The greenhouse gas emissions performance of cellulosic ethanol supply chains in Europe
© Slade et al; licensee BioMed Central Ltd. 2009
Received: 30 January 2009
Accepted: 14 August 2009
Published: 14 August 2009
Calculating the greenhouse gas savings that may be attributed to biofuels is problematic because production systems are inherently complex and methods used to quantify savings are subjective. Differing approaches and interpretations have fuelled a debate about the environmental merit of biofuels, and consequently about the level of policy support that can be justified. This paper estimates and compares emissions from plausible supply chains for lignocellulosic ethanol production, exemplified using data specific to the UK and Sweden. The common elements that give rise to the greatest greenhouse gas emissions are identified and the sensitivity of total emissions to variations in these elements is estimated. The implications of including consequential impacts including indirect land-use change, and the effects of selecting alternative allocation methods on the interpretation of results are discussed.
We find that the most important factors affecting supply chain emissions are the emissions embodied in biomass production, the use of electricity in the conversion process and potentially consequential impacts: indirect land-use change and fertiliser replacement. The large quantity of electricity consumed during enzyme manufacture suggests that enzymatic conversion processes may give rise to greater greenhouse gas emissions than the dilute acid conversion process, even though the dilute acid process has a somewhat lower ethanol yield.
The lignocellulosic ethanol supply chains considered here all lead to greenhouse gas savings relative to gasoline An important caveat to this is that if lignocellulosic ethanol production uses feedstocks that lead to indirect land-use change, or other significant consequential impacts, the benefit may be greatly reduced.
Co-locating ethanol, electricity generation and enzyme production in a single facility may improve performance, particularly if this allows the number of energy intensive steps in enzyme production to be reduced, or if other process synergies are available. If biofuels policy in the EU remains contingent on favourable environmental performance then the multi-scale nature of bioenergy supply chains presents a genuine challenge. Lignocellulosic ethanol holds promise for emission reductions, but maximising greenhouse gas savings will not only require efficient supply chain design but also a better understanding of the spatial and temporal factors which affect overall performance.
The production of transport fuels from lignocellulosic biomass using so-called second-generation conversion technologies is not yet commercial. Multiple conversion pathways are being investigated around the globe, but dominant pathways have yet to emerge and business models have yet to be proven. Nevertheless, expectations are running high and there has been significant investment in research and demonstration by private companies and public sector organisations in the US, Europe and Asia. The production of ethanol from lignocellulosic biomass is one of the most promising options, and in 2007 the US Department of Energy provided more than US$1billion toward lignocellulosic ethanol (LE) projects, with the goal of making the fuel cost competitive at US$1.33 per gallon by 2012 . The level of support provided by the European Union (EU) is far less, but is still significant (approximately US$68million in 2006 ). In addition to support for R&D and demonstration, LE, if it enters the market, would benefit from measures that seek to promote the use of currently available biofuels, that is, biofuels that are produced from agricultural commodities.
The political support for biofuels in the EU is, in part, predicated on their ability to reduce greenhouse gas (GHG) emissions . Quantifying the GHG savings that may be attributed to biofuels, however, is problematic for two reasons: (i) biomass production systems are inherently complex, spatially disaggregated and diverse, and (ii) the definition of system boundaries and the allocation of co-product impacts are highly subjective. Differing subjective interpretations have fuelled an active debate about the environmental merit of biofuels and, consequently, about the level of policy support that can be justified. Politically, however, decisions must be made before all the uncertainties are resolved .
This paper focuses on the production of ethanol from lignocellulosic feedstocks in Europe, exemplified using data specific to the UK and Sweden. The aim was to compare a number of plausible supply chains for LE production, to identify which common elements gave rise to the greatest GHG emissions, and to investigate the sensitivity of total supply chain emissions to variations in these elements. It builds upon, and is complementary to, a previous paper that investigated the factors affecting the commercial viability of LE production in Europe .
To meet this aim an emissions model was developed that permitted the comparison of different process concepts at the supply chain level. This model used simplified descriptions of LE conversion processes, together with emission factor estimates for feedstocks and supply chain operations, to determine the sensitivity of GHG emissions estimates to changes in the supply chain.
This paper is presented in three parts. This first part describes the context, reviews previous GHG estimates, and outlines why performance assessments of bioenergy supply chains are particularly susceptible to subjective interpretation. The basis for supply chain emissions comparison is defined and the basic structure of the model is described. The second part describes the components of representative ethanol supply chains. It identifies generic values for the most important parameters affecting emissions performance, as well as the range of values that these parameters may take and the influence of alternative allocation methodologies on their interpretation. The last part presents a comparison and detailed sensitivity analysis, and identifies which supply chain components have the greatest influence on GHG emissions.
The relative merit of alternative biofuels
The definition of system boundaries, the allocation of impacts, and the choice of data sources are inherently subjective.
Good quality data may not exist, or may not be readily accessible.
Spatial and temporal resolution is lost.
Rebound effects, where environmental and cost efficiency improvements are cancelled out by greater consumption, are not considered.
These limitations are well recognised and have led to calls for greater consistency, transparency and coherence in LCA studies .
In an attempt to compare different biofuel supply chains on a robust basis and provide the consistency and transparency demanded, a number of influential meta-studies have been conducted. These studies have re-analysed previous LCAs, drawing the system boundary around an individual production plant and its feedstock supply chain [8–10]. The studies differ somewhat in approach but agree upon a general conclusion: cellulosic ethanol results in greater carbon savings (75 to 150 gCO2e.km-1) than wheat (15 to 110 gCO2e.km-1) or maize (40 to 60 gCO2e.km-1) but not necessarily as great as from Brazilian sugar cane (125 to 175 gCO2e.km-1).
This conclusion, however, does not stand uncontested. Two subsequent studies assert that the system boundaries should be expanded to include the consequential impacts – for example, land-use change impacts – that may result from increased demand for agricultural commodities and land [11, 12]. They argue that these impacts have the potential to negate the benefits obtained from increased biofuel production unless the biofuels are produced from waste materials or on land with a low carbon-stock value that is not under productive use.
Biomass feedstocks are varied in nature, low energy density, geographically dispersed, and their availability for fuel production is dependent on interactions with existing markets; moreover, data relating to agricultural practices is scarce.
Logistics may contribute significantly to the overall environmental impact.
Environmental and technical performance is highly dependent on the detailed process configuration and the level of integration with other systems, for example, district heating .
By identifying which LE supply chain components and allocation methodologies have the greatest impact on GHG emissions, and by assessing the interpretation of these impacts, this paper goes some way towards addressing these issues.
The relationship between greenhouse gas emission projections, process development tools, and supply chain descriptions
GHG emission projections, like those for cost and commercial viability, are ultimately determined by the mass and energy balances of the conversion processes together with the structure of the feedstock-supply and product-distribution chains. Conversion processes for LE, however, have yet to be proven at commercial scale. Consequently, mass and energy balance estimates must be derived from flow sheeting models. These models are typically constructed to assist with process development and, although there are a number of good examples in the literature [14–17], they focus exclusively on the cost performance of alternative process designs. This emphasis on the conversion process ignores or sets as constant many of the factors that may influence overall supply chain cost performance. It also leaves the supply chain GHG performance unexplored.
Many of the components that make up feedstock-supply and product-distribution chains are already established; nevertheless, these chains are often poorly characterised and possess a high level of inherent variability. Whereas for cost projections, feedstock prices may reasonably be estimated by looking at pre-existing markets, the carbon emissions embodied in feedstocks cannot easily be divorced from the method of production. Consequently, a more detailed description of the feedstock supply is required. For agricultural commodities, for example, fertiliser use may have little impact on the market price but a large impact on embodied carbon emissions. Conversely, the product-distribution chain (percentage blend, level of subsidy and so on) has a large impact on the price obtained for ethanol when sold but little impact on emissions from combustion. (Ethanol has a lower volumetric energy density than gasoline but may be combusted more efficiently. The precise difference, in terms of MJ fuel per unit of useful work done, will depend upon an engine's compression ratio, drive cycle, percentage blend level, and so on, but is nonetheless small (± 5%) [18, 19].)
Developing a supply chain greenhouse gas model
Quantifying supply chain greenhouse gas performance
This analysis focuses on the carbon and energy intensity of the supply chain and only considers the direct energy consumption necessary to produce, transport and transform material inputs, and the GHG emissions associated with these operations. The energy required to construct, maintain or replace capital equipment whose lifetime far exceeds that of the fuel produced was not considered. This assumption is consistent with the Concawe well-to-wheels methodology .
The end point of the supply chain was considered to be the embodied GHG emissions of ethanol when delivered to the pump. The starting point was the production of biomass feedstocks. Emissions are reported as the mass of carbon dioxide equivalents per GJ ethanol, calculated from the summation of embodied carbon emissions across the supply chain. The advantage of this unit is that it is readily comparable with the carbon emissions associated with combusting gasoline (74.2 kgCO2e.GJ-1) .
Verification of model inputs
Our model is, in essence, a summation of embodied carbon estimates in which numerous assumptions and decisions are incorporated. It cannot be tested empirically, but to ensure its overall validity, multiple estimates for input parameters were considered, taken from a wide range of sources. Data and assumptions were discussed with experts and were moderated accordingly. The sensitivity analysis embodied within the model also helped identify those areas where greatest precision was necessary.
Our analysis focuses on a limited number of supply chains. Specifically, we consider ethanol produced from softwood or straw, using a dilute acid or enzymatic conversion process. These supply chains are amongst those with the greatest potential within Europe. Softwood and forest fuels are of interest because of their abundance (approximately 411 TWh.y-1 ) in Northern Europe and the low input intensity of silviculture compared with agriculture. Straw is of interest because of its abundance as a co-product of cereal production (approximately 63 to 227 TWh.y-1 ), and relatively low cost. For each supply chain, representative values for the most important parameters affecting embodied carbon emissions were identified and normalised.
Characterising feedstock parameters
- 1.The feedstock supply chain was assumed to be composed of three generic operations:
Production and forwarding to a roadside collection point.
Transport from the roadside collection point to the processing plant.
Size reduction, where required, in order that the biomass was in a form acceptable to the plant.
The conversion plant was assumed to only receive chips, or in the case of the straw process, bales.
Biomass was transported in the densest form possible; for example, for logs, chipping at the plant was given preference to chipping at the roadside. (Chipping at the plant is more efficient and, although biomass transport is not necessarily limited by the bulk volume, transporting low-density biomass may require specialist vehicles.)
Emission assumptions for transportation and size reduction operations.
Average Carbon Emissions from fossil fuel kgCO2e.odt-1
Transport from roadside
Softwood (all forms)
GHG emissions from biomass production were assumed not to vary with the quantity demanded.
Quantities were converted to oven-dry-tonne (odt) equivalents.
Normalised embodied carbon estimates for biomass feedstocks
(delivered to the plant as chips/bales)
It should be recognised that these base-case assumptions represent a substantial simplification. Relatively few data points were available, transport distances were assumed to be constant and the estimates exclude emissions that may arise as a consequence of increasing biomass utilisation. Nevertheless, the level of resolution corresponds with the quality and availability of data and is consistent with the analytical methods underpinning a number of UK Government reports, including the recent UK Biomass Strategy . A more in-depth treatment of logistics and consequential impacts is described and evaluated below.
Characterising the conversion process
Mass balance and assumptions for reference-case enzymatic and dilute acid conversion processes.
Dilute acid processb
(solid + liquid)
(solid + liquid)
Alternative allocation methodologies
The methodology used to apportion supply chain GHG emissions between co-products affects the interpretation of emissions attributable to ethanol. The inputs to the supply chain considered here were biomass and fossil-fuel derived inputs: diesel, fertiliser, and so on. The outputs were ethanol, surplus solid fuel or electricity (obtained from lignin residues), CO2 from fermentation and the combustion of biomass residues, and CO2 from the consumption of diesel, production of fertiliser, and so on. (It should be noted that the production of biofuels may give rise to GHG emissions from a variety of other sources, for example, N2O from fertiliser and CO2 from the oxidation of organic matter in soil; these potential sources were included in the analysis only insofar as they were included in the literature estimates of the GHG emissions associated with biomass production.)
Electricity is a significant input to the conversion process. If this electricity is imported from the grid, its carbon intensity (assuming average grid emissions) varies significantly between European countries, depending upon the generation mix. To account for these differences the following average carbon intensities were used: EU27 (340 kgCO2e.MWh-1), UK (472 kgCO2e.MWh-1), Sweden (44 kgCO2e.MWh-1) .
Modelling enzyme production
Cellulase manufacture assumptions
Inputs and parameters
Agitation power requirement
Air sparge power requiremente
Biomass feedstock composition: cellulose/lignin
% (dry basis)
Initial cellulose concentration
Corn oil anti foam
An alternative scenario to off-site enzyme production was also developed. This alternative, 'auto-generation' scenario, assumed that electricity was produced from the solid fuel co-product and used for enzyme manufacture on the same site as the ethanol plant, thereby reducing the quantity of electricity purchased. This scenario was modelled by adapting the mass balance for the enzymatic process so that the residual solid fuel from ethanol production, together with the lignin residue from enzyme manufacture, was converted to electricity at an efficiency of 30%. The quantities of electricity imported, and solid fuel exported, were correspondingly reduced.
Modelling transport logistics
The base-case supply chains described above assumed fixed transport distances. To investigate the contribution of transport to overall feedstock carbon emissions in greater depth, a further set of supply chains was developed using a simple logistics model to estimate emissions from transport as a function of the quantity of feedstock demanded, the spatial density of the resource and the bulk density of the biomass.
The average distribution of biomass collection points was constant and could be characterised as a fixed distribution density – ρ (odt.km-2).
Biomass for one year's operation – m (odt) – was supplied from a circular area with marginal radius – r' (km) – to a plant at the centre. Thus the marginal radius increased with the square root of the quantity of biomass demanded.
Road tortuosity was taken into account using an average winding factor (20.5).
The total number of km.tons (rm tot ) for one year's operation is thus given by the integral of the marginal radius with respect to mass, multiplied by the road tortuosity: rm tot = (2/3)3/2.m3/2.(∏.ρ)-1/2.
Logistics parameters a
Consideration of consequential impacts and land-use change
GHG emissions may arise from consequential impacts, the most important of which are arguably direct and indirect land-use change. Direct land-use change may occur if previously uncultivated land is used to produce biomass feedstocks; if the converted land had a high carbon stock value (for example, if it was forested) the GHG emissions from clearance and conversion may be significant. Indirect land-use change impacts may arise if increasing demand for biofuels increases commodity prices or displaces the production of other agricultural crops, and this, in turn, causes uncultivated land to be converted to agricultural production.
The Intergovernmental Panel on Climate Change (IPCC) provides guidance on the estimation of direct impacts (for example, the conversion of forest or grassland to annual or perennial biofuel crops) based on climate zone, ecological zone and soil type . In the case of by-products (for example, straw) and managed forestry which continues to be managed (for example, softwood in northern Europe), the direct impacts, following IPCC 'tier 1' guidance, are nil.
Yet the science, and convention, for determining indirect impacts is in its infancy. To estimate the impacts from indirect land-use change, the UK Renewable Fuels Agency identifies two contrasting approaches: partial equilibrium modelling  and the use of indirect land-use change (ILUC) factors [37, 38]. Both have been applied to first generation biofuels to investigate possible displacement effects resulting from the use of food crops for biofuels, but the indirect effects from the increased use of forest products (and residues such as straw) have received less attention.
For softwood, consequential emissions were assumed to arise from an equivalent mass of short rotation coppice (SRC) grown on arable land, which in turn results in land elsewhere being converted to arable usage. Using an average yield of 10 odt.Ha-1.yr-1 for SRC, and Fritsche's estimate for the conversion of high carbon content natural systems to arable land (4000 kgCO2e.Ha-1.year-1)  these assumptions yield a rough estimate of 400 kgCO2e.odt-1.
For straw, consequential emissions were assumed to be the same as the nutrient replacement value: 177.6 kgCO2e.odt-1 (that is, the 'high' estimate in Table 2).
Labelling individual supply chains
Many hundreds of supply chain permutations are possible. To clearly distinguish individual permutations the following labelling scheme is used:
Feedstock: Softwood = Spruce, Straw = Straw
Feedstock emissions: High = (HC), Medium = (MC), Low = (LC)
Process: Dilute acid = DA, Enzymatic = EH, Pentose fermentation = p
Capacity: 25 odt.h-1 = C(25)
For example, softwood with a low level of embodied carbon emissions, processed using an enzymatic process including pentose fermentation, in a plant with a capacity of 25 odt.h-1, would have the label: Spruce(LC)-EHp-C(25).
Results and discussion
Comparison of base-case supply chains
Base-case lignocellulosic ethanol supply-chains
Supply chain label
Feedstock embodied emissions
Electricity embodied emissions
All fossil carbon
emissions allocated to
ethanol and solid fuel
All the base-case chains lead to substantially reduced carbon emissions relative to gasoline (56 to 82% reduction). For enzymatic hydrolysis chains, the greatest contribution to emissions comes from enzyme production (50 to 60%), followed by electricity consumption in the conversion process (22 to 28%). For the acid hydrolysis chains, the greatest contribution to emissions comes from electricity consumption (40 to 55%). For the softwood chains, biomass contributes approximately 16 to 30%, whereas for the base-case straw chain the contribution is negligible. In all cases the emissions associated with other process chemicals are small (4 to 8%) and the emissions associated with transport and distribution are negligible (0.5 to 1.5%). Chains utilising pentose fermentation all show lower emissions than their non-pentose fermenting counterparts, demonstrating the advantage that may be obtained from incremental improvements in ethanol yield.
The impact of allocation and location decisions
It can be seen that allocating emissions to both ethanol and co-products reduces the emissions attributable to ethanol in all cases. With this method, a supply chain located in Sweden yields a greater carbon saving relative to gasoline (73 to 86% reduction) than the same chain located in the UK (28 to 63% reduction) or assuming the EU27 average electricity mix (42 to 70% reduction). This difference reflects the greater carbon intensity of electricity in the UK and EU27 compared with Sweden.
Using the substitution methodology, ethanol appears to have greatly reduced emissions compared with gasoline (74 to 84% reduction), but the impact of location is reversed: a chain located in Sweden appears worse than one located in the EU27 or UK. This is because the carbon credit obtained by displacing relatively clean Swedish grid electricity is approximately one tenth of that obtained by displacing UK or EU27 electricity; that is, the more carbon intensive the electricity system, the greater the apparent saving the substitution methodology will demonstrate.
It is arguable that the substitution method best reflects the situation on the ground for a specific plant. The disadvantage, however, is that expanding the system to include displaced electricity production (or other products) requires knowledge of the local situation. The products that are displaced may also change with variations in relative market price; consequently, generalisation from a specific instance may not be justified. Allocation on the basis of energy content avoids this problem to a certain extent, but it may be argued that this approach provides a less accurate reflection of the net impact of the bioethanol production system in the economy. Again, the issue of how to draw consistent boundaries arises because the solid fuel may be burnt directly or transformed into secondary products.
Comparing offsite and onsite enzyme production
The impact of transport emissions
Consequential impacts: indirect land-use change and fertiliser replacement
The ILUC estimate for wood included here is somewhat crude and may be overly pessimistic; nevertheless, it is clear that indirect land-use change could have an important impact if demand for lignocellulosic feedstocks were to indirectly increase competition for land. In the case of increasing the utilisation of waste products such as straw, the inclusion of fertiliser replacement has a similar effect. In this case, however, it might be argued that a portion of the consequential impacts should be attributed to the primary product, the grain. Ultimately this is a subjective judgement.
The relationships shown above clearly illustrate that emission estimates are influenced by a great number of assumptions. Testing the stability of the model output against variation in these assumptions is important to identify which have the greatest influence. In this analysis, the testing has been broken down into two steps.
In the first step, an elasticity analysis was conducted on each of the GHG emission factor parameters feeding into the model. The elasticity of a result with respect to an input parameter is defined as the ratio of the percentage change of the result to the percentage change in the parameter. A small change, much less than 1, denotes an inelastic parameter, that is, one that is forgiving of small uncertainties, whereas an elasticity close to 1 shows that the parameter has a greater influence on the model result and indicates that a more accurate input is required.
In the second step, the parameters identified as important were varied over a range of values and the change in results recorded. The outcome is presented graphically in the form of a spider diagram showing the change in the result as a function of the percentage change in each parameter.
Emission factor parameters included in the sensitivity analysis.
% Variation relative to base-case
Softwood production transport and size reduction
Range reflects the difference between high and low estimates for softwood emissions, excluding land-use change
Straw production and transport
This range is illustrative only and reflects variation around the lower estimate for straw
Range reflects the carbon intensity of enzyme manufacture using Swedish or UK electricity relative to EU 27
Process electricity purchased
Range reflects the carbon intensity of Swedish and UK electricity relative to EU 27
A range of -50% to +100% was considered sufficient
to cover uncertainties in production emissions
The model described here has been used to characterise and compare simplified LE supply chains applicable to Europe. Using this model it has been possible to identify which factors are most important in determining GHG emission savings and to compare a range of supply chain configurations. The impact of selecting alternative allocation methodologies on the interpretation of the results was also considered.
Quantifying GHG emissions from the production of cellulosic ethanol faces similar challenges to quantifying the emissions from conventional biofuels. There is limited empirical data, significant methodological variation, and results cannot easily be divorced from subjective interpretation. Nevertheless, although the precise level of emissions may be uncertain, the LE supply chains considered here all lead to GHG savings relative to gasoline irrespective of the allocation method applied. There is, however, one important caveat to this generalisation: the essentially unknown importance of consequential impacts, and, in particular, indirect land-use change. If LE production uses feedstocks that lead to indirect land-use change, the GHG performance of LE supply chains may be greatly reduced.
If the commercial deployment of LE technology is modest and does not exceed the carrying capacity of existing managed forestry, then indirect land-use change could reasonably be ignored. The problem for policy makers is that replacing a significant proportion of European transport fuel requires anything but a modest response. Indirect land-use change is therefore likely to remain firmly on the political and scientific agenda. Further analysis is needed, and it is important that this analysis is sensitive to the multi-scale nature of bio-energy systems and the anticipated level of deployment.
The debate about how impacts should be allocated to co-products is also unlikely to diminish. The UK Renewable Transport Fuel Obligation – arguably the most advanced framework for assessing the GHG benefit of biofuels – indicates that substitution is the preferred approach, but permits other approaches be considered where substitution is not practical. This flexibility has considerable advantages: for an individual plant the substitution methodology probably provides the best reflection of the situation on the ground, but for the purpose of policy formation, where it is necessary to forecast and generalise the environmental impact of multiple plants in multiple locations, allocating emissions on the basis of energy content is simpler and will yield a more consistent result.
Setting the issues of consequential impacts and allocation to one side, the most important factors affecting supply chain emissions are the use of electricity and the GHG emissions embodied in biomass production. For any operational assessment of GHG impacts that may be required as part of a reporting scheme, it will therefore be important for fuel producers to know the provenance of their feedstocks.
The enzymatic process entails greater GHG emissions than the dilute acid process owing to the large quantity of electricity consumed during enzyme manufacture, even though the dilute acid process has a somewhat lower ethanol yield. Co-locating ethanol production, electricity generation and enzyme production in a single facility may improve performance, particularly if this allows the number of energy intensive steps in enzyme production to be reduced, or if other process synergies are available. Determining the extent of these synergies requires a more detailed process comparison than the one presented here. What is clear, however, is that without these synergies, integrating enzyme production on site is simply equivalent to selecting a system expansion methodology to allocate environmental impacts in preference to one that apportions impacts to products and co-products.
While process integration provides scope to decrease electricity consumption and advances in microbiology have the potential to increase enzyme yield and efficacy, the emissions embodied in biomass production are potentially more difficult to reduce: improvements will ultimately depend on yield improvements and efficiency gains. Transport emissions, although often discussed in the literature, appear to be relatively unimportant.
If biofuels policy in the EU remains contingent on favourable environmental performance then the multi-scale nature of bioenergy supply chains presents a genuine challenge. LE holds promise for emission reductions, but maximising GHG savings will not only require efficient supply chain design but also a better understanding of the spatial and temporal factors which affect overall performance.
List of abbreviations
dilute acid conversion process
dilute acid conversion process incorporating pentose fermentation
enzymatic conversion process
enzymatic conversion process incorporating pentose fermentation
filter paper unit
life cycle assessment
New Improvements in Lignocellulosic Ethanol (An EU Framework Programme 7 project)
National Renewable Energy Laboratory
oven dry tonnes
short rotation coppice.
The authors thank Guido Zacchi and Per Sassner of the University of Lund for their comments and insights. We gratefully acknowledge the support provided by the European Commission Framework Programme 7, through their sponsorship of the New Improvements in Lignocellulosic Ethanol (NILE) project. Contract number: 019882. Website: http://www.nile-bioethanol.org.
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