Pery Francisco Assis Shikida, Adele Finco, Barbara Fran^oise Cardoso, Valdir Antonio Galante, Daliane Rahmeier, Deborah Bentivoglio and Michele Rasetti

Abstract Industrialized countries’ dependence on fossil fuels has been distressing for a long time for countries that do not have self-sufficiency, whether for environ­mental, economic, geopolitical, or other reasons. In this context, it is understood that the burning of fossil fuels contributes to greenhouse gas emissions (GHG) increasing the risk of intensifying climatic disturbances that can deteriorate the processes of production, consumption, and welfare in the world. Therefore, the development of alternative energy sources can provide solutions for the gaps, since reducing exposure to the vulnerability of supply and price volatility, environmental issues, and even the development of new investment opportunities in these coun­tries. This is due to the possibility of developing innovations in the production and processing industry, which would contribute to the economic activity. Thus, increasing the use of bioenergy is one of the existing ways to reconcile the need to [7] [8]

expand the supply of energy with the slowdown in global warming, i. e., the most important and disseminated use would be the biomass power generated by the consumption of biofuels, once it reduces GGE emissions.

1 Introduction

Global ethanol and biodiesel production are projected to expand at a slower pace than in the past. Ethanol markets are dominated by the USA, Brazil, and, to a smaller extent, the European Union. Biodiesel markets will likely remain domi­nated by the European Union and followed by the USA, Argentina, and Brazil.

The world biofuels production reached almost 124 billion liters in 2011; 80 % of that global production of liquid biofuels consists of ethanol and 20 % consists of biodiesel. The European Union produced in 2011 about 9.5 million metric tons of biodiesel, but in 2011, the production decreased about 10 % compared to 2010. However, the share of biodiesel is rapidly increasing due to emergence of new pro­ducing countries in Southeast Asia. The USA and Brazil are the largest ethanol producers, with 54 and 34 % of global ethanol output in 2009, respectively; while the European Union accounts for 57 % of global biodiesel production.

Brazil is the world’s second biggest producer of fuel ethanol (about 23 billion liters in 2011) and the world’s biggest exporter of fuel ethanol. The production started in the early 1970s by a program which led to the development caused by local automobile companies with flex-fuel engine technology. Presently, around half of all Brazilian cars use these hybrid engines, which can run with any mixture of pure ethanol and gasohol (around 80 % gasoline and 20 % ethanol). In 2010, cars used nearly equal volumes of gasoline and ethanol.

The chapter aims at revisiting the recent developments in biofuels markets and their economic and environmental impacts. The analysis compares the perfor­mance of ethanol versus biodiesel produced in Brazil and Europe, respectively.

This chapter is organized as it follows: Sects. 2 and 3 discuss the scenario of Brazilian ethanol and European biodiesel in terms of policies, production, sup­ply, and demand. Section 4 examines the environmental impacts of both biofuels. Finally, we draw key conclusion.

The prices for feedstocks are critical for the economically viable production of bio­fuels. In addition to raw material prices, crude oil as key competitor product also influences the profitability of biofuels. Prices for both are interrelated. Increasing oil prices tend to fuel demand for alternative sources of energy and thus the prices for raw materials. A positive correlation between the prices for crude oil and global grain commodities has been demonstrated in a model by Chen et al. (2010).

In order to project raw material prices for biofuels, we analyse the relation between the price of biofuel raw materials (pB) of type k (maize, wheat, rapes oil, palm oil and wood) and past crude oil prices (pO) while also considering other major drivers of raw material prices, including a price index for agricultural prod­ucts (pA), growth in world population (POP), growth in wealth (per capita income: GDP/POP), change in energy consumption per capita (EN/POP) and global infla­tion (pGDP). The linear regression model to be estimated reads as follows:

pBk, t = a + ei, kpOt + P2,kPAt + e3,kpGDPt + xi, k POPt + X2,k GDP/POPt

+ X3,kEN/P°Pt + &k, t > (1)

with t being a time index for months, a being a constant, в and x being parameters to be estimated and є being a time and k-specific error term. We take the following monthly price data for five different biofuel raw materials k.

• Maize: US No. 2 Yellow, FOB Gulf of Mexico ($/t)

• Wheat: No. 1 Hard Red Winter, ordinary protein, FOB Gulf of Mexico ($/t)

• Rapes oil: Crude, fob Rotterdam ($/t)

• Palm oil: Malaysia Palm Oil Futures (first contract forward) 4-5 % FFA ($/t)

• Wood: average price ($/m3) for softwood (average export price of Douglas Fir, U. S. Price) and hardwood (Dark Red Meranti, select and better quality, C&F UK port)

The data for crude oil prices were obtained as an average of Dated Brent, West Texas Intermediate and Dubai Fateh (Euro/barrel). Raw material prices were taken

All prices are average prices per year Ein barrel Rohol sind 159 L

Die Dichte von Rohol schwankt zwischen 0.8 bis 1 kg/l—beim Vergleich mit Rohol rechnet man

im Allgemeinen mit einer Dichte von 0.883 kg/l

Als mittlere Dichet von Holz wurde 600 kg/m3 angenommen from www. indexmundi. com. Table 1 shows average annual prices for the five biofuel feedstocks as well as for crude oil, based on monthly data from April 1982 to April 2010. The historical price overview shows significant differences in price developments for the different types of raw material. For example, the palm oil price has doubled between 2006 and 2010, while during the same time prices for wood remained almost stable.

Annual data on population, GDP, energy consumption, inflation and agri­cultural prices were taken from the ‘World Development’ and converted into monthly data through linear interpolation. We measured all prices, GDP and

energy consumption in US Dollars and converted them into Euros using monthly exchange rate averages.

An ARMAX (Harvey 1993) modelling approach with a one month autoregres­sive term of the structural model disturbance and additive annual effects was used (see the results in Table 2). It is obvious that the price of crude oil is significantly correlated to prices for biofuel feedstock. Crude oil has the weakest impact on prices for wheat and maize, while rapes oil and palm oil prices are heavily influ­enced. The influence on wood is in between these two groups. The results indi­cate that both rapes and palm oil have been used as energy inputs to a significant degree in the past and are therefore more closely related to oil price changes than wheat and maize. These are still predominantly used as input for food production.

Future prices for biofuel feedstock in 2015 and 2020 are based on the estimation results in Table 2. For the calculation, projected values for all independent vari­ables are necessary. In regard to prices for crude oil, we refer to oil price scenarios that have been published by IEA (2007) and the International Energy Outlook. We then investigate the effects of crude oil prices per barrel of Euro 50, Euro 100, Euro 150 and Euro 200 in 2020. For 2015, crude oil prices are calculated through linear interpolation of the 2011 value and the 2020 scenario. In regard to the other vari­ables, we assume a 1 % p. a. increase in world population, a 2.5 % p. a. increase in GDP per capita, a 1.25 % p. a. increase in energy consumption per capita, a 5 % p. a. increase in agricultural prices and a global inflation of 6 % p. a. These assump­tions are close to the average rate of change of each variable during 1982 and 2010. For simplicity reasons, business cycle effects are not taken into consideration.

Dependent on different crude oil price developments, biofuel raw material prices for 2015 and 2020 are determined. Table 3 reports projected prices for 2015 and 2020 as well as actual and predicted prices in 2010. Prices are expressed in Euros per tonne, as production cost scenarios use tonne units for all material inputs. For crude oil, we assume a mass density factor of 0.883 kg/L and for wood a mass density factor of 0.6 kg/dm3. Except for palm oil, predicted 2010 prices are higher than they really were. This indicates that the price level in 2010 was lower than one would have expected if prices had followed the typical development of the past three decades. A calming effect on commodity due to the economic crisis may be one of the main reasons for that. The 2010 price level for all raw materials, except palm oil, was below the peak of the pre-crisis level in 2006 and 2008, while crude oil prices in 2010 were close to the pre-crisis peak. As mentioned before, we refrain from considering any type of business cycle effects on prices but focus on longer term trends in raw material prices. For this reason, we do not adjust projected prices for 2015 and 2020 to the ‘prediction error’ in 2010 but consider the higher predicted prices for 2010 (and consequently for 2015 and 2020) as reflecting an upcoming upwards trend of commodity prices in case the world economy recovers.

Wheat, rapes oil and maize prices are expected to undergo the largest rise until 2020. In the Euro 50 scenario, prices for these three types of biomass will increase by 89, 85 and 66 %, respectively, compared with the actual prices in 2010, which were rather low. In the Euro 200 scenario, price advances will be significantly higher. Changes are all above 100 %. In regard to palm oil, prices are expected to

Table 2 Results of ARMAX model estimations

remain stable in the Euro 50 scenario but increase substantially in the Euro 200 scenario. This reflects the stronger link between crude oil and palm oil prices. As for wood, all scenarios except the Euro 200 scenario expect tumbling prices. The latter estimates constant prices for the time period between 2010 and 2020.

Waste material is another important group of raw material for biofuels. However, there are no world market prices available, due to waste rarely being traded internationally, because of high transport costs per unit and small unit val­ues. In our scenario analysis, we assume that the prices for waste lignocellulosic material are constantly 1/4 of the price of maize and the price for waste oil is 1/2 of the price of palm oil. At this point, we assume that producers are price takers and that production functions are linear homogenous.

It is important not to lose sight of the impact that climate change itself will have on biofuels into the future, together with determining the most appropriate place for biofuels in the long-term battle to reduce global GHG emissions. This section deals with these two issues.

4.2 Effects of Climate Change

It is uncertain whether existing current climatic conditions will prevail, with many scientists contending that anthropogenic climate change is already taking effect across the globe (Cook et al. 2013). There are a number of critical factors associ­ated with climate change that need to be taken into account. First, and as intro­duced above, there may be increased uncertainty with regard to rainfall patterns. This will problematize when to plant with annual crops (such as those used for first-generation biofuels), and will also place increased pressure on water use, with potential social repercussions outside the agricultural arena. Second, there may be increased and more severe meteorological phenomena, with floods wip­ing out entire fields, and storms damaging or destroying entire harvests (Charles et al. 2009). Uncontrolled fires resulting from drought, thunderstorm activity or human action could also have similar effects. Third, there may be an increased severity and incidence of pestilence, with changed climatic conditions making crops destined for biofuel production more susceptible to pest outbreaks (Malcolm et al. 2012). This would have the added environmental implication that there could potentially be an increased need to employ pesticides, herbicides or fungicides, with all the negative outcomes associated with the use of these materials signalled above compounded by increased chemical usage.

Taken together, these issues suggest that it will be more difficult to plan for weather — and climate-related phenomena into the future. Nations will clearly be unable to rely solely on domestic biomass cultivation for their biofuel needs (Larson 2008). It follows that increased energy security associated with biofuel production will need to be tempered with the understanding that existing agri­cultural techniques certainly do not guarantee constant and predictable harvests in the face of regular climatic uncertainty. Yet climate change, as is generally expected, will exacerbate this high level of uncertainty, regardless of whether it is anthropogenic or otherwise, or indeed a combination of both man-made and natu­ral processes. Regardless of these issues, it is essential that biofuel policy takes a path that does as much as possible to ensure that it assists with anthropogenic cli­mate change mitigation, rather than exacerbating the problem.

Oil products account for approximately 95 % of the energy used for transportation in the world in their various modes. The technological standards for the use of this energy source, which has been strongly disseminated in the world, developed over more than a century.

However, several liabilities accompany its hegemonic use, since the reduction of available stocks of this essential non-renewable resource (petroleum), pollution, and GHG emissions (Seabra 2008: 83).

Therefore, the continuation of fossil fuel energy resources use provides strate­gic and environmental drawbacks, seeing that the use of non-renewable sources is revealed as a way of releasing elements captured in a remote past, which expose the modern lifestyle to a not properly dimensioned future risk.

On the other hand, the production and the consumption of biofuel obtained from agricultural biomass (renewable resources) entails a GHG balance (CO2 eq.) close to neutrality. Thus, unlike fossil fuels, the biomass has sustainable features, since human systems capitalize on energy use with little interference in the GHG balance (ANEEL 2008; Macedo et al. 2008; Garcia 2011).

According to Table 10, the sugarcane has the best energy efficiency (9.3) among the different sources of biomass available in Brazil and it has the highest reduction percentage of GHG emissions (89 %). These indicators are much higher than those obtained by corn (US option) or beet (an option used in Europe).

When the Life Cycle Assessment (LCA) of some biofuels was performed, ethanol was highlighted due to the high percentage of GHG reduction, as depicted in Fig. 9.

Even though the options of energy production are within the renewable status, they are not free of interfering negatively on the environment. One of the most important liabilities is the interference in the soil and the formation of monocul­tures over large areas. However, these problems can be mitigated by techniques and processes that increase biomass productivity per area. An example of this is that Brazil produces 6,800 l of ethanol per hectare of sugarcane, while the USA produces 3,100 l/ha of maize (ANEEL 2008).

In Brazil, several crops have the potential to produce bioenergy, among them soy, sugarcane, castor bean, and palm oil. The cultivation of sugarcane has been highlighted in the production of ethanol. With a focus on increasing productivity, the mills have opted for mechanical harvesting, including suitability for the cur­rent legislation which restricts fires of sugarcane straw for the crop.

Another element of this sustainable supply chain is the use of bagasse to pro­duce electricity through thermal power plants (ANEEL 2008).

The techniques and processes evolution and R&D also contribute to the increased efficiency in the various stages of the production process, such as har­vesting sugarcane in Brazil, which is abandoning the straw burning for the harvest and better studies about the emission levels in the various stages of production and processing of this biomass (Table 11).

The governance structure is related to the transaction characteristics of a given production chain. Table 1 illustrates the characteristics of each transaction accord­ing to the aforementioned theoretical attributes.

Table 1 Main transaction attributes of palm oil processing plants with family farmers

Transaction Transaction attributes Predominant

Buyer o Business Frequency Degree of governance

Seller specificity uncertainty

The Industrial Organization (that was also called Industrial Economy, in Great Britain and Europe) is not recent, where the central focuses of this study are as follows: (1) competition, as the engine of most modern markets, and (2) the power of monopolies that interfere with the good results of competition (De Jong and Shepherd 2007). The Industrial Organization also focuses on the study of public policies, where the first studies analyzed the governmental policies, in order to prevent the existence of monopolies, to eliminate, or at least restrict, the effects of the existing monopolies. The public policies studies mainly include as follows: antitrust policies, in order to prevent or reduce the power of monopoly; regulation, so as to contain the natural monopolies; deregulation, which removes restrictions, hoping that competition will grow, and the creation of estates that seek to support the public interest when competition does not work.

However, a growing research area, within the Industrial Organization, is iden­tifying the industrial concentration level, where one seeks understanding the rela­tionship between the concentration level and this industry’s price/profitability ratio, where much evidence point to a positive relationship between market con­centration and the sector’s profitability (Peltzman 1977). The basic assumption for this purpose is that high concentration enables collusion and, as a consequence, the manipulation of market prices.

Peltzman (1977) said that the relationship between the market structure and productions costs is long known, where a technological breakthrough in a not con­centrated industry can produce a natural monopoly, since there will be an increase of the operational efficiency through time, generating competitive advantages for a specific organization. On the other hand, according to the author, the pro­cess through which old technology becomes economically obsolete also implies a reduction (or at least no increase) of the offered goods. Whatever force is operat­ing this system, it is crucial to understand what the concentration level is, so as to control the excessive power of some firms within its industry.

Industrial structure and industrial concentration issues have concerned econo­mists and politicians for at least a century (Jacquemin and Slade 1986), while the industrial concentration level is tightly connected to the margins firms keep in the market, since competitiveness drops according to the increase of concentration level, creating opportunities for firms to price in a differentiated manner. The mar­ket concentration analysis, on the other hand, of a specific industry stems from the idea of how it is distributed in terms of production and participation of their firms, in a determined market. In this context, Bain and Qualls (1968) define industrial concentration according to property, considering the control of a great proportion of aggregates of economic resources or activities, by a small companies’ proportion.

George and Joll (1983) states that the industrial concentration regards the size distribution of firms that sell a specific product, with a significant dimension of the market structure, for having an important role regarding a company’s behavior and performance. Besides, the number and size distribution of these firms influence the expectations regarding the competitors’ behavior. In this context, Possas (1985) comments that the industrial concentration is closely linked to the internal profit accumulation and corporate technical progress.

According to Bain and Qualls (1968), the market structure regards the organiza­tional features that determine the relationships with the agents, being an important part of the competitive environment of firms, in order to influence the competitors’ pattern. For the author, this means that the market structure features have a strategic influence on the nature of competition and on determining prices in the market.

These are fuels that are produced from edible crops. Bioethanol is generally derived from commonly grown food crops such as sugar cane, sugar beet, maize (corn), sorghum and wheat. First-generation processes for bioethanol production, in the case of plants such as corn and wheat, rely on starch from plant kernels or, with respect to sugar cane and sugar beet, on the sucrose contained within parts of the plant (McCormick-Brennan et al. 2007). These starches and sugars are fer­mented and are then distilled, in much the same way as the production of alcohol destined for other purposes. The types of crops employed for first-generation bio­fuel production also have lower energy content than conventional petroleum prod­ucts per volume, something which exacerbates the issues surrounding the use of this technology (McCormick-Brennan et al. 2007). With regard to first-generation biodiesel, crops such as rapeseed, palm oil, Jatropha and soya beans are gener­ally used. The oil from these crops is then converted to biodiesel, together with a co-product called glycerol, which can be used for a variety of non-energy-related purposes. Waste vegetable oil (WVO), if cleaned up sufficiently, can also be used to produce biodiesel (Parida et al. 2011).

1.1 Ethanol Policy Scenario

With the growing concern around climate and environment, the viable alternatives to replace fossil fuels with biofuels provided Brazil the possibility of an array of interests among the agents involved in the ethanol production chain. This arrange­ment allowed the creation of the National Alcohol Program (PROALCOOL) in 1975, in which the main objective was to leverage the Brazilian ethanol produc­tion through incentives and subsidies. It is pointed out that, even after the discon­tinuation of the Program in the early 1990s, it has continued acting in institutional arrangements formed with its creation allowing expansion of ethanol production (Shikida and Perosa 2012).

The Brazilian government started subsidizing ethanol production with the beginning of PROALCOOL, and even at the end of this program, the subsidies are indirectly maintained by the Federal Law 8723/1993, which enforce the 20-25 % proportion of ethanol in gasoline. However, there are no subsides of gasoline in the strict sense. There are cross-subsidies between petroleum derivatives such as variation in the tax burden of the ethanol and control of prices of petroleum products (because this prices affect transportation) due to anti-inflationary policy. Indirectly, the variation in the percentage of ethanol in gasoline can also encourage or discourage the gasoline consumption. The international sugar and oil prices also affect ethanol consumption. According to the Sugarcane Industry Union (UNICA) (2011: 11), ‘gasoline pricing remains artificial, with cross-subsidies between petroleum derivatives. In addition to causing problems to the industrial sector, this also distorts the market where hydrous ethanol competes directly with gasoline.’

In the last decade, the alcohol sector began a new phase of expansion with the permission of the European Union to import Brazilian sugar. However, the increase in exportation of sugar caused an increase in ethanol’s price and a decrease in its consumption, since both use the same raw material. Another fact is the appearance of flex-fuel cars in Brazil, which allows the use of any combination of ethanol and gasoline on the same engine.

In recent years, the decrease in sugar prices in the international market has reduced the stimulus for expansion of this sector. The price control policy adopted by the Brazilian government, which is stimulated by the lobbying of the alcohol sector, has raised the interference in the ethanol market. In addition to offering low interest loans to sugarcane production, the percentage of ethanol in the gasoline was increased and it promoted greater tax relief in the sector.

In our model, feedstock prices are exogenous variables and therefore independ­ent from production scale. This assumption is based on the fact that transportation costs are the main driver for raw material prices and the costs per unit increase with the scale of a plant as transport routes become longer. The rationale behind this assumption is that each company aims to operate at the optimal production scale in the light of (a) the tension between scale benefits and (b) increasing cost of capital associated with transportation costs. Cost advantages driven by learning

and scale are significant endogenous parameters and therefore main determinants in our calculation model.

For each type of biofuel, we assume learning-based cost-reduction potentials which diminish over time applied to all process steps as defined in Fig. 3. In regard to second-generation biofuels, we estimate learning curve effects of 40, 30 and 20 % for the corresponding time frames 2005-2010, 2010-2015 and 2015-2020. This in return leads to progress coefficients of 60, 70 and 80 %, respectively. For first-generation bioethanol, progress coefficients of 70, 80 and 85 % were estimated. In autoregressive time series models, these progress coefficients are sequentially multiplied with previous values to derive operational and total production costs for specific points in time. Based on our scale size estimates for the different types of biofuel (see Fig. 2), scale effects were incorporated into biofuel conversion costs depending on the output of biofuel product (10, 50, 100, 250 and 250 kilotonnes per year). Table 4 is one example for the application of our assumptions in order to calculate conversion costs. It represents the results for first-generation bioethanol.

1.1.2 Total Production Costs

Depending on the type of raw material, different numbers of litres of biofuel can be produced from one tonne of feedstock. A conversion factor was implemented in order to translate prices for one tonne of raw material into the prices per litre of produced biofuel. Production costs were calculated as the sum of raw material costs and conversion costs. For a better comparison, energy density factors (in Millijoule per litre, MJ/L) were taken into account and normalised to the average energy den­sity of fossil fuel. The results were adjusted production costs, based on the specific density of biofuels. Reference scenarios were calculated for 2015 and 2020. This model enables the calculation for different production scales in place and planned or hypothetical scales (e. g. simulation of not yet realised production scales).

As previously mentioned, the price of fossil fuel is the decisive factor for biofuel market success. Therefore, it is essential that biofuel production costs can compete

Table 4 Modelling of conversion costs for first-generation ethanol Scale Investment Depreciation Operational costs Total costs = operational expenses plus depreciation 2005 2010 2015 2020 2005 2010 2015 2020 (kt> (ml) (m Euro) (m (Cent/1) (m Euro) (Cent/1) (m Euro) (Cent/1) (m (Cent/1) (m Euro) (Cent/1) (m Euro) (Cent/1) (m Euro) (Cent/1) (m Euro) (Cent/1) (m (Cent/1) Euro/ Euro) Euro) year) Process step 1: enzymatic hydrolysis Learning curve 0.70 effect 2005-2010 Learning curve 0.80 effect 2010-2015 Learning curve 0.85 effect 2015-2010 10 13 10 0.50 3.95 6.00 47.40 4.20 33.18 3.36 26.54 2.86 22.56 6.50 51.35 4.70 37.13 3.86 30.49 3.36 26.51 50 63 35 1.75 2.77 12.00 18.96 8.40 13.27 6.72 10.62 5.71 9.02 13.75 21.73 10.15 16.04 8.47 13.38 7.46 11.79 100 127 50 2.50 1.98 18.00 14.22 12.60 9.95 10.08 7.96 8.57 6.77 20.50 16.20 15.10 11.93 12.58 9.94 11.07 8.74 250 316 75 3.75 1.19 24.00 7.58 16.80 5.31 13.44 4.25 11.42 3.61 27.75 8.77 20.55 6.49 17.19 5.43 15.17 4.79 500 633 100 5.00 0.79 30.00 4.74 21.00 3.32 16.80 2.65 14.28 2.26 35.00 5.53 26.00 4.11 21.80 3.44 19.28 3.05 Process step 2: fermentation Learning curve 0.70 effect 2005-2010 Learning curve 0.80 effect 2010-2015 Learning curve 0.85 effect 2015-2010 10 13 15 0.75 5.93 9.00 71.10 6.30 49.77 5.04 39.82 4.28 33.84 9.75 77.03 7.05 55.70 5.79 45.74 5.03 39.77 50 63 53 2.63 4.15 18.00 28.44 12.60 19.91 10.08 15.93 8.57 13.54 20.63 32.59 15.23 24.06 12.71 20.07 11.19 17.68 100 127 75 3.75 2.96 27.00 21.33 18.90 14.93 15.12 11.94 12.85 10.15 30.75 24.29 22.65 17.89 18.87 14.91 16.60 13.12 250 316 113 5.63 1.78 36.00 11.38 25.20 7.96 20.16 6.37 17.14 5.41 41.63 13.15 30.83 9.74 25.79 8.15 22.76 7.19 500 633 150 7.50 1.19 45.00 7.11 31.50 4.98 25.20 3.98 21.42 3.38 52.50 8.30 39.00 6.16 32.70 5.17 28.92 4.57

effect

2005-2010

Learning curve 0.80

effect 2010-2015

Learning curve 0.85

effect 2015-2010

with those of fossil fuels. This was the main focus for our comparative analysis. To compare production costs, historical prices for raw materials were extrapolated in the course of reference scenarios of the fossil fuel price. The identification of eco­nomically promising biofuel technologies was then enabled through modelling of projections for technological advancements in respect to production scale and learn­ing effects. In other words, our approach enables the comparison of different biofu­els’ production costs while considering the specific development state, economies of scale in context of realistic scenarios for the market prices for biomass. Plausibility checks based on current data as well as consistency of the results across production technologies enhanced the accuracy of the results. At the same time, we assessed the comparability of data and performed corresponding adjustments if necessary.

If the continued exploitation of carbon-based fuel sources do indeed pose a consid­erable threat to the earth via anthropogenic climate change, it will be necessary to consider the optimal use of an energy source that, in a way, could prolong the adop­tion of truly carbon-free technologies. The role of biofuels in transitioning human­ity away from carbon-based energy sources needs to be considered dispassionately and beyond the influence of short-term political manipulation. Biofuels will clearly be an important component in any future energy mix, though the extent to which they will be used remains a subject for debate (Charles et al. 2011). Eggert et al. (2011) emphasize this level of uncertainty and forcefully argue that the view that first-generation biofuels should be supported by policymakers so as to pave the way for second-generation biofuels is inherently faulty—and indeed counter-productive to promoting the market entry of more environmentally friendly biofuels, espe­cially since the feedstocks and production techniques are so very dissimilar. Their argument that investment subsidies for first-generation biofuels should be removed immediately so as to allow a ‘learning by doing’ approach to improve the economic efficiency of immature second-generation technologies has much to recommend it.

Whatever the case, it appears highly unlikely that biofuels will ever be able to replace petroleum-derived products on a one-for-one basis (Di-Lucia and Nilsson 2007), especially if current growth in the transport sector continues unabated. Indeed, the IEA (2012) reported a continued increase in CO2 emissions, particularly in developing countries such as China and India, owing to growth in the consump­tion of fossil fuels. As mentioned previously, biofuels have a clear advantage over other emerging transport energy solutions, such as those relying on stored electric­ity, electricity produced from chemical reactions (e. g. in fuel cells), or hydrogen, in either gaseous or liquid form. This is because they are able to be deployed and con­sumed, in blended form, by existing infrastructural systems—and the internal com­bustion engine in particular—without major technological modifications.[18] Indeed, most existing vehicles can operate with a small proportion of biofuel (usually cited as 10 %) without the need for any modification. In Brazil, the majority of vehicles (around 90 %) sold are able to run on pure bioethanol in its hydrated form (E100) if required to do so thanks to FlexFuel technology, though E20 or E25 is much more commonly used (Eggert et al. 2011). Switching costs are therefore dramatically reduced (Charles et al. 2009). A danger, here, is that reliance on biofuels might pro­long our existing lock-in to technologies that are manifestly dangerous to the envi­ronment, such as the internal combustion engine or the gas turbine. When looking at possibilities associated with new technologies, the network externalities that these technologies are likely to face must be considered, more so in light of the ‘lock-in’ effect of existing technologies (Katz and Shapiro 1986).

There also remains the possibility that biofuels, together with the engines that they have the ability to power, will be made largely redundant, in time, by other mobile energy technologies. In some respects, this would be the optimum outcome, since the preferred transport energy paradigm would clearly be almost completely, if not entirely, de-carbonized—something which can obviously never be achieved with the combustion of biofuels, no matter how de-carbonized their production becomes. Some of these potential contributors to reducing global GHG emissions across all sectors could include nuclear energy (particular if problems associated with the disposal of contaminated waste products are resolved, how­ever unlikely that may seem at present), cleaner second-generation (and third — and fourth-generation) biofuel production processes, the development of a hydrogen economy (predicated on the availability of clean, renewable energy, with potential links to nuclear energy) and other energy paradigms, e. g. geothermal, hydroelec­tric, photovoltaic and wind, all of which could contribute either directly or indi­rectly to de-carbonized mobility (Charles et al. 2011).

Of course, up until the point that other technologies become more cost effective, biofuels would have an important place in alleviating the existing reliance on carbon — based forms of transport energy. A balance must therefore be reached between (1) biofuels taking over from traditional petroleum-based transport energy fuels (which seems highly problematic, at least with existing technologies) and (2) the emergence of the environmentally optimum outcome of a completely de-carbonized transport sector throughout the world. In effect, the transition from liquid carbon-based energy transportation, based on a combination of fossil fuels and biofuels, to a more gen­uinely sustainable paradigm will need to be governed carefully, while the ongoing suitability of biofuels as part of this transition will need to be monitored closely. As Sharpe and Hodgson (2006, p. 6) have observed, there is “a significant danger that, by wringing more capability out of our existing systems, we may fail to tackle more fundamental issues”. In this respect, biofuels of whatever type must not be allowed to impede the bringing to market of more long-term transport energy technologies.

Given the current inability of second-generation biofuels to find their way to mar­ket, it is likely that substantial political support, with attendant policy mechanisms, will be required. Yet, as Eggert et al. (2011) point out, it will be necessary to avoid any political or technological lock-into biofuels of any sort. Governments clearly must balance support for second-generation biofuels with support for other alterna­tive mobile energy sources. As a consequence, they argue that policies that promote even second-generation biofuels will need to be flexible, while support programs should be able to be terminated at short notice if it becomes clear that alternative technologies are more desirable in the long run. In effect, and as Eggert et al. (2011, p. 9) aptly put it, “policies for promoting R&D for cellulosic ethanol should only have as their aim to uncover the technology’s true potential (which is so far not clear), and not operate with ambitious goals for the technology’s future market penetration”.

To demonstrate this point, one need only think of existing political commitment to first-generation biofuels, which has proved difficult to withdraw, even though these fuels have not shown the environmental potential once commonly ascribed to them. The same must not occur with respect to second-generation biofuels if other technol­ogies emerge as offering greater long-term potential. A particular threat is that first — generation technologies will continue to be supported by politicians and stakeholder interest groups, particularly in agrarian-based societies, because second-generation production, together with third — and fourth-generation, will typically be far more cap­ital intensive and less labour intensive, and therefore may have more limited immedi­ate economic impacts on the local area as a result of reduced employment prospects in the local community (Larson 2008). This issue is gaining increasing attention in the biofuel policy space as existing multilateral arrangements continue to focus on promoting international trade rather than overall global sustainability (Lima 2009).

5 Concluding Remarks

There is clearly a need for producers of biofuels to look carefully at their biomass sources so as to ensure that they are not creating a market for unsustainable agri­cultural practices. Indeed, without sufficient scrutiny from these purchasers of bio­mass, agricultural producers may be prompted to cultivate the requisite biomass in a highly unsustainable fashion (Mathews 2008). Advances in biotechnology, and the increasing possibility of replacing fossil fuels with second — and probably third — and fourth-generation biofuels, could potentially address many challenges related to both energy and food security in a relatively sustainable manner. However, there is a need to (1) further investigate the environmental impacts of advanced biofu­els through more comprehensive analysis in individual circumstances to ensure that they are truly reducing the global carbon footprint without affecting exist­ing ecology and (2) create effective governance and institutional arrangements across national boundaries to ensure that the biofuel industry looks beyond the visible horizon and does not advantage some regions at the cost of others. While biofuel technology is likely to evolve over time, thereby making the production processes more sustainable from an environmental, social and economic perspec­tive, the developed world will undoubtedly need to play a strong leadership role. This could be achieved by supporting the commercialization of cutting-edge bio­fuel production processes instead of protecting their respective local economies by subsidizing biofuel crops that are not particularly friendly to the environment. A greater focus must be placed on non-edible biocrops (including algae) and com­mercializing advanced biomass-processing techniques that will emit less GHGs, consume less land and yield high-energy outputs. In short, moral and ethical con­siderations must prevail over the arguably short-term political and economic out­comes currently associated with the global biofuel industry.