Applying consequential LCA to support energy policy: Land use change effects of bioenergy production

Science of the Total Environment 472 (2014) 78–89

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Applying consequential LCA to support energy policy: Land use change effects of bioenergy production Ian Vázquez-Rowe ⁎, Antonino Marvuglia, Sameer Rege, Enrico Benetto Public Research Centre Henri Tudor (CRPHT), Resource Centre for Environmental Technologies (CRTE), 6A, avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Partial equilibrium (PE) model created for the agricultural sector in Luxembourg • PE model combined with a consequential LCA approach to support energy policy • The impact of LUCs due to the additional production of maize for energy was modelled. • Three different consequential decision contexts were presented. • Lame environmental benefits of introducing energy crops in Luxembourg • PE + LCA method useful for regional contexts with high data availability

a r t i c l e

i n f o

Article history: Received 4 July 2013 Received in revised form 23 October 2013 Accepted 27 October 2013 Available online 27 November 2013 Keywords: Consequential LCA iLUCs Luxembourg Partial equilibrium General equilibrium Land use change

a b s t r a c t Luxembourg aims at complying with the EU objective of attaining a 14% use of bioenergy in the national grid by 2020. The increase of biomethane production from energy crops could be a valuable option in achieving this objective. However, the overall environmental benefit of such option is yet to be proven. Consequential Life Cycle Assessment (CLCA) has shown to be a useful tool to evaluate the environmental suitability of future energy scenarios and policies. The objective of this study was, therefore, to evaluate the environmental consequences of modifying the Luxembourgish agricultural system to increase maize production for biomethane generation. A total of 10 different scenarios were modelled using a partial equilibrium (PE) model to identify changes in land cultivation based on farmers' revenue maximisation, which were then compared to the baseline scenario, i.e. the state of the agricultural sector in 2009. The results were divided into three different consequential decision contexts, presenting differing patterns in terms of land use changes (LUCs) but with minor shifts in environmental impacts. Nevertheless, energy from maize production would imply substantially higher environmental impacts when compared with the current use of natural gas, mainly due to increases in climate change and agricultural land occupation impacts. The results are discussed based on the consequences they may generate on the bioenergy policy, the management of arable land, the changes in import–export flows in Luxembourg and LUCs in the domestic agricultural system. In addition, the specific PE + LCA method presented intends to be of use for other regional studies in which a high level of site-specific data is available. © 2013 Elsevier B.V. All rights reserved.

⁎ Corresponding author. Tel.: +352 425 991 4926. E-mail address: [email protected] (I. Vázquez-Rowe). 0048-9697/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.scitotenv.2013.10.097

I. Vázquez-Rowe et al. / Science of the Total Environment 472 (2014) 78–89

1. Introduction In an attempt to comply with the 20/20/20 targets set by the European Union (EU) in 2009 (EC, 2009), which pledges a substantial reduction of greenhouse gas (GHG) emissions by 2020, member states are forced to implement a series of interdisciplinary laws, schemes and measures to attain the objectives dictated. In particular, nations are required to augment their share of total energy production arriving from renewable sources to 20% by 2020. This increase includes attaining 11% of renewable source biofuels in final energy consumption (EC, 2007, 2009; MECE, 2010). These objectives are an important matter of concern for all EU states. However, depending on the energy production and consumption profile from different nations, the level of difficulty to comply with the marked reduction scenarios may vary substantially. For instance, Luxembourg has to deal with a very high energy consumption rate, increasing energy imports from neighbouring countries and large number of border commuters entering the country annually (EEA, 2010a). Additionally, it is important to note the limited land use potential of Luxembourg, given its reduced size and high population density (EEA, 2010b). Nevertheless, due to the limitations that the implementation of other renewable energy sources entails, the use of biomass for biogas generation in power plants has arisen as one of the major initiatives that authorities and stakeholders are promoting to account for GHG emission reductions (Ragwitz et al., 2007). The use of agricultural crops for energy production has, however, been shown to cause relevant impacts in other environmental dimensions, such as acidification, eutrophication or toxicity, in some cases higher than those generated by conventional fossil fuels (EC, 2008; Scharlemann and Laurance, 2008). Furthermore, recent scientific findings also highlight the dwindling benefits that biofuels can produce in terms of GHG emissions due to land use changes — LUCs (Searchinger et al., 2008; Hertel et al., 2010). For instance, Hertel et al. (2010) pointed out that the attractive prices linked to energy crop production are causing the conversion of pastures and forests into cropland on a worldwide scale, with a subsequent loss of biodiversity and carbon storage in the biosphere. Nevertheless, other studies have pointed out the advantages of energy crops within specific land use considerations (Tilman et al., 2006). Given these disparities between studies, the use of comprehensive assessment tools, such as Life Cycle Assessment (LCA), appears to be an appropriate way to assess the environmental suitability of a given biofuel enhancement policy. One of the main advantages of LCA is the broad set of environmental impacts it includes, providing a life-cycle based assessment of the product or process evaluated (ISO, 2006). Nevertheless, many LCA studies focus exclusively on the internal flows of a production system, without considering the effects that the system and its final flows may have on other related economic systems. This perspective, known as the attributional LCA approach (ALCA), has been predominant in life cycle thinking, but this perspective does not account for the consequences that increased energy crop cultivation generate on food production or biodiversity preservation (Reinhard and Zah, 2011). A more recent approach, named consequential LCA (CLCA), seeks an environmental assessment that takes the evaluation a step further, in order to analyse how physical flows and, therefore, environmental burdens, may vary in response to changes with (marginal or structural) market implications in a specific life cycle beyond the foreground system (Ekvall and Andræ, 2006; UNEP, 2011). Regarding its application to bioenergy, it can track the environmental consequences from potential changes induced in the production system, due to expansion, displacement or intensification (Kløverpris et al., 2008). Moreover, despite its recent development, CLCA has been shown to provide a different interpretation framework as compared to ALCA (Reinhard and Zah, 2009; Hertel et al., 2010), especially in terms of informing policymakers and decision makers on the indirect effects of a specific strategy (Sánchez et al., 2012).

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The main aim of the current research is to analyse from a CLCA perspective the environmental consequences of increased maize production for biomethane generation in Luxembourg to meet the 20/20/20 targets, granting special attention to the indirect land use changes (iLUCs) derived from these practises. The selection of maize is linked to the fact that this crop currently constitutes a commonly employed resource for energy purposes in Luxembourg (Jury et al. 2010; Udelhoven et al. 2013). Hence, a set of different CLCA scenarios were modelled and computed based on an economic partial equilibrium (PE) model, in order to determine the socio-economic mechanisms via market information that will potentially occur in the analysed production system (Marvuglia et al., 2011; Earles et al., 2013). In addition, this article constitutes a comprehensive assessment of the Luxembourgish agricultural system in terms of LCA, providing the inclusion of consequential indirect effects of land use changes in the assessment. 2. Materials and methods 2.1. Goal and scope definition The main goal of this study is to evaluate the direct and indirect lifecycle environmental impacts linked to an additional requirement of 80,000 tonnes of maize for bioenergy production, in order to meet EU biofuel production objectives by 2020. This value was calculated based on the 144 GWh of electric energy from biogas targeted by the Luxembourgish government in 2020 (Marvuglia et al., 2011). Moreover, it assumes that this amount of biogas would be supplied domestically through an increase in the production of maize to produce biomethane (for more details see Section S1 in the Electronic Supplementary Information — ESI). A preliminary survey carried out by the Service d'Economie Rurale (SER) highlighted the fact that the only feasible mechanism identified in order to respond to an increase in the demand of biomass in Luxembourg is the replacement of crops or imports. These two mechanisms already constitute common activities in Luxembourg due to high land use competition and to the fact that agricultural production is below the national demand. Therefore, intensification and expansion are not likely to be put into effect in Luxembourg. The former is not a feasible strategy given the existing high yields for crop cultivation and the strict legislative regulations. The latter is forbidden according to current Luxembourgish laws (SER, 2012). Before starting the crop replacement modelling, a preliminary estimation of the dimension of the changes potentially produced on a global scale by the introduction of the shock on the Luxembourgish agricultural system (i.e. 80,000 tonnes of maize) was carried out applying a computable general equilibrium (CGE) approach using the Global Trade Analysis Project (GTAP) database in accordance with the methodology followed by prior studies (Kløverpris et al., 2008, 2010). Given the lack of specific information and data relative to Luxembourg contained in the GTAP database, the shock was introduced for a cluster representing Belgium and Luxembourg. The simulations showed that only when a shock equivalent to an increase of at least 100% of the Luxembourgish national crop production is introduced, do changes in trade partner countries, such as Belgium, France or The Netherlands start to be noticeable (Thomas Dandres, personal communication, CIRAIG, March 2012). Consequently, given that the selected CGE model (i.e. GTAP) is not suitable for this case study, since the size of the intended shock is not big enough to generate any effects at the level of granularity handled by GTAP, the development of a PE model was specifically advanced in order to consider the market constraints and the reaction to the production of maize demanded for a given time horizon and at the specific regional scale (Rege et al., submitted for publication). 2.2. Assessment method for the PE model The PE model was implemented in order to provide a market-linked operational tool to calculate the LUCs in the Luxembourgish agricultural

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system by 2020. The inventory data used to run the PE model reflected the data shown in Table 1. Based on these data, the model was run in the General Algebraic Modelling System (GAMS) in order to obtain the change in crop production patterns (Rosenthal, 2011). Two different perspectives were considered, within the same model, regarding the production system. On the one hand, one model structure was developed which is based exclusively on the opportunity cost of planting a different crop from the existing one, minimising the sum of all opportunity values subject to restrictions on the maximum possible replacement permissible for each crop (Rege et al., submitted for publication). In the case of the farmer, the opportunity cost is the economic sacrifice related to the second best (i.e. second most remunerative) crop choice available. Hence, the opportunity values in €/ha are calculated for each crop considering yield, market price, subsidies, fertilisers or variable and fixed costs. Furthermore, it is important to highlight the fact that the agricultural sector is considered a single entity rather than a wide range of small-holdings, i.e. the entire Luxembourgish agricultural system is modelled as an average farm. On the other hand, the model maximised the profits from cropping and livestock operations on the representative farm. The prices of crops were exogenously specified from past data and given this set of prices the farmers would then maximize the land allocated to each crop, subject to permissible bounds on changes in crop replacement in Luxembourg. The cost involved fixed and variable costs besides fertiliser costs and the response of crop yields to fertiliser use. Given the NPK fertiliser use for each crop and the expenditure on fertilisers in the base year 2009, the crop yield response curves to fertiliser use were estimated. This enabled the model to endogenise the decision to intensify the production. The by-products from crops, namely straw, oil and cake, wherever applicable, were also incorporated in the decision to produce feed for animals. Livestock in the base year were subject to a minimum metabolic requirements level arriving from a variety of sources (grains, straw and cake of different crops). The prices of animal products (meat and milk) were exogenously specified based on data for 2009. Luxembourg was deficient in animal feed in 2009 and still is. The gap between demand and supply for animal feed is covered by competitive products imported from abroad. However, in the model it was assumed that this gap is automatically satisfied by imports, as modelling cheaper imports would mean the whole animal feed requirement would be imported, a procedure discouraged by the government and unfeasible in practise. This would also lead to spurious values for profits from the Table 1 Agricultural area of Luxembourg per crop in 2009 (Source: STATEC, 2010). Crop

Area (ha)

Production (tonnes)

Maize dry matter (DG) Pastures Meadows Other forage crops Wheat (animals) Wheat (humans) Barley (winter) Triticale Barley (spring) Rapeseed Other crops Oats Vineyards Rye Potatoes Grain maize Spelt Crops NES Dried pulses Mixed grain Beans TOTAL

16,079 58,320 9023 7981 6866 6575 5863 4055 3507 4487 1850 1384 1242 1101 604 409 400 392 305 242 77 130,762

219,872 479,689 74,212 109,135 45,444 43,761 36,044 25,415 18,354 18,132 90,735 7197 13.481 6924 20,044 2453 1857 2433 1206 1272 271 –

1

Output in hectolitres.

optimization perspective. Finally, it should be noted that given the characteristics of the PE model developed, a direct application of the shock was modelled by 2020, rather than a progressive annual application. The model is therefore of the comparative static type, i.e. it compares two status quo situations: the one in 2009 and the one in a future moment in time when the changes being evaluated are supposed to be operational (in our case we hypothesise this moment to be the year 2020). A detailed description of the PE model is provided in Section S4 of the ESI. 2.3. Consequential LCA decision contexts and scenarios As previously discussed in literature, CLCA modelling is strictly dependent on the decision context setting, including the decision question to be addressed, the relevant stakeholders involved and the final goal, i.e. planned use and communication of the results from the study (Marvuglia et al., 2011; Earles et al., 2013; Sánchez et al., 2012). Accordingly, three different decision contexts were defined in order to understand the expected environmental consequences in the Luxembourgish agricultural system. In the first approach (Approach A) the agricultural system was assumed to suffer substitution processes occurring due to revenue maximisation, but without the existence of an 80,000 tonne shock of maize production for biogas generation. This approach allows understanding the environmental consequences of a business-as-usual (BAU) perspective, where farmers progressively optimize their operations and, therefore, how the crop production patterns would evolve, from an optimization perspective, without biogas generation. This approach represents the baseline scenario, and provides farmers with information regarding the environmental consequences for the progressive optimization of farming activities. A second approach (Approach B) took into consideration the entire shock of increased maize production, focusing on the changes occurring in the national agricultural system, i.e. without considering imports– exports. It also considers a farmers' approach, by evaluating the environmental impacts linked to the changes in the agricultural system associated with maximisation of revenues and an increase in maize cultivation for bioenergy production. It essentially informs the farmers about the consequences (related to the Luxembourg's agricultural system only, i.e. to their scope of influence and decision-making) on the maize production increase for biomethane. Finally, a third approach (Approach C) expands the assessment's scope, by including not only the consequences on the domestic agricultural sector and its predicted changes, but also the import/export flows due to crop expansion/displacement, as well as the entire biomethane production process up to its use in the national grid. This approach is aimed at aiding policy development, providing policy-makers with predictive forecasts and analysis regarding the whole analysed production system. All three approaches have certain disparities concerning the system function which, therefore, affect the methodological implications of each of them. Furthermore, given the diversity of decision variables and actions underlying these decision contexts, a total of 10 scenarios were modelled and computed in the PE model following the two modelling perspectives previously described (see Table 2 and Table S4 in the ESI), as compared to the baseline scenario (BAS), which refers to the actual crop land distribution in Luxembourg in 2009. 2.4. Function and functional unit selection The main objective of the system under analysis is the production of the additional 80,000 tonnes of maize for energy generation in Luxembourg. However, it is important to note that due to the consequential nature of the study, the three different decision contexts considered are not meant to encompass directly comparable functions. In Approach A the absence of a bioenergy shock on the system disregards the previous function description as the main carrier. Hence, the function may be described as the predicted LUCs in the Luxembourgish

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Table 2 List of scenarios considered for the PE model. Abbreviation Scenario

Brief description Allocation

BAS

Baseline scenario

CR_NSH

Change in 2020 without maize shock and excluding animal costs Change in 2020 including maize shock and excluding animal costs Change in 2020 without maize shock and including animal costs Change in 2020 including maize shock and animal costs Minimum animal requirement at base level without maize shock Minimum animal requirement at base level including maize shock

CR_SH AN_NSH AN_SH MAR_NSH MAR_SH

MP_NSH MP_SH RP_NSH RP_SH

Approach

N/Ap

N/Ap

No additional demand for maize, but changes in crop allocation for crops to maximize gain. Additional demand for maize at 80,000 tonnes with all crops undergoing land use change. No additional demand for maize, but changes in crop allocation for crops to maximize gain. Additional demand for maize at 80,000 tonnes with all crops undergoing land use change. No additional demand for maize, but changes in crop allocation for crops to maximize gain. Additional demand for maize at 80,000 tonnes with minimal animal requirement (MAR) fixed at base level to detect effects of livestock feeding patterns. Meadows/pastures are allowed to undergo No additional demand for maize, but changes in crop allocation LUC without maize shock for crops to maximize gain. Meadows/pastures are allowed to undergo Additional demand for maize at 80,000 tonnes with the LUC including maize shock meadows and pastures undergoing land use change. Rapeseed is allowed to undergo LUC No additional demand for maize, but changes in crop allocation without maize shock for crops to maximize gain. Rapeseed is allowed to undergo LUC inAdditional demand for maize at 80,000 tonnes. Lower bound of cluding maize shock land use change was fixed at 80% for rapeseed. Only rapeseed, together with maize, allowed to have LUCs.

agricultural system in 2020 whenever farmers seek a maximisation of revenues, assuming a ceteris paribus situation (BAU). Approach B, which can be considered the core of the analysis, attempts to link a specific increase in maize production for bioenergy with the consequential environmental impacts that this action would imply. In the same way as mentioned in Approach A, the conferred decision context seeks a maximisation of revenues by farmers. Hence, in both approaches the derived consequences were limited to evaluating the environmental burdens related to farmers' decisions for improving their economic revenue based on current trends in terms of crop prices and harvest yields. This decision context led us to limit the system boundaries to the direct and indirect LUCs which occur within the Luxembourgish agricultural system. The LUCs stemming from this CLCA perspective, which should not be associated with the geographical nature of its limits, but to the decision context, have been named bounded indirect land use changes. Coherently, the possible surplus (or deficit) from crops production and related consequences' chains are not included in the analysis as they are beyond the scope of the decision context, which is centred on the farmers' perspective. Based on this rationale, the selected functional unit (FU) in Approach A was represented by the 126,434 ha dedicated to agricultural production in Luxembourg. The same FU was chosen for Approach B. However, Life Cycle Impact Assessment (LCIA) results in Approach B were rescaled to 1 tonne of maize production at the gate of the processing plant in Luxembourg. This procedure was performed to allow manageable values when interpreting the results. Hence, the results should be only interpreted in the context of the described shock, since environmental consequences may be scale-dependent. The rationale behind the FU was linked to the fact that the aim of the study was to induce the domestic agricultural sector to produce an additional amount of maize for bioenergy purposes, with the aim of achieving the highest avoided environmental impacts. A land use FU perspective (e.g. 1 ha of maize production) was, therefore, disregarded. An energy FU was also excluded due to the fact that the system boundaries were limited to the agricultural stage of production. Finally, the main function in Approach C is to provide environmentally significant results to support policy-makers in deciding the appropriateness for enhancing bioenergy production in Luxembourg. To provide these results, the system boundaries need to be extended to account for the different consequences related to new import and export flows of

Crop opportunity costs maximisation exclusively. Crop opportunity costs maximisation exclusively. Opportunity cost maximisation for livestock and crops. Opportunity cost maximisation for livestock and crops. Opportunity cost maximisation for livestock and crops. Opportunity cost maximisation for livestock and crops.

Opportunity cost maximisation for livestock and crops. Opportunity cost maximisation for livestock and crops. Opportunity cost maximisation for livestock and crops. Opportunity cost maximisation for livestock and crops.

agricultural products, as well as including the entire bioenergy production chain from maize. The LUCs stemming from this perspective, which includes an expansion of consequential depth in CLCA, have been named extended indirect land use changes. For this approach, one of the FU perspectives disregarded in Approach B, the energy FU, was considered the most appropriate. Hence, the energy delivered from the selected maize production shock was rescaled for the production of 1 MJ of biogas injected into the natural gas grid, as described by Jury et al. (2010). In a similar way as in Approach B, the results' computation was rescaled to 1 MJ after a full scale calculation. 2.5. System boundaries System boundaries in CLCA are not limited to the evaluated production system as in ALCA studies, due to the necessary enlargements caused by the predicted changes in the Luxembourgish agricultural system. Hence, not only maize production was modelled and included in the system analysed, but also the inventories from all the other crops present in the agricultural sector in Luxembourg and, therefore, affected by changes in LUCs (Fig. 1). This approach is named system expansion, since the alternative production functions are included within the system (Thomassen et al., 2008). Livestock, despite its inclusion in the PE model, was excluded from the system boundaries and, therefore, not included in the Life Cycle Inventory (LCI). The rationale behind this decision is linked to the fact that the PE model structure when animals are included assumes a fixed minimum feed requirement (MFR) per day for the animals. Therefore, the actual number of cattle, as well as their weight or the slaughtering and milking patterns are not altered within the model, since it only calculates feeding (i.e. fodder) redistribution patterns to maintain the MFR, based on opportunity cost. The assessment boundaries in approaches A and B were limited to the agricultural crop production stage, excluding processing, wholesaling, retailing and consumption stages. The transport of the crops up to the processing plant's gate was included within these boundaries. The substitution or replacement effects due to LUCs in the analysed system will generate a cascade effect in terms of certain operational inputs, such as fertilisers, field operations or application of pesticides. Moreover, the occupation and transformation patterns on different crops are also important consequences to be taken into account when LUCs

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Fig. 1. Schematic representation of the consequential land use implications of maize production for energy purposes in Luxembourg depending on the selected modelling approach.

take place. The availability of derived products in Luxembourg also suffers important variations. This issue becomes more complex when the co-product status of most crops is considered. Finally, regarding Approach C, system boundaries were expanded to meet the function's requirements. The conversion of maize into bioenergy was therefore included, as well as the imports and exports that are needed to level out the excess and lacking agricultural products in the predicted new scenarios. Assumptions were made that substituted crops were imported raw from neighbouring countries (i.e. Germany, Belgium and France), based on expert opinion and the decision-tree model (Schmidt, 2008). 2.6. The role of biogenic carbon LUCs may imply important carbon transfers from vegetation and soil to air and vice versa, depending on the direction of these transformations. For instance, if cropland is transformed to forest, a higher carbon capacity is expected both in the soil and in the aerial vegetation (Poeplau et al., 2011). However, studying the carbon retention in the analysed areas is not within the scope of this study, which rather aims at assessing the changes in this storage due to LUCs (Müller-Wenk and Brandão, 2010). A methodological limitation that was encountered in this study was the fact that carbon stocks are usually calculated based on biomes, rather than on individual crops (Milà i Canals et al., 2007). Therefore, given the high level of detail in the current study in terms

of crop differentiation, as well as the lack of detailed sampling values for soil organic content, the soil was excluded from the system boundaries on the production system under analysis. 2.7. Life Cycle Inventory (LCI) and Life Cycle Impact Assessment (LCIA) methods The LCI stage was modelled based on the three different approaches considered in this study. Hence, for Approach A the LCI is limited to all the currently existing crops in the Luxembourgish agricultural system (see Table 1 and Table S11 in the ESI). For Approach B the situation highly resembles the LCI described for the previous perspective. However, it was necessary to model the production system for the maize crops that would be implemented in the fields for biogas production. Therefore, the maize cultivation stage in the production system described by Jury et al. (2010) was improved and used for this purpose, adapting the attributional LCI (ALCI) to the multifunctional characteristics of consequential LCIs (see Table S10 in the ESI). Finally, Approach C feeds off the core LCI of Approach B, but adds two additional aspects: firstly, the entire supply chain for biogas production from maize cultivation, and the individual LCI processes for crop cultivation sites outside Luxembourg that account for the displacement of agricultural processes in Luxembourg. Secondly, it integrates into the inventory the changes in the import/ export flows of agricultural products. Details on the data acquisition for the LCIs are available in section S3 of the ESI.

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In order to obtain an overarching perspective of the environmental impacts and damages identified in the chosen production system, and to check the consistency of the results, several different LCIA methods were computed. The analysis of midpoint impacts, as well as endpoint damages, was calculated using Impact 2002+ and ReCIPe (Jolliet et al., 2003; Goedkoop et al., 2009). 2.8. Combined PE + LCA method The synergistic combination of the PE model and consequential LCI, as proposed by Marvuglia et al. (2013) aims at providing a meta-model in which economic modelling is integrated together with relevant environmental flows that are subject to change in agricultural systems. In fact, despite its application in this case study to the specificities of the Luxembourgish agricultural sector, the method characteristics may apply to other case studies with regional impact. Therefore, the additional demand for maize to produce biogas in the Luxembourgish domestic market is calculated using the PE model, which provides the substitution factors for the different crops present in the system based on the abovementioned market optimization process. These values are then used to carry out a LCA in which not only the maize production system, but also the crop patterns changes due to crops substitution within the domestic agricultural system are included (Approach B). Moreover, in Approach C the import/export flows generated by the LUCs are accounted for. Consequently, the joint inclusion from market driven aspects and the traditional LCI conception based on the ecoinvent® database aims at improving market-dependent production systems modelling on a regional scale (Frischknecht et al., 2007). 3. Results

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MAR_NSH and MAR_SH demonstrate how relatively low changes in feed requirement for animals may have important implications regarding domestic LUCs. 3.2. Environmental consequences without bioenergy policy — Approach A The results obtained for Approach A represent the estimated fluctuations in environmental impacts based on farmers' revenue maximisation in the absence of a bioenergy policy. As shown in Fig. 2 (see also Fig. S5 and Tables S13 and S14 in the ESI), results in this approach show important shifts in environmental impacts, not only when compared to the BAS, but also when the five different scenarios are compared between each other. For instance, the approach in which only crop revenues are considered (CR_NSH) shows an important decrease in agricultural land occupation (ALO) impacts due to the reduction of intensive cereal crops production (i.e. barley or wheat) and an increase of pasture and meadow area. Moreover, due to these alterations, an increase in climate change (CC) impacts is observed associated with a diminution of aerial carbon content in the available crops. In contrast, scenario AN_NSH, which includes livestock revenues, shows a reverse situation, where ALO impacts strongly increase due to an expansion in cereal crops and GHG emissions fall as a consequence of an elevated carbon retention rate in the fields. When the different crops are assessed individually in terms of subsystem, as can be observed in Fig. S5 in the ESI, the mineral fertiliser production and transport appears as one of the main carriers for environmental impacts in most impact categories, together with field operations and, to a lesser extent, seed production, pesticide production and field emissions from fertilisers and pesticides (toxicity categories). 3.3. Environmental consequences of bioenergy policy (farmers' decision context) — Approach B

3.1. Predicted land use changes for the assessed scenarios A total of 10 different scenarios for the agricultural system in Luxembourg were run in the PE model (the BAS scenario was not included), obtaining a wide range of LUCs (see Table S12 in the ESI). Results throughout the computed scenarios suggest three different types of crops in terms of their substitution capability regarding opportunity cost minimisation: i) expanding crops, which are those crops that show an increase in their surface area in the 2009–2020 period, regardless of the selected scenario (e.g. meadows, maize); ii) swing crops, which show variable trends depending on the constraints included in the model in each specific scenario (e.g. barley, wheat, rapeseed); and, iii) narrowing crops, which show a general trend to a decrease in surface area (e.g. oats, mixed grain). Scenarios CR_NSH and CR_SH showed a widespread reduction in cereals, such as wheat, barley or spelt, substituted by meadows and pastures for the non-shock scenario, and meadows and especially the maize shock in the bioenergy policy scenario. However, if the opportunity costs linked to livestock are included in the model (scenarios AN_NSH and AN_SH) the results become more complex to interpret. For instance both scenarios present a sharp reduction in the area destined to pastures (roughly 12,000 ha), while they both present a considerable increase in land use for meadows (3339 ha), and cereals, such as barley or rye. Interestingly, the maize shock in the AN_SH scenario was achieved mainly thanks to a reduction in wheat production for animal feed and winter barley. The remaining scenarios also include costs linked to livestock, but with specific constraints in the modelled system. Therefore, results shown for these scenarios are highly dependent on these particular constraints. In the first place, the two scenarios regarding LUCs for meadows and pastures exclusively show a strong reduction in pasture land that would be occupied by meadows, and also by maize for bioenergy in the MP_SH scenario. Secondly, the scenarios in which the constraints linked to rapeseed fluctuations are loosened (RP_NSH and RP_SH) show a significant reduction for this crop. Finally, scenarios

The direct LUCs (dLUCs) and iLUCs related to maize production expansion in Luxembourg show considerable replacements between the analysed crops. Concerning the environmental profile expected in 2020 based on the five different scenarios, results show a stable picture in terms of overall variation of environmental impacts, with slight environmental improvements ranging from 2% to 8% depending on the scenario. However, when analysing the specific impact categories separately, ALO shows a strong increase when compared to BAS due to more intensive agricultural practises (see Fig. 2 and Section S8 in the ESI). Moreover, this is also the case for fossil depletion (FD), associated with increased field operations in Luxembourgish fields. In contrast, CC impacts fall sharply, despite the increase in fossil fuel consumption, as a consequence of LUCs that imply a higher level of carbon sequestration in the fields. 3.4. Environmental consequences of bioenergy policy (policy makers' decision context) — Approach C The use of maize for bioenergy production in Luxembourg generates important shifts in the import/export agricultural products' flow with neighbouring countries (e.g. Germany or Belgium). As shown in Fig. 2, an expansion of maize production for bioenergy would bring about slight increases in the overall environmental impact, ranging from 1% to 4% depending on the scenario selected. However, it is important to stress that a great part of the environmental impact is linked to the post-agricultural biogas production stages (see Fig. S11 in the ESI). Therefore, if the scope is limited to the agricultural stage exclusively, increases in environmental impact would range from 5% to 31% (see Section S9 in the ESI for more details). These swings in LUCs would have an important impact on the environmental profile of the energy system in Luxembourg since procuring energy from maize production would imply environmental impacts 20–25% higher than the impacts related to the use of conventional natural gas (see Fig. 3). In fact, the use of bioenergy rather than conventional gas would only imply benefits in two impact categories: ozone

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Fig. 2. Approach A) Single score ReCiPe endpoint values for each of the selected scenarios in Approach A in terms of variation as compared to the baseline scenario — BAS. Data per FU = Luxembourgish agricultural system (126,434 ha); Approach B) Single score ReCiPe endpoint values for each of the selected scenarios in Approach B in terms of variation as compared to the baseline scenario–BAS. Data per FU = 1 tonne of maize for bioenergy production; Approach C) Single score ReCiPe endpoint values for each of the selected scenarios in Approach C in terms of variation as compared to the baseline scenario–BAS. Data per FU = 1 MJ obtained from maize cultivation. NOTE: CC [HH] = climate change–human health; OD = ozone depletion; HT = human toxicity; POF = photochemical oxidant formation; PMF = particulate matter formation; IR = ionising radiation; CC [Ec] = climate change–ecosystems; TA = terrestrial acidification; FE = freshwater eutrophication; TET = terrestrial eco-toxicity; FET = freshwater eco-toxicity; MET = marine eco-toxicity; ALO = agricultural land occupation; ULO = urban land occupation; NLT = natural land transformation; MD = metal depletion; FD = fossil depletion; CR_NSH = scenario modelling crop rotation (excluding livestock) and no additional maize production in the PE model; CR_SH = scenario modelling crop rotation (excluding livestock) and additional maize production in the PE model; CR_SH′ = scenario modelling crop rotation (excluding livestock) and additional maize production in the PE model, as well as computation of new import/ export flows; AN_NSH = scenario including livestock modelling and no additional maize production in PE model; AN_SH = scenario including livestock and additional maize production in PE model; AN_SH′ = scenario including livestock and additional maize production in PE model, as well as computation of new import/export flows; MAR_NSH = minimum animal requirement at base level without maize shock; MAR_SH = minimum animal requirement at base level with maize shock; MAR_SH′ = minimum animal requirement at base level with maize shock, as well as computation of new import/export flows; MP_NSH = meadows and pastures undergo LUCs in the PE model without maize shock; MP_SH = meadows and pastures undergo LUCs in the PE model with maize shock; MP_SH′ = meadows and pastures undergo LUCs in the PE model with maize shock, as well as computation of new import/export flows; RP_NSH = rapeseed undergoes LUCs in the PE model without maize shock; RP_SH = rapeseed undergoes LUCs in the PE model with maize shock; RP_SH′ = rapeseed undergoes LUCs in the PE model with maize shock, as well as computation of new import/export flows.

depletion and fossil depletion. This increase would be mainly due to the consequential effects of maize expansion, especially those associated with the new import/export flows from neighbouring nations and, to

a lesser extent, the increased use of fertilising agents and more intensive field operations. Nevertheless, it should be mentioned that the increase in fertilisation requirements would be partially compensated by the

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Fig. 3. Single score ReCiPe endpoint values for selected CLCA approaches as compared to the use of natural gas in the grid. Data per FU = 1 MJ. NOTE: CC [HH] = climate change–human health; OD = ozone depletion; HT = human toxicity; POF = photochemical oxidant formation; PMF = particulate matter formation; IR = ionising radiation; CC [Ec] = climate change–ecosystems; TA = terrestrial acidification; FE = freshwater eutrophication; TET = terrestrial eco-toxicity; FET = freshwater eco-toxicity; MET = marine eco-toxicity; ALO = agricultural land occupation; ULO = urban land occupation; NLT = natural land transformation; MD = metal depletion; FD = fossil depletion; AN_SH′ = scenario including livestock and additional maize production in PE model, as well as computation of new import/export flows; CR_SH′ = scenario modelling crop rotation (excluding livestock) and additional maize production in the PE model, as well as computation of new import/export flows.

availability of digestate residues produced in the biogas production phase. The main environmental impact carriers, as shown in Figs. 2 and 3, are CC, not only due to the use of fossil fuels, but also to the inclusion of biogenic CO2 emissions resulting from biomethane combustion for energy production, ALO, due to the increase in intensiveness of the cultivation methods and to relocation of crops in other countries which still nourish livestock and human consumption in Luxembourg, and finally FD. 4. Discussion 4.1. LUCs in the Luxembourgish agricultural sector The results obtained show that an increase in energy crop cultivation in Luxembourg can have a series of important effects: i) a reduction in the arable land destined to food/feed consumption; ii) an increase in the amount of crops that should be imported from elsewhere due to displacement; iii) a considerable variation in terms of land use in the domestic agricultural system; and iv) the additional production of a considerable amount of digestate that can be used in substitution from marginal mineral fertilisers. However, it should be noted that these observations are extracted from a joint interpretation of the three approaches to the system under study that were included in the assessment: I) Reduction of food/feed production. Approach B reveals a reduction of the arable surface area destined to feed and human consumption, which would ultimately lead to an increase in the import flows for these products, as represented in Approach C. This increase in imported agricultural goods for nourishing purposes is expected to be a similar concern in neighbouring countries, where the pressure on crops due to augmenting bioenergy uses is also a current topic. Moreover, recent studies are starting to highlight the importance on maintaining fields worldwide for human consumption, due to increasing demographics and depleted hunting and fishing stocks, leaving agriculture and derived livestock activities as the main carriers of human nutrition (Von Braun, 2007; Worm et al., 2009). In fact, some studies point out the highly inefficient process linked to biofuel production (Michel, 2012). Finally, increased biofuels production, with

consequent increases in benefits by farmers may lead to lobbying to push for further allowable land for agriculture, increasing the environmental effects of iLUCs, since this would probably imply, in the case of Luxembourg, the expansion of energy crops in abandoned land or forest (Giampietro and Mayumi, 2009). II) Crop displacement. Table S12 in the ESI shows the expected shifts in land use by 2020 due to maize expansion. However, changes in crop patterns may cause important alterations in a whole set of different production systems. For instance, grain and straw crops destined to animal feed can be substituted by other expanding crops with similar protein, energy or fibre characteristics, with the aim of maintaining the same nourishment requirements. If this input from expanding crops (excluding those for bioenergy) were insufficient, the remaining stock would have to be imported (Approach C). In a similar way, food products that are no longer produced in Luxembourg (e.g. barley for beer production) would have to be imported. Conversely, an increase in production for any of these products would imply a surplus that would have to be absorbed by foreign markets based on the domestic ceteris paribus approach considered in this study. Given the domestic settings used for the PE model, assumptions regarding import/export flows were made on the basis of previous CLCA studies in which simplified models are considered to obtain manageable systems (Ekvall and Weidema, 2004). Therefore, taking into consideration the opinions of agricultural experts in Luxembourg (Gérard Conter and Martina Arenz, personal communication, October 2012), all import/export flows were assumed to affect neighbouring regions in Germany, Belgium and France. Due to the finding that no land-use changes would occur in these nations due to energy crops augmentation in Luxembourg, no additional expansion for crop production in these countries was assumed. Nevertheless, in a companion article some insights were performed regarding the importance of assumption choices in CLCA, including the use of several auxiliary LUCs modelling methods (Vázquez-Rowe et al., 2013a). The results linked to the inclusion of import/export flows and biogas production shown for Approach C show that extended indirect land use changes beyond Luxembourgish borders neutralize the minor environmental benefits on the different scenarios presented for Approach B (Fig. 3).

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III) Domestic LUCs. The dLUCs and iLUCs observed in the different scenarios for approaches B and C are considerable, but were not found to trigger significant changes in domestic environmental burdens. Approach B highlights the importance of direct and indirect LUCs whenever maize cultivation is expanded in Luxembourg, displacing those crops that present the lowest economic benefits. It is important to take into account the fact that these direct and indirect LUCs show limited environmental burdens when compared with the expansion of maize/corn cultivation for bioenergy elsewhere (Searchinger et al., 2008; Hertel et al., 2010). The reasons for this are mainly linked to two factors. On the one hand, land clearing for crops is a process that in most European countries was undergone many centuries ago, while in other areas of the world is still an on-going operation (Houghton, 2003). Nonetheless, LUCs in Europe and their potential to alter terrestrial carbon stocks should not be underestimated since phenomena such as afforestation or agriculture intensification still occur in many European regions with lower land use competition than Luxembourg (Schulp et al., 2008; Muñoz-Rojas et al., 2011). On the other hand, agricultural legislation in EU countries, which is greatly influenced by the Common Agricultural Policy (CAP), imposes strong restrictions in terms of LUCs, especially when this involves the shift from arable land to other land use types, such as forests or urban land, or vice versa (Lavalle et al., 2011). In fact, current arable land extension in Luxembourg is fixed. Therefore, as observed in the results section, it is barely surprising that shifts in land use are entirely produced between arable crops, reducing the potential variation in carbon storage and sequestration. IV) Digestate surplus. Digestate production in the fermentation stage of biomethane was assumed to be spread directly on fields in Luxembourg, substituting mineral fertilisers and, therefore, compensating to some extent the predicted increase in fertiliser use. However, it is important to consider that current legislation trends in North Western Europe are starting to enforce digestate treatment prior to field spreading with the main objective of lowering ammonia emission (Rehl and Müller, 2011; Golkowska et al., 2012). However, the technologies for digestate pre-treatment were not considered in the CLCA perspective in this study. 4.2. Confronting the different decision contexts: Is this possible? The direct comparability between the three different decision context approaches presented in this case study should be tackled with care. For

instance, each decision context has been modelled to answer specific research questions. Nevertheless, it is important to highlight the fact that all three measure the environmental consequences of changes in the Luxembourgish agricultural system stemming when comparing years 2009 and 2020 (the latter with the constraint to meet the target of electricity production from biogas set for Luxembourg by the 20/20/20 directive). Therefore, considering that all three CLCA models are projected towards the 2020 horizon, it seems plausible that the different contexts should be compared in order to deliver actual consequences from a cross-policy perspective, rather than limit the discussion to the temporal perspective presented until now. However, the comparability process should be managed cautiously, since the methodological assumptions behind each approach, as discussed in Materials and Methods, are not fully homogeneous. Approaches A and B, in the first place, engender from a very similar CLCA modelling structure, since they both have the same system boundaries, and a farmer-oriented perspective. In fact, their structure is identical except for two specific aspects: the existence or absence of the 80,000 t shock and the FU used to report the results. Consequently, in order to advance with the comparison of these two scenarios, the use of a common FU was needed. Hence, given the lack of a bioenergy shock in Approach A, it was determined that the use of an equivalent FU to the one used for Approach A should be implemented: the management of the entire Luxembourgish agricultural surface in 2020 (i.e. 126,434 ha). Finally, the temporal scale was modified so as to measure the environmental consequences between the two perspectives in 2020, rather than the BAS in 2009. The results for the comparative AB Approach, presented in Fig. 4, suggest slight improvements in the overall environmental profile for 4 out of 5 scenarios if maize production is encouraged linked to a reduction in CC impacts. However, given the exclusion of soil trade-offs in terms of CO2 emissions, these results should be interpreted with care, especially considering the increased impacts related to FD, ALO and PMF if maize production were to be expanded. Nevertheless, the overall difference between environmental consequences would range from 4% to 7% improvements depending on the scenario (except for the RP scenario in which environmental impacts would actually increase by roughly 3%). In fact, when compared to the environmental impacts obtained for the base year, the results suggest that the range of variation between the BAU scenarios in 2020 and those for bioenergy policy are similar to the expected change in the timeframe 2009–2020. A cross-comparison between other approach combinations was considered not pertinent for the specific objectives of the study. On the one

Fig. 4. Single score endpoint values for each of the selected scenarios when comparing the expected environmental consequences between Approach A and Approach B (AB Approach) in 2020. Data per FU = Luxembourgish agricultural system (126,434 ha). NOTE: CC [HH] = climate change–human health; OD = ozone depletion; HT = human toxicity; POF = photochemical oxidant formation; PMF = particulate matter formation; IR = ionising radiation; CC [Ec] = climate change – ecosystems; TA = terrestrial acidification; FE = freshwater eutrophication; TET = terrestrial eco-toxicity; FET = freshwater eco-toxicity; MET = marine eco-toxicity; ALO = agricultural land occupation; ULO = urban land occupation; NLT = natural land transformation; MD = metal depletion; FD = fossil depletion; AN = livestock modelling included in PE model; CR = only crops (no livestock) included in PE model; MAR = minimum animal requirement at base level considered in the PE model; MP = meadows and pastures undergo LUCs without constraints in PE model; RP = rapeseed undergoes LUCs without constraints in the PE model.

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hand, no comparability was deemed appropriate between approaches B and C, since the latter just constitutes an expansion in terms of system boundaries of the former. On the other hand, the comparability between Approaches A and C could be attained by enlarging the system boundaries for Approach A (e.g. the inclusion of new import/export flows or the simulation of an alternative energy source to biomethane). However, this comparison was discarded given the fact that the modelled changes in this study deal with the agricultural system and are mainly accounted for through the AB Approach (see Fig. 4). 4.3. Utility of the joint use of the PE model and LCA The identification of which will be the correct products that are substituted by co-products can have large impacts on the results of CLCA (Brander et al., 2008). In fact, many studies adopt the so-called consequential system delimitation for agricultural LCA approach (Schmidt, 2008), which seeks expert criteria to model economic assumptions. However, the use of the PE model in this particular study allowed a detailed analysis of the Luxembourgish agricultural system (including livestock) through the collection of economic data that permitted predicting future equilibriums between supply and demand (Ekvall and Andræ, 2006). Nonetheless, it should also be noted that while this line of reasoning is plainly valid for Approach B, due to the limited consequences that were considered, this is not the case for Approach C, since import/export flows beyond Luxembourg's borders need to be accounted for. Therefore, the PE model does not provide any information regarding what iLUCs will take place to account for surplus or deficit crops, but we know that according to the preliminary simulations developed using the GTAP model, these effects will not have global repercussions. This issue demonstrates the fact that despite the economic models' usefulness, these can only cover a limited amount of the cascade effects that changes in a production system generate on marginal systems and markets (Ekvall and Andræ, 2006; Vázquez-Rowe et al., 2013a). Another important issue is the fact that the energy return on investment (so-called EROI) of agro biofuels, including the maize use for biogas, is usually relatively low as compared with other sources of energy, namely fossil fuels (Cleveland et al., 2006; Murphy and Hall, 2010; Gupta and Hall, 2011; Hall et al., 2009, 2011), even though the use of agricultural residues may turn out economically and technically viable, under some operational conditions (Messineo et al., 2012). Therefore, it is expected that Luxembourgish farmers would substantially increase the intensification of the bioenergy crops to maximize the outputs, which would probably be a result of increasing fertilisation and plant protection, with a consequent effect in terms of environmental impacts, such as climate change, toxicity or biodiversity (Pedroli et al., 2013). However, an important limitation of the PE model used in this case study is that it does not account for potential intensification. Finally, it is also important to note that the model does not take into account the iLUCs and dLUCs that are expected to occur on an annual basis from 2009 to 2020, since it is only a comparative static model with no specification of the path of conversion that considers the maximisation of revenues based on the 11-year leap. This limitation hinders the accuracy and final reliability of the computed cumulated changes and related environmental consequences. 4.4. The importance of CLCA in terms of decision-making The results obtained provide useful information for the implementation or revision of policies in Luxembourg, not only from an agricultural perspective, but also in terms of the energy and environmental strategies for the nation in the frame of the EU (Söderberg and Eckerberg, 2013). However, despite the utility of CLCA in terms of policy-making, it is also important to bear in mind that there are certain underlying constraints in the approach that are worth discussing.

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In the first place, in accordance with what was explained in the previous section, completeness in the CLCA is difficult to attain, since the marginal effects may cascade down a wide range of production systems. Fortunately, it is presumable that the level of these consequences dissipates as the marginal production systems move away from the main system and become a minor source of uncertainty in the final calculations (Ekvall and Weidema, 2004). Moreover, the case study, given its depth, analysing the entire domestic agricultural sector, is prone to have higher data gaps than, for instance, ALCA studies in which one specific system is assessed in detail (Ekvall, 2002). Secondly, it should be noted that the main reasons behind the policy support of European nations and institutions for the development of a bioenergy industry is linked to the belief that it will be a source of GHG emissions reduction, to the assumption that bioenergy is a source of energy independence in terms of macro-scale strategy and that it may recover rural development (Giampietro and Mayumi, 2009). The results presented in this article show that reductions in GHG emissions would be very limited or even inexistent. Nevertheless, they do not show a doubling in GHG emissions as in the case of some studies conducted for corn in the US (Searchinger et al., 2008). Energy independence in a nation like Luxembourg, where current energy consumption per capita is amongst the highest in the world, can constitute a tricky discussion topic. On the one hand, it is true that a total of 144 GWh would be used in the grid substituting fossil fuels arriving from third countries. On the other hand, the bioenergy production system in Luxembourg would still be powered mainly by fossil fuels (Smil, 2008). In fact, as shown in Fig. 3, there are no substantial differences in FD impacts between the conventional energy supply and bioenergy produced from maize. Therefore, in order to power the bioenergy sector in Luxembourg with renewable bioenergy, an extended amount of land use would be needed, which would further constrain the domestic supply of food and feed (with a subsequent higher dependency from abroad), and trigger the possibility of land use changes with other biomes, such as forest land, beyond the current prediction that only crop land would be affected by LUCs, increasing potential GHG emissions (Giampietro et al., 2006; Pimentel et al., 2007). It seems plausible that the extent of the bioenergy shock modelled in this study would be assumable by the Luxembourgish domestic energy strategy, as a way of diversifying the energy carriers that power the nation's economy, although an improvement on the energy system's environmental profile would not be attained. Therefore, further policies or subsidies to enhance the production of bioenergy in Luxembourg should be neutralized beyond the extent of the modelled shock, since they may become a considerable problem as compared to the relative innocuousness from the scenarios shown in this study. 4.5. Uncertainties in using CLCA Uncertainties in CLCA are considered to be higher than those in ALCA studies due to a greater number of assumptions that are made, as well as the elasticity of the system boundaries (Ekvall and Weidema, 2004; Wang et al., 2012; Vázquez-Rowe et al., 2013a). As stated throughout the study, uncertainties due to the former were limited through the use of the PE model, reducing the amount of decisions based on expert opinion to those occurring beyond the domestic market. For the latter source of uncertainty, the exclusion of the soil from the system boundaries constituted a major issue, since biogenic carbon sequestration was only accounted for partially by monitoring the aboveground biomass changes. Moreover, there are a set of natural and market driven sources of uncertainty that are worth discussing. On the one hand, regarding natural sources, the PE model does not take into account any prediction time in terms of crop yield for that year. In fact, previous studies have demonstrated that there can be important inter-annual fluctuations in environmental impact that can be attributed exclusively to natural production or abundance (Ramos et al., 2011; Vázquez-Rowe et al., 2012). The

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perspective that is considered focuses rather on the potential yields that may be attained by the year 2020. On the one hand, market-driven uncertainties can be internal, due to the maximisation of revenues performed by farmers, which can derive in positive environmental feedback, using the increased revenues to purchase environmentally friendly technologies (Hertel et al., 2010; Tonini et al., 2012), or negative consequences, due to a thirst to expand bioenergy crops beyond sustainable thresholds. On the other hand, external sources of uncertainty may go from minor changes in price elasticity, to structural changes regarding the functioning of agricultural trade worldwide (Boehlje, 1999). 5. Conclusions The results obtained in this study are in line with previous published scientific articles, suggesting that the use of maize-based biofuels increase GHG emissions and other environmental impacts, such as land occupation or FD. However, it should also be noted that these augmentations are notably limited in Luxembourg when compared with other areas of the planet (e.g. Brazil or the US) due to the current status quo in terms of arable land extension in many European countries, limiting the conversion of, for example, forest area, with a high carbon sequestration rate, to arable land and vice versa. The sharp increase expected in terms of land use impacts regarding agricultural land occupation or natural land transformation highlights the importance of monitoring the qualitative changes that may occur in the soil due to LUCs for agricultural purposes. Therefore, we believe that the findings from this study should set a baseline to assess specific soil parameters such as organic content, erosion resistance or biodiversity loss in Luxembourgish arable soils, rather than maintain a quantitative perspective in terms of land surface (see Vázquez-Rowe et al., 2013b). Moreover, future perspectives within the analysed system should have the goal of improving and extending the consequential perspective. For instance, energy trade-offs from a CLCA may be introduced within the assessment, as well as including a small-holding perspective at the farming stage, accounting for different behaviours, i.e., the socalled farmer-effect (Morse et al., 2007). Finally, in terms of policy support, the results demonstrate the lame environmental benefits of introducing an energy crop regime in the Luxembourgish agricultural system. Hence, from an environmental perspective it remains to be seen whether the potential introduction of energy crops in Luxembourg would help complement and foster the energy independence of the country, or whether energy crop implementation will become a mainstream strategy that would trigger further iLUCs, with a subsequent increased stress on the environment. Conflict of Interest The authors declare no conflict of interest. Acknowledgements This article was developed thanks to funding from the National Research Fund Luxembourg (FNR) in the frame of the LUCAS project. The authors would also like to thank Gérard Conter, Martina Arenz and Thomas Dandres for valuable scientific exchange and Lesley Rowe and Marco Alatrista for kindly revising the grammar and style of the manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2013.10.097. References Boehlje M. Structural changes in the agricultural Industries: how do we measure, analyze and understand them? Am J Agr Econ 1999;81:1028–41.

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