Quality control of the final product requires a large and varied number of chemi­cal analyses to evaluate the physical and chemical parameters in comparison with quality standards, usually established by regulatory legislation. Table 7 shows the specifications and analytical methods for the quality control of ethanol, an impor­tant Brazilian biofuel.

These data highlight the large number of techniques required to ensure ethanol quality, from classical techniques (volumetry, gravimetry, and direct potentiometry) to instrumental techniques (ion chromatography, GC-flame ionization detector, and AAS). The method for each analytical technique needs to be rigorously and systematically applied in order to enable accurate comparison between samples and to accurately assess the quality of the sample.

Figure 8 shows a flowchart for the use of AAS for quality control of ethanol. AAS is a rapid technique for the determination of the presence and concentration of several metals and some nonmetals. Nevertheless, preparation steps require attention because this step will release the analyte into the solution to be meas­ured. If not all of the species is released into the solution, inaccurate results will be obtained. The analytical result could be obtained as a concentration (mg kg-1 or mg L-1) or as a mass percentage in a certain volume (% m/v), depending on the individual’s interest or standard regulation.

Since 1850, corn and beets crops have been used as raw materials for ethanol pro­duction in the USA. Ethanol was a popular fuel for lighting during the first half of the nineteenth century, and in 1860, 13,157,894 gallons of ethanol were burned in the USA for lighting (Herrick 1907). The ethanol tax initially imposed a fee of 20 cents per gallon in 1862 and reached $2.08 per gallon in 1864 (Herrick 1907).

Between 1919 and 1933, ethanol was forbidden to increase the demand for products such as gasoline. During World War II, the production of ethanol rose to 600 million gallons per year. The US interest in ethanol fuel has grown since the oil crisis of the 1970s. The USA started using 10 % ethanol blended into gasoline at the end of 1970. The Energy Tax Act of 1978 (ETA) officially defined 10 % as the required volume of a non-fossil-fuel blend with gasoline (Solomon et al. 2007).

In addition, the demand for ethanol produced from corn has increased since the main product that was added to gasoline at one time (namely, methyl tert-butyl ether, or MTBE) was revealed as a contaminant of groundwater. The use of MTBE in gasoline was banned in almost 20 American states in 2006, and since then, etha­nol has become its main substitute (RFA 2011).

Today, the sharp growth in the production and consumption of ethanol is asso­ciated with federal legislation that was created to reduce oil consumption, increase energy security, and reduce CO2 emissions in the country. From 1983 to 2005, the production costs for making ethanol from corn decreased by 65 %, and further­more, the industrial processing costs decreased by 45 % (Hettinga et al. 2009).

The use of ethanol has been expected to expand since the Energy Policy Act of 2005 established a production target of 7.5 billion gallons of renewable fuel by 2012. The Security Act of 2007 raised this target and required the annual use of 36 billion gallons of renewable fuels until 2022 (RFA 2011). With these incentives and the maturity of the industry, the USA is currently the world’s largest producer of ethanol and it represents approximately 60 % of the world’s production.

In addition to ethanol, biodiesel is currently used as a biofuel in the USA. The two major feedstocks for biodiesel production are soybeans and rapeseeds. The fed­eral government plays a key role in determining the course and especially the scale of biodiesel development, and it gives incentives such as tax exemptions, price con­trols, production targets, and direct subsidies (Lin et al. 2011).

Advances to stimulate biodiesel were proposed by Congress, and President Bush signed the Energy Independence and Security Act of 2007. The scale of production has grown significantly, and furthermore, plants are now distributed in various parts of the country. Today, the total production of biodiesel is nearly 1 billion of gallons.

In some parts of the USA and Canada, camelina (Camelina sativa (L.) Crantz) is emerging as an oilseed feedstock for biodiesel that is intended for use in avia­tion fuel, as it can be grown on wheat fields that would otherwise be left fal­low without harming the soil. In fact, growing camelina in these fields usually improves their fertility. The USA has announced plans for using algae as a feed­stock for future generations of biofuels and is promoting the biofuel industry by providing grants and sponsorships.

Our initial analysis examines ethanol production volumes between now and 2040. We consider four different scenarios altering the presence of the EPA RFS 2 man­dates (EPA 2009) and market penetration costs. Figure 2 presents ethanol production

volumes with and without penetration costs under a mandate. When a drop-in type fuel is produced avoiding market penetration costs, we find increased ethanol mar­ket supply. Our second comparison analyzes the impact of penetration barriers when mandates are not present (Fig. 3). There without a mandate, less crop ethanol is pro­duced than with mandates and more cellulosic ethanol is produced after 2020, mainly due to lower processing costs. Furthermore, the removal of market penetration costs has a stronger impact on ethanol volumes reflecting the greater flexibility allowed.

Next, we examine the impact of adding carbon prices. First, we consider the case with no mandates in place, but with market penetration barriers (Fig. 4). There total ethanol production volume increases slightly under increasing car­bon price, and ultimately reaches about a 10 % increase in total production. Simultaneously, cellulosic ethanol replaces crop ethanol production due to its enhanced GHG emission offset efficiency (see McCarl and Sands (2007) for esti­mated offset rates).

We also examine the projections of future ethanol production under “no mandates and no penetration costs in place” scenario for three points of time (Fig. 5). It can be observed that removal of market penetration costs drives ethanol volumes up. In 2020, the amount of total ethanol produced fluctuates between 25 and 30 billion gal­lons per year (depending on the GHG price); in 2030, the amount of total ethanol is in the range of 30 and 35 billion gallons per year; and in 2040, the total etha­nol amount reaches 40 billion gallons per year under GHG price of $50 per ton of CO2e. GHG payments provide additional revenues and increase ethanol volumes. At the same time, we see that only under scenarios with a carbon payment and no penetration costs does the total volume of ethanol produced reach the RFS2 biofuel mandate levels. Thus, it appears that in the absence of carbon trading schemes or a drop-in fuels it is highly unlikely that the EPA RFS2 mandate will be ever met.

Finally, we examine impact of penetration costs removal on the total volume of ethanol produced. At the same time, we assume that scenario with no penetration costs could be a case of all drop-in biofuels which do not require adjustment in infra­structure before their distribution in the market. Some innovative liquid biofuels, like butanol or methanol, are free from corrosive properties and they could be distributed and sold to the end-consumer through the currently existing distribution networks and pumping stations. As one can observe in the Fig. 6, removal of penetration bar­riers raises the total ethanol production by around 5 billion gallons per year unde

Fig. 5 Projection of future crop and cellulosic ethanol production under varying GHG prices (at three points of time) for “no mandates and no penetration costs in place” scenario all considered GHG prices in 2020. In 2030, the situation looks slightly different. Under $0 carbon price and scenario with no penetration costs the amount of ethanol produced is around 6-7 billion gallons higher than under the scenario with penetra­tion costs in place. However, under $100 carbon price this difference between two scenarios amounts to 10 billion gallons per year. Some discrepancies could also be noticed in the projections for 2040. Under $0 carbon price, the total amount of etha­nol under no penetration costs exceeds the total amount of ethanol under scenario with penetration costs by 10 billion gallons per year. However, once the carbon price reaches $100 per ton of CO2e, the gap between both scenarios amounts to almost 15 billion gallons per year. In general, these projections display the pattern which reflects the impact of penetration costs removal on the amount of total ethanol pro­duced. Clearly, removal of penetration barriers enables ethanol to be absorbed by the market and encourages growing consumer ethanol demand. On the other side, exist­ence of penetration barriers and lack of investments aiming at their reduction might hamper further development of the ethanol industry.

As experience has shown, the production of lignocellulosic ethanol in the USA has not been launched on the industrial scale so far. It is believed that more techno­logical progress is needed to lower processing costs of cellulosic ethanol production. Enzymes used for fermentation have to become cheaper and biochemistry of reactions

needs to become more efficient. So far, cellulosic ethanol production is limited to operations in pilot plants; therefore, it is difficult to estimate processing costs of cellu — losic ethanol per gallon. Uncertainty related to potential location of cellulosic ethanol plants makes it challenging to assess feedstock and other materials costs, transporta­tion costs, and capital costs related to cellulosic ethanol production. Until now, one of the available projections is the estimation made by the National Renewable Energy Laboratory (NREL) which presents $3.29 per gallon as a viable unitary process­ing cost (see Fig. 1 for the specific estimation of cost). In our next analysis, we try to analyze the impact of further cellulosic ethanol processing cost reductions to see how much the processing costs have to fall from $3.29 per gallon level for cellulosic ethanol production to become economically profitable. We also look at what level of processing costs the volume of ethanol produced approaches volumes contemplated by the Energy Independence and Security Act and the EPA RFS2 mandates. In this analysis, we hold crop ethanol production costs constant. Figure 7 shows the effect of decreasing processing costs on production volumes for three different points in time.

Decreasing processing costs are found to reduce crop ethanol and increase cel — lulosic ethanol volumes in the absence of mandates. For example, we find that 50 % cost drop in processing costs of cellulosic ethanol causes amount of crop ethanol produced in 2020 to drop to around 5 billion gallons per year and in 2040 to drop to around 1 billion gallon per year (Fig. 7, panel a). These amounts are much smaller compared to current levels of crop ethanol of around 13-14 billion gallons.

At the same time, the 50 % cost drop causes the volume of cellulosic ethanol to increase from 13.3 billion gallons per year to 19 billion gallons per year in 2020. It is also worth mentioning that RFS2 mandate schedule requires cellulosic ethanol to be produced at the level of 16 billion gallons per year by 2022. From the projec­tion of cellulosic ethanol production in 2020 in Fig. 7 (panel a), we observe that this volume is only achievable under 25 % decrease in processing cost. When it comes to the total ethanol volume, the EPA 2022 mandate of 31 billion gallon per year is never achieved, even if the processing costs drop by 60 % (Fig. 7, panel a).

2 Conclusions

We find that ethanol mandates create volumes that are generally higher than would occur in the free market and that market penetration costs and carbon prices are big influences in ethanol market penetration. Namely:

• positive carbon prices, lower infrastructure costs, or some other cost reduction are needed to provide economic incentive for second-generation liquid biofuels production if they are to reach mandated levels;

• technological progress is essential to reduce processing costs and, thus, produc­tion costs of the second-generation biofuels and to make the second-generation biofuels cost competitive.

The use of enzymes for the pretreatment of biomass feedstocks can significantly reduce the capital cost, under the condition that enzymes are produced by microorgan­isms during the process of fermentation, also known as simultaneous saccharification

and fermentation (SSF) (Olofsson et al. 2008). However, the major drawbacks of SSF are the need to find optimal conditions of temperature and pH for both the enzymatic hydrolysis and the fermentation, and the difficulty to recycle the fermenting organism and the enzymes (Olofsson et al. 2008). Nevertheless, the application of enzymes can facilitate the fast, efficient, and cheap conversion of cellulose to glucose. Enzymatic hydrolysis can give higher yields of sugars in contrary to acid hydrolysis. However, the enzyme system for hydrolyzing lignocellulose is quite complex and involves the cellulase system as shown in Table 4. The efficacy of the enzyme systems depends on various factors that should be overcome to achieve the maximum yields of glucose. The key barriers that impede the action of enzyme system are as follows: (1) unreac­tive nature of crystalline cellulose; (2) the presence of lignin-blocking reactive sites; (3) low substrate surface area; (4) low rates of hydrolysis; (5) substrate and product inhibition; and (6) enzyme denaturation.

In order to develop an effective enzymatic hydrolysis process, it is important that inhibitors that impact the enzyme activity are removed (Taherzadeh and Karimi 2007). Another issue that requires attention is the cost reduction of the enzymes, which can be achieved probably by recycling the enzyme or by producing micro­bial enzymes during the SSF process. Recycling of enzymes can be achieved by repeated batch hydrolysis of feedstocks and immobilization of enzymes on an inert material (Das et al. 2011). The application of immobilized enzyme enables easy post­hydrolysis separation of the enzyme from the reaction mixture (Das et al. 2011). The advantage of enzyme immobilization is that it ensures the enzyme structure and conformation is preserved in addition to imparting improved thermostability. Enzyme modifications and active site mutations could possibly provide much effec­tive enzymes with high rates of hydrolysis, reusability, and resistance to denaturation. Modified/novel enzymes have the potential to reduce the cost of enzymatic hydrolysis.

Biofuels have often been seen as a way to enhance the agricultural sector. This is especially the case in the developed world, where locally produced food crops find it increasingly difficult to compete at a global level because developing and underde­veloped nations produce the same at a much lower cost. In these cases, governments provide considerable subsidies, promote low-interest loans and impose various trade barriers to incentivize farmers to produce these crops at a competitive price and thereby sustain their agricultural sector. Given that biofuels, especially first-gener­ation biofuels, rely on edible crops as a feedstock, they create an alternative market for such agricultural products as a valuable input for the energy sector. In this sec­tion, we look at the degree to which rural economies, where farming is the livelihood for most people, are influenced by the burgeoning biofuel industry.

One of the central arguments in favour of biofuels is its contribution to rural development through increased employment opportunities and higher income. It has been estimated that the biofuel industry requires approximately 100 times more labour than the capital-intensive fossil fuel industry to produce the same energy output (Renner and McKeown 2010). This is because there is a wider array of jobs associated with biofuel production. These positions can relate to farming through to biotechnological research. Scaramucci and Cunha (2007) estimated that more than 5 million jobs could be generated in Brazil by the year 2025 if 5 % of global gasoline demand is replaced by sugarcane-based bioethanol from Brazil. Jobs also result from indirect employment, such as those involved in the sales of biofuels and transport of biomass. In 2006, all types of biomass operation in the United States employed about 136,999 people directly and another 310,000 across the supply chain (Domac et al. 2005). While the numbers are substantial, rational­izing pro-biofuel policies simply based on potential job creation can be problem­atic. This is because the net economic benefits depend on a multitude of factors.

For example, production capacity and level of mechanization can influence the scope for job creation. While a heavily mechanized production system increases labour productivity, it also minimizes employment opportunities. Likewise, a large refinery may achieve higher economies of scale, but the number of workers required per unit of output is low. Brazil’s policy to control the rate of mechaniza­tion and provide support for small-scale refineries has assisted with controlling unemployment and poverty in the region (APEC 2010). In 2006, 351 plants were able to provide employment for approximately 700,000 people to produce 17,900 million litres of ethanol from 5.9 million hectares of land. In this context, the Brazilian Social Fuel Seal (Selo Combustfvel Social)[6] initiative, which supports biofuel producers through tax incentives, is worth mentioning here as it promotes diversification of jobs within biofuel-producing regions and encourages the ongo­ing participation of family-based feedstock production firms in the nation’s biofuel industry (Padula et al. 2012). However, large-scale production is crucial for biofuels to compete with fossil fuels (DfID 2007). This may negate the expectations of regional development emanating from the biofuel industry. Indeed, potential bene­fits from new or expansion of existing biofuel facilities are often overestimated. This is because refinery building or expansion provides construction-related jobs to those generally living outside the local area. As a result, most of the initial impact is not felt locally (APEC 2010; Hillebrand et al. 2006; Moreno and Lopez 2008).

Net employment may also vary depending on the land displacement effect. Switching from existing food crops for biofuel production does not always result in additional employment (Jaeger and Egelkraut 2011). Rather, it simply exchanges one market for another. With regard to the impacts of biofuel policy on employment, analysis based on dynamic and long-term general equilibrium adjustments, includ­ing shifts in jobs in agriculture among biomass-producing regions, has found that biofuel policies would not provide any additional economic activity. This is because the increase in bioethanol output would be offset by a reduction in livestock pro­duction (Dicks et al. 2009), especially because land-use changes take effect. Furthermore, de Gorter and Just (2010) claim that higher fuel prices induced by bio­fuel subsidies magnify the inefficiency of the preexisting wage tax by reducing real wages and thus discouraging work. This would reduce labour supply and generate deadweight costs because the tax base becomes eroded as consumers move away from the taxed good and use substitutes. On the contrary, if the land used for bio­fuel production was not in use or was abandoned, any job created would potentially increase net employment and foster economic growth (Diop et al. 2013).

As with employment expectations, it is perceived that biofuels increase the income levels of those engaged in the industry. Parcell and Westhoff (2006) found that, in 2006, the average annual salary of ethanol-related salary was much higher than the average US salary. However, this may not always be the case as earnings and job security can vary significantly across a number of factors. Skilled labour working in technical roles has a much higher income potential than unskilled labour working in the field or in the refinery. In fact, there are fewer white-collar jobs compared to blue-collar jobs. Depending on the type of feedstock, employ­ment opportunities may vary. In the case of Brazil, the high seasonality of sug­arcane production means that the ratio between the number of temporary and permanent workers is significant (DfID 2007). As a result, many workers do not have a biofuel job throughout the year. Failures of biofuel projects are becoming increasingly common, and these failures adversely affect the livelihood of many vulnerable farmers in regional areas (APEC 2010).

While one objective of biofuel policies is to help farmers, landowners stand to benefit the most from increases in crop prices. Crop growers who lease land there­fore only benefit until higher profits associated with rising feedstock prices are captured by higher land values and land rents. Take corn for example. Though dis­puted by Ajanovic (2010), as corn prices rise, domestic pork and poultry producers reliant on this crop to feed their livestock will potentially reduce their international competitiveness, thereby causing a reduction in production levels if higher prices are not absorbed by consumers (Brown 2008). Although the flow of profits from these facilities may initially stimulate rural economies, a rise in crop prices over time owing to demand has the potential to minimize these benefits. There will also potentially be a reduction in livestock farming in these same areas (Dicks et al. 2009), especially as land-use changes take effect. This could eventually work to offset this advantage.

To understand how the biofuel industry has influenced rural development, we look at the employment data of three major biofuel markets, these being the United States, Brazil and the EU (it must be understood, however, that income may vary significantly within the sector itself). If one takes into account that abso­lute numbers of employment may only tell part of the story, unemployment and employment data in the agricultural sector are presented in the form of percentage of total labour force and of total employment, respectively. As can be observed from Fig. 4, bioethanol production/consumption does not seem to have increased employment in agriculture in the United States. Employment in agriculture is relatively stable during the observed period, despite the substantial increase in domestic biofuel production, and has even slightly declined. With respect to the overall impact on employment, the unemployment rate has increased in recent years. Figure 5 illustrates the case for Brazil. Once again, bioethanol production/ consumption has not had the effect of increasing employment in the agricultural sector. Indeed, the employment in agriculture has declined significantly in recent

bioethanol production (‘0000 barrels per day )

times, even though biofuel production/consumption has increased sharply. The reason may be that a greater use of mechanical harvesting has resulted in fewer jobs being generated. Yet there seems to be some positive impacts on overall

employment as a drop in the unemployment rate has been observed since 2006. As in the United States and Brazil, biodiesel production/consumption does not increase employment in agriculture in the EU. Like the United States, employment in agriculture has also slightly declined, despite a significant observable jump in biofuel production and consumption. Furthermore, biofuels seem to have a neutral impact on overall employment (Fig. 6).

So, despite the fact that first-generation biofuels use crops currently grown by farmers within the respective domestic biofuel markets investigated, there is no clear overall benefit with respect to the number of people employed in the agricul­tural sector. While jobs are obviously being created in terms of biofuel processing, the same positive effects do not seem to flow through to the agricultural sector in the economies discussed.

The observations made above have significant implications. As it is eventually realized that more sustainable forms of biofuel production beyond first-generation processes are necessary, this will arguably also have significant impacts on local or regional economies reliant on the growing and processing of particular feed­stocks. In many cases, food crops currently being used for biofuel production will not be optimum for later-generation bioethanol production, which can use all man­ner of biomass (Blottnitz and Curran 2007). Once demand for biofuels grows, the cost equation of producing biofuels from these less energy-intensive crops will undoubtedly force producers to look for crops that can produce the most energy at the least cost (McCormick-Brennan et al. 2007). In many cases, this might mean that regions currently producing biofuel feedstocks will not be well placed to grow the preferred types of biofuel crops. This will clearly have detrimental impacts on economies that are closely tied to long-held agricultural traditions, especially if market conditions continue to militate against their ability to compete with other economies in the open food market. Yet this might be precisely the reason why governments continue to support first-generation biofuels, for moving to later — generation processes brings with it the spectre of moving from labour-intensive to more technology-based production.

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

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

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

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

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

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

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

Agriculture has brought about widespread environmental degradation, with this degradation intensifying as it became increasingly mechanized and reliant on man-made inputs. It is therefore important to bear in mind the potentially negative impacts that intensified farming practices will have on ecosystems in areas where organic material for biofuel production is being grown, often where less-intensive agricultural methods have previously been employed, such as in the developing world. This is because the exploitation of biofuels, being made from organic mate­rial, has the potential to have a number of significant negative effects on ecol­ogy. In this section, the impact of biofuels from an ecological perspective will be explored, particularly as a result of unregulated use of (1) herbicides, pesticides and fertilizers, and (2) land-use changes.

4.1 Broader Impacts of Herbicides, Pesticides and Fertilizers

The growing of crops is made problematic by pests, which can destroy whole fields of crops. To enhance productivity and ensure saleable crops in sufficient volumes, pesticides are regularly used to keep unwanted organisms under con­trol. Yet pesticide run-off from agricultural land has the potential to pollute local watercourses and can result in a loss of biodiversity when food supplies for higher organisms are reduced (Charles et al. 2009). This is especially the case with respect to insects, many of which are highly detrimental to agricul­ture, but are of vital importance to creatures higher up in the food chain. The presence of harmful chemicals found in pesticides can also flow throughout food chains, thereby leading to chemical build-up in higher organisms, espe­cially avian fauna, and raptors in particular, the strength of whose eggs is affected by chemicals such as DDT, thereby leading to greater infant mortality (Sodhi et al. 2011).

Furthermore, production processes and distribution relating to pesticides, her­bicides and fertilizers can also contaminate water supplies, something of especial importance in developing nations such as India, where clean, disease-free reticu­lated water might not be readily available to all citizens (Rajagopal 2008). With respect to water, Eisentraut (2010, p. 10) notes that, while water requirements for second-generation crops could be less than for first-generation biofuels, depending on the crop type and local environmental conditions, the total demand for water could be higher owing to additional water treatment steps in the production pro­cess. In addition, run-off from nitrogen-rich fertilizers can profoundly increase the incidence of algal bloom in freshwater aquatic environments. Such outbreaks can result in these watercourses becoming starved of oxygen, while the presence of thick layers of light-seeking algae at the surface of the watercourse can block out sunlight and impede the ability of other plants to photosynthesize effectively (Bergkamp et al. 2000). The result can be a catastrophic loss of biodiversity in affected watercourses, particularly when organisms at the lower end of the food chain are threatened. In many cases, watercourses have become depleted of oxy­gen owing to algal blooms, thereby creating hypoxic or ‘dead’ zones where only a few organisms can survive (Dale et al. 2010).

The use of fertilizers can also result in the acidification of soils (Eisentraut 2010). The phosphorus, nitrogen and potassium contents in fertilizers, when they become dissolved in water, alter the pH balance of the soil. High acidity reduces the functioning of nitrifying bacteria responsible for the breakdown of organic matter into ammonium and nitrate for plant uptake. This might not be problem­atic for biomass cultivation, depending on the species being cultivated, but could have disastrous impacts in adjacent areas affected by fertilizer run-off. Finally, the very production of fertilizer and its distribution are generally energy inefficient. In most countries, it is reliant on carbon-based forms of energy and therefore contrib­utes to GHG proliferation, all of which, as noted previously, needs to be taken into account in an overall LCA.

The concept of sustainability is derived from ‘sustainable development,’ which has been defined in the Brundtland report as ‘development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs’ (WCED 1987: 45). The concept of sustainable development has traditionally focused on three pillars (i. e., social, environmental, and economic), and in recent years, it has evolved including other components such as policies and institutions (Diaz-Chavez, 2011).

The EU, since first announcing its intention to set a mandatory biofuels target, has maintained that any production or use of biofuels must be sustainable (European Federation for Transport and Environment 2009). The Renewables Directive (2009/28/EC) aims to ensure this ambition is met through the use of mandatory sustain­ability criteria. The criteria set out three main requirements which biofuels must meet in order to be counted toward the target or to be eligible to receive tax rebates or subsidies:

• The greenhouse gas emission savings from the use of biofuels and bioliquids must be at least 35 % (rising to 50 % in 2017) compared to fossil fuels;

• The feedstock of biofuel is not to be derived from land with high biodiversity value such as high biodiversity grassland; and

• The feedstock of the biofuel is not to be derived from land with a high carbon stock. These criteria apply to biofuels and bioliquids and for both imported and domestically produced feedstock.

A significant part of biofuels debate since 2009 focused on indirect land — use change and its exclusion from the EU sustainability criteria. ILUC is not accounted in the Renewables Directive, and therefore, the emissions resulting from ILUC are not included in the greenhouse gas life cycle analysis calculations (Amezaga et al. 2010).

For biofuels, the length and complexity of the supply chains make the sustainabil­ity issue very challenging. Biofuels’ pathways include several successive segments over the fuels life cycle (e. g., feedstock production, conversion of the feedstock to biofuels, wholesale trade, retail, and use in engines) and multiple actors (e. g., feed­stock suppliers, biofuels producers, biofuels consumers, and public authorities).

In order to be sustainable, biofuels should be carbon neutral, especially consid­ering the necessity of fossil fuel substitution and global warming mitigation. Also, biofuels should contribute to the economic development and equity. Moreover, they should not affect the quality, quantity, and use of natural resources as water and soil, should not affect biodiversity, and should not have undesirable social consequences (Lora et al. 2011).

Several authors have recently raised concerns about the environmental costs benefits and social implications of biofuels production such as underlying uncer­tainties over the life cycle emissions of greenhouse gas emissions (GHG), pos­sible deforestation for feedstock production, degradation of soil and air quality, increased water consumption, possible loss of biodiversity, possible competi­tion with food production, and other potential social imbalances (Ajanovic 2011; Gnansounou 2011; Finco et al. 2012; Padella et al. 2012).

Land-use change is considered one of the most important environmental impacts to address, mainly because of its impacts on GHG and wider ecosystems. Recently, many studies are working on land use, direct and indirect (LUC, ILUC). For exam­ple, the research studies of Brazil show that the amount of new land required for sugarcane production would be relatively small (Arima et al. 2011; Macedo et al. 2012). In the same way, the LUC module based on a transition matrix developed by Ferreira Filho and Horridge (2011) and calibrated with data from the Brazilian Agricultural Censuses of 1995 and 2006 shows how land use changed across different uses (crops, pastures, forestry, and natural forests) along those years. The results obtained by general equilibrium models approach show that the ILUC effects of ethanol expansion are of the order of 0.14 ha of new land coming from previously unused land for each new hectare of sugarcane. This value is higher than the values found in Brazilian literature (Ferreira Filho and Horridge 2009, 2011).

Careful assessment of these impacts has given rise to criticisms from economists, ecologists, NGOs, and international organizations, who call for additional analysis of biofuels’ effects. Furthermore, the European Union and several countries have adopted certification schemes to biofuels to respond to these growing concerns and to address the sustainability issues derived from the expanding production of biofuels.

Current and future biofuels production could have important environmental and ecological impacts. One of the major reasons for producing biofuels is to reduce greenhouse gas emissions and to mitigate the effects of global warming produced by fossil fuels. However, some unintended impacts of biofuel production are land, air, water, and biodiversity.

This section is divided into two parts. The first provides a brief description of the social arrangements of palm oil. Additional details of these arrangements can be found in the works of Cesar (2012) and Cesar and Batalha (2013). Sustainability and social development are also referenced, though more briefly, by Fischer et al. (2006). The second section addresses aspects of NIE, which are classified accord­ing to the social arrangements in focus.

Armenta et al. (2008) established the creation of the term green analytical chem­istry based on: (1) sample treatment; (2) oriented scanning methodologies; (3) alternatives to toxic reagents; (4) waste minimization; (5) recovery of reagents; (6) online decontamination of wastes; and (7) reagent-free methodologies. Thus, it should be considered that the analysis of biomass should be based on the 12 principles of green chemistry proposed by Anastas and Warner (1998), since the context of its use is reflected in the sustainability of feedstock and processes.

Some of the 12 principles of green chemistry are closely related to the imple­mentation of green analytical methodology, which are as follows: (1) atomic and

energy economy; (2) use of catalytic reactions instead of stoichiometric reactions; (3) decreasing solvent use; and (4) a decrease in residues (Anastas and Warner 1998). The application of these principles will contribute to achieve a more sus­tainable analytical methodology, as can be seen in Fig. 9.

In some cases, it is very difficult to apply all of those principles presented in Fig. 9, because each analytical method has its particularities and limitations. Then, we need to seek other principles as waste prevention, design for energy efficiency, use of real-time analysis for pollution control, and inherently safer chemistry for accident prevention; this strategy will ensure a greener chemical analysis and ana­lytical chemistry.

3 Conclusions

Chemical analysis of biomass is an important branch of analytical chemistry because it can provide information about the constitution of feedstocks, processes, products and by-products, and residues. Analytical techniques are at the core of the analytical laboratory, and the understanding of its principles is necessary for real-world applications. Then, this can be applied on a whole biofuel chain to solve many technical and scientific problems, as: best uses for a biomass, improvement of conversion processes, increase in the quality of biofuel, and control of residues.

Nowadays, green chemistry and sustainability of processes and products are themes that passed from academic discussion to practical use. Then, analytical chemistry as part of chemical sciences should follow this current trend, which can contribute to a bioeconomy based on biomass use instead of non-renewable raw sources, as the oil, and an advance in biomass knowledge to develop their best uses.