometry, voltammetry, polarography, and amperometry—can be applied for quality control of biofuels (Takeuchi 2007);

• Chromatography (liquid and gas) identification and quantification of organic compounds (volatile, semi-volatile, and nonvolatile) and inorganic, polar, and non­polar, such as sugars from sugarcane or starch, and its products of conversion pro­cesses: high performance liquid chromatography (HPLC) or ultra-high performance liquid chromatography (UPLC) with refractive index, ultraviolet-visible, diode array, fluorescence, mass spectrometry, and light scattering detectors; gas chroma­tography (CG) with flame ionization, thermal conductivity, electron conductivity, and mass spectrometry detectors—can be applied for feedstock, processes monitor­ing, and quality control of biofuels (Mischnick and Momcilovic 2010);

• Spectroscopy and spectrometry identification and quantification of organic and inorganic compounds, polar and nonpolar, such as metals and by-products in bio­fuel synthesis, by means of radiation interaction or radiation production: nuclear magnetic resonance, Fourier transform infrared, X-ray diffractometry and fluo­rescence, ultraviolet and visible spectrophotometry, atomic absorption spectrom­etry (AAS), optical emission spectrometry—can be applied for feedstock, process monitoring, and quality control of biofuels (Shuo and Aita 2013; Orts et al. 2008);

• Mass spectrometry identification and quantification of organic compounds, by means of molecular fragmentation—can be applied for process monitoring, to verify the product purity, and for metabolic engineering approaches of microor­ganisms (Orts et al. 2008; Jang et al. 2012);

• Microscopy (e. g., scanning electron microscopy, transmission electron micros­copy, and atomic force microscopy): observation of surface atomic composition and disposition of biomass components (morphology)—are frequently used for natural polymers and fibers (Hu 2008).

Table 6 presents some general uses of analytical techniques in chemical analysis of biomass for liquid biofuels production.

It is generally desirable to apply the highest possible number of techniques to obtain the greatest amount of information about a biomass. For example: Sugarcane could be analyzed by HPLC-refractive index detector to determine the sugar content, its molecular characteristics could be characterized by near-infra­red spectroscopy, and its energy content by differential scanning calorimetry. This same analytical approach could be applied to an oil crop for biodiesel production: GC-flame ionization detector for content of fat acids and esters in is grains; near­infrared spectroscopy for molecular characteristics, and differential scanning calo­rimetry for energy content.

Since 2008, the Brazilian ethanol market has shown a growing gap between the effective supply and the potential demand for this product. The ethanol demand is being vigorously stimulated by the flexible-fuel vehicles market, which totaled 20 million units in 2013 (ANFAVEA 2013); this total represents approximately 60 % of the vehicles in Brazil. Unfortunately, the capacity to produce ethanol in Brazil was not able to follow this growth. With the increase in ethanol demand and a corresponding supply reduction, it is essential to consider that there is an opti­mal point for the consumer’s decision about using ethanol or gasoline in vehicles. Currently, for Brazilian consumers using ethanol is only viable when the price of it is at least 70 % of the gasoline price, due to the differences in the efficiency of gasoline and ethanol, which is popularly called the 70 % ratio.

According to the data shown in Fig. 6, whereas in 2008 27.1 billion liters of ethanol were produced, in 2013 it is estimated that approximately 23.4 billion lit­ers will be produced, which represents a decrease of 13.6 %. In contrast, sugar production in 2008 was 31.5 million tons, but in 2013 its estimate is 38.3 million tons, which indicates an increase of 21.5 %.

2008 2009 2010 2011

Sugarcane —•— Sugar Ethanol production

Fig. 6 The production of sugarcane, ethanol, and sugar in Brazil from 2008 to 2012. Source UDOP (2013)

Because the production areas for sugarcane remain stable, the supply of ethanol in Brazil is mainly dependent on the price of sugar in the international market, which interferes with the production process (Fig. 6).

Unlike the ethanol supply, biodiesel has several raw material substitutes, as it is not dependent on only one source of feedstock. Among the sources used for production, we can mention beef and pork fat, used cooking oil, cottonseed oil, jatrophas, canolas, castor beans, and soybean oil. With the variety of options of raw material for biodiesel production, Brazil has anticipated an increase in the ratio of biodiesel in its diesel mix.

The demand for biodiesel is now fixed at 5 % in relation to the total diesel con­sumption, which was 2.7 million cubic meters in 2012. However, the installed industrial capacity for biodiesel production can produce double what is actually processed, or 500,000 m3 per month. Therefore, it is estimated that by 2015, bio­diesel consumption will increase by 10 % and the demand will expand by 50 %.

Figure 7 shows the occupancy rate data for biodiesel plants in Brazil. Note that because there is an idle installed capacity in the regulatory period and throughout the series, the actual production is below than 50 % processing capacity.

Nevertheless, one of the constraints of the biofuels supply in Brazil is the con­centration of production. According to ANP, it is estimated that the Brazilian mid­west region represents 43 % of the total production of biodiesel, and the southern region represents 34 % of the national production. Therefore, when this combined percentage (77 %) is analyzed, a concentration on production is found in these regions, whereas in Brazil’s north and northeast regions, there is a high rate of idleness of the biofuel facilities due to the climate and agriculture characteristics in those regions, as well as a disruption of the supply chain.

This pattern of disruptions has a direct impact on the final price of biodiesel, as the logistics support in the biofuel chain extends from the primary source of the agri­cultural inputs to the delivery of biofuel to distributors at the point of consumption or in ports. The price of transportation has a significant impact on the total price, and therefore, the locations farthest from the production center have higher sales prices.

Biodiesel prices are different in each Federal Brazilian state, which is especially due to the logistical costs for transferring it, primary and secondary warehousing costs, and final distribution costs. In this context, it is clear that there are different price rela­tionships between ethanol/gasoline and diesel (Goldemberg 2007) in different areas.

The domestic market for biodiesel is made through auctions. Therefore, a nearer biodiesel refinery for feedstock production decreases the price of the prod­uct and thereby increases the local competitiveness of biodiesel.

Government strategies to encourage a regular supply and increase the com­petitiveness of biodiesel in distant regions are conducted primarily through tax incentives. This policy mainly covers disadvantaged regions, as it seeks to include family farmers in biodiesel production.

Currently, ethanol production in the USA is stimulated by mandates set by the US EPA. Renewable Fuel Standard (RFS2) creates requirements which oblige fuel blenders to mix ethanol into fuel blends. In our analysis, first, we make a projec­tion of future volume of ethanol production with mandates in place until 2040. Then, we observe how these volumes change once market penetration costs are removed. This endeavor helps us in understanding how adjustments in current fuel distribution network and car fleet could influence total amount of ethanol produced and sold in the USA. Second, we look at the projected amount of ethanol pro­duced under situation with no mandates in place. That investigation provides us with projection of possible ethanol production should the US EPA decide to waive all renewable fuel mandates. Again, we look how these estimated amounts are impacted by removal of ethanol market penetration barriers.

Our next steps include examination of changes in volume of ethanol produced as a response to increasing CO2e prices. By doing this, we are able to see what level of CO2e price stimulates higher volumes of ethanol production, and we can verify at which CO2e price ethanol production reaches volumes mandated by the RFS2. We repeat the same exercise for two cases: first one with a market situation with no mandates in place but with market penetration barriers present, second one with no mandates and no market penetration barriers. In our analysis, we assume that the presence of carbon trading markets is a substitute for the EPA mandates because car­bon trading mechanism is supposed to provide incentives similar to standard quantity requirements. Therefore, we do not examine the impact of changes in CO2e prices on the volume of ethanol produced when the EPA mandates hold. At the end, we compare CO2e price effect on ethanol produced under two scenarios: with and without mar­ket penetration barriers in place in order to look at the magnitude of impact of mar­ket penetration removal on total ethanol produced in the USA. All in all, the outcomes of these scenarios provide enough information for decision makers to assess potential benefits which could arise from introduction of carbon pricing and trading mecha­nisms as well as positive environmental and economic consequences from removing market penetration barriers. Finally, we look at the impact of technological progress on volume of ethanol produced. We investigate how decrease in processing costs of cellu — losic ethanol influences quantity of crop and cellulosic ethanol produced at three points of time (i. e., 2020, 2030 and 2040). By doing this, we attempt to quantify the level of processing cost decrease necessary for ethanol production to become cost competitive.

Although a plethora of pretreatment methods have been developed in the recent years, very few could be applied in pre-commercial stage. It is therefore difficult to ascertain which method is the best in terms of its efficiency. The term ‘effi­ciency’ includes several criteria, and for a pretreatment method to be an efficient one, it should suffice all or partly. The key criteria for an efficient pretreatment technology and process are as follows (IEA 2008; Kumar et al. 2009):

• must reduce the crystallinity of cellulose and increase porosity of the material;

• increase the yields of both hexoses and pentoses in downstream processing;

• avoid the loss/degradation of sugars;

• recover lignin for further combustion;

• minimize the inhibitors of enzymatic action and fermentation;

• fungibility with different feedstocks;

• avoid expensive capital cost on biomass comminution;

• minimize waste products and use low-cost chemicals; and

• should have low overall capital cost with low energy requirement.

Currently, none of the pretreatment method is suitable for a range of biomass feed­stocks owing to their different degree of action and varying strengths and weak­nesses. Different feedstocks respond to each pretreatment method in a varying way, and it is difficult to find a single method for all feedstocks type. Presently, the dilute and concentrated acid, and steam explosion are very near to commercializa­tion, in spite of their high capital cost. As described in Table 3, each of the methods has its own limitations, which are inherent to the process and difficult to overcome. Therefore, a single pretreatment method cannot have the potential to commerciali­zation unless integrated/combined with other methods. It is imperative to conduct research on combined pretreatment methods to minimize the limitations and overall reduce the capital cost and improve the efficiency of hydrolysis. In fact, all the pre­treatment methods described in Table 3 are in the varying stages of R&D and require extensive trials before any of them reach the commercial viability.

Biofuels are expected to enhance sustainability and minimize GHG emissions. The argument in favour of biofuels with respect to reducing emissions is that biofu­els, especially cellulosic-based biofuels, emit much less carbon dioxide than con­ventional petroleum fuels. Yet there are many economic issues that currently work against these interests, these being (1) the high production costs of biofuels, partic­ularly advanced (second-generation onwards) biofuels and (2) the comparatively low conventional fuel prices that do not yet internalize the cost of GHG emissions associated with its extraction, production and combustion. This section provides an insight into the economic issues relating to shifting towards a biofuel regime that intends to realize GHG abatement goals.

As discussed earlier in Sect. 3, the production costs of biofuels, except for sugarcane-based bioethanol produced in Brazil, are much higher than those of fossil fuels (IEA 2007; UN 2008). Furthermore, the substitution of fossil fuels with first-generation biofuels raises concerns with respect to social and ecologi­cal sustainability, and also the scope to reduce net GHG emissions (Searchinger et al. 2009). Advanced biofuels could overcome the disadvantages associated with first-generation biofuels, but they are yet to be produced en masse. The technolo­gies employed for advance biofuel work very well at a laboratory scale, but the most significant challenge is to find ways to produce these biofuels at a commer­cial scale, and at a competitive price (EMBO 2009). The EMBO report added that biofuel companies are often too optimistic with their biofuel plans given that they tend to look at projected production costs based on the availability of mature tech­nology at commercially feasible prices.

Let us consider the case of Shell and its advanced biofuels projects. In 2008, Shell was working on ten such projects, most of which have now been shut down (Shell 2013). Furthermore, none of those that remain is ready for commercializa­tion. Shell has admitted that bringing these biofuels to the market will take longer time than expected (Economist, 2013). Acknowledging the issues of producing advanced biofuels at a competitive price, and consequently the limited incentive for biofuel producers, the United States Environmental Protection Agency (EPA) revised its target for cellulosic biofuels from about 76 million litres between 2010 and 2012 to 53 million litres for 2013 (IEC 2013). The two potential drivers of a truly sustainable biofuel regime thus appear to be the following: (1) an increase in the price of fossil fuels as we move towards a post-peak oil period, or as conven­tional fuel becomes depleted and the cost of extracting unconventional fuel (from oil sands or shale) becomes uneconomical and (2) the potential decrease in the costs of biofuel production (mainly advanced) as technology slowly matures.

First, we discuss the likelihood of the former, i. e. an increase in the price of fossil fuels. Since the golden age of oil discovery in the 1950s and 1960s (Fleay 1995), the rate of oil consumption has risen steeply (Grant 2007; Leder and Shapiro 2008). Kilsby (2005) reported that the world is consuming oil four times faster than the rate at which it finds new petroleum sources. Although the quantity of world’s oil reserves and the end of the fossil fuel age are highly debat­able (Hirsch 2005; Leder and Shapiro 2008), there is little doubt that this point will eventually be reached. This does not mean that the stock of fossil fuels will run out; rather, ‘cheap oil’ will certainly come to an end (Kilsby 2005). To illus­trate, let us look at the post-peak oil period, when oil reserves and overall supply begin to shrink. In the face of rising demand, this situation would create a sub­stantial imbalance between oil supply and demand (Grant 2007), and the price of oil would rise rapidly as a consequence (Hirsch 2005; Leder and Shapiro 2008). Furthermore, as the world’s stocks of fossil fuels decrease, exploration and extrac­tion activities of the remaining reserves will become increasingly uneconomical, while the energy costs associated with doing so will also rise (Hall et al. 2008; Bardi 2009). These costs could conceivably push the oil price high enough to ena­ble the global biofuel market to evolve sustainably. From an economic perspective, one of three possibilities may occur: (1) oil is the only source of energy supplied in the economy when the price of oil is lower than the price of backstop energy; (2) both oil and backstop energy are supplied in the economy when the price of backstop energy becomes competitive vis-a-vis the price of oil; or (3) backstop energy dominates energy supply in the economy when backstop energy tech­nologies mature and the price of oil is high. At present, with pro-biofuel policies favouring first-generation biofuels, we are experiencing the case of both fossil and subsidized biofuels being supplied in the market.

The second potential driver is the technological advances in the production of advanced biofuels, such as cellulosic-based biofuels. The three main technological conversion pathways for cellulosic biofuel production are selective thermal process­ing, hydrolysis and gasification (Baker and Keisler 2011; Bosetti et al. 2012). Each of these pathways consists of two major steps. The first step involves breaking down the biomass into an intermediate product consisting of simpler substances, while the second step involves processing the same intermediate product into a commercial fuel. The technologies involved in the latter process, such as biooil and biocrude refining, are similar to those used in fossil oil refining. These technologies are relatively mature compared to the technologies involved in the first step. Fischer- Tropsch is worth mentioning here as it is one of the most cost-effective and estab­lished technologies used in the second step. The overall cost efficiency of cellulosic biofuels therefore mainly depends on technological advances for the first step of primary biomass conversion, in particular gasification and hydrolysis (Mandil and Shihab-Eldin 2010; Bosetti et al. 2012). With growing public and private funding towards research and development of advanced biofuels, these technologies are expected to mature by 2030 (Bosetti et al. 2012). Future projected costs (USD/lge) for these technological paths are summarized in the following Table 4, where it is assumed that the feedstock used is switchgrass costing USD 70/tonne.

Given that the increasing demand for biofuels cannot fully be met by first — generation biofuels derived from food crops, the market for advanced biofuels seems to be large enough to accelerate the development and commercialization of advanced biofuel technologies. At present, most of the market demand for biofuels is policy driven. For example, the recently introduced Renewable Fuel Standard 2

(RFS2) in the United States and the Renewable Energy Directive (RED) in the EU both require a reduction in GHGs emission by at least 20-35 %. This can only be achieved by increasing the share of advanced biofuels, which, in turn, creates sig­nificant demand for these fuels. Furthermore, demand comes from industries pur­suing an interest in biofuels for enhancing a socially responsible image, or because they recognize that their business will need to shift to a cost-effective renewable fuel in the future if it is to survive. For example, the US Navy has announced that it wants to source half its nonnuclear fuel from renewables by 2020 (DofNavy 2010), and particularly advanced biofuels, since these avoid the controversial food-versus-fuel issue. Likewise, major commercial airlines (e. g. United, British Airways, Lufthansa and Qantas) that are aiming to become carbon neutral by 2020 have expressed their interest in including cellulosic biofuels within their fuel mix. With the increasing costs of conventional jet fuels owing to the implementation of carbon taxes (e. g. Australia’s carbon tax requires airlines to pay more than AUD 20 per emitted ton of carbon) and increasingly stringent climate change regulatory policies around the world, the airline industry sees renewable energy as a key to its continuing growth (Qantas 2013; IFPEN n. d.).

Despite the market potential discussed above, a neoliberal approach, where only market forces prevail, will not allow advanced biofuels to reach sufficient global market penetration at the required level so as to meaningfully combat GHG emissions from the transport sector. This is because it is unlikely that conventional fuels will ever be priced—at least in the immediate future—at a level that internal­izes all external costs, including the cost of GHG emissions associated with their extraction, production and combustion. It is therefore desirable that some form of government intervention takes place so as to ensure the growth of the biofuel industry, particularly if the projected GHG emission reductions are to be realized at a lower cost than would be the case in a business-as-usual scenario.

Thus, an increased adoption of biofuels at a global level will largely depend on the position that governments take on the trade-off between the environmental and economic justification of biofuels, more so given that current pro-biofuel policies are claimed to be very costly and have a negligible net effects on emissions. For example, taking the US biofuel market into consideration, Jaeger and Egelkraut (2011) found the then approach to be 14-31 times more costly than alternatives such as increasing the gasoline tax or promoting energy efficiency improvements.

In addition, RFS2 and RED have sparked a debate over their effectiveness in reducing GHG emissions owing to potential ‘carbon leakage’ that may occur in other sectors and countries not covered by the same sustainability standards. For example, these standards would provide incentives to bioethanol producers to use relatively clean inputs (e. g. natural gas), while the dirtier inputs (e. g. coal) that might otherwise have been used are shifted to other uses not covered by the sus­tainability standards. Carbon leakage also happens at an international level when Indonesia exports sustainable biodiesel and consumes unsustainable biodiesel at home, or when the United States purchases Brazilian bioethanol to comply with its RFS2, while Brazil imports emission-intensive corn-based ethanol from the United States that does not meet RFS2. Significant volumes of bilateral trade of bioethanol between the United States and Brazil driven by their different biofuel policies have been seen in recent years, but no global changes to emissions were achieved (de Gorter and Just 2010; Meyer et al. 2013).

In the end, of course, the two potential drivers signalled above will have a more important role. In other words, for advanced biofuels to be sustainable in the long term, they will need to be economically competitive vis-a-vis conventional fossil fuels without government subsidies, especially if one takes into account an appro­priate credit allocation for emissions reduction. When the above two driving forces become more entrenched, partially as a result of strategic government intervention, the biofuel industry will be ready to operate independently and according to the precepts of free-market economics.

Gunter Festel, Martin Bellof, Martin Wurmseher, Christian Rammer and Eckhard Boles

Abstract The current taxation benefits for biofuels are only temporary. Therefore, biofuel production costs need to be able to compete with those of conventional fuels in order to gain market share in the future. However, highly complex influ­encing factors make a comparison of biofuel production costs with those of fossil fuels challenging. This chapter has three major goals: (1) a projection of future feedstock prices for biofuels based on the development of the price for crude oil, (2) a simulation of the effects of likely economies of scale from scaling-up produc­tion size and technological learning on production costs and (3) a scenario analysis comparing different biofuels and fossil fuels. European biofuel production costs for 2015 as well as 2020 are projected based on a calculation model for biofuel production. Our scenarios assume prices for crude oil between Euro 50 and Euro 200 per barrel for both reference years. Our results indicate that mid — to long-term, second-generation biofuels are very likely to achieve competitive production costs, if technological learning and economies of scale are factored in. Bioethanol made

G. Festel (*)

Festel Capital, Mettlenstrasse 14, 6363 Fuerigen, Switzerland e-mail: gunter. festel@festel. com

G. Festel • M. Bellof

Autodisplay Biotech GmbH, Merowinger Platz 1a, 40225 Dusseldorf, Germany G. Festel • M. Wurmseher

Butalco GmbH, Mettlenstrasse 14, 6363 Fuerigen, Switzerland G. Festel • M. Wurmseher

Department of Management, Technology, and Economics, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland

C. Rammer

Centre for European Economic Research (ZEW), Mannheim, Germany E. Boles

Institute of Molecular Biosciences, Goethe-University Frankfurt,

Frankfurt am Main, Germany

A. Domingos Padula et al. (eds.), Liquid Biofuels: Emergence, Development and Prospects, Lecture Notes in Energy 27, DOI: 10.1007/978-1-4471-6482-1_5, © Springer-Verlag London 2014

from lignocellulosic biomass and biodiesel from waste oil promise the highest cost-saving potential in all crude price scenarios and are capable of outperforming fossil fuels and first-generation biofuels in the future.

Keywords Biofuels ■ Production costs ■ Scale effects ■ Learning curve effects

1 Introduction

The economic dependence on fossil fuels and the potential replacement of crude oil by biomass have been investigated in research chapters. Biomass use for bio­fuels competes with residential applications, heat/power generation and the pro­duction of food, animal feed and other industrial products. This is the reason for negative influences of first-generation biofuel production on global food prices. Whenever crude oil prices rise, the positive correlation between the production scale of biofuels and food prices becomes clearly visible, due to arbitrage effects (Chen et al. 2010). For example, a rising oil price has significantly influenced production volumes, and prices of agricultural grain on a global basis, as the pro­duction of biodiesel and bioethanol from soybeans and corn, respectively, have grown accordingly. The fact that biomass can serve as raw material for chemicals and numerous other applications is not solely the fuel industry that drives prices (Swinnen and Tollens 1991; Hermann and Patel 2007). Due to substitution effects, the price for raw materials is not only dependent on the development of biomass markets but also on the cost of fossil raw materials, such as crude oil. Conversion costs are driven by scale effects as well as time-dependent learning effects.

In order to better understand complex energy production systems under various policy objectives, numerous different calculation models have been developed. Both technical bottom-up approaches as well as macroeconomic top-down approaches have been utilised to describe the entire energy system (de Wit et al. 2010). Other authors evaluate whole supply chains for bio-based products (Stephen et al. 2010; Kim et al. 2011), biorefinery concepts (Fernando et al. 2006; Clark 2007; Francesco 2010) as well as the potential of biofuels for individual countries (Martinsen et al. 2010). For example, a mixed integer linear programming model that allows the selection of fuel conversion technologies, capacities, biomass locations, as well as the logistics of transportation from the raw material locations to the conversion sites and then to the final markets has been established by Kim et al. (2011).

Numerous research chapters have evaluated biofuels, such as biodiesel (Zhang et al. 2003; van Kasteren and Nisworo 2007; Araujo et al. 2010), or simulated biofuel processes with specialised software, such as Aspen HYSYS (West et al. 2008). By contrast, comparisons of one biofuel production process with other production processes (biofuels or fossil fuels) that take scale and learning curve effects into account are rare, even though production costs are imperative to the demand of biofuels. Some studies focus on a single process step, such as enzymes (Tufvesson et al. 2011; Klein-Marcuschamer et al. 2012), while others compare biofuel types through production cost analysis (Bridgewater and Double 1994; Giampietro and Ulgiati 2005; de Wit et al. 2010; NREL 2011). de Wit et al. (2010), for example, show that biodiesel is the most cost competitive type of fuel, dominating the early market of first-generation biofuels. Lower oil crop feedstock prices compared to those of sugar — or starch-containing crops are one of the rea­sons for biodiesel’s better cost performance compared with first-generation bioeth­anol. In addition, capital and operating expenditures for the transesterification of oil to biodiesel are below those for hydrolysis and fermentation of starch to bioeth­anol (de Wit et al. 2010). This chapter intends to calculate the production costs for various types of biofuel in Europe. It will also compare them with production costs of fossil fuels. Raw material, conversion and capital costs are taken into account as well as different scenarios of price development for raw materials and crude oil.

Four steps are central to the developed calculation model in order to analyse and compare biofuel production costs: (1) the definition of biofuel production scenarios in 2015 and 2020, (2) an estimation of future raw material prices based on assump­tions for crude oil price development and the relation between crude oil price and prices for biofuel raw materials which has been observed in the past, (3) the model­ling of scale — and time-dependent costs for capital expenditures and the conversion of biomass and (4) a calculation of the total production costs as a total of raw material, capital and conversion costs. Our model is based on publicly available data for single production process steps and the whole production process. The input data have been collected in expert interviews and intensive literature research during the past 5 years (Festel 2007, 2008). As model output, we have chosen production costs in Euro-Cent per litre, as this is a measure which end users, such as car drivers, can refer to.

Within the next 5-10 years, estimates do not see biofuels gaining a market share larger than 15 % globally (Gnansounou et al. 2009; Bagheri 2011). European Union (EU) targets support this estimation. The EU has set a target market share of 10 % in terms of all petrol and diesel transport fuels by 2020 (EU Commission 2003). That is why future fuel markets prices will still be driven by fossil fuels. Today, it is government regulations and subsidies that enable biofuels to compete with fossil fuels. However, our hypothesis is that government incentives will have a decreasing influence on biofuel demand medium to long term and that demand will be more and more driven by cost competitiveness with fossil fuels through, e. g., new tech­nologies, reduced costs in the production process, improved logistics. In the case that production costs of biofuels will be lower than those of fossil fuels, we expect demand to be high enough to absorb all produced volumes of biofuel. In our model, we do not take the connection between biofuel demand and biofuel prices into account, as the market share of biofuels is determined by its production costs.

In our model, we neglect the option of biofuel import from outside Europe but rather assume that all demand for biofuels within Europe will be met by European biofuel producers. The more developed production infrastructure, economies of scope to other production activities and a close proximity to end users may be a benefit for European production sites. Our input data for production costs are solely focused on Europe. However, our model could easily be adapted to other regions if input is changed accordingly.

Smith and Searchinger (2012) argue that existing life cycle assessments (LCAs) pertaining to biofuels seriously overestimate carbon absorption on the part of bio­energy crops and do not take sufficient account of GHG emissions resulting from the cultivation and harvesting of these crops.

The type of land, i. e. unfertilized grassland, forest land or traditional cropland, used for biomass feedstock is an important determinant of GHGs emitted from the soil (EPA 2006). Preparing fallow or underutilized land for agricultural production usually requires clearing off the majority of the animal and plant species. This can destabilize the soil by releasing significant amounts of stored carbon (EPA 2006). Some studies conclude that conversion of native land such as forest, grassland and abandoned land for biofuel crops leads to carbon debts[15] ranging from one to sev­eral 100 years (Fargione et al. 2008; Gibbs et al. 2008; Fritsche 2008). For exam­ple, Fargione et al. (2008) estimated the carbon debt of producing palm oil on forest land (releasing 3452 tCO2/ha) to be approximately 423 years. Table 5 below provides an overview of the estimated payback periods for a range of biofuels.

In contrast, biofuel crops grown on traditional croplands are less threatening to the environment since they have less embedded soil organic carbon (SOC) (Englund et al. 2011). However, intensive biofuel cultivation, especially if using annual crops, could lead to a substantial release of SOC. This is due to frequent disturbance to the soil (i. e. via tillage), which exposes protected organic matter and increases the rate of mineral decomposition, thereby resulting in lower SOC storage (Grandy and Robertson 2007).

Aside from tillage, farming and irrigation practices could also affect the net carbon balance of biofuels. Mechanized farming or the use of fossil-fuel-powered machinery for soil preparation, sowing, planting, weeding and harvesting activi­ties releases GHGs. Likewise, water for irrigation of biofuel crops is often sourced from rivers, lakes, canals, dams and groundwaters. While this reduces water avail­ability for other uses, it also leads to soil salinization when the irrigation process is poorly managed (Englund et al. 2011). These impacts can be mitigated where rain harvesting systems such as terraces, bunds and small dams are available.

Another issue related to harvesting is mono-cropping, or the planting of only a single species or cultivar. While harvesting a particular biofuel crop on a large-scale over several years makes the process more economical, it can also increase the environmental footprint. Repetitive harvesting of a single variety of crop results in a

lack of biodiversity and a decline in soil fertility. To control pests and maintain yields in such environments, more chemical input and fertilizers are generally applied (Englund et al. 2011), which can lead to serious ecological impacts (more in Sect. 5 in A Comparison Between Ethanol and Biodiesel Production: The Brazilian and European Experiences). However, as Dale et al. (2010) report, such impacts can be minimized by adopting sustainable land management practices.[16]

Studies of LCAs have shown that GHG emissions can vary substantially between biofuels, but are mostly lower than those associated with conventional fossil fuels. Through a meta-analysis of LCA literature, Davis et al. (2008) found that the results range between -89 MgCO2 per hectare per year for corn-based biofuel (Farrell et al. 2006) to 9.6 MgCO2 per hectare per year[17] for biofuel pro­duced from switchgrass (Searchinger et al. 2008). Results also varied between authors for biofuels produced from the same crop. For example, Shapouri et al. (2002) found that corn ethanol reduces CO2 emissions by 1.2 Mg per hectare per year, while Delucchi (2006) determined that it increased CO2 emissions by 5.14 Mg per hectare over the same period. Some studies reported the results in terms of change in GHG emissions compared to fossil fuels. The variation in this case was once again large and ranged between -114 % for switchgrass (Adler et al. 2007) to 93 % for corn (Searchinger et al. 2008). LCAs, however, have often overlooked the impacts of LUC on overall GHG emissions. When Bailis and Baka (2010) compared biodiesel from Jatropha in Brazil with conventional biodiesel without considering LUC, they noted a 55 % reduction in GHGs. In contrast, when they included LUC, the net emissions were estimated to increase by 59 %.

Despite providing a cradle-to-grave assessment, LCAs therefore reach varying conclusions on any biofuel depending on the methodological approach adopted. While using an LCA should ideally be an ongoing process for handling and prior­itizing information as new data comes to hand, it is worth noting the “seven grand challenges” that McKone et al. (2011) identified for undertaking a comprehensive LCA of biofuels. These are

• Understanding farmers, feedstock options and practices.

• Predicting biofuel production technologies and practices.

• Characterizing tailpipe emissions and their health consequences.

• Incorporating spatial heterogeneity in inventories and assessments.

• Accounting for time in impact assessments.

• Assessing transitions as well as end states.

• Confronting uncertainty and variability.

A proper understanding of these issues will have profound implications with respect to what feedstocks should be used for biofuel production, together with what lands are most suitable for environmentally sustainable feedstock produc­tion. Any conclusion reached from an LCA must consequently be tempered by the knowledge that the same assessment could provide a different result at another point in time.

The cost of producing biodiesel depends on a number of factors, including the feedstock used in the process (i. e., the production cost of biomass), the capi­tal and operating costs of the production plant, the current value and sale of by­products, and the yield and quality of the fuel and by-products. Table 8 provides total and unit production costs of a representative European biodiesel plant (Italy) using rapeseed oil as feedstock (2010), which is a good example that includes the average characteristics of Italian plants, on the base of the information collected through firm survey (Finco 2012). The plan has capacity for 150,000 tons and pro­duces 150,000 tons of biodiesel.

Table 8 shows that the major economic factor to consider for input costs of bio­diesel production is the feedstock, which is about 80 % of the total production cost. This means that the market trend commodities prices highly influence the result of the biodiesel industry. In particular, feedstock costs can vary significantly from region to region due to their availability and market fluctuations, which can also make biodiesel production costs vary over time. Vegetable oils prices have changed significantly in the last 5 years. The prices have been rather stable until end of 2006, while from 2007 to 2008, they are more than doubled, declining again in 2009 reaching the 2006 level. In the second semester of 2010, the price registered another increase followed by a slight fall in 2012 (OECD-FAO 2012).

Table 9 shows the net margin of our representative plant. Nowadays, our plant perceives a negative economic result because revenues do not cover production costs. This result is mainly driven by the biodiesel price that is fixed by the refiner­ies and it is not connected with the production costs.

There are two components that influence the value of biodiesel: the diesel price on Platts and a premium price. The premium is determined by the refinery indus­try, and it depends on the vegetable oils price and the contractual power of the biodiesel plant. Technically, the premium price should correspond to the difference between the production costs and the diesel price on Platts, which biodiesel pro­ducers widely call the ‘business margin.’

However, according to the data from biodiesel plants, the premium price per­ceived corresponds to approximately 65 % of the ‘business margin.’ Moreover, this percentage depends on the policies adopted by the Governments, such as tax excise reductions or subsidies.

It is important to underline that biodiesel plants use a blend of vegetable oils and, consequently, the price can probably be lower than the rapeseed oil price that was used in the Table 9. Taking this into account, the results present an accurate representation of the Italian biodiesel industry.

However, the increased price of vegetables oil, the economic crisis, and policy changes at European level had negative impact on biodiesel production. For exam­ple, in Italy, the reduced tax exemption in 2009 and the subsequent abolition has diminished the profitability of the biodiesel plant realizing losses.

Lastly, the third attribute refers to the specificity of the assets involved in the trans­action. Assets are specific if their return depends on the continuity of a specific transaction. The more specific the asset, the greater the agents’ dependence on achieving the negotiation and therefore the greater the loss from an opportunistic behavior by one of the parties.

Williamson (1985) also proposes classifying the different ways a given transac­tion is performed, starting with the spot market, continuing with long-term con­tracts and concluding with the hierarchy (a single firm securing the transaction in question). If the asset specificity is null, the TCs are negligible, requiring no con­trol over the transaction; therefore, the spot market would be more efficient than other organizational forms. If, instead, the asset specificity is high, the costs asso­ciated with breaching the contract will be high, which would imply greater control over the transactions.

Also according to Williamson (1981), asset specificity is the most important critical dimension, as it is related to the type of investment. Thus, after perform­ing the specific investment, the seller and the buyer will operate in a bilateral exchange relationship for a considerable period of time (irreversibility cost). Williamson (1991a, b) discriminates six types of asset specificity:

a. locational: those whose application in a given transaction generates cost sav­ings in transport and storage, meaning specific returns to these productive units;

b. physical: those more suitable for a specific purpose (e. g., specific inputs for the production of a specific product);

c. human: related to the use of specialized human capital for an activity. This type of specificity is related to accumulated knowledge by the continuous execution of a particular activity;

d. dedicated: specific assets for a given transaction (e. g., to service a specific customer);

e. brand: refers to capital—not physical or human—manifested in a company’s brand, which is particularly relevant in the franchising world; and

f. temporal: refers to the value of the assets related to the period when the trans­action is processed. Thus, this asset becomes especially relevant in the case of negotiating perishable products.

According to Azevedo (2000), as it is not possible to determine a relationship that contains all eventualities, in some cases, renegotiation is inevitable. However, as an opportunistic behavior is a possibility, this renegotiation is subject to one of the parties taking advantage of the gains, which in turn results in losses to the other party. Thus, in economic transactions, based on the issue of opportunism, one side could try to take advantage of the other due to the impotence of predicting future events. Hence, agents often have to resort to safeguard contracts, which in turn contribute to increase some TCs.

There are some forms reported in the literature that enable controlling the prob­lems of post-contractual opportunism, namely increase the resources to monitor transactions, reduce information asymmetry, and adopt contractual incentives rewarding the agents’ compliance or good performance. The vertical integra­tion itself can eliminate conflict of interest, especially in transactions between an organization and its suppliers, reducing TC, though this integration could increase operating costs (Bonfim 2011).

On account of the intrinsically qualitative competitive process, the literature generally does not address the governance structures and the theory of competi­tiveness. This supposes, mistakenly, that the coordination of supply chains occurs efficiently or that more efficient structures through mechanisms associated with competitive rivalry are used (Farina 1999).

Coutinho and Ferraz (1995) pointed out that strategies are the basis of the dynamics of competitiveness, which seek to expand and renew the companies’ capacity required by the standards of competition (or “rules of the game”) in the market they are embedded.

Buainain et al. (2007) deem that competitiveness will only be achieved by including practices that encourage cooperation between the economic agents of a supply chain, including the government. According to the authors, considering that a company’s competitiveness is linked to the system it is inserted in could mean significantly changing the way such company views and manages its business. Thus, the authors emphasize the importance of vertical and horizontal manage­ment within a system to gain competitiveness. According to Buainain et al. (2007), a serious problem is the lack of works and experiences that report the problems of internal management in the family farmers’ network, as well as the relationship between them and their customers and suppliers.

Thus, competitiveness is reflected by these companies’ greater or lesser ability to adopt governance structures that reduce TC, enable greater integration with the agricultural production, and set conditions for systemic competitiveness (Batalha and Souza Filho 2009).