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.

Although several challenges remain in the trail toward algae biofuels commercialization and its adoption as a biofuel, as seen so far, an increasing number of companies and policy makers seem to believe the rewards outweigh the risks. Thus, the expectation pathway for algae-based biofuels remains uncertain.

Theoretically, microalgae have been shown to be a potential source to produce biodiesel because of their many advantages as a sustainable feedstock for biodiesel production compared to other feedstocks (Ahmad et al. 2011). Nevertheless, not only more innovations are still needed for the development of technologies that reduce costs while increasing the yields of production (Singh and Gu 2010), but it is also required a comprehensive set of policies to assist the development of micro­algae technology.

In the management area, it is extremely important in the early phases of this prom­ising industry, to deliberate new business models that look at the bioenergy poten­tial of algae through the transportation fuels market, as well as production of other higher-value products so as to make the economics practicable (Singh and Gu 2010).

2 Conclusion

The continued use of fossil fuels for energetic purposes is gradually becoming clearer to the society that is unsustainable. Innovative technologies and sources of energy must be developed to replace fossil fuels. In this context, biofuels play a vital role in meeting the energy needs of human beings. There is reason to believe they will continue to do so in the future albeit in a different manner. The basic economic motivation for biofuels is that they are a convenient, low-priced, domes­tically producible and a substitute for oil. However, alternative sources of biofuel derived from terrestrial crops such as sugarcane, soybeans, maize, and rapeseed inflict a lot of pressure on the global food markets, contribute to water scarcity, and precipitate the destruction of forests. Besides that, many countries cannot grow most of the terrestrial crops due to climate factors or lack of fertile cultiva­tion areas for energetic purposes. In this context, algal biofuels can really make a contribution for the future world sustainability.

In the presented chapter, it is clear that algae are now being intensively researched as a potential biofuel feedstock. In addition to their potentially high yields per unit land area, algae can grow in unsuitable land for agriculture, includ­ing industrial areas. Thus, their exploitation offers the possibility of a feedstock for producing biofuel that avoids damage to ecosystems and competition with agricul­ture associated with other biomass resources. Although many testing and start-up companies are in operation in several countries, cost information is scarce. Along the aforementioned literature review, a consensus was found that biofuels from algae are, in any case, still at the research and development stage and face numer­ous obstacles related to energy and water needs, and productivity.

Consequently, we revisited the recent developments in biofuel algae-based mar­kets and their technical issues, political standpoints, and environmental impacts. From a research and technology perspective, we stressed the importance of the US bioenergy policies and the European SET plan, as well as by the scenarios from IAE in 2010. These policies inform that several countries have introduced mandates and targets for biofuel expansion and, moreover, that production, international trade, and investment have increased sharply in the last few years.

The introduction of these new policies is essential for lowering the costs of algae biofuels, encourage investment, and develop greater diffusion of this emer­gent technology. Otherwise, in the lack of public policy, currently production costs will eventually remain too high to replace fossil fuels. In the same manner, it is expected that these policies will stimulate innovation to tackle some of the prob­lems in this emerging market.

The problems concerning large-scale production of biodiesel from algal farms on non­arable land include inconsistent and insufficient algal productivities, uncertain capital and operating costs, volatile market prices, and unknown levels of government support. Our survey permits to conclude that although intensive work is being done on many technolog­ical issues, economic studies and respective data are scattered, incomplete, and divergent.

With the onset of new policies, incentives, and massive investment in the pri­vate and public spheres, more researchers are forging new understanding in the science required to make algal biofuels economically feasible. In the present situ­ation, however, the technology to efficiently produce biodiesel from microalgae is not yet competitive. However, with policy support and incentives, we believe that the algal biofuel industry will continue to develop and assuming that this technology follows renewable energy cost trends, costs will decrease to even­tual economic viability. In parallel, processes must be developed to reduce costs and increase production. In this respect, the currently fast rate of development of algae biofuel technology and the actual rising of petroleum-based fuels prices are encouraging algae-based biofuels feasibility in the next few years.

Nevertheless, as shown in this chapter, we are witnessing a rise of companies’ strategies of entering new markets. Reports and news of new activities of algae — based companies are frequently on the news nowadays. These are signs that the uncertainties around the commercialization of this still not mature technology are not sufficient to hinder investment decisions.

Acknowledgments Lauro Andre Ribeiro and Patricia Pereira da Silva would like to acknowledge that this work has been partially supported by FCT under project grant PEst-C/EEI/ UI0308/2011 and from the Brazilian National Council for the Improvement of Higher Education (CAPES). This paper has been framed under the Energy for Sustainability Initiative of the University of Coimbra and supported by the R&D Project EMSURE Energy and Mobility for Sustainable Regions (CENTRO 07 0224 FEDER 002004).

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.

The combined effects of microporous HZSM-5 (Si/Al = 80) catalyst and mesoporous acidic AlSBA-15 on the degradation of HDPE are presented by data in Table 6. It was observed that a conversion of about 100 % yield toward liquid, gaseous, and residue products without any formation of solid waxy compound was achieved. According to literature, these two catalysts constitute principally Lewis and Bronsted acid sites with variable surface areas and pore sizes (Ooi and Bhatia 2007).

However, results shown here are in accordance with the shape selectivity effect in microporous and mesoporous materials. It was associated with the narrow pores to access active sites. It was noted that for acidic enhancement SBA-15 catalyst, the larger pore size and channels allowed the formation of higher hydrocarbon products such as light liquid products. The high yield of gaseous products shown by HZSM-5 catalyst was mainly due to the slow diffusion of cracked products within the internal pores. The findings made in this work were consistent with those reported by pre­vious researchers. It has been reported that the diffusion rate of hydrocarbon mol­ecules could be retarded using microporous catalysts (Urquieta et al. 2002).

Overall, the combined effects of HZSM-5 (80) and AlSBA-15 catalysts gave rise to positive improvements as compared to the performance of the individual cata­lyst. For all three combinations of catalyst ratio, light liquid degradation product yields were higher than 20 wt. %. The optimum liquid yield was demonstrated by the HZSM-5 (80) to AlSBA-15 catalyst ratio of 1:2 (which was 26.5 wt. %). Although the increment of both catalysts increased the liquid yield as compared to the catalyst ratio of 1:1, further addition of AlSBA-15 could provide higher liquid yield while simultaneously suppressing the yield of gaseous product more effec­tively. Another advantage of applying the 1:2 catalyst loading ratio was that it also inhibited the solid coke formation on the catalyst. This could be explained from the theory that larger composition of HZSM-5 catalyst will contribute to higher coke

Fig. 14 Composition of gas products at 673 K using composite HZSM-5(80) and AlSBA-15 catalysts at different ratios

Fig. 15 Liquid degradation products at 673 K using composite HZSM-5 (80) andAlSBA-15 catalysts at different catalyst ratios

formation and more rapid deactivation. In this respect, the mesoporous catalyst is generally more effective than the microporous catalyst in suppressing the formation of large molecules and thus causing less carbon deposits (Urquieta et al. 2002).

As can be seen in Fig. 14, the gas products distribution was quite uniform. For the catalyst ratio of 1:1, the smallest compositions were the carbon chain of C1 (12.4 %) and C5 (12.7 %) while the highest were the carbon chain of C3 (28.9 %) and C4 (28.6 %). However, all of them were marginally different. For unequal cat­alyst mixing ratios such as 2:1 and 1:2, the uniformity of product distribution was not able to be maintained. For HZSM-5(80) to AlSBA-15 catalyst ratio of 2:1, the smallest compositions were the carbon chain of C1 and C5 (about 13.0 %) while the highest proportion was the carbon chain of C4 (31.7 %). On the other hand, for catalyst ratio of 1:2, increasing amount of AlSBA-15 composition resulted in lower production of C5 carbon chain (8.9 %) while showing higher composition in the carbon chain of C3 (31.4 %).

Meanwhile, the effect of varying catalyst mixing ratio on the liquid phase degradation products is illustrated in Fig. 15. Mixture of HZSM-5(80) and AlSBA-15
catalysts with catalyst loading ratio of 1:1 produced nearly 89 % of carbon chain range C8-C12, C13-C16, and C17-C20 from the overall liquid composition. Increasing the ratio of either HZSM-5(80) or AlSBA-15 amount in the catalyst mixture appar­ently shifted the product distribution toward heavier carbon chain. The effect of adding HZSM-5(80) was more dominant as it increased the carbon chain range of C17-C20 to 35.7 % while adding AlSBA-15 only increased the carbon chain range of C13—C16 to 28.4 %.

4 Conclusions

The results reported and discussed in the present work demonstrate that micro — and mesoporous materials show promising properties to be used as catalysts in the degradation of HDPE into gaseous and liquid hydrocarbon fuels at 350-500 °C. HZSM-5 (14), HZSM-5 (80), SBA-15, and AlSBA-15 were used under various operating conditions to obtained liquid biofuels. Mixture of HZSM-5 (80) and AlSBA-15 with ratio of 1:2 exhibits higher degradation activity to yield higher liquid biofuels with valuable gas product at 400 °C. In addition, significant HDPE conversions into liquid fuel with lower coke contents were achieved in a batch reactor over the HZSM-5 catalyst as compared to mesoporous silica catalyst SBA-15. The pore shape of zeolites was very important for deter­mining their activities and product selectivity in the degradation of polymeric materials because it influenced the degradation and deactivation rates simulta­neously. SBA-15 containing aluminum catalyst was of a potential interest in the cracking of heavier feedstock such as palm oil waste into biofuels. The weaker acid properties exhibited by the mesostructured catalysts, i. e., SBA-15 and Al-SBA-15 were responsible for their reduced gaseous product production capaci­ties. However, their presence coupled with the combination of HZSM-5 catalysts in the conversion of polyethylene materials promoted a substantial conversion of the original long-chain hydrocarbons into lighter liquid hydrocarbon products (up to 26.5 wt. %). Furthermore, larger pore dimensions exhibited by these sol­ids did not allow for any product selectivity, resulting in the possible formation of a wide range of branched hydrocarbon and alkyl derivative aromatic products. These results suggest that catalytic degradation of HDPE leads into higher liq­uid hydrocarbons yield at lower temperature. It is stated that such type of chemi­cal recycling, i. e., conversion of waste HDPE into hydrocarbon feedstock used as resource for biofuel has been recognized as an ideal approach and could sig­nificantly reduce the net cost of disposal. It is concluded that under appropriate reaction conditions, suitable catalysts such as HZSM-5 (80) and AlSBA-15 have the ability to control both the product yield and product distribution from HDPE degradation, potentially leading to a cheaper process with more valuable products such as biofuel.

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.