A top-down approach to assess physical and ecological limits of biofuels

Energy 64 (2014) 506e512 Contents lists available at ScienceDirect Energy journal homepage: www.elsevier.com/locate/energy A top-down approach to assess physical and ecological limits of biofuels Carlos de Castro a, *, Óscar Carpintero b, Fernando Frechoso c, Margarita Mediavilla d, Luis J. de Miguel d a Department of Applied Physics, Campus Miguel Delibes, University of Valladolid, Spain Department of Applied Economics, Av. Valle Esgueva 6, University of Valladolid, Spain Department of Electric Engineering, Fco. Mendizabal, University of Valladolid, Spain d Department of System Engineering and Automatic Control, Paseo del Cauce, University of Valladolid, Spain b c a r t i c l e i n f o a b s t r a c t Article history: Received 4 May 2013 Received in revised form 11 September 2013 Accepted 17 October 2013 Available online 9 November 2013 The aim of this article is to analyse the physical and ecological limits of biofuels (in particular, ethanol). To this end, three aspects are discussed. First of all, the territorial element, i.e., the real productivity of energy crops (in particular, ethanol), to show that, in general, the productivity is much lower than most of the literature on the subject suggests. Similarly, we then stress the high total territorial impact (the Ecological Footprint) associated with this kind of energy production. Thirdly, we offer data concerning the very modest nature of the energy balance of biofuels (EROEI) and their real power density, considering the productivity of the crops grown for biofuel. As a comparison, these three aspects are estimated for other forms of energy, in particular, photovoltaic energy. We conclude that when the set of estimated parameters has been analysed, there exist reasonable doubts concerning the use of biofuels on a regional and global scale, so they should not, in principle, be promoted as a renewable energy source, nor is it desirable on such a scale. Ó 2013 Elsevier Ltd. All rights reserved. Keywords: Biofuels potential Energy return Ecological footprint Biofuels productivity 1. Introduction There is currently an important social, economic, political, ecological and technological controversy surrounding the use of biomass as a biofuel to produce energy. There are many opposing studies and discussions in the scientific literature concerning the production and use of these biofuels in such matters as: the emissions of gases associated with the greenhouse effect [1e6], the energy return (EROEI) [1,7e10] and other energy related issues such as the exergetic evaluation [11e14], the real yields productivity [15e18], and the environmental, economic and social costs and benefits [17,19e29]. Given that the use of biomass would, in principle, be included within the scope of renewable energies, we should take into account not only the usually stressed advantages [1,18,23], but also the evaluation of the physical and ecological limits associated with its use. * Corresponding author. E-mail address: ccastro@termo.uva.es (C. de Castro). 0360-5442/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.energy.2013.10.049 Following a holistic approximation, top-down methodologies have been proposed and explained for wind potential [30,31] and solar power on a global scale [32]. Prieto and Hall [33] use also a top-down approach based on the whole Spanish photovoltaic installed base instead of the classic individual plants of a given technology of a given latitude (bottom-up methodology) and use this approach to analyze the extended boundaries of the energy inputs required to the whole photovoltaic system. Bottom-up methodologies are based on available concrete cases and estimations of the land productivity, power density, EROEI, exergy, etc. of a particular area of a region or country, which are generalized and extrapolated to all this region or country and sometimes to the World as a whole (see Table 1). Against this methodology, we propose [30,32] a top-down methodological approach to assess the very same issues, considering global and/or regional statistics, for instance, for biofuel production: satellite data of the total land occupation of plantations, total biofuel production, etc. (see next sections); always with a holistic approach that takes into account related issues like: eroded land, use of water and materials, global greenhouse emissions, etc. In this work we aim to use this same methodology for the biomass case, making a global analysis of some of the most C. de Castro et al. / Energy 64 (2014) 506e512 Table 1 Reported or inferred land yields for sugarcane and biofuel yields from Brazilian sugarcane and the World. See text and Appendix. Authors Land yield (Tn/Ha) Biofuel yield (L/Ha) Connor & Hernández [34] 73.5 (Brazil) 60.7 (India) 82.4 (Brazil) 5475 (Brazil) 4522 (India) 6500 (Brazil) 5300 (India) 5500 (Brazil) 6712 (Brazil) 6314 (Brazil) 6250 (Brazil) 6641 (Brazil) 5227 (World) 5684 (Brazil) 4737e5559 (Brazil) (harvested) Worldwatch Institute [15],a Pimentel & Patzek [35],b Macedo et al. [36],b Ramírez-Triana [7],b Giampietro [37] Balat & Balat [18],c Johnston [16],d This work e 77 77 77 75 (Brazil) (Brazil) (Brazil) (Brazil) 64.5(World), 70.2 (Brazil) 60.2e69.9 (Brazil) a Notes: These are the values given as “typical yields” in Table 3.1 of the book (bottom-up methodology). However, from Table 23.1 of the same book, from the data given there (Brazil total land occupation of sugarcane, % dedicated to ethanol production, Brazil ethanol production), the productivity for Brazil can be calculated as 5213 L/Ha (top-down methodology). b The data of Pimentel & Patzek [35] and Macedo et al. [36] have been taken from the revised work of Ramírez-Triana [7] dedicated to the calculation of the EROEI of four authors. Pimentel & Patzek [35] and Macedo el al [36]. give the extreme values. c Balat & Balat [18] give the productivity for the ethanol produced from sugarcane or other crops from different regions and countries; from their data, the ethanol average for Brazil, China, the EU27 and North America is 4223 L/Ha. The great difference is due to the fact that the productivity of the sugarcane of Brazil is much higher than that of other countries and that of other crops (mainly corn). d Johnston [16] provides data concerning the bioethanol produced by the sugarcane from around the world, while only the average is shown here. e To be able to compare data with other authors, we give here our estimated value of the productivity of real harvested land. However, as we explain in the text, the total real production per hectare dedicated to crops (what is grown every year is not always harvested) is lower. We believe that the latter should be the real “productivity” which should be used in the global calculations. See the text for more detailed explanations. important aspects to be taken into account in its generalized use for biofuels. 2. Theoretical calculations and results 2.1. Yield productivity of land for biofuels Let us commence by remembering the stated productivity from different authors (or at least what is inferred from their work) for the bioethanol from sugarcane in Brazil or other countries and regions (Table 1). We also compare these data with those provided by Johnston et al. [16] for the world average and with what we calculate in this work for Brazil, a country where the productivity is above the average. Johnston et al. [16] show that the productivity of many crops has been overestimated (and this still happens) by 100% or more using a methodology based on actual, reported yields derived by spatially-explicit agricultural census data. In addition, the updating of the studies cited in this work concerning the average values found by Johnston et al. [16], in general, not only lower the productivity of the land, but also the calculations which depend on this average, or typical, productivity. This occurs, for instance, in the case of the EROEI or the balance of greenhouse gases with respect to the consumption of fossil fuels. In fact, the work by Johnston et al. [16] can even be considered to be optimistic since its world average is close to the performance of Brazil, while the value it gives for Brazil is higher than the highest we have found. In effect, if we take Brazil as a reference, as the world’s principal producer of biofuels together with the USA and a big region, the 507 data are revealing. According to the Brazilian Ministry of Agriculture, Livestock and Food Supply [38], and taking five year periods to avoid the influence of anomalous years, the area of sugarcane harvested in Brazil went from an average of 4.9 million hectares in the period 1997e2001 to 5.61 million hectares in the period 2002e 2006, to finish in 8.18 million hectares in the period 2007e2011. Meanwhile, the production of crushed sugarcane for the same periods went from 295.04 million tonnes to 373.19 million tonnes, to finish in 568.12 million tonnes. In terms of crop productivity, this supposes an increase from the 60.24 Tonnes/hectare in the first of the abovementioned periods, to 69.86 Tonnes/hectare in the last period. However, not all the sugarcane crop is dedicated to the production of ethanol: an important part is also dedicated to the production of sugar. If we concentrate on the TRS (Total Recoverable Sugar), that is, the part of the sugarcane crop for industrial use (ethanol or refined sugar), the progressive move towards ethanol has become evident over the last few years (Fig. 1). In the case of ethanol, the same reference data allows us to obtain its productivity for Brazil (see Appendix) and also to lower the performance figures obtained in the previous works. Thus, if we consider the three abovementioned five year periods, the average production of ethanol in Brazil grew from 12.8$109 L to 25.1$109 L. With these figures, the country’s productivity was between 4737 and 5559 L/Ha, similar to the conservative estimates of Connor & Hernández [34] and of Pimentel & Patzek [35], and some way below the rest of the estimates included in Table 1. The statistics used by the authors of Table 1, and those that we ourselves have previously used, are based on the harvested land dedicated to sugarcane. Although the productivity calculations are made on the basis of the harvested surface (what the Brazilians call “disponivel para colheita”), it is necessary to look closely at the total territorial requirements (both direct and indirect) that the cultivation of sugarcane has through the cultivated area (which, besides the harvested surface in any year, also includes what is called “em reforma”, or not yet available for harvest). The total area occupied by the sugarcane crop increases on average when this last type of land is included. In the case of Brazil, there are no complete statistics of the differences between the real territory occupied by the sugarcane crop and the harvested crop. However, for the state of Sao Paulo (with over 50% of Brazil’s production), there are recent measurements taken using advanced satellite data tools [39], which have already been incorporated into open-access databases (UNICA [40], 2012). A comparison between the cultivated areas and Fig. 1. Brazilian destination of the total recoverable sugar of sugarcane. Source: Brazilian Ministry of Agriculture, Livestock and Food Supply [38]. Own calculations. 508 C. de Castro et al. / Energy 64 (2014) 506e512 the harvested areas in the state of Sao Paulo between 2003 and 2011 show that the average harvested area is 91% of that of the cultivated area. If this proportion is maintained for the whole of Brazil, the litres of ethanol obtained per cultivated hectare would be in the range of 4300e5100. On the other hand, even the productivity of sugarcane on a regional, national and world scale are often conditioned by negative factors that reduce the crop’s productivity (e.g., such climatic problems as unexpected and excessive rainfall during the harvest season in some areas, unexpected flowering or frost, etc.). Such circumstances have always existed and will continue to do so, and such events must be taken into account. For instance, climate change itself may make unexpected climatic and ecological conditions more probable and more frequent. 2.2. Biofuels energy return (EROEI) The considerations of the above section tend to decrease the EROEI calculated by other authors, as they are usually based on a higher productivity of the cultivated land than the real one. Of course, if the productivity per hectare in the calculations of the EROEI is reduced, both the outputs and energy inputs in the tables and the LCA (life cycle assessments) used for such calculations are also reduced. Nevertheless, they will not be reduced in the same proportion and thus the EROEI estimated by the different authors will not be conserved. This is so because, for instance, if it is estimated that a typical mill needs 22,000 ha, supposing a productivity of 80 tonnes/ha [15], then it is probable that with a smaller productivity, the average mill will really need more hectares and the kilometres covered by trucks from the processing plant to the fields where the crop grows, cover, on average, greater distances. Other energy inputs such as those due to the use of fertilizers, insecticides, etc., which have been used on crops that, in the end, turn out to have a low productivity, or none at all, must also be taken into account for the regional EROEI calculations. Thus, for instance, Ramírez-Triana [7] aims to compare and standardise the results of four studies of the EROEI of ethanol from Brazil [35,36,41,41]. To do so, he supposes a productivity of 77 tonnes/Ha (6314 L/Ha). With this theoretical productivity he reduces or increases the outputs calculated by the different authors, while leaving the calculated inputs unchanged. He thus reaches EROEIs of 2.63 (Pimentel & Patzek [35]), 3.19 (Oliveira et al. [41]), 7.52 (Macedo et al. [36]) and 8.84 (Boddey et al. [42]). Following the same methodology and standardisation as in Ramírez-Triana [7], but with the productivity we have calculated here, we would obtain lower values for all of them. The dispersion in the EROEI calculations is so high (see Table 2), it is clear that, at least in part, the calculations are biased. There are frequently mutual accusations concerning the overestimation or underestimation of some inputs. Thus, for example, Giampietro et al. [49] argue that the boundaries of the inputs should be widened to include the energy due to the human work effort, while others (Macedo et al. [36]) suggest that this should not be considered because such persons would necessarily consume the same energy in another place. At the same time, the latter authors argue that all the by-products of the manufacture of the biofuels should be considered as outputs (for instance the glycerine manufactured), whether they are used to produce energy or not. However, the former authors have the exact opposite belief, reasoning that energy is not obtained with these byproducts and that, in fact, their manufacture, use and disposal consumes energy. As things stand, if the energy of the by-products is included, the energy associated with the human work effort in the case of biofuels should also be included, since it is probable that this work effort is much higher than that required by other sources of energy. Another by-product not usually considered in the studies of the EROEI and which, in our judgement, is correctly estimated in Dong et al. [46], consists of quantifying the soil loss of the crops and, from that, the energy content of the organic material which “disappears” with it. For the case of wheat in China, the calculation of Dong et al. [46] gives 7.83 GJ/Ha$yr. A similar approximate calculation for the bioethanol of Brazil can be estimated by taking into account the fact that about 12 tonnes of soil per hectare/year are lost [50] (chapter 7); that, with an average organic material content of 5% (Bianchi [51] gives values of 0.72e18.9% as extremes, and an average of 7.7% for soils of Parana State) and taking an energy content of 20MJ/kgC (Dong [46] takes 22.6 MJ/kg of organic matter), this would suppose about 12GJ/Ha-yr of losses, which would be around 10% of the energy content of the ethanol produced. Pereira & Ortega [43] give 1.82 kg of topsoil eroded per litre of ethanol, which would give 1.82 GJ/L or a loss of 8% of the energy content of the ethanol itself. If this energy loss were included in the studies of the EROEI cited by Ramírez-Triana [7] and with his standardisation, the highest EROEI (that of Boddey et al. [42] (8.84)) would come out at 5.1. Further, if we take into account the fact that the real productivity is lower than that taken by Ramírez-Triana [7]; the result would be even smaller. It would seem reasonable to believe that, with these criteria, the highest estimates of the EROEIs for the ethanol of Brazil could be overestimated. Furthermore, if we also take into account the fact that the Brazilian EROEI for ethanol is probably higher than that of other countries such as India (with a lower productivity), our estimate in Table 2 could also seem optimistic. In this estimation, we should also take into account the differences due to calculations of the EROEI based on bottom-up Table 2 Some reported EROEIs and the EROEIs used in this work for biofuels. Source Reported EROEIs or output energy/Input energy This work (optimistica) Sugarcane ethanol Brazil Corn ethanol U.S. Giampietro & Mayumi [10]: 1.5, Oliveira et al. [41]: 3.7, Pereira & Ortega [43]: 7.2, Macedo et al. [36]: 8.9, Triana [7]: 2.63e8.84 5 Pimentel & Patzek [35]: 1.12, Pimentel et al. [44]: 0.69, Oliveira et al. [41]: 1.1, Hill et al. [23]: 1.25, Worldwatch Institute [15]: 1.34e1.38, Wang [45]: 0.7e1.5 Dong et al. [46]: 0.18e1.48 (China), Giampietro & Mayumi [10]: 1.1 (Italy), Dong et al. [46]: 1.19 (Italy) 1.25 Pimentel et al. [44]: 0.92, Pleanjai & Gheewala [47]: 2.42e3.58, Yee et al. [48]: 3.54 Yee et al. [48]: 1.44 Hill et al. [23]: 1.93, Worldwatch Institute [15]: 1.43e3.4 Dong et al. [46]: 1.09 3 1.5 Corn ethanol (other countries) Palm oil Rapeseed oil Soybean Wheat China a The EROEI shown here is not a calculation but a rough estimate based on the analysis of the reported EROEIs that are considered optimistic (see text) and which will be used as global world average values for four sources: sugarcane, corn, palm oil and other sources. 509 C. de Castro et al. / Energy 64 (2014) 506e512 methodologies with respect to the results that could be given by top-down methodologies. Normally, in bottom-up EROEI calculations, statistics are gathered from studies of concrete cases which are extrapolated to the country or region and type of crop being dealt with. The methodology proposed here would try to make the estimation on the basis of global or regional data. In this way, if we look, for instance, at the “Brazilian Energy Balance” provided by Brazil’s own Ministry of Mines and Energy [52] (page 46, 2011), we can see that the production of crushed sugarcane in 2010 was 160.33 Mtm, and whose power content, taking that obtained per metric tonne according to Beharry [53], would be around 39 million metric tonnes oil equivalent or 1.64 EJ. However, the official data show that the energy used to produce that crushed sugarcane is equivalent to 13.2 million metric tonnes oil equivalent (page 68) (0.55 EJ). This gives an EROEI for the crushed sugarcane of around 3. In 2010, 50.5% of the production of crushed sugarcane was dedicated to ethanol production and the rest to sugar. In Brazil, it is normal for the production of sugar and ethanol to be carried out in the same factories, so it is, in practice, very difficult to calculate how much energy is used in each process. If we suppose that the energy needed to produce ethanol and sugar is similar, then the EROEI factor of the crushed sugarcane for ethanol of 3 would be conserved. This means that the EROEI of a derivative of the crushed sugarcane (whether ethanol or sugar) must be lower, even though a part of the crushed sugarcane is used as an energy source to produce ethanol. From these figures, it can be concluded that, either the Brazilian ethanol has an EROEI <3 or that the data provided by the said report are not correct. In the first case, it would point in the direction mentioned in this work, that our calculations and estimations are still optimistic. On the other hand, it is important to take into account the fact that the productivity of Brazil or the USA for ethanol depends mainly on the importation of fossil fuels from around the world associated with the growing quantities of fertilizers per hectare [54]. Thus, it would be difficult to achieve such productivity for the world as a whole, given the nature of the Earth’s closed system. So this increasing fertilization, which depends on the rest of the world, tries to compensate precisely the ever increasing loss of nutrients that occurs with the working of the land and the harvesting. Neither could this be compensated for by the majority of the world’s arable land being given over to the production of ethanol. 2.3. Power density of biofuels We shall now look at the real power density of biofuels. A good top-down approach taking socio-ecological and real biomass energy yields is what Campbell et al. [55] did. These authors argued that “the area with the greatest potential. that reduces net warming and avoids competition with food production is abandoned/degraded lands”. From this premise, Campbell et al. [55] calculate that the extractable gross heat content in 386 MHa would be 27 EJ/yr (0.86 TW) or 0.222 Wheat/m2 (Wheat is calorific power). Although they suggest that there are less than 386 MHa available for cultivating biofuels, they do not take into account the land that is now used as pasture and which the expected increase in degradation, population and consumption will involve their use for other ends. Obviously, if this energy were used to produce electricity with 10e 30% efficiency in the conversion [53] or biofuels with an efficiency of approximately 15% (see below), there would be less than 10 EJ of useful energy in the form of electricity, or less than 5 EJ in the form of biofuels (the latter with a very low power density of <0.033 W/ m2 of occupied land). Of course, highly productive land is currently being used to produce biofuels, which is why the power density could be greater (but also unsustainable, according to Campbell et al. [55] own criteria). In this sense, Table 3 calculates the power densities that can be inferred from the work of other authors and they are compared with what is offered in this article. For the case of the highly developed bioethanol of Brazil, and taking into account the fact that 767 GJ/Ha$yr (2.43 Wheat/m2) is the power content of 75 Tn/Ha of sugarcane [53], the result is that the power content of the ethanol with respect to the power content of the sugarcane is approximately 15% (calculations from Table 1), and even less if we take into account the energy losses during manufacture. Thus, a top-down calculation of the power density for biofuels should begin with an estimation of the real area used for the world production of biofuels. However, the available estimates of the power density are based on a hypothetical mean productivity of a particular region or country; in general, they are based on concrete cases that use the bottom-up methodology. As argued above, there are doubts about this methodology and, therefore, the estimates of global land occupation for biofuels are normally underestimated. For instance, UNEP [56] estimates that 36 MHa were dedicated to fuel crops in 2008 and points out that 64.5$109 L of ethanol and 11.8$109 L of biodiesel were produced. Multiplying by their respective energy contents (see note on Table 3), a gross energy of 1.75 EJ or 0.155 W/m2 was obtained for that year. In reality, the power density is less as more than 36 MHa were surely used to produce that much, as indicated by our top-down calculations for Brazil. The methodology used by the UNEP [56] is bottom-up and has been shown to be conservative, as the UNEP itself recognises in the light of the work by Johnston et al. [16]: “some of the land requirement data. may represent rather conservative estimates” (UNEP [56], page 63). In addition, the net energy is inferior to the gross energy due to the EROEI ratios. Taking into account the optimistic EROEIs estimated in Table 2, the biofuel production of Table 3 Power densities calculated from the productivities reported or inferred by different authors. Source of biofuel Inferred gross power densities (W/m2) Gross power densities: This work (W/m2) Sugarcane 0.47 (Macedo et al. [50], Brazil), 0.37 (Worldwatch Institute [15], India), 0.35 (Johnston et al. [16], World mean) 0.264 (Balat and Balat [18], US) 0.141 (Balat and Balat [18], China), 0.12 (Johnston et al. [16], World mean) 0.627 (Worldwatch [15], Malasya), 0.29 (Johnston [16], World mean) 0.06 (Hill [23]), 0.073 (Worldwatch [15], Europe), 0.043 (Johnston et al. [16], World mean) 0.155 (UNEP [56]) 0.30e0.36 (Brazil) Corn ethanol Palm oil Soybean Global biofuelsa Note: the light heat content is: 22.1 MJ/L for ethanol and 33 MJ/L for biodiesel. For our Brazil calculations, we take the range 4300e5100 L/Ha estimated before. a See text for the calculation. <0.15 510 C. de Castro et al. / Energy 64 (2014) 506e512 the different world regions provided by the UNEP [47] (reflected in Table 4) and the net energy production of an occupied surface area greater than 36 MHa; then we reach a global net power density of biofuels lower than 0.073 W/m2, which could be bigger than the estimation for abandoned land using Campbell’s [55] criteria (0.033 W/m2), but which confirms roughly the estimations of Giampietro and Mayumi [10] (<0.05 W/m2). Thus, the net 0.073 W/m2 is an optimistic calculation, because we have used an optimistic value for the gross power density (see Table 3) and because we have used the high spectrum for the calculated EROEIs. In comparison, from Prieto and Hall [33] data we could calculate the electric power density produced at approximately 4.8 We/m2 in the Spanish photovoltaic parks, that with a EROEI of 2.41, gives a net energy density of 2.8 W/m2, roughly 40 times bigger. 2.4. Ecological Footprint of biofuels The inverse of the power density is related to the land requirements for delivering this power. This idea allows us to directly approach the concept of the Ecological Footprint [57], which has a great political relevance as it is often used by policymakers [58]. In the case of fossil fuels, the Ecological Footprint (EF) calculates the associated emissions of CO2, subtracts a percentage which is absorbed by the oceans, and estimates the woodland surface area needed to absorb this CO2 from the atmosphere. The result is given in “global” hectares (gHa) per capita, which, for 2008, was 1.47 gHa/ per capita [59]. In the case of biofuels, Oliveira et al. [41], compare the footprint of gasoline with other biofuels in concrete cases. In this sense, and for what interests us now, we can calculate this Ecological Footprint (EF) per unit of power potential (the inverse of the calculated power density) on a global scale. The approximate result is shown in Table 5. In the case of biofuels, it is also necessary to add to the calculated EF both the emissions associated with the fossil fuels used in their manufacture and those associated with the change of use in the land and its erosion, so our result is clearly conservative. For the renewable energies, the EF per watt generated can also be calculated in this way, considering the inverse of its power density and applying the factor that, according to the methodology of the EF, is assigned to each type of land or surface area in order to convert it to gHa (see Table 5). In the light of this impact indicator, it can be seen that the biofuels would be surprisingly much worse than even the fossil fuels. 3. Discussion and conclusions The following table (Table 6) shows a summary of the results found in this work: It is clear from these figures that, in general, biofuels should not be considered by policymakers as a source of renewable energy, since the energy flow and materials needed for their production include not only the land but also soil loss and a considerable Table 5 Land needs per watt produced and Ecological Footprint per generated watt and per capita to replace 12 TW of fossil fuels. Source (w12 TW) m2/W gm2/W gHa/Per capita Fossils Biofuels Solar electricity Wind 6.5 (forest) >6.7 (cropland) 0.21 (cropland) 0e2 (cropland) 8.2 >16.3 <0.49 0e5 1.47 >2.87 <0.09 0e0.9 Note: In the case of the fossil fuels, we use the necessary woodlands and “global” m2 (gm2) to absorb the emissions associated with its use in accordance with the methodology of Wackernagel and Rees [57]. For the biofuels, the land needs are calculated as the inverse of the power density calculated in this work (<0.15 W/m2). For photovoltaic power, we use 4.8 We/m2 (see text); the table shows the worst of the cases: installing parks in areas of cropland, and for wind power, we use 0.5 W/ m2 with calculations based on data from de Castro et al. [30] and Smil [60]. In the case of wind power, the occupation of the necessary infrastructures is much smaller if we consider that the wind farms allow a double use and that they are compatible, for instance, with cropland and pastureland. In the case of photovoltaic power, if we use the net power (2.8 W/m2) we will arrive to 0.156 gHa/per capita, but at present, the associated fossil emissions required from the photovoltaic industry [42] could reach the Ecological Footprint to roughly 1/3 of fossil fuels instead of roughly 1/10 in croplands. Ecological Footprint in a finite, non-renewable planet, in addition to the use of fossil fuels (especially when the EROEI is low). The use of biofuels on a global scale over the last decade makes neither ecological nor sustainable sense. Among the arguments that lead us to this conclusion are the following: i) their reduced net power density; ii) the corresponding high Ecological Footprint; iii) the percentage of GHG (Greenhouse gases) in comparison with petroleum, which is possibly higher than the emissions of this fossil fuel in terms of net energy (low EROEI) when the emissions of N2O are also taken into account [4]; and iv) the negative balance of use and land conversion in the real world [2,3,24,61]. In addition to these disadvantages which have been described in the paper, it is important to mention other problems that have been pointed out by other research works. These reasons are supported by the data given in this work. However, in addition to these disadvantages, it may also be necessary to add other, equally important ones which arise with the large-scale proliferation of biofuels. Among these are, for instance: i) the change in use of agricultural land dedicated to feeding humans or animals and the directly [24] or indirectly associated deforestation (the displaced crops are transferred to forested areas [2]) in a world with severe problems of hunger, deforestation and loss of biodiversity [22,28,29,62e64]; ii) the high water use needed for its cultivation and industrial processing in a world with ever greater water shortages [25,65]; and iii) the loss of organic material from the soils due to the erosive processes associated with the energy crops in a context where reversing the erosion process is urgent [22,43,46]. The above considerations, unfortunately, convert the generalisation of biofuels into a worrying example of an unsustainable energy source with negative socioeconomic implications [20]. When considering the question of energy it is, in general, better to be prudent and not confuse the concrete technological potential Table 4 Production of biofuels by region in 2008. Region/fuel Gross production (litres) EROEI Net energy (EJ) US ethanol Brazil and others ethanol UE biodiesel Others biodiesel 38$109 26.5$109 5.9$109 5.9$109 1.25 5 1.5 3 0.168 0.469 0.065 0.130 Note: For production see UNEP [56]. For the EROEI see Table 2. Net energy is calculated as: net energy ¼ gross energy$(EROEI-1)/EROEI. The assigned energy is 22.1 MJ/L for ethanol and 33 MJ/L for biodiesel. The global EROEI gives 1.85. Table 6 Brazil ethanol and World biofuels yield, EROEI, density power and EF. Brazil ethanol World biofuels Real yield (L/Ha) EROEI Gross density (W/m2) EF (gHa/MW) 4300e5100 <2120 <5 <1.85 0.3e0.36 <0.15 >680e815 >1630 Note: Real yield is the litres obtained per hectare really used to produce the biofuel. The Ecological Footprint is given in global hectares needed to produce a continuous power of one MW (compare this with the 820 gHa/MW calculated for fossil fuels). C. de Castro et al. / Energy 64 (2014) 506e512 with its translation to reality on a global scale. Normally, it is not enough simply to visualise our dilemma or trilemma [26], imagining that the technology could be viable if it were used in the best cases and discarded in the worst. The calculations and estimations carried out here are very far from the best cases because the real world is much more complex and subject to internal (e.g. average practices) and external (e.g. climate change) inefficiencies, as well as to scientific, economic and political interests [10] which, unfortunately, are capable of promoting solutions which are far from being good practices. To summarise, the above pages have clearly shown the “technical” limitations of the potential production of biofuels. It is a question of limitations that also affect other renewable energy sources [30,33,32]. As biofuels on a large scale are highly dependent on fossil fuels, the increase in their production would have an effect (for example, in CO2 emissions) at most equivalent to a more efficient use of petroleum. In this latter case, it would mean a lower impact on land use. The scenario considered in this article is BAU (business as usual), and therefore, modifications concerning changes in the social organisation of consumption and production have not been incorporated. Nevertheless, although changes in dietary patterns, for instance, have not been considered here, such changes have been shown to have a very relevant influence on the energy potentials of biomass [61], perhaps even more so than the expansion of the yields due to technological improvements. Although these questions also depend on the technical side, in the light of the latter’s limitations, the effectiveness of the socioeconomic changes and the incentives for advancing towards a more environmentally sustainable and socially just model should not be ignored. Given the current circumstances, and taking into account criteria of social equality and ecological sustainability, it would be convenient if, on a global scale, the technology associated with biofuels were subject, at the very least, to a moratoria, putting a stop to its expansion and even, perhaps, tending to a progressive decrease in its use. It is to be hoped that this conclusion concerning environmental policy, reached through the information and analysis given here and the arguments set out with their referenced works, should not result in misinterpretations of the said conclusion. Author contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Acknowledgement This work has been developed within the project CGL200914268 funded by Spanish Ministry of Science and Innovation (MICINN). Óscar Carpintero would also like to thank financial support from the Spanish Ministry of Science and Innovation (Project ENE2010-19834, Project CSO2010-21979, and Project HAR2010-18544). Annex. Ethanol productivity calculations for Brazil To calculate the performance of the ethanol, we carried out a reversible process starting from the contents of the TRS of the sugarcane. We did so in the following way: 1. Using the MAPA [38] data and the proportion of mean TRS contained in the annual harvest of crushed sugarcane, and the needs of the TRS per litre of ethanol (anhydrous and hydrated) 511 provided by MAPA (1 L of anhydrous ¼ 1.812 kg of TRS; 1 L of hydrated ¼ 1.7412 kg of TRS), we have calculated the quantity of TRS needed to obtain the ethanol produced in that year. 2. 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