Challenge clusters facing LCA in environmental decision-making—what we can learn from biofuels

Goal and scope setting is the first step in performing a LCA and thus influences the subsequent phases (Tillman 2000). It falls to the trained practitioner to enforce the validity of goal and scope setting for the required analyses. For example, the level and quality of data collected must be sufficient in order to fulfill the original aim of the study (Singh et al. 2010). The system boundaries are set during this stage, and these are key to any analysis (Bird et al. 2011). The choice of system boundary in comparative studies may lead to different conclusions and decisions about which products to promote, as in the classic debate surrounding nappies/diapers (Bond 2005), and have an influence on rankings through variation in scope or the impact that is assessed (Suh et al. 2004). Bio-based (poly)lactic acid, for example, out-performed conventional polymers on energy consumption and GHGs; however, clear ranking was lost when ecosystem quality metrics were assessed (Yates and Barlow 2013).

Allocation methods (the way in which impacts are assigned to co-products) give quantitatively different results, which can also lead to incomparability (Wang et al. 2011). An assessment of UK wheat ethanol with regional N₂O parameters reported GHG emissions (midpoint values) of 51, 64 and 68 g CO₂eg/MJ for energy allocation based on energy, economics and substitution, respectively (Yan and Boies 2013). This affects both comparison and compliance, as policies mandate different allocation types: by substitution on the RTFO, RFS2, LCFS and RED for electricity co-production, by energy content in RED and as fall back for RFS2 and LCFS and by economic value as a fall back for the RTFO. Thus, the same pathway ranks differently across policies. Direct GHG emissions for bioelectricity from rapeseed using allocation choices from primary renewable policies improved on the reference case by 60 % (sub peas) or 21 % (sub soy) with substitution, 33 % for energy allocation and 16 % for economic allocation (Wardenaar et al. 2012). Such incompatibility can occur even with methodologies compliant with industry or regulatory standards for biofuels GHG reporting (Whittaker et al. 2011).

System boundaries can become inconsistent because of allocation decisions or because the scope or the goals of analysis changes. For example, methods for reducing the overall farm environmental footprint will not necessarily result in a low carbon crop (both of which are assessed with valid LCAs) and vice versa, and the differing purposes lead to different boundaries, which are often conflated (Whittaker et al. 2013). The farm-based approach considers the overall environmental footprint of a farm site, whereas a crop-based analysis focuses on crop- and product-specific aspects that go beyond the farm gate. Agricultural assessments are particularly sensitive to scope changes in response to purpose (Roy et al. 2009), which has major implications for bioenergy policy decisions. For example, the RED was designed to promote the production of biofuels from ‘non-food’ biomass feedstocks and does this by specifying in the GHG accounting methodology that cereal residues are not allocated upstream emissions from cultivation. This immediately places these feedstocks at an advantage but is an example where the intention and approach of the analysis has been heavily influenced by policy (Whittaker 2014). Perhaps more significantly, the detail of goal and scope is seldom conserved when analyses are merged into the policy process, because data, for example GHG values, are taken from a range of studies to inform policy or regulation or to be the basis of financial incentives, such as feed-in tariffs. Often, several studies, which may have been originally produced for a different purpose, are taken to inform policy (McManus and Taylor 2015).

This tendency to aggregate the GHG estimates for comparative decisions is logical but overlooks the complexity and detail specificity associated with GHG analyses for bioenergy systems. For example, Farrell et al. (2006) conducted a meta-analysis of early corn ethanol LCAs and found that, even for direct effects (attributional analysis), the GHG and net energy results varied substantially. Studies with the least favourable results for corn ethanol yielded a carbon-intensity of nearly 120 g CO₂eq/MJ, which is approximately 30 % higher than gasoline. However, those studies failed to allocate any biorefinery impacts to co-products and used outdated data for key industrial processes. For rapeseed biodiesel in Europe, a careful analysis found calculated emissions ranging from 5 to 170 g CO₂eq/MJ (Malça and Freire 2011), arising primarily from differences in modelling soil emissions and land use, co-product allocation and high uncertainty associated with some emissions parameters.

Currently, GHG assessments dominate the biofuels sustainability debate; for example, more than half of 53 sampled LCA studies of lignocellulosic biofuels published between 2005 and 2011 were GHG focused (Borrion et al. 2012). Although bioenergy is a global commodity, local values and drivers may differ, and effects like GHG emissions are not always observed near their cause. Location-specific metrics are expanding, though, particularly as water captures more attention and the relevance of other resource competition emerges in the discourse. However, issues other than GHG tend to be specific to the location of production or processes. For example, Scown et al. (2011) demonstrated the location specificity of water consumption/withdrawal and pollution in biofuel production systems. Integrating such impacts is hampered since LCA is, traditionally, not a tool that examines local impacts and thus has crucial gaps. For example, water, particulate emissions and wider impacts on ecosystem services are often not well modelled and may lack spatial or biophysical data. Gaps in data availability and data quality, as addressed elsewhere, are generally not highlighted in the final result, which are often distilled down to a single number (e.g. N₂O emissions from soil under the IPCC guidelines (De Klein et al. 2006)).

Analyses with complex boundaries often result in multiple models in the final analysis. Ecosystem, market and social dynamics contribute to impact processes. Structural changes, such as infrastructure and technology changes and other progress along the innovation trajectory and adoption rates and patterns, contribute to scenarios for impact and are also beyond LCA’s scope. All of these introduce the need to rely on and integrate increasingly complex and speculative modelling and shape the development of assessment tools (Wicke et al. 2015) and lead to efforts to develop general quality criteria for modelling and recommend best-practice LCIA models for particular impacts from the plethora available (Hauschild et al. 2013). Because advances in elucidating or representing the mechanisms that underpin models occur in many different academic disciplines (see, e.g. Arbault et al. 2014), integrating the sets of data or models to give a picture of the life cycle impacts is challenging, though essential for LCA to contribute policy setting or analysis (Wicke et al. 2015). In connecting models to describe various portions of the overall value chain and potential consequences, the overall complexity and level of detail in the analysis can mask areas where boundaries conflict, even for relatively narrow studies. Ensuring that the full set of models integrate harmoniously and preserve reasonable levels of transparency, rigour and robustness is an ongoing challenge that worsens as use of custom-built tools proliferate (see, for example, CA-GREET 2014).

System sustainability is often estimated by summing the impacts of value chain components and attributional LCAs (see, e.g. Bento and Klotz 2014). In order to assess the sustainability of biofuel production, it is necessary to consider each biofuel from each feedstock, according to its own merits, and alongside specified sustainability criteria (Royal Society 2008). There is, however, a lack of the necessary codified sustainability criteria and a recognition of data needs to support them (Hecht et al. 2008), and most progression in LCA-based reporting methods has focused on developing a single method (predominantly based around GHG measurement (McManus and Taylor 2015)) to assess all biofuels in a similar manner. The success of this is mixed, especially with the increasing importance of social and economic sustainability parameters, and with mismatches between regulatory mechanisms in methodological approaches such as allocation and default values for fuels and systems.

To be included in an assessment, mechanisms for interactions among portions of the physical system or value chain are also needed. The fundamental interactions are bio/geophysical, such as in soil carbon accumulation and mobility, or in nutrient and water cycles, and give rise to feedbacks at a variety of spatial and time scales (Bagley et al. 2014). These mechanisms draw on scientifically active areas where knowledge is evolving rapidly with both high and low uncertainties (see, e.g. Balvanera et al. 2014; Greene et al. 2015) and provide an idea of the necessary scale of the system boundary needed to capture relevant impacts. Sustainability assessments of biofuel systems show very strong sensitivity to soil emissions, particularly nitrogen. Using different N₂O emission methods gave GHG values (midpoint) differing by 25 % when using IPCC Tier 1 methods or a UK-specific model within the same assessment of UK wheat ethanol (Yan and Boies 2013). A meta-analysis of LCA studies on European rapeseed biodiesel found that GHG intensities correlated directly with how soil emissions were modelled (Malça and Freire 2011). Some of these mechanisms differ substantially between first generation and advanced bioenergy systems. For example, perennial crops can have belowground carbon allocations more than four times higher than a traditional corn-soy rotation, and belowground biomass increased by 400–750 %, measured for miscanthus, switchgrass and native prairie during establishment ( Anderson-Teixeira et al. 2013). The potential for perennial bioenergy crops to increase soil carbon under some conditions has contributed directly to their preferential ranking in over first generation pathways in policy instruments.

Multi-sector analyses or so-called consequential factors introduce additional complex mechanisms for interactions among the value chain and potential consequences. One route is through mechanisms that do not directly include the primary product. For example, Scown et al. (2014) showed that the net GHG and water impacts of utilizing lignin for heat and power at cellulosic biorefineries or exporting lignin for co-firing with coal varied greatly depending on long-term trends in power plant retirement and new construction. If the export of additional biopower to the grid encourages early retirement of aging coal-fired power plants, the GHG footprint of ethanol in their scenario was predicted to be approximately 50 % lower than if biopower exports encourage deferred construction of new natural gas combined cycle (NGCC) power plants.

Interaction mechanisms can also be through market mediation that emerges within or across borders and/or sectors, which may rely on speculative global market dynamics. Though such consequential approaches started with general product life cycles (Weidema 1993; Zamagni et al. 2012), attempts to assess the impact of global commodity market-driven land use dynamics in relation to biofuels have made it a common concept to guide policy in attempting to avoid unintended consequences in the form of indirect land use change (McManus and Taylor). The drawback is that consensus around the approach has not yet emerged (Marvuglia et al. 2013; Rosegrant and Msangi 2014; Schmidt et al. 2015); thus incorporating such market-mediated impacts for consequential analysis increases the variability dramatically ( Vázquez-Rowe et al. 2013). For example, estimates of ILUC GHG impacts ranged from about 3 to above 220 g CO₂eq/MJ rapeseed biodiesel and ~5 to ~100 g CO₂eq/MJ for bioethanol from maize over the last 5 years, even in current analyses when ranges for such values have tightened nor are the authors of that analysis optimistic about such uncertainty decreasing soon (Ahlgren and Di Lucia 2014). Thus far, ILUC has received the bulk of attention among consequential LCA of biofuels. But market-mediated mechanisms are also being explored for other properties, such as indirect fuel use (Rajagopal et al. 2011) and rebound effects (Vivanco and van der Voet 2014; Smeets et al. 2014). Likewise, social impacts, crucial for sustainability assessments, arise through cross-cutting mechanisms (see, e.g. Benoît et al. 2010). About 20 % of annual LCA publications touch on or address social factors (McManus and Taylor 2015) and guidelines and methodologies are developing broadly because of the labyrinthine relationships among groups and the range of potential impacts (Wu et al. 2014). Many of these are reflected qualitatively in international sustainability standards (see, for example, FAO’s Compilation of Bioenergy Sustainability Initiatives 2011). In nearly all these mechanisms, the science or state of knowledge is changing rapidly. As new insights emerge, accounting for impacts in the LCA that depend on them is lagging.

The absence of spatiotemporal components, which underlie most mechanisms in LCA, is of particular concern for bioenergy. Time is most commonly incorporated with set-year analyses (e.g. N years in the future) and linear annualization. While the former is a viable, if limited, approach, the latter is problematic for agriculture. Simple annual crop rotations that make up the bulk of agriculture (corn, soy, wheat, etc.) are well represented this way, but, for perennial crops, where yields develop over time and management practices may vary from year to year, simple annualization is not always reflective of reality. The difference between establishment and production periods for such crops are key to emissions estimates; simulations of nitrogen loss from 2-year-old switchgrass were 360–410 % higher than for mature stands at the same fertilization levels, depending on harvest number, resulting in a 15-year average of 20–30 % that of cotton under the same conditions (Sarkar et al. 2011). Likewise, the temporal mismatch between conversion or harvest and carbon uptake in forest-based resources introduces decadal and longer time frames, and concerns over the potential carbon debt caused between harvesting and re-establishing timber stands have become an important issue for climate and bioenergy policy (Lamers and Junginger 2013).

Location-dependent issues for LCA are not limited to siting information and land use changes; they are dominated by the limitations in inventories from location dependence in input data and the location dependence of impact metrics water or biodiversity (Seager et al. 2009) as well as trade, market mediated and social impacts, all addressed in other sections. Spatial characteristics of the feedstock production system are often assessed extraneously in economic terms. Scown et al. (2013) found that, in their scenario that incorporated corn stover, wheat straw and Miscanthus in the USA, only 80 % of available biomass was geographically concentrated enough to warrant utilization for biofuel production; the remaining biomass was too dispersed, resulting in prohibitively high transportation costs. While highly sensitive to supply chain characteristics, LCA is still evolving to incorporate the intersection between developing infrastructure and logistics, such as transportation modelling (Strogen and Horvath 2013; Strogen and Zilberman 2014). The spatial aspects of resource management will also begin to contribute in bioenergy LCA, as in critical resource assessment (Sonnemann et al. 2015), potentially to pivotal effect, because there are more critical resources than just land in biofuels, and land is a critical resource in more than biofuels. Generally, these issues introduce reliance on multi-model, multi-scale systems. Data integration, uncertainty integration, error propagation, certainty and multi-parameter output representation all become important and are difficult to convey succinctly.

Agriculture and farming in particular deserve special mention as competition for land increases. Financial incentives awarded to the farmer to encourage planting of bioenergy crops create an interaction among LCA, policy and farming practices (Glendining et al. 2009; Natural England 2013), as does the global commodities feed/food market. The effectiveness of such incentives to serve climate policy goals depends on the accuracy of calculated avoided life cycle GHG emissions and the system boundaries considered. Farmers are key to the provision of empirical data; increasing the certainty of the assessments for that portion of the life cycle. However, because agricultural practices differ by region, even over relatively small distances, and management decisions have large impacts in bioenergy (Davis et al. 2013), transferability of data or existing analysis from a region where much is known to one where little is known (a common technique in other sectors) is questionable.

LCA for agricultural production is well-established (Roy et al. 2009), but LCA for policy planning in bioenergy incorporates the potential implementation of large-scale biomass production, which lacks certainty. Improvements in agricultural yields have been substantial over the past decades (e.g. US corn yields have roughly doubled since 1976, USDA data). Projections of productivity changes over time are speculative but important for long-term planning. For example, carbon payback times decrease 30–50 % when crop yields reach the 90th percentile in global yield, representing crop productivity increases and/or substantial management changes across global averages (Gibbs et al. 2008), in effect identifying the benefits of addressing the global yield gap. There are potential benefits in non-GHG impacts also. For example, in advanced bioenergy landscapes, it is possible for very small trade-offs in the economic balance to have a large favourable impact on biodiversity under particular land conservation regimes (Evans et al. 2015). These represent challenging aspects for cross-sector agricultural LCAs to include. Other complicating factors that are of increasing importance include conservation mechanisms which impact biodiversity (see, e.g. Evans et al. 2015), precision agriculture where practices vary between or even with fields (Weekley et al. 2012), alternate cropping systems (Perrin et al. 2014) and land sparing practices (e.g. Cohn et al. 2014) which are often multifunctional. Multifunctional landscapes provide many sustainability benefits (Perfecto and Vandermeer 2010) but expand the system boundaries increasing data requirements (Rossing et al. 2007) and uncertainty (Jung et al. 2013).

Risk, policy uncertainty, nascent markets, infrastructure and supply chains threaten adoption, which makes projection of roll-out rate or the extent of production challenging. This increases reliance on representing statistical projections in data and uncertainty and shifting policy estimates of expansion (Rajagopal and Plevin 2013), including how to reflect the ranges in the results, and whether knowledge of the demographics can contribute to analyses or types of analyses that are more useful from a policy perspective (Plevin 2010).

Data availability, transparency, curation and sharing are key to the long-term success of LCA both generally and for comparisons used in bioenergy planning. Reliability of the results from LCA studies strongly depends on the extent to which data quality requirements are met, where common problems include lack of transparency and data variation and gaps. For lignocellulosic ethanol, for example, data inconsistencies contribute to conflicts in published LCA results of GHG emissions (e.g. Wiloso et al. 2012), ranging from −1.25 to 0.84 kg CO₂eq/km travelled when using E100 (Borrion et al. 2012). There are specific cases where there is minimal data, for example, enzyme production (Spatari et al. 2010), or where data is outdated, for example, pesticide production (Audsley et al. 2009). Primary data sets, despite their overall strength, also sometimes have errors (see, e.g. Cooper et al. 2012). Data set compositions vary regionally, and sometimes sectorally, in the completeness of their data on which GHGs are included (Ansems and Ligthart 2002). Reusability of data is highly dependent on sufficient data documentation, and standards are emerging (see, e.g. Sonnemann et al. 2013) to address this, for example, in the area of LCAs used for Environmental Product Declaration (EPDs) (Ingwersen et al. 2012).

While transferability is a common approach to supply missing data, using the value from another region for a missing parameter, the approach frequently fails for bioenergy. Data for non-global environmental impact categories frequently vary by region, often for categories to which agricultural assessments are highly sensitive (e.g. ecotoxicity). Some impact categories, such as water quality and use impacts in bioenergy, can be local, regional or both (Dale et al. 2010) and are further complicated by the importance of the local water context (e.g. drought to excess). Water consumption, for example, may range from 5 to 2138 L per litre of ethanol depending on regional irrigation practices in the USA (Chiu et al. 2009)). Region-specific data are also often not available (for example land use and biodiversity (Dale et al. 2010)). Industrial data for emerging processes and technologies is frequently limited due to commercial confidentiality and also as the data for such systems and an understanding of their impact is nascent. New scientific understanding of various impacts, such as the impact of air pollutants on human health (see, e.g. Hajat et al. 2015; Novák et al. 2014), or newer issues such as ‘black carbon’ (see, e.g. Cai and Wang 2014; Otto et al. 2014), also introduce data or method limitations.

Under some conditions, data sets/databases used in a LCA reflect external value judgments, of which users may or may not be aware or take time to discover. For example, there are levels of detail within and between datasets, some with wider boundaries than others. There are also value judgments embedded in ‘off the shelf’ impact assessment methods. Some are more explicit than others, for example EcoIndicator 99 and ReCiPe both show three options ranging from an ‘absolutely only proven cause and effect’ to a precautionary approach. Pragmatically, these databases and software packages are frequently the most accessible solution for the practitioner since primary data is either unavailable or too time consuming to gather (Hetherington et al. 2014). This is reflected in the literature, where uptake of these databases is rapid; studies citing the widely used ECOINVENT database launched in 2000 (Frischknecht et al. 2004) had reached 2240 by mid-2014 (scopus search, 18th July 2014). However, these proprietary databases and software tools can limit transparency, independent reproducibility and transferability as the underlying data cannot be shared in publications. The high-cost setting up and maintaining the quality of database impedes open access (Hellweg and Canals 2014). A number of open data sets and standardization efforts seek to address this, in particular the European Platform on LCA’s European reference Life Cycle Database and the International Reference Life Cycle Data system common data format standard, as well as and the USDA LCA Commons and NREL US Life Cycle Inventory Database.¹

Because a wide range of stakeholders use the results, indications of data quality are increasingly important. Qualitative indicators can be used along with their quantitative counterparts to address data transparency issues such as data source and to address the confidence and uncertainty of data (see for example, Ansems and Ligthart 2002) under particular circumstances, such as in the electricity sector (see, e.g. Garraín et al. 2015). Qualitative indicators help to contextualize the relevance of the data so that policymakers are able to make more informed decisions about the circumstances to which the data apply, thus making policy judgements based on the available data more reliable. Such indicators are sometimes contained within commercial software packages (for example SimaPro—Pre Consultants (2014) and GaBi - PE International (2014)). While these packages can aid very quick and effective calculations, it is also very easy for general users to employ them and default databases without an understanding of the uncertainty or quality of the data contained within them.

Communicating scientific uncertainty is relevant both within the LCA community itself, where practitioners often disagree in terms of practice and culture, and with an increasingly broad range of stakeholders, such as the public and policymakers. Given the growing use of LCAs to help guide decisions in controversial topics cutting across different policy domains, such as bioenergy, communicating the inherent complexity and uncertainty becomes more challenging and important. A key component for policy in these controversial topics is that often there is no single solution. For example, geographical and/or temporal dimensions of sustainability impacts produce uneven effects, magnified by the globalization of biofuel production and trade. Awareness of these global impacts is growing; at the same time, local impacts are becoming more widely known (Raman and Mohr 2014). Other tools also address these, such as Environmental Impact Assessment (EIA), and a variety of footprinting with a range of indeterminacies (Andrew et al. 2009; Johnson and Tschudi 2012; Newell and Voss 2011). Policymakers may have varied knowledge of what is, or is not, encompassed in the assessment approach.

At the highest level of detail, the evolution of issues included in an LCA is effective, but at the broader level, the evolution is less smooth. These limitations need to be communicated. Ranges in results and standard uncertainty in data decrease confidence in the results beyond the LCA community. Policymakers generally rely upon straightforward, pragmatic information and while they also require transparency, they often do not feel comfortable with communication at the level of complexity that LCA practitioners feel is necessary. Uncertainty could be considered to be a weakness in LCA, particularly where a ‘single answer’ may be considered by some to be preferential. Through use of the correct tools, however, uncertainty assessment can in fact be used to help support the results by providing a more comprehensive account of the likely range of results (Björklund 2002) and a comprehensive understanding of the problem and its possible solutions (Hellweg and Canals 2014). For example, triangulation with qualitative data enables policymakers to consider quantitative assessments in the broader context of social judgements on what is considered important and why.

Controversial topics often also invoke different value sets that vary by community and region (Walls et al. 2005; Corner et al. 2014), as seen in bioenergy projects. Analysis of the past management of such issues and differences in the outcomes/policy measures may provide insights to assist in bioenergy governance and its use of LCA. For example, genetically modified organisms generally see policy support in the USA and opposition in the UK/EU (Mohr et al. 2012; Marques et al. 2014), while climate agendas show the opposite trend (Howell 2013; Corner and Randall 2011). Where successful, it is likely that broader value-based questions were taken into account (Howlett 2012), and engagement before technical and policy positions became entrenched contributed to success (Mohr et al. 2013).

The presence of uncertainty creates challenges in assigning responsibility and thus accountability. For the biofuels/bioenergy supply chain, the impacts that occur at each stage vary in certainty and in the level of operator control, as illustrated in Fig. 2. For each stage, there are varying levels of operator control, ranging from issues such as indirect land use (very little operator control) to water use in a bio-refinery (significant operator control). For example, on farm, fuel combustion is both controllable (through machine production, selection and driver control) and the emissions associated with it are relatively certain. Areas of low certainty are often those where there is little control, such as indirect land use change (ILUC). Many of these are considered both ethically important and challenging in the policy process (Nuffield 2011).

As shown in Fig. 2, these levels of certainty and control also have implications at each stage in the LCA process. Because the value chain consists of distinct but (inter) connected sections, LCA’s inclusion of these can vary. This may be most apparent in the preliminary description, where some components may contain sections that are well described with existing data, to aspects of the value chain that are based on more speculative technology where data may be either highly variable or estimated. In order to move from inventory to impact parameter, both a mechanism and responsibility must be represented. Variability in how well either is known may or may not be reflected in the output. From a method-development perspective, research practitioners are aware of these issues but that this area of the LCA method is under constant development which may not be apparent outside of the practitioner community. Similar issues arise where LCA integrates with other tools, such as in the representation of market-mediated effects. In such cases, there may be variation in mechanism or model for inclusion and variation in data drawn on, as well as differing connection to other tools. The framing that underlies the analysis also reflects decisions about what is controllable, who has responsibility and how necessary it is to include components that have higher than average uncertainty associated with them.

Because policy setting is often concerned with comparing across several options, lack of comparability at best leads to fortuitous agreement and at worst erroneous conclusions and decreased confidence in the analytical community. Use of results in different communities does not always bring the originating community’s caveats with them, increasing the challenge of using LCA studies across disciplines. For bioenergy, these caveats can include describing the range of results may have, either from modelling or from lack of/variability of data, assumptions about time horizons and projection differences across studies, and situations where the original uncertainty range is large. Subjective factors, or those where causality is not fully established, also contribute to policy development, but the challenges around these factors are seldom communicated effectively. As a result they are either ignored or taken as concrete, both of which make ranking and comparisons problematic.

Bioenergy policy-making relies on projecting forward new technology, development and use because it is concerned with future courses. Such projections draw on assumptions about future economic growth, fossil fuel prices, energy generation costs, population and similar variables that are often implicit or embedded in a separate analysis (see, e.g. use of IEA scenarios in projected GHG emissions from energy) and on technology performance projections. These projections of maturity of emerging technologies are inherently uncertain and involve, among other things, assessing potential learning, changing impacts with scaleup, structural change and adoption (Hetherington et al. 2014). LCA can incorporate learning developments by estimating differing efficiencies along the supply chain, and, with some caveats, scaleup by drawing upon estimates for efficiencies of scale, process modelling and industry experience (Lloyd and Ries 2007; Hetherington et al. 2014). However, the difference between measured data and modelled numerical estimates is easily blurred and the two are often treated as interchangeable (Youngs and Somerville 2014). Prospective analysis, where future technologies are assessed, will have higher uncertainty than retrospective analysis than models existing or past production systems (Kendall and Yuan 2013). For biofuel policy, these prospective analyses are common; where they include indirect, or market-mediated effects, the uncertainty increases significantly (Kendall and Yuan 2013).

Drivers for and perceptions of both biofuels and bioenergy vary globally. Some countries, such as Brazil and Scandinavia, have high levels of bioenergy due to funding and acceptability, among other factors (see discussion in for example, Ericsson et al. 2004; Walter et al. 2012, and Lynd et al. 2011). The motivation and drivers in these areas are different from those in regions where bioenergy has lower public acceptance (see, e.g. Dragojlovic and Einsiedel 2015). Consequently, how policymakers frame questions to be analyzed with LCA and prioritize impacts or outcomes differ, reflecting these drivers, perceptions and local constraints. This is relevant particularly for biofuels moving in international trade or subject to international sustainability standards.

The viability of current sustainability frameworks to support policies internationally still needs to be determined. Likewise, the extent to which LCA contributes to quantifying or differentiating among issues related to perceived drivers and barriers in countries with substantial biofuels or bioenergy trade (especially corn and sugar ethanol, pellets and biodiesel) needs to be codified to support compliance with international agreements. Currently, LCA is used as a static tool, while being asked dynamic questions. Forward assessment of sustainability impacts using LCA lies at a complex interface among applied and basic science, engineering and crucially policy. It is also both coupled and dynamic; all of these aspects act on each other simultaneously.

Value judgments are often taken as distinct from the overall LCA process; however, they may have a material effect on the results of the analysis. They can shape what is included within the boundaries of an analysis due to a combination of concern over and knowledge about a particular issue. Through selection of specific boundaries, impact categories or assessment methods, they can also focus analyses to a single location or problem, when in reality, observing regional and sectoral variation may provide a more complete or practical assessment, which may have a strong influence on the final results if crucial impacts are not included (Whittaker et al. 2013). Determining ISO-appropriate boundary criteria (CEN 2006b), however, requires some prior knowledge of the system. LCA production and evolution is influenced by different voices, with a range of familiarity with LCA as a technique or with what is needed by those who rely on the results. This has begun to change the shape of the tool, its use and interpretation. Once the purview of trained practitioners answering specific questions, LCA is now increasingly used to address more broad questions posed by users with less expertise (Whittaker et al. 2013). There is apparent confusion over the role that LCA for one specific question has over addressing broader concerns (Brander et al. 2009). Value judgments, thus, can be a serious issue when LCA is used for comparative analysis of scenarios that will guide policy decisions. For example, the European Commission aims to address indirect land use change in calculations involving biofuel regulation; however, it could be argued that this is not solely a bioenergy issue (Adams et al. 2013).

The way in which value judgments are prioritized raises ethical questions, particularly as they are frequently implicit. For example, which impacts to include in a study, the choice of functional unit and system boundaries may be driven by balancing trade-offs where groups, areas or communities are affected differentially (Bustamante et al. 2014; Calvin et al. 2013). Capturing these trade-offs is what creates challenges and opens new space for LCA methods as they develop. Decision ranking reflects the framing values (Taylor 2012). How effectively they can be included depends on uncertainty, which is fluid term in this particular context. It thus affects the support for including ethically important but less concrete factors in the analysis.