Firstly, the limited number of impact potentials that were quantified shows that the field of LCA has seen a considerable methodological development in the last 15 years. Secondly, the lack of data that led to the ozone depletion potential being discussed qualitatively in the study illustrates the data availability issue that LCA practitioners traditionally have to deal with. For example, the authors of the study described the locations where cooling equipment was used, as well as the LCA of Food and Agriculture


refrigerants used, but could not quantify the resulting environmental impacts because data about refrigerant leaking was not available at that time. 29.3.1.3 Bread One of the first studies of a whole food product was an LCA of bread production carried out by Andersson and Ohlsson (1999). The main aim of this study was to compare different scales of baking. A secondary goal was to identify environmental hotspots. This study was selected for this chapter to illustrate some typical pitfalls of LCA application, which often arise due to the complexity of the modelled systems in real life, data unavailability and lack of transparency of the modelling choices made. In cooperation with industrial partners from the bakery industry that provided data, the authors compared the environmental impacts of 1 kg of white bread ready for consumption at home produced in four different Swedish scenarios. Two of these were industrial bakeries

Hauschild et al., LCA: Theory and Practice


Two of these were industrial bakeries. The first produced bread distributed through the entire country, the second operated at a regional scale, the third a small local bakery, whilst the fourth corresponded to the baking of bread at home. Included in the system boundaries were the farm inputs, wheat cultivation, wheat milling and baking, production of packaging material, the household stage (freezing the bread), waste handling and transport. Capital goods were excluded and so were all other ingredients of the bread other than wheat. These ingredients were excluded because little variation was observed between the recipes. Unless the impacts of these ingredients were minor, which has not been tested, excluding them makes it hard to conclude on the environmental hotspots: an unknown part of the impacts are not quantified. The wholesale and retail steps were also excluded because they were expected to contribute little to the overall impacts


The wholesale and retail steps were also excluded because they were expected to contribute little to the overall impacts. Impacts from wheat cultivation and milling were allocated to wheat and flour, respectively. Allocation of the impacts of the bakeries was done differently. For the two industrial bakeries, allocation was done on basis of the mass of the products produced there. In contrast, economic allocation was used in the local bakery scenario. This option was chosen because data for the masses produced were not available. The impact categories and indicators included in the study were energy use, land use, global warming potential, eutrophication potential, acidification potential and photo-oxidant formation potential. No normalisation or weighting step was done. The results are presented in Table 29.5. We will not discuss the details of the results here, but summarise the main findings of the hotspot analysis


No normalisation or weighting step was done. The results are presented in Table 29.5. We will not discuss the details of the results here, but summarise the main findings of the hotspot analysis. A hotspot was defined as a sub-system that contributes more than 20% to the impact in a given impact category or flow indicator. With the exception of primary energy use and photo-oxidant formation, the farm inputs and agriculture stages were hotspots in all of the impact categories for all scenarios. Eutrophication impacts were dominated by wheat cultivation. Transport was a large contributor to global warming and acidification potentials in the industrial bakery scenarios. In the bakeries using T.J. Dijkman et al.


natural gas or oil in their ovens, the baking process also was a hotspot for the global warming impacts. Food processing, i.e. ethanol released when baking bread, was the main hotspot for the photo-oxidant formation impacts, even though transport was also a hotspot in this impact category. Summarising, we see that in contrast to what was observed in the previous chapters, the agricultural stage is not the single dominating stage in this case study. The main aim of the authors was to compare different scales of bread production. It is, however, doubtful how useful the results were to study differences of scale, because of a number of methodological inconsistencies that a practitioner should try to avoid. First, the system boundaries were set in a way to include the consumers’ transport to buy the bread, or the raw materials to bake the bread at home, except in the local bakery scenario. The authors claim that the local bakery is visited on foot or by car on the way home


The authors claim that the local bakery is visited on foot or by car on the way home. As the transport by car hence does not add extra kilometres, the impacts were not accounted for, while the impacts due to the transportation could also have been allocated to both the travel back from work and the bread production. It is not clear from the study why this would not be the case when purchasing industrially produced bread at supermarket. Lack of transparency in modelling choices makes it difficult to identify if the systems compared are equal. Second, the allocation procedure is not done consistently: the impacts from the industrial bakery were allocated by mass, those from the local bakery economically. The authors justify the approach by mentioning that the fractions of impacts allocated to the bread were found to be similar for the industrial bakeries on one hand and the local bakery on the other hand


However, this does not validate the approach: the similarity in outcomes may be accidental. A factor that further complicates the interpretation of the comparison relates to the differences between bakeries. One of the industrial bakeries used a natural gas oven while the local bakery had an oil-fuelled oven. In the second industrial bakery and the home baking scenarios, electric ovens were used. It is not clear from the Table 29.5 Summary of the results of the LCA of bread production Studied systems Primary energy (MJ) Global warming (kg CO2-eq) AcidificaƟon (mol H+) EutrophicaƟon (g O2) Photochem. oxidant formaƟon (g C2H4-eq) Industrial bakery 1 (naƟonal) 0.15 5.4 Industrial bakery 2 (regional) 0.1 3.2 Local bakery 0.1 2.6 Home baking (heat from electricity) 0.078 2.4 Home baking (heat from oil) 0.09 2.6 Functional unit 1 kg of white bread ready for consumption at home


Electricity production from waste incineration is assumed to replace electricity produced from oil combustion (Andersson and Ohlsson 1999) LCA of Food and Agriculture


study whether or not the use of different fuels for the oven was related to the different scales of the bakeries. If not, this would mean that the systems compared were not equivalent regarding the goal set, i.e. compare different scales of baking. However, if the aim of the study had been to compare most common practices of baking at a given scale, the same definition of systems would have been fair to answer the question aimed. Summarising, when conducting an LCA it is important to act consistently and to design a model of a product system that suits the aim of the study. 29.3.1.4 Tomato As an example of an LCA of a vegetable product, we will discuss here the study on tomatoes produced in a greenhouse in Spain, carried out by Torrellas et al. (2012a). The study was carried out to investigate how the environmental performance of greenhouse tomatoes could be improved


(2012a). The study was carried out to investigate how the environmental performance of greenhouse tomatoes could be improved. The functional unit of the study was 1 tonne of loose tomatoes at the farm gate, indicating that this study is a cradle-to-farm gate study. Included in the system boundaries were the manufacturing of greenhouse components, auxiliary equipment such as the irrigation and water collection systems and the substrates in which tomato plants are grown, products needed for greenhouse management such as fertilisers, pesticides, water and electricity, waste handling including transport to waste management from the site of the greenhouse. The environmental impacts were calculated for six categories and flow indicators: cumulative energy demand, abiotic depletion, acidification, eutrophication, global warming and photochemical oxidation. If we use the same definition of a hotspot here as in the last paragraph, i.e


If we use the same definition of a hotspot here as in the last paragraph, i.e. a process or group of processes to which more than 20% of an impact can be attributed, then the results (see Fig. 29.2) showed that the structure of the greenhouse was a hotspot in all of the categories. The steel in the frame and the plastic of the cover were the main contributors. Auxiliary equipment represented a hotspot in all impact categories apart from eutrophication. For acidification, eutrophication and global warming, fertiliser production and application constituted a hotspot as well. A large part of the environmental impacts was associated with the greenhouse itself: for the impact categories abiotic depletion, acidification, photochemical oxidation and cumulative energy demand the sum of greenhouse structure and auxiliary equipment accounted for more than 75% of the total impacts. For eutrophication and global warming the percentages were 46 and 66%, respectively


For eutrophication and global warming the percentages were 46 and 66%, respectively. Therefore, the impacts of the greenhouse were relatively high. Because the studied greenhouse was located in Spain, no heating was needed. Another study that assessed different geographical greenhouse tomato scenarios showed that the most important contributor was the greenhouse heating, highlighting the need to reduce energy consumption and use renewable energy sources where greenhouse heating is necessitated (Torrellas et al. 2012b). T.J. Dijkman et al.


Since the study by Torrellas et al. (2012a) was a cradle-to-farm gate LCA, the role of transport in the life cycle of vegetables and fruit is not highlighted. Other work, for example the aforementioned study by Sim et al. (2007) on French beans flown from Kenya to England, showed that transportation may be determining for the environmental impacts of vegetables and fruit. The same was concluded by Mithraratne et al. (2010), who analysed the carbon footprint of a tray of kiwifruit produced in New Zealand and consumed in Europe. Here, shipping contributed to 44% of the footprint. 29.3.1.5 A Full Diet In the previous chapters various food items were discussed in isolation, with functional units of 1 kg (or 1 tonne) of the food item. Although this gives a good overview of the impacts of single food products, it provides little information about where environmental impacts arise in a diet. After all, humans usually do not consume identical amounts of different foods


After all, humans usually do not consume identical amounts of different foods. Therefore, we finish this chapter by reviewing a study by Muñoz et al. (2010), who did a full LCA of an average Spanish diet. The functional unit of their study was given as ‘the supply of food for a Spanish citizen in the year 2005’. The authors included all processes needed to provide food to the consumer in the study: agriculture stage, processing of food, distribution, retailing, storage at home and preparation. Furthermore, the end of life of food was considered via food waste management and wastewater treatment of human excretion. Impact assessment was limited to global warming, acidification, eutrophication and primary energy use. The results, presented in Fig. 29.3, showed that food production, including agricultural and processing stages, represented the largest source of impacts in all categories


The results, presented in Fig. 29.3, showed that food production, including agricultural and processing stages, represented the largest source of impacts in all categories. Meat and dairy alone made up 54% of the global 75% 100% 50% waste 0% 25% GW CED AD AA EU PO pesƟcides ferƟlizers auxiliary equipment climate system structure Fig. 29.2 Results of the LCA of the production of 1 tonne cold greenhouse tomatoes. Adapted from Torrellas et al. (2012a) LCA of Food and Agriculture


warming potential. Wastewater treatment contributed 17% of the impact potential when including emission of biogenic CO2 while it was only 3% when excluding these emissions. These CO2 emissions can be excluded from the impact assessment, as they are formed from carbon that was taken up by the plant during its growth. Meat, dairy and beverages represented 60% of the eutrophication potential. Here, wastewater treatment was the second most important stage, contributing 17% to the total impact. Home storage and cooking, which was considered as one process, was the second contributor to the acidification potential, with a contribution of 12%. This process was also the second largest user of primary energy (22%). With regard to the percentages above, the authors mentioned that there was a fair level of uncertainty involved in the study. As an example, for some food products data were missing


With regard to the percentages above, the authors mentioned that there was a fair level of uncertainty involved in the study. As an example, for some food products data were missing. For other products the data used were collected for Danish rather than Spanish circumstances, following a consequential approach, instead of the attributional approach used by the authors. From the results it is clear that for a full diet, the agricultural stage and the production its inputs are the stages contributing most to the impacts. Among the different products, meat production was associated with the largest impacts in all of the four impact categories studied. Dairy and beverages were other considerable contributors to environmental impacts. The authors of this study did not discuss options to reduce the environmental impacts of diets. Other authors recommend exploring a reduction of food waste and Fig. 29.3 results for the LCA study of the average Spanish diet. Taken from Muñoz et al


Other authors recommend exploring a reduction of food waste and Fig. 29.3 results for the LCA study of the average Spanish diet. Taken from Muñoz et al. (2010) T.J. Dijkman et al.


switching to a (partly) vegetarian diet. In the study of the Spanish diet, the authors found that 23% of food purchased was discarded. This waste consisted of a smaller fraction of inedible parts of the food products, but the largest waste fraction were edible parts not consumed for one reason or another. Regarding switching to a diet containing less meat, Saxe and Jensen (2014) compared the environmental consequences of switching from an average Danish diet (ADD) to a New Nordic Diet (NND). In the NND, focus is on local ingredients, produced organically. In addition, the diet contains less meat, but more fish, wholegrain products, nuts, fruits, berries and vegetables than the average Danish diet. The NND was shown to reduce environmental impacts, but at the expense of increased cost for the consumer


The NND was shown to reduce environmental impacts, but at the expense of increased cost for the consumer. 29.3.2 Case Study: Conventional, Integrated and Organic Farming Most agricultural practices in Western countries are chemically intensive, aiming at maximising the production by using external inputs. This production mode is associated with environmental problems mostly due to nutrients and pesticides emissions to environmental compartments and decreased near farms. In order to reduce the environmental burdens of agriculture, new production methods have been introduced. Organic agriculture aims at producing while sustaining the health of soils and people and preserving biodiversity. In practice, this form of agriculture differs from conventional agriculture in the sense that it avoids the use of synthetic-agrochemical pesticides and mainly uses manure and compost as fertiliser


In practice, this form of agriculture differs from conventional agriculture in the sense that it avoids the use of synthetic-agrochemical pesticides and mainly uses manure and compost as fertiliser. In organic milk farming, as discussed below, cows spend most of their time outside in order to stimulate their natural behaviour. Feed should consist of 60% of roughage, produced organically, preferentially on-farm. Another alternative is integrated farming. Based on the principles of integrated pest management, it aims to achieve optimal long-term results from both an environmental and economic point of view. Furthermore, pesticide application has to be targeted and limited, the soil has to be protected in winter and the crop rotation needs to be diversified. In this chapter, two comparative cases are discussed: organic and integrated farming in Switzerland, and organic and conventional milk production in the Netherlands. 29.3.2.1 Crop Production Nemecek et al


29.3.2.1 Crop Production Nemecek et al. (2011) compared the environmental impacts of conventional/ integrated and organic farming in Switzerland. Environmental impacts were reported as yearly averages calculated from 7-year crop rotations. The authors LCA of Food and Agriculture


identified three functions of agriculture, and defined functional units accordingly: land management, providing income to the farmer and production of food. The focus in this chapter will be on the first and third functions. In the land management function, the impacts are expressed per hectare per year. In the food production function, they are reported per kg dry matter or per MJ net energy content, depending on the food or feed product. The environmental impacts were assessed using the Swiss Agriculture Life Cycle Assessment (SALCA) framework. In this framework, the usual impact categories and flow indicators energy resources, global warming potential, ozone formation, eutrophication, acidification, terrestrial and aquatic ecotoxicity and human toxicity are considered along with biodiversity and soil quality. The results found for normal fertilisation levels are shown in Fig. 29.4. The results for biodiversity, not given in Fig


The results found for normal fertilisation levels are shown in Fig. 29.4. The results for biodiversity, not given in Fig. 29.4, showed that biodiversity was higher in an organic farming system, mainly as a consequence of banning synthetic pesticides. The soil quality indicators did not vary much between the three systems. The results presented in Fig. 29.4 show that, when looking at the land management function, the potential impacts of organic farming were considerably less than those caused by conventional/integrated farming. However, the land required to produce the same amount of product was 25–30% higher for organic farming. For that reason, the differences between organic and conventional/integrated farming were smaller when looking at the results from the production function. Still, the authors showed a significantly lower impact for organic farming for most of the impact categories when looking at the impacts per kg dry matter, especially for toxicity impacts


Still, the authors showed a significantly lower impact for organic farming for most of the impact categories when looking at the impacts per kg dry matter, especially for toxicity impacts. The exceptions here were land use, which was higher for organic farming, and ozone formation, acidification and eutrophication, where the differences were not statistically significant. The large difference in toxicity-related impact categories was explained by the reduced use of pesticides in the organic farming systems. Not taken into account in the method applied was the use of copper as a fungicide in organic farming. Therefore, the toxicity impacts of organic farming have been underestimated. The reduction in energy demand and global warming potential in the organic farming systems was due to reduced use of mineral fertiliser in this type of farming


The reduction in energy demand and global warming potential in the organic farming systems was due to reduced use of mineral fertiliser in this type of farming. et 100) Land management funcƟon (impacts per ha per year) Food producƟon funcƟon: impacts per kg dry maƩer aƟve (C/I set to 100 RelaƟve % BD BO C/I Fig. 29.4 Relative environmental impacts for crop rotations of three types of farming in Switzerland. Impacts for conventional/integrated farming set to 100% (reference system). Abbreviations BD bio-dynamic, BO bio-organic, C/I conventional/integrated T.J. Dijkman et al.


In addition to calculating the impacts associated with crop rotations, the authors also looked at the impacts of individual crops in organic and integrated crop production. Here it was found that some organic products had higher environmental impacts in some categories than their conventional/integrated counterparts. This was partially explained by lower yields in organic farming. Based on this the authors stressed the importance of looking at product systems instead of looking at products in isolation. Summarising, depending on the selected function, the outcome is either that organic farming is the environmentally favourable option, or that the results are not conclusive to decide on the most environmentally friendly option for agriculture. 29.3.2.2 Milk Production Based on Dutch data from 2003, Thomassen et al. (2008) performed a comparative LCA of conventional and organic milk production


29.3.2.2 Milk Production Based on Dutch data from 2003, Thomassen et al. (2008) performed a comparative LCA of conventional and organic milk production. This cradle-to-farm gate study used 1 kg of fat and protein corrected milk leaving the farm gate as the functional unit. The studied system included the on-farm processes and the inputs required on the farm: breeding of animals, production of feed concentrates, roughage and bedding material, transport of animal manure used as fertiliser. In addition, the conventional milk production also required pesticides and artificial fertilisers for feed and roughage production. The studied systems had a number of multifunctional processes, most notably the cow which produced not only milk, but also meat, calves and hides. In order to deal with these co-products, economic allocation was applied. The study considered five impact categories and flow indicators: land use, energy use, climate change, acidification and eutrophication


The study considered five impact categories and flow indicators: land use, energy use, climate change, acidification and eutrophication. Stratospheric ozone depletion was excluded because previous studies had shown that milk production does not produce significant ozone depletion impacts. Furthermore, human toxicity, terrestrial and aquatic ecotoxicity were excluded. The reason for this was the absence of data on pesticides and heavy metals used. The impacts obtained are summarised in Table 29.6. The difference in land use observed in Table 29.6 was mainly explained by the lower yields for feed production and a lower intensity of animals per hectare in the organic system. The difference in energy use is related to the absence of fertiliser and pesticide production in the organic system and the smaller use of concentrate in the feed mix


The difference in energy use is related to the absence of fertiliser and pesticide production in the organic system and the smaller use of concentrate in the feed mix. Likewise, the production and use of concentrates and artificial fertiliser explained the higher eutrophication impacts in the conventional farming system. The small difference in acidification impacts may seem counterintuitive: the conventional system emitted more ammonia from manure storage and application, and fertiliser application per hectare. However, the animal intensity was much higher in the conventional system, leading to lower overall impacts when looking at mass of milk produced. The climate change impacts were similar for both systems. The organic system had higher on-farm impacts because of the higher number of LCA of Food and Agriculture


cows needed to produce the same volume of milk. In contrast, the off-farm emissions were higher in the conventional system, mainly due to the purchase of feed concentrates. Concentrates are feed products that contain a high density of digestible nutrients and are usually low in fibre content (FAO 1995). This case shows the importance of the perspective of the study, as reflected in the functional unit, on the outcome of the study. For example, taking the perspective of a farmer with a fixed land area at his disposal who wants to produce milk in a more sustainable way, switching from conventional to organic farming would probably result in lower environmental impacts. In this case, the functional unit will be area-related (e.g. hectare per year). However, the main motivation of a farmer usually is to maximise income for the agricultural production. Looking at how to minimise environmental impacts per currency unit might be more realistic from the farmer viewpoint


Looking at how to minimise environmental impacts per currency unit might be more realistic from the farmer viewpoint. In that case, the functional unit will be income-related (e.g. €). As can be seen from Table 29.6, using the perspective of producing food, the functional unit chosen was kg of milk. The picture of which option is most favourable to the environment is not clear. In both the crop and the milk examples, results showed that neither conventional farming nor organic farming was a more environmentally favourable practice in all respects. LCA was powerful at identifying environmental hotspots and margins of improvement of the studied systems. Agriculture is multifunctional and LCA outcomes depend a lot on the agricultural function studied, especially when comparing systems with contrasted intensification levels. This conclusion is also found in studies conducted by other authors, such as Cederberg and Mattsson (2000), De Backer et al. (2009) and the review by Foster et al


This conclusion is also found in studies conducted by other authors, such as Cederberg and Mattsson (2000), De Backer et al. (2009) and the review by Foster et al. (2006). In addition, based on a review of 34 agricultural LCAs, Meier et al. (2015) argue that many LCA studies do not sufficiently capture the differences between two production systems, for a number of reasons. The goal and scope definition does not differentiate the characteristics of the farming systems. The inventory data used for N and C flows from the field are based on models, often developed for modelling conventional farming, and do not represent actual circumstances. Finally, LCA studies apply LCIA indicators of all readily available impact categories. Other important categories for agriculture, such as land use impacts on biodiversity and soil quality, water use and (terrestrial) toxicity are currently the object of fast scientific development and will be normal practice in a few years


Table 29.6 Comparison of environmental impacts of conventional and organic milk production per kg of fat and protein corrected milk leaving the farm gate (Thomassen et al. 2008) Impact category Conventional system Organic system Land use (m2) 1.3 1.8 Energy use (MJ) 5.0 3.1 Eutrophication (kg NO3 −-eq) 0.11 0.07 Acidification (g SO2-eq) 9.5 10.8 Climate change (kg CO2-eq) 1.4 1.5 T.J. Dijkman et al.


29.3.3 Case Study: ‘Local’ Food Consumers are increasingly aware of environmental problems connected to food production. Many food products are transported over long distances: air-freighted French beans from Kenya, apples from Chile or peas from Egypt can be found in European and North American supermarkets. The “food miles” concept (defined in 1994 by Paxton as the distance food travels from producer to consumer) has become popular in the UK and USA and has led to increased interest in local food, which has been produced in close proximity of where it is consumed. Apart from questionable environmental benefits, local food is also associated with other values: taste, naturalness, local economy, to mention a few examples given by Edwards-Jones et al. (2008). These authors have analysed the interest in local food and food miles from an environmental and ethical point of view. Here we will focus on the environmental aspects that have been analysed using LCA


Here we will focus on the environmental aspects that have been analysed using LCA. Transporting food products over large distances may appear as a waste of resources and an unnecessary cause of GHG emissions. Especially air transport causes high emissions per tonne kilometre. In order to have a measure of the distance over which food is transported the concept of ‘food miles’ is used. However, when looking from an LCA perspective, transport is only one of the stages in the life cycle of a food product where GHG and acidifying emissions occur. Apart from the distance, the transportation mode is of the utmost importance for the environmental impacts of the transportation phase. Moreover, for many field-grown crops the production of fertiliser has a large global warming impact potential, whilst in crops grown in greenhouses the use of electricity for heating and lighting can cause considerable impacts


Therefore, only looking at the transported distance is not sufficient to conclude on the environmental benefit of local food. To underline this, the authors give an example of an assessment of global warming potential for apples consumed in the UK, taking a life cycle perspective. Researchers from the UK had found that local production results in the lowest GHG emissions, whilst researchers from New Zealand found the opposite: apple production in New Zealand followed by transport to the UK results in the lowest global warming impacts. Therefore, there are two contrasting conclusions. Which one is right? The answer is, surprisingly, both. The two studies used different system boundaries. When looking at a full calendar year, and including the cold storage that is required to store the apples between harvest and consumption, it can be shown that apples from the UK are favourable in most parts of the year


However, when these apples have to be stored for a long time, importing freshly harvested apples from New Zealand is environmentally favourable. Similarly, it was found that, in terms of energy, it is more efficient to import off-season tomatoes from Spain to the UK, rather than growing the tomatoes locally in heated greenhouses (Smith et al. 2005). Unfortunately, in terms of water use impacts, the imported tomatoes have much greater impacts than their local counterpart (Payen et al. 2015). In addition, in the study about Danish pork meat exported to the UK, which was described in Sect. 29.3.1 the ‘food miles’ concept was criticised. The authors LCA of Food and Agriculture


pointed out that the different transport steps contribute less than 1% to the overall global warming impacts. Hence looking at reducing these impacts, transport is not the place where significant reductions can be obtained. The authors of the pork study call the concept of food miles misleading. Food-miles can be concluded to be a simple social representation of a complex system that help people engage but it is not reliable as an indicator of the environmental impacts of a product system. 29.4 Methodological Issues In the previous sections, a number of Life Cycle Assessments of food and food products has been discussed. These studies showed that in the full life cycle of a food product, it is often the farm inputs and agricultural stages where most environmental impacts arise. Within these agricultural stages, a few trends can be observed. Firstly, global warming impacts can be attributed to animal husbandry or fertiliser production


Within these agricultural stages, a few trends can be observed. Firstly, global warming impacts can be attributed to animal husbandry or fertiliser production. Secondly, acidification and eutrophication impacts are associated with the production and use of fertiliser or animal manure. Finally, toxic impacts are related to pesticide use and are still seldom considered. The two papers described in the section about organic, integrated and conventional farming not only gave an indication of the differences in environmental impacts between these farming systems, they also illustrated the importance of considering land use in LCAs. The case studies on Swiss farming practices and local foods stressed the need to consider systems as a whole. Studying crops in isolation or only at a certain moment in time might lead to deceptive conclusions. Besides those, a number of other methodological issues relating to the environmental assessment of food and food production remain


Besides those, a number of other methodological issues relating to the environmental assessment of food and food production remain. First of all, the choice of the functional unit of a study has to reflect a product’s function. As discussed in Sect. 29.3.2, some authors ascribe three functions to agriculture: land management, providing an income for farmers and production of food. The choice of the functional unit will depend on the goal and scope of the LCA study (see Chaps. 8 and 9). It will often be relevant to express the results by different functional units within the same study to give a fair picture of the compared systems, especially in the case of highly contrasted intensification levels. From a consumer’s point of view, the primary function of food products is to provide sufficient energy and nutrients to the human body


From a consumer’s point of view, the primary function of food products is to provide sufficient energy and nutrients to the human body. Beyond the mass of food produced, research is therefore looking for more qualitative functional units, taking into account the food’s nutritional value in a harmonised way. As an example, increasing yields of wheat may negatively affect the nutritional quality of the grains. It is therefore recommendable to clearly define the nutritional quality of a food product in the functional unit, especially in comparative LCA studies (Schau and Fet 2008). A second issue is the definition of the system boundaries (see Chap. 9). This discussion has two important aspects. The first aspect is setting the border between T.J. Dijkman et al.


technosphere (the product system) and ecosphere (the natural environment). This border is essential in LCA, as only material and energy flows crossing it are considered inputs or outputs. Especially in LCAs of agricultural and food products this boundary might be hard to identify clearly, because in food production the technosphere is closely linked to the ecosphere. An example can be found in the emissions of pesticides to agricultural soil. It can be argued that these should be marked as emissions to the ecosphere because the pesticides might affect various forms of life in the soil: worms, beetles, which are not necessarily the target organisms for the pesticide. On the other hand, one can reason that the soil of a field is part of the technosphere, because it is manipulated by humans to an extent where it is incomparable to natural soils


On the other hand, one can reason that the soil of a field is part of the technosphere, because it is manipulated by humans to an extent where it is incomparable to natural soils. Setting the system boundaries is dependent on the goal and scope of the LCA study: it is not possible to objectively define one correct boundary setting that works for all agricultural LCAs (Dijkman 2014; Rosenbaum et al. 2015). For this reason, it is important to explicitly define the system boundaries in the goal and scope definition. Ideally, the boundaries between technosphere and ecosphere, and thus of LCI and LCIA for modelling the inflows and outflows, should be defined uniformly in order to produce a consistent LCA study. The second aspect of setting the system boundaries relates to the processes that are included in the study. Often, a cradle-to-farm gate study is done because it is assumed that most impacts arise at the agricultural stage or because the post-farm gate processes are identical


Often, a cradle-to-farm gate study is done because it is assumed that most impacts arise at the agricultural stage or because the post-farm gate processes are identical. This might result in overlooking product losses during processing or consumption, while the reduction of food waste can contribute to lowering environmental impacts elsewhere in the life cycle. A third issue in the LCA of food products is the inclusion of the diversity of production systems. Most LCA studies in the past have relied on a very small number of farms, while agricultural production systems are generally very diverse due to the interaction between the farmers’ skills and practices and their environment. In studies where the variability of systems has been explored, the variability of LCA results is larger within one production group than between the studied alternatives. This also leads to the question of uncertainty of LCA results, which is generally not evaluated (see Chap. 11)


This also leads to the question of uncertainty of LCA results, which is generally not evaluated (see Chap. 11). As a fourth issue, the modelling of flows from the agricultural field can be improved. Meier et al. (2015) argued that the modelling of nutrients needs to be improved in LCA practice, because especially nitrogen flows are responsible for many environmental impacts from on-field processes. Many studies reviewed by Meier et al. (2015) did not calculate the N balance from the field. When the N balance was calculated, differences in the N surplus (defined by Meier et al. (2015) as the nitrogen potentially emitted to the environment via different pathways) between conventional and organic farming systems were not always reflected in differences in the eutrophication potentials calculated for both farming systems


However, because the N surplus is a measure of the amount of N that is available for leaching to surface water or other environmental compartments, differences in the eutrophication potential should be related to differences in the N surplus. Moreover, Meier et al. (2015) found that the N emissions exceeded the N surplus in four processes representing Swiss agriculture in the EcoInvent database. LCA of Food and Agriculture


In addition, N emissions from manure, which are dependent on the excretion of N by livestock animals, are seldom adjusted for differences in the animals’ dietary composition. Because the N balance of a field is dependent on many factors, such as the chemical or organic fertilisers used, the uptake into the crop and local soil and climate conditions, a simple model of the N balance of a field for the use in LCA is not available. The authors also stated the need for better modelling of flows of carbon from the field and manure. Apart from nutrient flows, modelling of pesticide flows can be improved. Databases with inventory data, such as ecoinvent (Ecoinvent Centre 2007) and the US LCI database (NREL 2003), often assume fixed emissions to one or two environmental compartments, independent of the pesticide applied, the application technology used, the climatic circumstances and the crop or soil onto which the pesticide is applied. Here, models such as PestLCI 2.0 (Dijkman et al


Here, models such as PestLCI 2.0 (Dijkman et al. 2012) and the forthcoming results of ongoing pesticide consensus work can be used to better represent the influence of chemical properties and local circumstances on pesticide emissions (Rosenbaum et al. 2015). A final issue in agricultural LCAs is the limited number of impact categories and flow indicators that are usually included: global warming, acidification, eutrophication, ozone depletion and energy use. Land use-related impacts are increasingly considered. This is an important impact category, because land is a scarce resource. Most land that is suitable for agriculture, is currently already in use as such. Moreover, considering land use may also help to illustrate the trade-offs of, e.g. organic farming: more land is required per unit of product, resulting in an expanded use of land for that product when switching to organic farming (under the assumption that the demand for the product remains unchanged)


This direct land use change in turn results in indirect land use change (iLUC) because the organic product displaces another product, which ultimately results in the conversion of grasslands or forest into agricultural land. At the same time, chemical pesticide use is avoided, and the nutritional quality of grains may be at optimum when grown below maximum yield levels. So, even though all farmers switching to organic farming would considerably reduce certain environmental impacts of agriculture such as toxicity impacts, the amount of food available may be reduced as well on the short term. Another impact category that is usually omitted in LCAs is toxicity (Meier et al. 2015). Because of neglecting toxicity, the effects of pesticide use are not well reflected in LCA results. Historically, toxicity was excluded because of the unavailability of emission data and impact assessment methods for these categories


Historically, toxicity was excluded because of the unavailability of emission data and impact assessment methods for these categories. On the LCI side, models such as PestLCI 2.0 can now be applied to calculate pesticide emissions to air, surface water and groundwater (Dijkman et al. 2012). Likewise, in impact assessment, models such as USEtox (Rosenbaum et al. 2008) can be used to calculate toxicity impacts. However, LCI and LCIA models do not necessarily apply the same boundaries between ecosphere and technosphere, both in terms of time and space. In order to overcome this inconsistency, and to provide guidance to LCA practitioners about modelling pesticide emissions to the environment and their impacts in LCA, a series of international workshops has been T.J. Dijkman et al.


held in an effort to establish a global consensus. Rosenbaum et al. (2015) report the objectives of the effort and the recommendations of the first workshop. Finally, water use-related impacts are frequently omitted in LCA studies. Given the importance of water resources for economic activities in general and for food production in particular as well as for human and ecosystems health, this impact category has rapidly evolved in the last few years. Nowadays, operational methods based on regional water stress are available (e.g. Berger et al. 2014, Pfister and Bayer 2014) and the new AWARE consensus impact assessment method (WULCA 2016) to asses water deprivation impacts (see Sect. 10.15). Despite these developments, much work to improve environmental relevance of methods addressing the consequences of water use on the environment is ahead. Water use is also associated with other long lasting problems such as salinisation (Payen et al. 2016) and desertification (Núñez et al


Water use is also associated with other long lasting problems such as salinisation (Payen et al. 2016) and desertification (Núñez et al. 2010), included in a number of LCA pilot studies. Therefore, even though quite some steps have been made in the field of LCA of food products during the last 25 years, a number of challenges and methodological issues remain to be improved. 29.5 Applying LCA to Food Products in Southern Countries In a context of demographic increase, especially in cities, southern and generally poor countries are facing immense challenges in terms of food security, poverty reduction, food safety and environmental protection. In such contexts, global assessment tools such as LCA can help stakeholders in food supply chains focus, improve and develop the most promising technical alternatives and support the eco-design of livestock and cropping systems


However, the application of LCA to food products in southern countries is recent and associated to difficult and numerous challenges. All the previously mentioned methodological challenges are relevant, but in an even more critical way due to the extreme diversity of production systems (often including perennial crops) and practice, the data scarcity on production systems, the lack of knowledge and appropriate models for estimating the field fluxes and finally the predominance of environmental impacts for which no consensual methodology exists. This concerns all environmental impacts associated to the use of land and water, including aspects of biodiversity, water deprivation, soil quality and fertility, carbon balance and GHGs from soils, salinisation impacts, etc. This also concerns all toxicity impact categories which are complex and for which available inventory approaches are either not valid or difficult to implement due to data scarcity


This also concerns all toxicity impact categories which are complex and for which available inventory approaches are either not valid or difficult to implement due to data scarcity. Particularly important in these situations are also the social impacts associated to food supply chains for which ambitious research programmes are starting. Finally, caring for the environment in such subsistence economies where the priority is to feed the people, often appears as a luxury for wealthy people. Therefore, awareness-raising and education about eco-friendly practices, LCA of Food and Agriculture


human health risks and environmental sustainability are important priorities in association to more methodological studies to implement LCA in southern countries. Some research teams have started to implement LCA to food products in southern countries with examples on peri-urban tomato in the South of Benin (Perrin 2013), palm oil in Indonesia (Bessou et al. 2016), clementine in Morocco and mango in Brazil (Basset-Mens et al. 2016). 29.6 Conclusions We have seen that LCAs of food products can be divided into six stages: inputs production and transportation, agricultural stage, processing, distribution, use and waste management. A large number of LCA studies are cradle-to-farm gate studies, and include only the two first stages. As a consequence of production and flows of nutrients and pesticides from the field, as well as from livestock and manure handling, the agricultural stage is often found to be the major contributor to many impact categories


This was illustrated in a number of case studies. Different impacts can arise in the food production stage. Environmental impacts from the use stage are often related to energy consumed in food storage and preparation. In the waste management stage, impacts arise due to food waste handling and treatment of human excretion. Throughout the life cycle of a food product, food waste is a major problem. The case study about conventional and organic farming showed that, depending on the choice of functional unit and the impact categories included in the assessment, LCA can be used to conclude in favour of both conventional and organic farming practices. The case study about local food showed that local food is not by definition more sustainable. Transport is not often the decisive factor when it comes to environmental impacts of food products, so the circumstances during production weigh more heavily in determining where and how locally produced food is more sustainable


Overall, the key strength of LCA lies in the identification of the hotspots and margins of improvement of each system. Despite methodological improvements, a number of challenges remain for agricultural LCA. Firstly, the functional unit is currently often defined on basis of mass produced, or production per unit of area, without considering the nutritional quality of the product. Secondly, setting the system boundary between technosphere and the ecosphere is difficult in those production systems showing a direct interface with nature. In the life cycle inventory, modelling of flows of nutrients, water, salt and pesticides can be improved in many studies. A challenge in the LCIA phase remains the limited number of impact categories included in most LCA studies. Moreover, some impact categories relevant for agriculture, such as land use-related impacts including soil quality aspects and biodiversity damage, remain to be further developed and operationalised. T.J. Dijkman et al.


Abdullah, K.: Use of water and land for food security and environmental sustainability. Irrig. Drain. 55(3), 219–222 (2006). doi:10.1002/ird.254 Andersson, K., Ohlsson, T.: Life Cycle Assessment of bread produced on different scales. Int. J. Life Cycle Assess. 4(1), 25–40 (1999). doi:10.1007/bf02979392 Antón, A., Torrellas, M., Raya, V., Montero, J.I.: Modelling the amount of materials to improve inventory datasets of greenhouse infrastructures. Int. J. Life Cycle Assess. 19(1), 29–41 (2014). doi:10.1007/s11367-013-0607-z Basset-Mens, C., McLaren, S., Ledgard, S.: Exploring a comparative advantage for New Zealand cheese in terms of environmental performance. Presented at 5th international conference LCA in foods, Gothenburg, Sweden, 25–26 Apr 2007 (2007) Basset-Mens, C., Vannière, H., Grasselly, D., Heitz, H., Braun, A., Payen, S., Koch, P., Biard, Y.: Environmental impacts of imported and locally-grown fruits for the French market: a cradle-to-farm-gate LCA study


Fruits 71(2), 93–104 (2016). doi:10.1051/fruits/2015050 Berger, M., Van der Ent, R., Eisner, S., Bach, V., Finkbeiner, M.: Water accounting and vulnerability evaluation (WAVE): considering atmospheric evaporation recycling and the risk of freshwater depletion in water footprinting. Environ. Sci. Technol. 48(8), 4521–4528 (2014). doi:10.1021/es404994t Berlin, J.: Environmental Life Cycle Assessment (LCA) of Swedish semi-hard cheese. Int. Dairy J. 12(11), 939–953 (2002). doi:10.1016/S0958-6946(02)00112-7 Bessou, C., Basset-Mens, C., Latunussa, C., Vélu, A., Heitz, H., Vannière, H., Caliman, J.-P.: Partial modelling of the perennial crop cycle misleads LCA results in two contrasted case studies. Int. J. Life Cycle Assess. 21(3), 297–310 (2016). doi:10.1007/s11367-016-1030-z Cederberg, C., Mattsson, B.: Life Cycle Assessment of milk production: a comparison of conventional and organic farming. J. Clean. Prod. 8(1), 49–60 (2000)


doi:10.1007/s11367-016-1030-z Cederberg, C., Mattsson, B.: Life Cycle Assessment of milk production: a comparison of conventional and organic farming. J. Clean. Prod. 8(1), 49–60 (2000). doi:10.1016/S09596526(99)00311-X Cordell, D., Drangert, J.O., White, S.: The story of phosphorus: global food security and food for thought. Glob. Environ. Change 19(2), 292–305 (2009). doi:10.1016/j.gloenvcha.2008.10.009 Cristea, A., Hummels, D., Puzzello, L., Avetisyan, M.: Trade and the greenhouse gas emissions from international freight transport. J. Environ. Econ. Manag. 65(1), 153–173 (2013). doi:10. 1016/j.jeem.2012.06.002 Dalgaard, R., Halberg, N., Hermansen, J.E.: Danish Pork Production: An Environmental Assessment. DJF Animal Science No. 82. University of Aarhus, Tjele (2007) De Backer, E., Aartsens, J., Vergucht, S., Steurbaut, W.: Assessing the ecological soundness of organic and conventional agriculture by means of Life Cycle Assessment (LCA): a case of leekproduction. Br. Food J


Br. Food J. 111(10), 1028–1061 (2009). doi:10.1108/00070700910992916 De Vries, M., De Boer, I.J.M.: Comparing environmental impacts for livestock products: a review of Life Cycle Assessments. Livest. Sci. 128(1–3), 1–11 (2010). doi:10.1016/j.livsci.2009.11.007 Dijkman, T.J., Birkved, M., Hauschild, M.Z.: PestLCI 2.0: a second generation model for estimating emissions of pesticides from arable land in LCA. Int. J. Life Cycle Assess. 17(8), 973–986 (2012). doi:10.1007/s11367-012-0439-2 Dijkman, T.J.: Modelling of pesticide emissions for life cycle inventory analysis: model development, applications and implications. Ph.D. thesis. Technical University of Denmark, Kongens Lyngby (2014) Djekic, I., Miocinovic, J., Tomasevic, N., Smigic, N., Tomic, I.: Environmental life-cycle assessment of various dairy products. J. Clean. Prod. 68, 64–72 (2014). doi:10.1016/j.jclepro. 2013.12.054 Ecoinvent Centre: Ecoinvent Reports No. 1–25


J. Clean. Prod. 68, 64–72 (2014). doi:10.1016/j.jclepro. 2013.12.054 Ecoinvent Centre: Ecoinvent Reports No. 1–25. Swiss Centre for Life Cycle Inventories, Dübendorf (2007) EU-JRC: International Reference Life Cycle Data System (ILCD) Handbook. General Guide for Life Cycle Assessment—Detailed Guidance. Publications Office of the European Commission, Luxembourg. Retrieved 20 Apr 2016 from http://eplca.jrc.ec.europa.eu/?page_id=86 (2010) LCA of Food and Agriculture


Edwards-Jones, G., Milà i Canals, L.L., Hounsome, N., Truninger, M., Koerber, G., Hounsome, B., Cross, P., York, E.H., Hospido, A., Plassmann, K., Harris, I.M., Edwards, R.T., Day, G.A. S., Tomos, A.D., Cowell, S.J., Jones, D.L.: Testing the assertion that ‘local food is best’: the challenges of an evidence-based approach. Trends Food Sci. Technol. 19(5), 265–274 (2008). doi:10.1016/j.tifs.2008.01.008 Ellen MacArthur Foundation: Growth Within: A Circular Economy. Vision for a Competitive Europe. Retrieved 20 Apr 2016 from https://www.mckinsey.de/sites/mck_files/files/growth_ within_report_circular_economy_in_europe.pdf (2015) European Commission: The Environmental Footprint Pilots. Retrieved on 21 Apr 2016 from http://ec.europa.eu/environment/eussd/smgp/pef_pilots.htm (2016) Fantke, P., Juraske, R., Antón, A., Friedrich, R., Jolliet, O.: Dynamic multicrop model to characterize impacts of pesticides in food. Environ. Sci. Technol. 45(20), 8842–8849 (2011)


Environ. Sci. Technol. 45(20), 8842–8849 (2011). doi:10.1021/es201989d FAO: Interactions between Livestock Production Systems and the Environment. Retrieved on 3 May 2016 from http://www.fao.org/wairdocs/lead/x6123e/x6123e00.htm#Contents (1995) FAO: FAOSTAT Land Use Data. Retrieved on 25 Apr 2016 from http://faostat3.fao.org/ download/E/EL/E (2011) FAO: World Agriculture Towards 2030/2050: The 2012 Revision. ESA working paper no. 12-03. Retrieved on 8 June 2016 from http://www.fao.org/docrep/016/ap106e/ap106e.pdf (2012) FAO: FAO Statistical Yearbook 2013—World Food and Agriculture—Part 4: Sustainability Dimensions. Food and Agriculture Organisation of the United Nations, Rome (2013) FAO: Water Withdrawal by Sector, Around 2007. Retrieved on 20 Apr 2016 from http://www.fao


Food and Agriculture Organisation of the United Nations, Rome (2013) FAO: Water Withdrawal by Sector, Around 2007. Retrieved on 20 Apr 2016 from http://www.fao. org/nr/water/aquastat/tables/WorldData-Withdrawal_eng.pdf (2014) Food, S.C.P.R.T.: ENVIFOOD Protocol, Environmental Assessment of Food and Drink Protocol, European Food Sustainable Consumption and Production Round Table (SCP RT), Working Group 1. Food SCP Round Table, Brussels (2013) Foster, C., Green, K., Bleda, M., Dewick, P., Evans, B., Flynn, A., Mylan, J.: Environmental Impacts of Food Production and Consumption: Final Report to the Department for Environment, Food and Rural Affairs. Manchester Business School, Manchester (2006) González-García, S., Hospido, A., Moreira, M.T.: Environmental Life Cycle Assessment of a Galician cheese: San Simon da Costa. J. Clean. Prod. 52, 253–262 (2013). doi:10.1016/j


J. Clean. Prod. 52, 253–262 (2013). doi:10.1016/j. jclepro.2013.03.006 IPCC: Contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge (2007) ISO: Environmental Management—Life Cycle Assessment—Principles and Framework (ISO 14040). ISO, The International Organization for Standardization, Geneva (2006a) ISO: Environmental Management—Life Cycle Assessment—Requirements and Guidelines (ISO 14044). ISO, The International Organization for Standardization, Geneva (2006b) Kim, D., Thoma, G., Nutter, D., Milani, F., Ulrich, R., Norris, G.: Life Cycle Assessment of cheese and whey production in the USA. Int. J. Life Cycle Assess. 18(5), 1019–1035 (2013). doi:10.1007/s11367-013-0553-9 Kulikowski, M.: Mayday 23: World Population Becomes More Urban than Rural. North Carolina State University Newsroom


Int. J. Life Cycle Assess. 18(5), 1019–1035 (2013). doi:10.1007/s11367-013-0553-9 Kulikowski, M.: Mayday 23: World Population Becomes More Urban than Rural. North Carolina State University Newsroom. Retrieved on 26 Apr 2016 from http://news.ncsu.edu/releases/ 2007/may/104.html (2007) Meier, M.S., Stoessel, F., Jungbluth, N., Juraske, R., Schader, C., Stolze, M.: Environmental impacts of organic and conventional agricultural products: are the differences captured by life cycleassessment? J. Environ. Manag. 149, 193–208 (2015). doi:10.1016/j.jenvman.2014.10.006 Mithraratne, N., Barber, A., McLaren, S.J.: Carbon footprinting for the kiwifruit supply chain: report on methodology and scoping study. Final report. Landcare Research, Auckland (2010) Muñoz, I., Milà i Canals, L.L., Fernández-Alba, A.R.: Life Cycle Assessment of the average Spanish diet including human excretion. Int. J. Life Cycle Assess. 15(8), 794–805 (2010). doi:10.1007/s11367-010-0188-z T.J. Dijkman et al.


Nemecek, T., Debois, D., Huguenin-Elie, O., Gaillard, G.: Life Cycle Assessment of Swiss farming systems: I. Integrated and organic farming. Agric. Syst. 104(3), 217–232 (2011). doi:10.1016/j.agsy.2010.10.002 NREL: US LCI Database. Retrieved on 6 Sept 2013 from www.nrel.gov/ (2003) Núñez, M., Civit, B., Muñoz, P., Pablo Arena, A., Rieradevall, J., Anton, A.: Assessing potential desertification environmental impact in Life Cycle Assessment. Part 1: methodological aspects. Int. J. Life Cycle Assess. 15(1), 67–78 (2010). doi:10.1007/s11367-009-0126-0 Paxton, A.: The food miles report: the dangers of long distance food transport. The SAFE Alliance, London (1994) Payen, S., Basset-Mens, C., Perret, S.R.: LCA of local and imported tomato: an energy and water trade-off. J. Clean. Prod. 87(15), 139–148 (2015)


The SAFE Alliance, London (1994) Payen, S., Basset-Mens, C., Perret, S.R.: LCA of local and imported tomato: an energy and water trade-off. J. Clean. Prod. 87(15), 139–148 (2015). doi:10.1016/j.jclepro.2014.10.007 Payen, S., Basset-Mens, C., Núñez, M., Follain, S., Grünberger, O., Marlet, S., Perret, S.R., Roux, P.: Salinisation impacts in Life Cycle Assessment: a review of challenges and options towards their consistent integration. Int. J. Life Cycle Assess. 21(4), 577–594 (2016). doi:10.1007/ s11367-016-1040-x Perrin, A.: Evaluation environnementale des systèmes agricoles urbains en Afrique de l’Ouest: implications de la diversité des pratiques et de la variabilité des émissions d’azote dans l’Analyse du Cycle de Vie de la tomate au Bénin. Ph.D. thesis. AgroParisTech- Sciences agronomiques et écologiques, Paris (2013) Pfister, S., Bayer, P.: Monthly water stress: spatially and temporally explicit consumptive water footprint of global crop production. J. Clean. Prod


J. Clean. Prod. 73, 52–62 (2014). doi:10.1016/j.jclepro. 2013.11.031 Rosenbaum, R.K., Bachmann, T.M., Gold, L.S., Huijbregts, M.A.J., Jolliet, O., Juraske, R., Koehler, A., Larsen, H.F., MacLeod, M., Margni, M., McKone, T.E., Payet, J., Schuhmacher, M., Van de Meent, D., Hauschild, M.Z.: USEtox: the UNEP-SETAC toxicity model: recommended characterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment. Int. J. Life Cycle Assess. 13(7), 532–546 (2008). doi:10.1007/ s11367-008-0038-4 Rosenbaum, R.K., Antón, A., Bengoa, X., Bjørn, A., Brain, R., Bulle, C., Cosme, N., Dijkman, T. J., Fantke, P., Felix, M., Geoghega, T.S., Gottesbüren, B., Hammer, C., Humbert, S., Jolliet, O., Juraske, R., Lewis, F., Maxime, D., Nemecek, T., Payet, J., Räsänen, K., Roux, P., Schau, E.M., Sourisseau, S., Van Zelm, R., Von Streit, B., Wallman, M.: The Glasgow consensus on the delineation between pesticide emission inventory and impact assessment for LCA. Int. J


Int. J. Life Cycle Assess. 20(6), 765–776 (2015). doi:10.1007/s11367-015-0871-1 Saxe, H., Jensen, J.D.: Does the environmental gain of switching to the healthy New Nordic Diet outweigh the increased consumer cost? J. Food Sci. Eng. 4(6), 291–300 (2014). doi:10.17265/ 2159-5828/2014.06.004 Schau, E.M., Fet, A.M.: LCA studies of food products as background for environmental product declarations. Int. J. Life Cycle Assess. 13(3), 255–264 (2008). doi:10.1065/ica2007.12.372 Sim, S., Barry, M., Clift, R., Cowell, S.J.: The relative importance of transport in determining an appropriate sustainability strategy for food sourcing. Int. J. Life Cycle Assess. 12(6), 422–431 (2007). doi:10.1007/s11367-006-0259-3 Smith, A., Watkiss, P., Tweddle, G., McKinnon, A., Browne, M., Hunt, A., Treleven, C., Nash, C., Cross, S.: The validity of food miles as an indicator of Sustainable development. Final report prepared by AEA Technology Environment for DEFRA


Final report prepared by AEA Technology Environment for DEFRA. DEFRA, London (2005) Sonesson, U., Janestad, H., Raaholt, B.: Energy for preparation and storing of food—models for calculation of energy use for cooking and cold storage in households. SIK-Rapport 709. The Swedish Institute for Food and Biotechnology, Gothenburg (2003) Swaminathan, B., Sukalac, K.E.: Technology transfer and mitigation of climate change: the fertilizer industry perspective. Presented at the IPCC Expert Meeting on Industrial Technology Development, Transfer and Diffusion, Tokyo, Japan, 21–23 Sept 2004 (2004) Ten Hoeve, M.: Environmental consequences of pig slurry treatment technologies: a life cycle perspective. Ph.D. thesis. University of Copenhagen, Copenhagen LCA of Food and Agriculture


Thomassen, M.A., Van Calker, K.J., Smits, M.C.J., Iepema, G.L., De Boer, I.J.M.: Life cycle assessment of conventional and organic milk production in the Netherlands. Agric. Syst. 96(1– 3), 95–107 (2008). doi:10.1016/j.agsy.2007.06.001 Torrellas, M., Antón, A., López, J.C., Jose Baeza, E., Perez Parra, J., Pere, M., Ignacio Montero, J.: LCA of a tomato crop in a multi-Tunnel greenhouse in Almeria. Int. J. Life Cycle Assess. 17 (7), 863–875 (2012a). doi:10.1007/s11367-012-0409-8 Torrellas, M., Antón, A., Ruijs, M., García Victoria, N., Stanghellini, C., Montero, J.I.: Environmental and economic assessment of protected crops in four European scenarios. J. Clean. Prod. 28, 45–55 (2012b). doi:10.1016/j.jclepro.2011.11.012 USEtox: USEtox 2.0 Official Launch. Retrieved on 12 Jan 2016 from http://www.usetox.org/ events/usetox-20-official-launch (2015) Van Middelaar, C.E., Berentsen, P.B.M., Dolman, M.A., De Boer, I.J.M.: Eco-efficiency in the production chain of Dutch semi-hard cheese


Livest. Sci. 139(1–2), 91–99 (2011). doi:10.1016/ j.livsci.2011.03.013 Van Zelm, R., Larrey-Lassalle, P., Roux, P.: Bridging the gap between life cycle inventory and impact assessment for toxicological assessments of pesticides used in crop production. Chemosphere 100, 175–181 (2014). doi:10.1016/j.chemosphere.2013.11.037 Williams, H., Wikström, F.: Environmental impact of packaging and food losses in a life cycle perspective: a comparative analysis of five food items. J. Clean. Prod. 19(1), 43–48 (2011). doi:10.1016/j.jclepro.2010.08.008 WULCA: The AWARE method. Retrieved on 21 Apr 2016 from http://wulca-waterlca.org/ project.html (2016) Author Biographies Teunis J. Dijkman has worked with method development in LCA since 2010, focusing on Life Cycle Inventory modelling. Interested in the boundary setting between technosphere and ecosphere for agrochemicals. Contributed to consensus building and model development for global pesticide emission quantification


Interested in the boundary setting between technosphere and ecosphere for agrochemicals. Contributed to consensus building and model development for global pesticide emission quantification. Claudine Basset-Mens agronomist and LCA scientist since early 2000s, adapting LCA to agri-food chains (animal and crop production) in a wide range of situations and countries. Her main research interests relate to inclusion of agricultural systems diversity, modelling of field emissions, modelling of perennial crops within LCA and the inclusion of uncertainty. Assumpció Antón expert in the application of LCA methodology in agriculture since early 2000s. Final goal of her work focus on the environmental improvement of agricultural systems. Main interests are development of agricultural LCI and LCIA methods related to agricultural processes (agrochemical emissions, land use and water consumption)


Main interests are development of agricultural LCI and LCIA methods related to agricultural processes (agrochemical emissions, land use and water consumption). Montse Núñez environmental scientist with interest in LCA, agriculture, and modelling of environmental impacts from resource use in agricultural activities. Involved in development of LCIA methods to assess impacts of land use and water use since 2007. T.J. Dijkman et al.


Chapter 30 LCA of Biofuels and Biomaterials Susanne Vedel Hjuler and Sune Balle Hansen Abstract Biofuels and biomaterials can today substitute many commodities produced from fossil resources, and the bio-based production is increasing worldwide. As fossil resources are limited, and the use of such resources is a major contributor to global warming and other environmental impacts, the potential of bio-products as substitutes for fossil-based products is receiving much attention. According to many LCA studies, bio-products are environmentally superior to fossil products in some life cycle impact categories, while the picture is often opposite in others. Bio-products is a highly diverse group of products and the environmental profile of bio-products relative to their fossil counterparts is case specific and to a high degree depending on the feedstock used


This illustrates the importance of conducting case specific LCAs for determining the environmental profile of bio-products relative to fossil ones, and emphasises the importance of including all relevant impact categories, in order to avoid problem shifting. 30.1 Many conventional petrochemical products, such as chemicals, polymers and fuels, can today be produced from biomass. There are multiple drivers for substitution of petrochemical products. One driver is that fossil resources are limited. Another driver is that fossil fuel consumption is a major contributor to global warming and to other important environmental impacts as well. Furthermore, biofuels and biomaterials (hereafter referred to together as bio-products) provide an option for basing production on more local feedstock, creating jobs and promoting reduced S.V. Hjuler  S.B


Hjuler  S.B. Hansen Division of Quantitative Sustainability Assessment, Department for Management Engineering, Technical University of Denmark, Produktionstorvet Building 424, 2800 Kongens Lyngby, Denmark S.V. Hjuler (&) Novozymes A/S, Krogshøjvej 36, 2880 Bagsværd, Denmark e-mail: hjuler.susanne.vedel@gmail.com © Springer International Publishing AG 2018 M.Z. Hauschild et al. (eds.), Life Cycle Assessment, DOI 10.1007/978-3-319-56475-3_30


dependence on fossil resources. Following this, the potential of bio-products as substitutes for fossil-based ones is high on the global political agenda, and the production worldwide is increasing. In 2003, 8–10% of the feedstock for the European chemical industry was biomass (Rothermel 2008), whereas the global share of bio-based chemicals was 2% in 2008 and is expected to be at least 22% by 2025, according to USDA (2008). For the global polymer production, the bio-based share has reached more than 8% (Carus et al. 2013). Approximately 2% of the global liquid fuel consumption was covered by biofuels in 2010 (EIA 2012). Direct substitution of some of the same fuels, chemicals and materials which are conventionally produced based on fossil resources is possible, but also the development of new materials and products with different properties is pursued


Along with the growing demand for bio-products, there is an equally increasing need for awareness of the environmental performance of the bio-products relative to their petrochemical counterparts. The aim of this chapter is to provide a review of the state-of-the-art within LCAs on bio-products, as well as an introduction to methodology and methodological challenges and uncertainties in the field. Definitions and system boundary choices are introduced initially to illustrate the systems in question, after which general LCA results in the field are presented, followed by discussions of specific methodological issues and their potential implication for the assessment results. An important portion of this chapter is based on, and for some parts taken from, Jørgensen (2014) and further details on many of the discussed issues can be found there. 30.1.1 Definition of Bio-products Bio-products are products made from biomass feedstocks


30.1.1 Definition of Bio-products Bio-products are products made from biomass feedstocks. The definition of biomass can vary among literature sources but one of the most comprehensive definitions can be found in directives from the European Union, e.g. the Renewable Energy Directive, EU-RED, (European Parliament 2009), stating that “biomass means the biodegradable fraction of products, waste and residues from biological origin from agriculture (including vegetal and animal substances), forestry and related industries including fisheries and aquaculture, as well as the biodegradable fraction of industrial and municipal waste”. Bio-products can also be defined in opposition to fossil products, made from e.g. oil or natural gas. Both fossil- and bio-based products come from biomass but the key difference is that in the case of fossil products, this biomass went through fossilisation


oil or natural gas. Both fossil- and bio-based products come from biomass but the key difference is that in the case of fossil products, this biomass went through fossilisation. Fossil resources have been formed from ancient biomass during millions of years of geological processing and storage. The carbon present in these resources was thus removed from the atmosphere through photosynthesis many millions of years ago and is no longer a part of the present day natural carbon cycle balance. This carbon is termed ‘fossil carbon’ (European Commission 2010). S.V. Hjuler and S.B. Hansen


On the contrary, bio-products are based either completely or partly on biomass feedstock, in the form of plants or biogenic residues/waste (Weiss et al. 2012). As plants take up and store CO2 from the atmosphere during their growth through photosynthesis, carbon exchanges between biomass, including bio-products, and the atmosphere are part of the present-day natural carbon cycle. The carbon pool that is constituted by carbon in biomass and bio-products is termed ‘biogenic carbon’. Whether this biogenic carbon can be considered ‘CO2 neutral’ or ‘carbon neutral’ is a specific key issue for bio-products and their relevance for climate change, and it is discussed in Sect. 30.4.4. There are also issues related to the timing of carbon sequestration and release which affects the ‘carbon neutrality’ aspect, as temporary release or storage of carbon may also play a role in terms of climate change, especially on short-term targets (e.g. Cherubini et al. 2012b; Jørgensen et al. 2015)


Cherubini et al. 2012b; Jørgensen et al. 2015). The issue of the potential role of temporary carbon storage in relation to climate change is addressed in Sect. 30.3.1. While the feedstock of bio-products consists of biogenic carbon, the production of the feedstock as well as other processes in the product life cycle may include fossil fuel consumption, or in other ways contribute to greenhouse gas (GHG) emissions. Thus, it is crucial to conduct an LCA in order to determine the actual climate change impacts of bio-products, along with other environmental impacts. There is no standard classification of bio-products. However, a proposal is given in Fig. 30.1 providing an overview of existing bio-products, considering bio-product application and type. The same distinction into biofuels and biomaterials was used throughout this chapter


30.1 providing an overview of existing bio-products, considering bio-product application and type. The same distinction into biofuels and biomaterials was used throughout this chapter. This chapter focuses on bio-products that serve for the substitution of conventional petrochemical products, such as fuels, chemicals and polymers, rather than bio-products based on traditional biomaterials such as wood, paper and textiles. However, many of the aspects discussed here apply for traditional biomaterials as well. Bioproducts Liquid fuels Solid fuels Gaseous fuels Bioproduct application Bioproduct type Examples of bioproducts Fuels Materials Traditional Synthetic Bioethanol, biodiesel Firewood, charcoal, wood pellets Biogas, biohydrogen Wood, paper, textile, essential oil Biocomposites, biopolymers Fig. 30.1 Overview of existing bio-products. Image Copyright © Anthony Benoist. Used with permission LCA of Biofuels and Biomaterials


Generally, bio-products are distinguished into different ‘generations’: First generation (1G) covers bio-products that are produced by conventional methods, based on sugar, starch, vegetable oil or animal fats. Most bio-products today are 1G, but this feedstock entails certain competition with food production and there is a major focus on moving on to the Second generation (2G). The use of the term 2G is not completely consistent in the literature. Predominantly, it refers to the change from the conventional technology to production of bio-products using lignocellulosic feedstock, e.g. agricultural residues or energy crops. However, some use the term to e.g. refer to a conversion route or end product, rather than feedstock (Cherubini 2010). Here, the first meaning of the terminology is used. Today, 2G biofuel production is reaching commercial scale


refer to a conversion route or end product, rather than feedstock (Cherubini 2010). Here, the first meaning of the terminology is used. Today, 2G biofuel production is reaching commercial scale. However, use of lignocellulosic feedstock also entails competition with its prior use, such as soil carbon replenishment for lignocellulosic residues, and land use competition for energy crops. Further, a so-called Third generation (3G) of bio-products is discussed in the literature, but like for 2G, the use of the term varies. The term 3G is often used to characterise the use of certain feedstocks like algae (e.g. Sander and Murthy 2010; Carus and Dammer 2013), or microalgae (Posten and Schaub 2009), due to its potential to address many of the concerns about 1G and 2G feedstock (Sander and Murthy 2010), its high yield potentials (Posten and Schaub 2009; Sander and Murthy 2010) and the different growing conditions, compared to terrestrial plants (Posten and Schaub 2009)


However, others use the term 3G to refer to a follow up on 2G, and do not refer to a shift in feedstock in the same way as the shift between 1G and 2G (Bessou et al. 2011). Bio-products beyond 1G are sometimes termed as ‘next generation’ or ‘advanced’ bio-products. As outlined, the use of terminology in this field is not consistent in the literature so caution should be taken when reading the terms. Biofuels can be classified into three categories (Fig. 30.1): 1. Liquid biofuels: the most common are bioethanol and biodiesel 2. Gaseous biofuels: Biogas, syngas and biohydrogen 3. Solid biofuels: Pellets, lignin, biochar and wood Among the biofuels, this chapter will concentrate on the liquid biofuels biodiesel and bioethanol


Solid biofuels: Pellets, lignin, biochar and wood Among the biofuels, this chapter will concentrate on the liquid biofuels biodiesel and bioethanol. These are among the focus areas in the EU-RED (European Parliament 2009) in Europe and the Renewable Fuel Standard, RFS (Schnepf and Yacobucci 2012) in the USA, both of which set the official (political) standards for permitted life cycle emissions of a biofuel to be labelled ‘renewable’. In both documents, the renewability of a biofuel is judged solely on GHG emissions. This in turn steers the industrial and to a large extend the academic focus onto GHG emissions, which could lead to potential problem shifting towards other impact categories. S.V. Hjuler and S.B. Hansen


Biomaterials can be classified in various ways—here two main distinctions are made (Fig. 30.1): 1. Synthetic biomaterials (e.g. biopolymers or biocomposites) substituting conventional materials, such as various types of petrochemical plastic, cement or chemicals. 2. Traditional biomaterials, e.g. wood, paper and textiles. The biomaterials in focus here are those of the first group. An example of such a biomaterial is polylactic acid (PLA), a widespread biopolymer for example used for biomedical applications, disposable cups and cutlery, food containers and biodegradable bags for compostable waste. 30.1.2 System Boundaries Bio-products are most often created to substitute fossil resource-based products. As such, it is of high importance that the system boundaries are set in a manner to make direct impact comparisons with fossil resource-based products possible


As such, it is of high importance that the system boundaries are set in a manner to make direct impact comparisons with fossil resource-based products possible. In assessments of bio-products, the most controversial system boundary decisions pertain to the assessment of land use change (LUC) (direct and indirect), the modelling of waste products, use stage and the ‘end-of-life handling’ (for biomaterials). Often, these aspects have simply been sidestepped or ignored. A number of studies have included these aspects and found that they are in most cases very important (e.g. Majer et al. 2009). As such, they should never be omitted from the assessment without proper documentation that they are negligible. Other types of system boundaries are pertaining to inclusion of impact categories and time horizons. These are dealt with in Sect. 30.2 Sustainability of Biofuels and Biomaterials and Sect. 30.3 Specific Issues for Biomaterials


These are dealt with in Sect. 30.2 Sustainability of Biofuels and Biomaterials and Sect. 30.3 Specific Issues for Biomaterials. It must be kept in mind that the system boundaries should be drawn to include what is important in relation to the goal of the study (see Chap. 8). Inclusion of aspects like LUCs and in particular indirect land use change (ILUC) may lead to increased representativeness of the study, but also increased uncertainties. There can thus be great variations in LUC emissions depending on land uses, soil type, geography, climate, etc.; and it can be difficult to determine which products are ultimately substituted. The needed data for a specific assessment are often difficult to come by and simplifications/assumptions may be necessary. It is important that these issues, along with simplifications and assumptions, are dealt with and described. Conventionally, attributional system boundaries (see Chap. 8) have been used for bio-products as with most other products


Conventionally, attributional system boundaries (see Chap. 8) have been used for bio-products as with most other products. In recent years, consequential system boundaries (see Chaps. 8 and 9) have been introduced; either for the entire system or partially, to describe (in)direct LUCs (see Sect. 30.4) and for the evaluation of the residue use and by-products, which cross the system boundaries. LCA of Biofuels and Biomaterials


30.2 Sustainability of Biofuels and Biomaterials More LCA results can be found on biofuels than on biomaterials (e.g. Patel et al. 2005), and most are based on first generation (1G), where fewer LCA results are available for the next generations of bio-products. For the environmental sustainability of bio-products relative to their fossil-based counterparts, conclusions cover a wide range. This is further complicated by LCA not having consensus on a standardised impact assessment methodology resulting in various impact categories being used in studies on bio-products (Singh et al. 2010). However, generally, LCA studies conclude the following: • With respect to fossil fuel consumption and climate change impacts, bio-products generally perform better than their petrochemical counterparts (Weiss et al. 2007, 2012; Cherubini and Strømman 2011; Wang 2010; Patel et al. 2005). This picture can, however, change if the feedstock is planted on previously high carbon stock land (Kim et al


2007, 2012; Cherubini and Strømman 2011; Wang 2010; Patel et al. 2005). This picture can, however, change if the feedstock is planted on previously high carbon stock land (Kim et al. 2009) or when including GHG emissions from ILUC, which can be substantial (Weiss et al. 2012). The issues of LUC and ILUC are covered in Sect. 30.4. • When it comes to other impact categories, bio-products do not necessarily perform better than their conventional counterparts. Many reviews conclude that bio-products often have a higher impact than conventional products in the case of eutrophication (e.g. Weiss et al. 2007, 2012; Cherubini and Strømman 2011) and stratospheric ozone depletion (e.g. Weiss et al. 2007, 2012). • For acidification, some reviews conclude that bio-products generally have a higher impact than their petrochemical counterparts (e.g. Cherubini and Strømman 2011; Taboneet al. 2010; Luo et al. 2009; Weiss et al. 2007)


Cherubini and Strømman 2011; Taboneet al. 2010; Luo et al. 2009; Weiss et al. 2007). Other studies, however, find inconclusive results for this impact category, which may indicate that the results vary between different types of bio-products (Weiss et al. 2012). • Some studies suggest that biomaterials have a lower impact in terms of human toxicity, terrestrial ecotoxicity, as well as carcinogenic potential, but a higher aquatic ecotoxicity, compared to conventional materials (Weiss et al. 2012), while other studies conclude that bio-product systems lead to increased human toxicity and ecotoxicity in most cases (Cherubini and Strømman 2011). However, those categories are often not included in LCA studies and results are based on few studies (Weiss et al. 2012). In addition, biomass feedstock production use land and thus include a number of impacts related to that land use (see more on impact assessment of land use in Sect. 10.14)


2012). In addition, biomass feedstock production use land and thus include a number of impacts related to that land use (see more on impact assessment of land use in Sect. 10.14). These are, however, in many cases not consistently included in LCA, as mentioned in Sect. 30.4.3. As mentioned, most current conclusions on LCA results of bio-products compared to their fossil-based counterparts are primarily based on 1G bio-products. For 2G bio-products, these results are expected to improve, as this feedstock is generally expected to have a better environmental performance than 1G. This is further discussed in Sect. 30.5. S.V. Hjuler and S.B. Hansen


These results make the current overall environmental performance of bio-products compared to conventional products inconclusive from a general point of view, and illustrate the importance of including all relevant impact categories when considering bio-based product sustainability, in order to avoid problem shifting. Further, it illustrates that environmental sustainability of bio-products relative to fossil counter products is rather case specific, emphasising the need for LCAs on a case level. 30.2.1 Biofuels or Biomaterials? Most LCA studies on bio-products focus on their environmental performance relative to petrochemical counter products. Comparing whether biofuels or biomaterials are preferable, in terms of optimal biomass use from an environmental perspective, has been less studied. However, Patel et al


Comparing whether biofuels or biomaterials are preferable, in terms of optimal biomass use from an environmental perspective, has been less studied. However, Patel et al. (2005) concludes that when comparing use in biofuels or biomaterials from the perspective of energy savings and reduced GHG emissions, biomaterials currently seem to be preferable. This conclusion is supported by Brehmer et al. (2012) stating that dedicated biochemical production can outperform biofuels in terms of fossil fuel replacement potential. As innovation continues in those technologies, these are preliminary results and further investigation is needed. Also in terms of to what extent the issue between biofuels and biomaterials is competition and to what extent it is complementary (Patel et al. 2005)


Also in terms of to what extent the issue between biofuels and biomaterials is competition and to what extent it is complementary (Patel et al. 2005). The potential complementarity between biofuels and biomaterials is to some extent pursued in the biorefinery concept—a bio-based analogue to the existing petrochemical refinery, producing both fuel and materials in an integrated process. However, even though some commercial and pilot scale biorefinery plants exist, the technology is still rather new and only few LCA studies are available. Cherubini and Jungmeier (2010) conclude that the biomass energy and material recovery is maximised if applying the biorefinery concept, joining a variety of technical processes. Apart from the potentially increased efficiency, LCA results resemble the general LCA results for bio-products, as the biorefinery concept is simply an integrated production of various bio-products


Apart from the potentially increased efficiency, LCA results resemble the general LCA results for bio-products, as the biorefinery concept is simply an integrated production of various bio-products. 30.2.2 Major Impact Processes in the Life Cycle The environmental impacts from bio-products originate from a number of activities, but some contribute more than others. Here, some of the major influencing aspects are highlighted: • LUCs can potentially contribute very significantly to especially the GHG emissions in a bio-based product system. LUCs are discussed in Sect. 30.4. LCA of Biofuels and Biomaterials


• A substantial share of environmental impacts from bio-products originate from industrial farming practices, e.g. from the use of fertilisers, pesticides and water. Further, solid and liquid wastes can cause significant impacts if not treated properly, but they also have the potential to be used directly as solid fuel or be used as feedstock for second generation bio-products and thereby improve the environmental performance of the system. • The production stage for bio-products can also have a large contribution to the overall environmental impact, e.g. for bioethanol (Tabone et al. 2010; Wang 2010). 30.2.3 Reliability of Results Conclusions of environmental impacts of bio-products compared to conventional petrochemical products from different LCA studies often differ substantially, as previously mentioned. Here, we distinguish between: • Variations due to real differences in studied systems, e.g


Here, we distinguish between: • Variations due to real differences in studied systems, e.g. due to difference in products, feedstock, production routes or spatial variability due to different regional scopes • Delimitations due to lack of the entire LCA perspective in studies, either by including only one or a few impact categories, or using a scope that does not cover the entire life cycle (e.g. using only ‘cradle-to-gate’ perspective, see Sect. 8.6) • Uncertainty due to limitations in data, insufficient knowledge on which to base reliable assumptions etc. Variations and Delimitations When discussing bio-products in general, there is bound to be variations due to the different types of bio-products. However, many LCA studies on bio-products also differ in, e.g. modelling choices of system boundaries, functional units, allocation procedure and life cycle scenarios (Weiss et al. 2012; Malca and Freire 2011)


modelling choices of system boundaries, functional units, allocation procedure and life cycle scenarios (Weiss et al. 2012; Malca and Freire 2011). Often, there are considerable variations in the LCAs on bio-products due to difference in product systems; e.g. in terms of feedstock type, production technology or yields (Weiss et al. 2012; Wang 2010; Dornburg et al. 2003). Furthermore, results vary with regional scope, as aspects such as electricity generation and end-of-life handling of products differ substantially between countries (Patel et al. 2005). Many LCA studies on bio-products are also delimited in their life cycle approach, either in terms of impact categories, scope or coverage of life cycle stages. For one thing, a substantial part of LCA studies on bio-products focuses only on fossil fuel consumption and GHG emissions (Von Blottnitz and Curran 2007). While this may be relevant in terms of specific political targets, it entails a risk of problem shifting


While this may be relevant in terms of specific political targets, it entails a risk of problem shifting. In later years, it seems that the inclusion of more impact S.V. Hjuler and S.B. Hansen


categories within this field has increased (Weiss et al. 2012). Some studies only include a cradle-to-gate perspective, rather than the full LCA perspective cradle-to-grave, or leave out important aspects in some life cycle stages, with the limitations and potential problem shifting that entails. Other points to be aware of are that there is an overweight of LCAs on European products, and that some results are based on pilot scale production while others are based on large industrial scale (Weiss et al. 2012) and others again are based largely on generic data. Such variations and delimitations make comparison of results difficult and meta-analysis has become a popular tool for reducing some of the differences. In meta-analysis, available studies are made comparable by, e.g. altering the functional unit and recalculating results accordingly (Weiss et al. 2012). Example 30.1 The interest in 3G biofuels has seen an important increase in the last decade


altering the functional unit and recalculating results accordingly (Weiss et al. 2012). Example 30.1 The interest in 3G biofuels has seen an important increase in the last decade. Many theoretical process chains have been proposed by coupling biomass production facilities (as open ponds or photobioreactors) and concentration and transformation processes (centrifugation, chemical separation, wet or dry extraction of compounds of interest, etc.). Since the first microalgal biodiesel LCA (Lardon et al., 2009), around 300 studies dealing with this topic have been published in scientific literature. All these studies try to capture the environmental drawbacks of these emerging systems for which real industrial data do not (yet) exist. The variability of LCIs combined with unclear system boundaries (as well-to-wheel or well-to-tank (see Sect. 27.1.3), co-product management rules, etc.) explains large variations in results. To illustrate this variability, Collet et al


27.1.3), co-product management rules, etc.) explains large variations in results. To illustrate this variability, Collet et al. (2013) reviewed 13 microalgal biofuel LCA publications; they found a range of climate change impact from −75 to 531 g CO2-eq per MJ produced. Collet et al. (2015) list 24 main issues driving variation in LCA results due to differences in goal and scope, LCI and LCIA steps, which should be assessed to allow result comparisons among 3G systems. Uncertainty Uncertainties in inventory data may be rather large and often vary substantially between bio-product studies (Weiss et al. 2012; Patel et al. 2005). One of the main reasons for this is the substantial use of assumptions (Davis et al. 2009), which is partially due to the fact that especially production of biomaterials is still relying on rather new technologies and LCA modelling is often based on small scale data (Weiss et al. 2007)


2007). In addition, when conducting an LCA on bio-products it must be kept in mind that especially biofuel is a highly politicised topic and that existing data and assumptions may be biased. A vast number of academic papers and reports have been published using a wide array of methodologies and assumptions, making comparison of studies difficult if not impossible in many cases. Thus, for the LCA of Biofuels and Biomaterials


purpose of conducting an LCA and collecting data, as well as choosing existing studies for comparison, strict review of the assumptions and data foundation in existing studies is important. The potentials for production of bio-products in developing and emerging countries are rapidly increasing. Specific data in these countries is often sparse, non-verifiable and not publicly available and it cannot be assumed that data from industrial countries apply. Note that Intergovernmental Panel on Climate Change (IPCC) guidelines (e.g. IPCC 2006) often do not apply to tropical conditions as the background data are collected from industrialised countries. Special focus on data integrity and clearly stated assumptions is thus necessary. 30.2.4 Areas Getting Increased Attention Current LCA results generally do not include ILUC impacts, but significant work is dedicated to tackling this issue in LCA


30.2.4 Areas Getting Increased Attention Current LCA results generally do not include ILUC impacts, but significant work is dedicated to tackling this issue in LCA. Further, several land use related impacts are receiving increased attention, such as water consumption and quality downgrading, changes in biodiversity, losses of soil carbon and erosion of soil, as well as changes in surface albedo and other biogeophysical impacts. However, many such land use related impacts have not yet been included in general LCAs (e.g. Weiss et al. 2012; Wang 2010; Patel et al. 2005). ILUC is further discussed in Sect. 30.4.1 and land use related impacts are further discussed in Sect. 30.4.2. N2O emissions from agricultural land using industrial and/or organic fertilisers is another issue, which has been neglected in many studies as data is sparse (Cherubini et al. 2009). However, some studies have highlighted the potentially very significant impacts from this strong greenhouse gas (e.g


2009). However, some studies have highlighted the potentially very significant impacts from this strong greenhouse gas (e.g. Crutzen et al. 2008) and recent studies have quantified some potential emissions, though generic data from IPCC (2006) is often used rather than site specific data (e.g. Majer et al. 2009). 30.3 Specific Issues for Biomaterials In many aspects, biofuels and biomaterials are comparable as they are based on the same feedstock types and thus have similar opportunities and challenges with respect to feedstock issues. For that reason, they can to a large extend be addressed together, which is what has been done in the previous sections in this chapter. However, there are some issues that differ between biofuels and biomaterials, due to one main difference, which is the product lifetime. Whereas the use stage for fuels is also the disposal stage and the fuel is not expected to have a long lifetime, this is different for some biomaterials


Whereas the use stage for fuels is also the disposal stage and the fuel is not expected to have a long lifetime, this is different for some biomaterials. This difference leads to a number of issues that need to be considered specifically for biomaterials. S.V. Hjuler and S.B. Hansen


30.3.1 Temporary Carbon Storage When biomaterials are produced, they store the biogenic carbon from their biomass origin, thus keeping it out of the atmosphere until, the carbon is released again, which e.g. happens if the product is incinerated at its end of life. There is an ongoing discussion on how, and even if, such temporary carbon storage contributes to reducing global warming issues, and many suggestions for the handling of temporary carbon storage exist (see, e.g. Brandão et al. 2013; Jørgensen et al. 2015). The ILCD handbook handles temporary carbon storage as follows (European Commission 2010): Temporary removal of CO2 from the air by e.g. storage in long-lived bio-products is accounted for in the inventory, but generally not included in the total impact calculation of the LCA, due to the general infinite time horizon in LCA. It should only be considered in the total impact calculation if the short-term perspective rather than the normal infinite perspective is considered


It should only be considered in the total impact calculation if the short-term perspective rather than the normal infinite perspective is considered. Generally, all emissions occurring during the first 100 years of the analysis are inventoried as normal elementary flows, whereas emissions occurring after those 100 years are inventoried as ‘long-term emissions’ and are not included in the short-term emissions. Thus, accounting for temporary carbon storage is done by introducing a ‘correction elementary flow’, using IPCC GWP100 factors and including the duration of the temporary carbon storage within the first 100 years of the analysis. Using this approach, the derived GWP100 impact factor is −0.01 kg CO2-eq for 1 kg CO2 stored 1 year.1 This way of handling temporary carbon storage is analogous to the handling of delayed emissions, with the exception that fossil delayed emissions do not have the benefit of prior uptake of CO2 from the atmosphere


Example 30.2 If a stock of biopolymer products has a mass of carbon equivalent to 1 tonne CO2 (*273 kg carbon) and is stored for 5 years, the correction flow will be: 1000 kg CO2  5 years  0:01 kg CO2-eq=kg CO2=year 1⁄4 50 kg CO2-eq ð30:1Þ If the study requires inclusion of the value of temporary carbon storage in the impact assessment, the correction flow of −50 kg CO2-eq means that 50 kg CO2-eq are subtracted from the total GHG impact of the product stock over its lifetime, thus lowering the carbon footprint of the products. 10.01 kg CO2-eq per kg CO2 stored 1 year corresponds to the GWP100 for 1 kg CO2 of 1 kg CO2eq over 100 years, if assuming linearity, and considering a short-term perspective, rather than the normal infinite one, as explained in the text. LCA of Biofuels and Biomaterials