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Cox, Kelly, Renouf, Marguerite, Dargan, Aidan, Turner, Christopher, &Klein-Marcuschamer, Daniel(2014)Environmental life cycle assessment (LCA) of aviation biofuel from microal-gae, Pongamia pinnata, and sugarcane molasses.Biofuels, Bioproducts and Biorefining, 8(4), pp. 579-593.

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1

Environmental life cycle assessment (LCA) of aviation biofuel from microalgae, 1

Pongamia pinnata, and sugarcane molasses 2

Kelly Cox, Boeing Research and Technology Australia, Brisbane Technology Centre, Brisbane, 3

QLD, Australia. 4

Marguerite Renouf, University of Queensland, School of Geography, Planning and Environmental 5

Management, St. Lucia, QLD, Australia. 6

Aidan Dargan, Boeing Research and Technology Australia, Brisbane Technology Centre, Brisbane, 7

QLD, Australia. 8

Christopher Turner, University of Queensland, Australian Institute of Bioengineering and 9

Nanotechnology, St. Lucia, QLD, Australia. 10

Daniel Klein-Marcuschamer, University of Queensland, Australian Institute for Bioengineering 11

and Nanotechnology, St Lucia, Australia; Joint BioEnergy Institute, Deconstruction Division, 12

Emeryville, CA, USA; Lawrence Berkeley National Laboratory, Physical Biosciences Division, 13

Berkeley, CA, USA 14

Correspondence to: Kelly Cox, Boeing Research and Technology Australia, Brisbane, QLD, 15

Australia. 16

E-mail: Kelly.Cox@boeing.com 17

18

ABSTRACT 19

The environmental benefits and trade-offs of automotive biofuels are well known, but less is known 20

about aviation biofuels. We modeled the environmental impacts of three pathways for aviation 21

biofuel in Australia (from microalgae, pongamia, and sugarcane molasses) using attributional life 22

cycle assessments (LCA), applying both economic allocation and system expansion. Based on 23

economic allocation, sugarcane molasses has the better fossil energy ratio FER (1.7 MJ out/MJ in) 24

and GHG abatement (73% less than aviation kerosene) of the three, but with the trade-offs of higher 25

water use and eutrophication potential. Microalgae and pongamia have lower FER and GHG 26

2

abatement (1.0 and 1.1; 53% and 43%), but mostly avoid the eutrophication and reduce the water use 1

trade-offs. All have similar and relatively low land use intensities. If produced on land where existing 2

carbon stocks are not compromised, the sugarcane and microalgae pathways would currently meet a 3

50% GHG abatement requirement, and some GHG mitigation would be required for the pongamia 4

pathway to achieve this. This is reasonably promising given the early developmental status of these 5

pathways. Based on system expansion, microalgae and pongamia pathways had lower impacts than 6

the sugarcane pathway for all categories except energy input, highlighting the positive aspects of 7

these “next–generation” feedstocks. The low fossil energy conservation potentials of these pathways 8

was found to be a, and significant energy efficiencies will be needed before they can effect fossil 9

energy conservation. Energy recovery from processing residues (base case) was preferable over their 10

use as animal feed (variant case), and crucial for favourable energy and GHG conservation. However 11

this finding is at odds with the economic preferences identified in a companion technoeconomic 12

study. 13

14

Supporting information can be found in the online version of this article. 15

Keywords: LCA; environmental impact; energy; greenhouse gas; alternative fuel; jet fuel; 16

17

1. INTRODUCTION 18

The aviation industry contributes around three percent of the radiative forcing from energy-related 19

greenhouse gas (GHG) emissions.1 The International Air Transport Association (IATA) has set a 20

target to halve emissions from the global aviation sector by 2050 compared with 2005 levels, and one 21

strategy is the use of aviation biofuels (IATA (www.iata.org)). While biofuels alone are not expected 22

to achieve this target, they can make a substantial contribution depending on the feedstock and 23

production pathway.2 Identification and development of optimal GHG saving pathways will require 24

collaboration between government, the aviation industry, research organisations and fuel 25

manufacturers. The Queensland Sustainable Aviation Fuel Initiative (QSAFI) project is such a 26

collaboration established to assess the feasibility of three pathways for aviation biofuels in Australia, 27

3

based on molasses from sugarcane (Saccharum spp.), photoautotrophic microalgae (Nannochloropsis 1

spp.) and pongamia seeds (Pongamia pinnata). The focus was on aviation turbine fuels. A 2

companion technoeconomic study has been published previously in this journal.3 The current work 3

assesses the environmental impacts of the pathways using environmental life cycle assessment 4

(LCA) based on the same process models, data and assumptions as the technoeconomic study 5

(QSAFI (www.aibn.uq.edu.au/qsafi)). 6

Past LCA studies of biofuels generally have found them to offer fossil energy conservation and GHG 7

mitigation, but the extent varies depending on the feedstock and the production pathway, and the 8

assessment methodology applied.4-7 Greater recognition of GHG emissions associated with land use 9

change has introduced caution for first-generation biofuels that necessitate expanded agricultural 10

production.5, 8 Impacts from nitrogen use have also been highlighted, both in regards to emissions of 11

nitrous oxide (N2O) and eutrophication potential.9-12 Other issues are increased stress on water 12

resources13-15 and land use change (both direct and indirect) in relation to the loss of eco-system 13

services16 and competition with food production.17, 18 With increasing recognition of the 14

environmental constraints of large-scale bio-production,19, 20 and the development of environmental 15

criteria for future investment in bio-production,21, 22 it is important for environmental implications of 16

new biofuel pathways to be fully understood so that their GHG abatement potential can be harnessed 17

sustainably. 18

The aim of this study was to evaluate the life cycle environmental impacts of the three selected 19

production pathways, to test if they are sustainable alternatives to their fossil-fuel equivalent. This 20

was done by comparing the (expected) GHG savings relative to aviation kerosene with current GHG 21

abatement standards,21, 22 and by considering other environmental impacts (land use, water use, 22

eutrophication, ecotoxicity). The environmental assessment was also considered alongside the 23

companion technoeconomic assessment3 to draw out synergies and conflicts between economic and 24

environmental objectives. 25

The three assessed feedstocks are promising because they can be produced in commercial quantities 26

under Queensland conditions. Sugarcane molasses is a by-product of producing raw sugar from 27

4

sugarcane, a major commercial crop in Queensland. Molasses is preferred over cane juice as a 1

fermentation substrate in the Australian context as it reduces diversion of sucrose away from raw 2

sugar production, which is the industry’s core focus. Pongamia is a leguminous, oilseed tree native 3

to India that has become naturalized in Australia. While not yet domesticated or grown 4

commercially, it has been the subject of research because of its potential to produce commercial 5

quantities of oil in Australian conditions.23 Microalgae are a family of simple-structured 6

photosynthetic organisms with high photosynthetic efficiency and lipid content, which can be 7

converted into microalgae oil. Microalgae are promising because they have the potential for high 8

biomass productivity per unit area, they can be grown without the need for arable land or fresh water, 9

and can utilise seawater, brackish waters and wastewaters.24 The engineering processes for producing 10

aviation fuel modelled in this study are based on a suite of technologies currently being investigated 11

in Queensland, and described further in 2.2. They do not represent aviation biofuel production in 12

general, and are in the early stages of development. 13

The production of first- and second-generation biofuels from sugarcane has been extensively 14

assessed using LCA. Sugarcane has consistently been shown to be amongst the best energy and 15

GHG saver relative to other feedstocks,4, 6, 7 but the first-generation pathways (from molasses and 16

cane juice) have potentially significant land-use, water-use and water quality trade-offs associated 17

with most first-generation biofuels.25 In effect, sugarcane biofuels can be regarded as the reference 18

case against which to compare other pathways. There have been far fewer LCA studies of algae 19

biofuels, and those available have found that the promise of high productivity has not yet translated 20

into favorable GHG abatement due to its early development status,26, 27 but other environmental 21

considerations have not been well examined. No published studies of the environmental profile of 22

pongamia-derived biofuels were identified. 23

Past research relates almost entirely to automotive biofuels, with very few published studies of 24

aviation biofuels.2 The different processes involved in producing and utilizing aviation biofuels 25

means that findings for automotive biofuels cannot be directly transposed, so examination of the 26

aviation biofuel pathways is warranted. This is the first environmental examination of pongamia oil 27

5

as substrate for biofuel, and the first to examine a range of environmental impacts, beyond energy 1

and GHG conservation, for microalgae. 2

2. MATERIALS AND METHODS 3

2.1. Overview 4

Scenarios were defined for each production pathway, which are mostly based on developmental 5

processes. An LCA was undertaken for each scenario with the aid of Simapro software V7.3.3 (Pre 6

Consulting (www.pre-sustainability.com)) to generate quantitative indicators of environmental 7

impacts per 100 MJ of fuel used. Methods were consistent with the International Standard for LCA.28 8

An attributional rather than consequential assessment was considered appropriate. Attributional LCA 9

evaluates the performance of systems using a static approach, whereas consequential LCAs consider 10

changes to a system 29. 11

All processes within the life cycle of each feedstock and processing pathway were assessed within 12

the scope of the LCA (from ‘cradle to grave’), and impacts assigned to their products and by-13

products. Economic allocation (EA) was used as the default approach for assigning impact to the 14

multiple products so the results could be compared with the GHG abatement standards which 15

stipulate EA.30 However results were also generated using system expansion (SE), as different 16

approaches are known to be influential.6, 31, 32 The other sensitivity considered was the utilisation 17

route for processing residues (meals) from pongamia and microalgae processing, with two 18

alternatives modelled. 19

Environmental impacts were quantified using mid-point indicators considered most important for 20

Australian agriculture-based processes,33 namely fossil energy input, consumptive water use, global 21

warming potential (GWP),34 eutrophication,35 ecotoxicity,36 and land occupation as a course proxy 22

for impacts to ecosystem services. 23

To test how the impacts of the aviation biofuels compared with those of the fossil fuels they 24

substitute and to estimate their GHG abatement potential, the results were compared with results 25

generated for aviation kerosene using life cycle inventory data for Australian production.37 The 26

biofuels modelled were assumed to have properties within the operational specifications of 27

6

acceptable jet fuel.38 In the absence of information about combustion characteristics, emissions from 1

combustion in jet engines were also assumed to be equivalent. 2

To distinguish discernible differences between the fuels, uncertainty ranges were generated using the 3

Monte Carlo function in Simapro. This was run over 500 iterations to generate 95% confidence 4

ranges based on known data ranges or assigned data quality pedigrees. 5

The reader is referred to the supporting information in the online version of this paper for the data 6

used in the analysis. 7

2.2. Scenario Descriptions 8

Assumed processes for each scenario are summarised in Fig. 1 and production outputs from each 9

stage are detailed in Table 1. 10

2.2.1. Feedstock production (sugarcane, pongamia seed and microalgae) 11

For the sugarcane scenario state average sugarcane production parameters reported by Renouf et 12

al.,39 were used. A growing cycle of six years was assumed, comprising one plant crop, four ratoon 13

crops, and a fallow period. Sugarcane production in Queensland is highly mechanised, employing 14

machinery for all stages of crop cultivation, mechanical harvesting, and truck and rail transport of the 15

harvested sugarcane to the mill. Nutrients are applied as synthetic fertilizers, and pesticides are 16

applied to control insect pests and weeds. Nitrous oxide (N2O) emissions from nitrogen fertiliser 17

application were assumed to be 0.0125 kg N2O-N/kg applied N in line with national GHG 18

methodology.40 Higher emissions are possible in some conditions (0.04 kg N2O-N/kg applied N on 19

average41), and the sensitivity of results to higher emissions was considered. Irrigation water is 20

applied to 60% of the crop. Around 61% is harvested ‘green’ with harvest residues (trash) retained in 21

the field. The other 39% is burnt prior to harvest with no retention of trash. Harvested sugarcane is 22

moved short distances by tractor to transport sidings for onward transport to sugar mills, which is 23

mostly by rail using the extensive sugarcane rail network, and to a lesser extent by road trucks. 24

The pongamia scenario is based on trial pongamia plantations at a couple of sites in Queensland, and 25

.production parameters were based on estimates from these research trials. It assumes a network of 26

pongamia plantations located in an inland region of Queensland within 100 km of the processing 27

7

plant, and where supplementary irrigation of 1ML/ha/yr was assumed to be required. Planting 1

densities were assumed to be 500 trees/ha, producing seeds over 25 years after a 5 year establishment 2

phase (Scott PT, 2013, pers. comm.). Each tree is assumed to produce 30 kg seed pods/tree/yr 3

resulting in a yield of 9.7 t seed/ha/yr42 containing 40% oil, or 3.9 t oil/ha.42, 43 . This assumed yield 4

is within the observed yield range (3-5 t oil/ha) (Scott PT, 2013, pers. comm.), and would be 5

representative of yield on agricultural land. Seeds are harvested using an umbrella shaker and the 6

kernels extracted from the pods on-site using machines similar to those used to de-shell peanuts. 7

Waste pods are applied as mulch for weed control 8

For the microalgae scenario, a hypothetical site was defined in the absence of a full-scale production 9

facility to model. The scenario assumes production of Nannochloropsis spp. in salt water in 10

compacted clay race-way ponds. Yield was assumed to be 20 g DM/m2/day, which is conservative, 11

being at the low end of yield reported in other studies.44-47 Production is assumed to occur on the 12

Queensland coast, in proximity to sources of carbon dioxide (CO2)-rich flue gas and sea water. Sea 13

water and ground water are fed into the ponds along with nutrients (phosphate and nitrate), aerated 14

and mixed with paddlewheels and injected with the CO2. Microalgae are harvested once the required 15

concentration is achieved. Anoxic conditions that promote N2O emissions were assumed to not be 16

present in the algae ponds and therefore direct N2O emissions were assumed to be zero or 17

insignificant.48 However indirect emissions of N2O from the N contained in wastewaters from the 18

algae process were accounted for in line with national GHG inventory methodology.40 19

2.2.2. Feedstock processing (to molasses, pongamia oil and microalgae oil) 20

Sugarcane milling was modelled on a typical Queensland sugar mill consistent with the 21

technoeconomic study.3 Cane is first crushed to extract juice, with the leftover fibre (bagasse) 22

combusted in a mill boiler to produce steam and electricity used internally to power the mill. Surplus 23

electricity is assumed to be exported to the grid rather than to the downstream fuel production 24

process because of inconsistencies in the timing of supply. Molasses is a co-product from the sugar 25

crystallisation process, containing residual sugars that are not further recovered. This scenario 26

assumes the use of A-grade molasses in fermentation and fuel refining. Other milling residues (mill 27

8

mud and boiler ash) are applied to cane fields, and wastewater is treated in aerobic ponds prior to 1

discharge to local rivers. 2

For pongamia, the kernels are first processed through a drying and flaking mill, then oil is recovered 3

using hexane solvent extraction and degumming to clean the oil. For microalgae, the harvested algae 4

slurry is first centrifugally concentrated to a dense paste, then subjected to hexane solvent extraction 5

and de-gumming. Energy requirements for pongamia and algae processing are met by grid electricity 6

and natural gas by default, but some portion of energy demand can be met by bio-gas generated from 7

by-products in some cases. A phospholipid co-product is produced from both the pongamia and 8

microalgae processes and is assumed to be a substitute for lecithin, a food additive. 9

The residual pongamia meal and hulls, and algae meal are assumed to be utilised in two different 10

ways. The base case assumes they are anaerobically digested to produce biogas, which is combusted 11

in a methane boiler to generate steam and electricity for feedstock processing and fuel production 12

Digestion effluent is discharged to the local sewerage system after treatment in the case of pongamia, 13

and in the case of microalgae returned to the ponds to recycle water and nutrients. The variant case 14

assumes they are used as protein-rich animal feed and all energy requirements met by fossil fuels. 15

2.2.3. Fuel production 16

For the sugarcane scenario, molasses is first fermented to produce farnesene. Residual fermentation 17

effluent is applied to cane fields. The farnesene then undergoes an emulsion dispersion process with 18

the addition of tergitol, sodium chloride and sodium hydroxide to produce oil. Hydrogen is then 19

added in a hydrocracking process to break down the oil followed by distillation. For the pongamia 20

and microalgae scenarios, the triacylglyceride oils are extracted and then refined into aviation fuel 21

using hexane extraction followed by a proprietary Honeywell UOPTM process, which involves 22

hydrogenation to produce synthetic hydrocarbons followed by selective hydrocracking and 23

distillation. Aviation fuel is the primary product from all these processes, co-produced with naphtha 24

and diesel. Light gases are also produced but are combusted in boilers to produce process heat. 25

Further details can be found in the technoeconomic study.3 26

9

2.3. System boundaries and data sources 1

The following aspects were included in the ‘cradle to grave’ system boundaries: 2

feedstock production – sugarcane,39 pongamia seeds,49 and microalgae;3 3

feedstock processing - sugarcane milling, oil extraction from pongamia and microalgae;3 4

fuel production – molasses fermentation, hexane extraction and UOP process, hydrocracking and 5

distillation;3 6

combustion and energy generation from bagasse (in the sugarcane scenario), biogas (in the 7

pongamia and microalgae scenarios) and light gas (in all scenarios); 8

anaerobic digestion of processing residues – algae meal, pongamia hulls and meal;3 9

land application of residues – sugarcane mill mud and boiler ash,50 effluent from molasses 10

fermentation, and biomass from pongamia and algae meal digestion; 11

production and supply of inputs for all the above processes (fuels, electricity, fertilisers, 12

pesticides, process chemicals);37 13

transport of inputs, and transport of feedstock to the processing plants (harvested sugarcane, 14

harvested seed, algae slurry); 15

emissions to air and water from sugarcane and pongamia growing, algae ponds, pre-harvest 16

burning of sugarcane,39, 40 feedstock processing and oil refining,3 and wastewater discharges; 17

production of agricultural machinery for sugarcane and pongamia, and pond construction for 18

microalgae;37 and 19

combustion of the produced aviation fuels in jet aircraft.37 20

The blending and distribution of fuels were assumed to be equivalent for all compared fuels, and 21

hence not included. Transport of substrate (molasses and oils) from feedstock processing to the fuel 22

production plant was not included as these were assumed to be co-located. Land transformation for 23

establishment was not included as specific geographic locations were not known and outside the 24

scope of the study. The construction of processing plants and the removal of the pongamia trees at 25

10

the end of their production life were not included, as the impacts amortized over the very large 1

production output over the life time of the plants / trees were considered to be insignificant. 2

When system expansion was applied, boundaries were expanded to account for avoided production 3

of commodities substituted by co-products (sorghum grain, lupin grain, naphtha and diesel). 37 4

Environmental input and output data can be found in the supporting information provided with the 5

online version of this paper. 6

2.4. Economic allocation (EA) 7

For economic allocation, impacts were assigned to multiple products based on the percentage 8

contribution of each product to the economic value of all products, using current trading prices 9

(Table 1). 10

For the sugar mill, impacts were allocated to raw sugar and molasses. Bagasse was modeled as a 11

waste, hence exported electricity produced from its combustion (the default disposal method) was 12

not considered a product, and instead avoided production of the average electricity mix in 13

Queensland was assumed. 14

For pongamia processing, impacts were allocated to the oil and the co-produced phospholipid. In the 15

base case, digestion of pongamia hulls and meal was modeled as a service process as it is conducted 16

for the purpose of energy production (over the default disposal route which is animal feed). Hence 17

the electricity and steam from combustion of the resulting biogas, which are utilised in the 18

downstream fuel production process, are also co-products with very small proportions of impacts 19

allocated to them. In the variant case, pongamia biomass is combined with the phospholipids and 20

used as animal feed instead, which was modeled as a waste treatment process. 21

For microalgae processing, impacts were allocated to the oil and the co-produced phospholipid in the 22

base case, and in the variant case to the oil and the meal. For the base case, digestion of algae slurry 23

was modeled as a waste treatment process (being the default disposal method due to its high water 24

content). Hence electricity and steam from combustion of the resulting biogas, which are fully 25

utilised in algae processing and fuel production, were not considered co-products. In the variant case 26

11

algae slurry is dried and combined with the phospholipids and utilised as animal feed, making it an 1

additional co-product with some impacts allocated to it. 2

For the fuel production process in each scenario, impacts were allocated to the aviation fuel, diesel 3

and naphtha. 4

2.5. System Expansion 5

For system expansion all impacts are assigned to the determining products, and the system expanded 6

to account for increased or decreased production of commodities displaced by the co-products.51 7

Quantities of substituted products were estimated based on qualified estimates of rates of substitution 8

(Table 1). 9

For the sugarcane mill, raw sugar is the determining product and molasses is the co-product. As 10

molasses is the feedstock of interest (whose default use is for animal feed), the impact of using it for 11

fuel production is taken to be increased production and use of sorghum grain, the marginal feedstock 12

for animal feed by virtue of its substitutable energy content. 13

For microalgae and pongamia processing, oil is the determining product. Co-production of 14

phospholipids (in the base case) and animal feed (in the variant case) is assumed to decrease 15

production of lupin grain Lupin grain was assumed to be the marginal feedstock for animal feed, by 16

virtue of its substitutable protein content. Therefore the avoided impacts of avoided animal feed 17

production (lupin grain) were taken into account. 18

For the fuel production processes aviation fuel is the determining product, and co-production of 19

diesel and naphtha are assumed to decrease the production and use of diesel and naphtha from fossil 20

fuels. 21

3. RESULTS 22

The contributional profile of results generated using EA (Fig. 2 right hand columns) show the 23

sources of environmental impacts across the life cycle of the assessed biofuels when only the 24

attributes of the production systems are considered. 25

Fossil energy input (Fig. 2(a)) for all feedstocks is dominated by electricity and/or chemical inputs to 26

feedstock processing and fuel production. The embodied energy of chemicals used in fuel production 27

12

(mostly hydrogen used in hydrocracking) is the most significant (45-55% for the pongamia 1

scenarios, 40-50% for the microalgae scenarios, and 58% for the sugarcane scenario), followed by 2

electricity input (34-35% for the pongamia scenarios, 43-45% for the microalgae scenarios). There is 3

no fossil-energy input to sugarcane milling because all energy needs are met by bagasse combustion. 4

Molasses fermentation is also less energy intensive than the hexane extraction and UOP processes 5

required for the oil substrates. This makes sugarcane molasses the least energy intensive of the three 6

feedstocks. Microalgae production requires significant energy for operating the ponds making it the 7

most energy-intensive feedstock overall. 8

GHG emissions contributing to GWP (Fig. 2(b)) originate from fossil-energy use, but also field 9

emissions of N2O from growing sugarcane and pongamia, and N2O and methane (CH4) from pre-10

harvest burning of sugarcane. For the pongamia and microalgae scenarios, which require a higher 11

input of electricity, the associated GHG emissions are significant (45-50% for the pongamia 12

scenarios, 63-68% for the microalgae scenarios) because the electricity mix is dominated by 13

emissions-intensive black-coal electricity. The energy self-sufficiency of sugarcane milling along 14

with the credit for displaced grid electricity means the sugarcane scenario has the lowest GWP. In 15

situations where N2O emissions are higher (0.04 kg N2O-N/kg applied N)41, the allocated GWP 16

result would increase from 2.2 to 3.2 kg CO2eq/ 100MJ, but would still be lower than the other 17

feedstocks. 18

Sources of eutrophication potential (Fig. 2(c)) common to all scenarios are nitrogen oxide (NOx) 19

exhaust gases from fuel combustion in jet engines, from combustion of bagasse, biogas and light 20

gases, and from other fossil-fuels. The sugarcane scenario also has the potential for field emissions of 21

nutrients (N and P) to waterways from sugarcane growing, resulting in it having higher 22

eutrophication potential than the pongamia and microalgae cases. While pongamia is a legume tree 23

with no nitrogen requirements, there was assumed to be some fertiliser application in the 24

establishment phase. However no nutrient losses to water were assumed due to the inland location 25

with less rainfall. The risk of nutrient loss is low for microalgae production because it occurs within 26

13

a controlled production process rather than an agricultural process thereby avoiding nutrient 1

exchanges with water bodies. 2

The sources of eco-toxicity potential (Fig. 2(d)) when the Australian toxicity method36 is used are 3

predominantly releases of heavy metals and organic compounds from production of electricity and 4

chemicals. The energy self-sufficiency of sugarcane milling along with the credit for displacement of 5

grid electricity means the sugarcane scenario has the lowest eco-toxicity potential using this method. 6

However if a different eco-toxicity method were used (such as Recipe52), which applies less 7

weighting to the toxicity impacts of metals and organics and more to pesticides, then the outcome 8

would be different. Potential losses of pesticide would be more significant for the sugarcane scenario 9

and would likely lead to it having the highest eco-toxicity potential. Due to the sensitivity of the 10

results to the eco-toxicity impact assessment method and consequently uncertainty in the eco-toxicity 11

results generated here, less emphasis has been placed on the eco-toxicity results when interpreting 12

findings. 13

The process responsible for water use is feedstock production – irrigation for sugarcane and 14

pongamia growing, and water feed of the algae ponds to compensate for evaporative losses. The 15

pongamia case has the lowest water use intensity (Table 2). In contrast sugarcane growing in 16

Australia can rely heavily on irrigation, but it is highly variable.39 In wetter growing regions where 17

no irrigation is required (as in the far north of Queensland), water use intensity may be comparable 18

with the other feedstocks. In the scenario modelled here, which represents average conditions, the 19

sugarcane molasses pathway has the highest water-use intensity. 20

Feedstock production is also the process responsible for land occupation. Land use per unit of fuel is 21

lowest for the pongamia case (Table 2) owing to its higher overall fuel productivity per hectare (2.5 t 22

fuel/ha for pongamia, 0.7 for sugarcane molasses and 0.4 for microalgae). However land use 23

intensities for all three pathways are relatively low when compared with the broader range of first 24

and second generation automotive biofuel pathways (2-20 m2/100MJ).7 The land use intensity for 25

algae from this study (6.8 m2/100MJ) was higher than reported in other past studies (0.3 m2/100MJ)7 26

because more realistic (conservative) productivity was assumed. 27

14

3.1. Influence of alternative allocation approach 1

When SE is applied, impacts occurring outside the system boundary due to co-product utilization are 2

also accounted for (Fig. 2 left hand columns). For all scenarios, there are avoided energy and GWP 3

impacts when naphtha and diesel from biofuel refining displace the production and use of naphtha 4

and diesel from crude oil. For the pongamia and algae variant cases, there are avoided energy, GWP, 5

eutrophication, land use and water use impacts when phospholipids and algae / pongamia meals 6

displace lupin grain as animal feed. For the sugarcane scenario, the impacts of feedstock production 7

and processing are those of sorghum grain rather than sugarcane, and can be seen to be considerable. 8

3.2. Influence of different co-product utilisation routes 9

The fate of processing meals in the microalgae and pongamia scenarios was found to influence 10

results (Fig. 2). Impacts are lower when the meals are used for energy production (the base case ), 11

than when used as animal feed (the variant case) . In the EA analysis, even though the impacts 12

allocated to oil are lower in the variant cases (allocation reduced from 99% to 78% in the pongamia 13

scenario, and 97% to 50% in the microalgae scenario), this is offset by the increased dependency on 14

grid electricity. In the SE analysis the effect of lower fossil-fuel energy demand in the base case is 15

greater than the effect of avoided lupin grain production in the variant case. Therefore energy 16

recovery from meal is the environmentally preferred route for utilising processing residues, which 17

was also observed in another LCA study of processing alternatives in Australian algae systems.53 The 18

discussions that follow will focus on the environmentally-preferred base case scenarios. 19

3.3. Ranking the aviation biofuels 20

Table 3 summarises the rankings of the base case aviation biofuel scenarios to aid the interpretation 21

of the results. It shows that different conclusions may be drawn about their relative environmental 22

impacts of the different pathways depending on which assessment approach is taken. 23

The EA results, as discussed in the previous section, only consider the attributes of the production 24

system and show the sugarcane molasses pathway to be best in terms of fossil energy input and GHG 25

emissions. However the algae and pongamia pathways have lower eutrophication potential and use 26

less water, and all have similarly favourable land use intensities. 27

15

The SE results on the other hand, also consider the wider product displacement effects and are 1

thought to provide a truer reflection of actual impacts.51 They show the algae base case pathway to 2

have the least impacts for all categories except for fossil energy input. Its advantages stem from the 3

fact that feedstock is produced in a contained system which avoids the impacts of agricultural 4

production (N2O emissions, water use and nutrient and pesticide losses to water), and that processing 5

residues (meal) are produced as a slurry that can be readily digested to biogas to enable some degree 6

of energy self-sufficiency. However its low FER flags the need to pursue more energy-efficient algae 7

harvesting and separation. The base case pongamia scenario also shows promise, as the perennial 8

nature and nitrogen- and water-efficient features of this legume tree crop minimise many of the 9

impacts of the agricultural production phase, and it also generates residues for energy self-10

sufficiency. This study is the first to quantify the promising characteristics of these two “next- 11

generation” pathways. 12

Sugarcane usually ranks as a preferred first-generation feedstock in terms of GHG offsets and land 13

utilisation when the substrate is cane juice4, 6, 7, 54, due to the very high yields of fermentable substrate 14

and the production of surplus energy. However in this study the substrate is molasses and the SE 15

results show that diverting molasses away from its existing use and necessitating substitute grain 16

production disadvantages the pathway modelled in this study.. 17

Overall, the very large confidence ranges generated by the Monte Carlo analysis (Fig. 2) mean that 18

uncertainties in the results are too high to confidently discern any particular preferences amongst the 19

pathways assessed at this stage. A gauge of the relative environmental performance of the fuels can 20

be gained by comparing the results with current standards and criteria for sustainable biofuel 21

production (in the next section). 22

3.4. Evaluating the biofuels against aviation kerosene and GHG abatement standards 23

The results were compared with aviation kerosene (Table 2), from which GHG abatement potentials 24

for the biofuels were calculated for comparison against the criteria of the Roundtable for Sustainable 25

Biofuels (RSB)22 and the European Union (EU) Directive for the use of energy from renewable 26

16

sources.21 Only the EA results are considered to be consistent with these standards which stipulate 1

EA in their methods.30 2

The fossil-energy inputs for all the base case biofuel scenarios are lower than for kerosene (Table 2). 3

However for the pongamia- and microalgae-derived fuels it is only marginally lower, requiring 4

almost as much fossil fuels to produce as kerosene. The less-preferred variant cases have energy-5

intensities higher than kerosene (Fig. 2). The fossil energy ratios (FER) (MJ out / MJ in) are 1.7 for 6

sugarcane molasses, 1.1 for pongamia and 1.0 for microalgae, compared with 0.92 for kerosene. 7

These are low compared with the range of values reported for automotive biofuels (1-7)55 This is 8

attributed to the higher energy demand for producing aviation fuels via the assessed pathways, 9

compared with the less energy-intensive processes for producing automotive fuels. The FER would 10

be expected to improve as the energy efficiency of these technologies develops over time. 11

GWP of all the base case biofuel scenarios are lower than kerosene. GHG abatement (i.e. the 12

percentage reduction in GHG emissions relative to the reference fossil fuel, aviation kerosene) was 13

calculated to be 73% for sugarcane, 53% for microalgae and 43% for pongamia. All pathways would 14

meet the minimum GHG abatement currently stipulated by the EU Directive (35%), but only the 15

sugarcane and algae pathways would meet those of the current RSB or the EU Directive from 2017 16

(both 50%). The pongamia pathway would require some GHG mitigation to achieve this. 17

For all the biofuels, land occupation, water use and eco-toxicity potential are considerably higher 18

than for kerosene. This is consistent with the finding from other LCA studies of bio-products that 19

have considered impacts other than GHG emissions.56, 57 However, for the base case microalgae and 20

pongamia pathways eutrophication is not discernibly higher than kerosene and water use is lower 21

than sugarcane. 22

These findings are consistent with the previously established trade-off between energy and 23

greenhouse savings and other environmental protection objectives. A novel observation is that the 24

“next-generation” pathways based on microalgae and pongamia can reduce or eliminate the water 25

use and eutrophication impacts. 26

17

3.5. Comparing economic and environmental objectives 1

The companion technoeconomic analysis3 found it was economically preferable to sell the co-2

produced meals from algae and pongamia as animal feed rather than recover energy from them. This 3

preference was most marked in the algae system, and less so in the pongamia system. In contrast, the 4

findings of the LCA suggest the environmental benefits of decreased reliance on grid electricity 5

when energy is recovered from meal far outweigh the benefits of diverting it to animal feed. 6

3.6. Applicability of the models and results 7

The results of the LCA are specific to the processes modelled and thus the results should not be 8

generalised. One limitation is that efficiencies of the systems modelled depend on the technologies 9

used by each production route. The process models were developed using publicly available data 10

whenever possible to ensure transparency and reproducibility of the models. However, this meant 11

that the most efficient scenarios may not have been assessed, for the sake of achieving higher data 12

integrity by modelling established and proven processes. An example is the choice of the algae 13

harvesting process. Dissolved air flotation (DAF) is potentially less energy intensive, but insufficient 14

data was available to model this technology accurately. 15

Another caveat concerns the methods used for representing water and land use impacts, which only 16

consider consumptive water use and land occupation without consideration of the status of water and 17

land resources used. This was because the geographic locations of the production systems were left 18

unspecified. The land occupation and water use results should be regarded as general indicators 19

based on the attributes of the production system. 20

4. CONCLUSIONS 21

When only the attributes of the three production pathways were considered (economic allocation), 22

the sugarcane molasses pathway was found to have the better fossil energy ratio FER (1.7 MJ out/MJ 23

in) and GHG abatement (73% less than aviation kerosene) of the three pathways assessed. This is 24

mostly due to the production of surplus energy when sugarcane is processed and the lower energy-25

intensity of the fermentation process compared to the oil extraction processes. The downsides of the 26

sugarcane pathway are higher water use and eutrophication potential. These observations are 27

18

consistent with past LCA studies that have compared and ranked biofuel feedstocks, and found 1

sugarcane to be a preferred feedstock in terms of GHG abatement. As such, the sugarcane pathway 2

has been used as a reference against which to compare the microalgae and pongamia pathways. 3

The microalgae and pongamia pathways were found to have less favourable FER and GHG 4

abatement (1.0 and 1.1; 53% and 43%) than sugarcane. 5

If produced on land where existing carbon stocks are not compromised, the sugarcane and algae 6

pathways would currently meet the 50% GHG abatement requirement of the Roundtable for 7

Sustainable Biofuels and the European Directive (from 2017). Some GHG mitigation would be 8

required as part of future process developments for the pongamia pathway to achieve this. However 9

the microalgae and pongamia pathways mostly avoid the eutrophication and reduce the water use 10

trade-offs that are downsides for the sugarcane pathway. 11

The microalgae and pongamia pathways consume only marginally less fossil fuel than does aviation 12

kerosene, and the sugarcane molasses pathway is at the low end of compared with biofuels generally. 13

This is a considerable limitation and a point of difference with automotive biofuels. Significant 14

energy efficiencies will need to be found for these processes before they can effectively conserve 15

fossil energy.All pathways were found to have similar and relatively low land use intensities when 16

considered against the broader range of biofuel feedstocks. The land use intensity for microalgae was 17

higher than previously reported as it was based on more realistic (conservative) biomass 18

productivities. No clear conclusions could be drawn in relation to eco-toxicity potentials due to 19

uncertainty regarding the choice of impact assessment method. 20

When the product displacement effects of co-product utilisation were considered (system expansion), 21

which is arguably a truer reflection of impacts, the microalgae and pongamia pathways had lower 22

impacts than the sugarcane pathway for all categories except energy input. This highlights the 23

positive aspects of these “next-generation” feedstocks which had not been well quantified previously. 24

Energy recovery from processing residues (base case) was found to be preferable over their use as 25

animal feed (variant case), and crucial for favourable energy and GHG conservation. However this 26

finding is at odds with the economic preferences identified in the companion technoeconomic study. 27

19

This suggests that economic versus environmental trade-offs may need to be grappled with in the 1

future development of these technologies. 2

These findings suggest that each pathway holds some opportunities for being sustainable routes for 3

aviation biofuel production. . The sugarcane molasses pathway can conserve fossil energy and 4

mitigate GHG emissions, but displacement effects when molasses feedstock is diverted away from 5

existing uses will need to be managed. The microalgae and pongamia pathways can avoid the water 6

use and water quality impacts, but energy efficiency will be crucial for fossil energy conservation 7

and GHG abatement. Other pathways such as conversion of lignocellulose, which have been shown 8

to be promising for automotive biofuels, 6, 58 may also be better options for aviation biofuels, and 9

should be explored. 10

5. ACKNOWLEDGEMENTS 11

This work was conducted by Boeing Research and Technology Australia as part of their contribution 12

to the Queensland Sustainable Aviation Fuel Initiative (QSAFI) project. Recognition goes to Brad 13

Wheatley and Shaun Jellett (Boeing Research and Technology) for their contributions in the early 14

stages of the project. The authors acknowledge the valuable input from Tim Grant (Life Cycle 15

Strategies Pty Ltd) in reviewing the work and providing guidance of appropriate allocation and 16

system expansion approaches. Thanks also go to the QSAFI consortium partners – Queensland State 17

Government, University of Queensland, James Cook University, IOR Energy, Mackay Sugar 18

Limited and Virgin Australia. DKM acknowledges help from the DOE Joint BioEnergy Institute 19

(http://www.jbei.org) supported by the US Department of Energy, Office of Science, Office of 20

Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence 21

Berkeley National Laboratory and the US Department of Energy. The authors are also grateful for 22

the useful input provided by the anonymous reviewers. 23

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3 4

23

Tables 1

Table 1 Production outputs from each stage1, allocation factors and substitute products 2 Values in parentheses are allocation factors used in economic allocation. 3 Values underlined represent the assumed increased (+ve) or decreased (-ve) production of substitute products, used in 4 system expansion. 5 Products Intermediary products Substitute products

Unit Sugarcane scenario Pongamia scenarios Microalgae scenarios

Base case Variant case Base case Variant

case Feedstock production, per ha per year Sugarcane t 85 - - Pongamia seed t - 18 - Microalgae (solids) 2 t - - 19

Processing of sugarcane, per tonne feedstock processed Raw sugar kg 62.7 (64.9%)3 - - Molasses (A-grade) kg 146.8 (35.1%) 3 - - Bagasse (to co-generation) kg 309.7 - -

Electricity from bagasse (exported) kWh 13.9 - Electricity from natural gas (substituting bagasse electricity) kWh -13.9

4 - -

Sorghum (substituting molasses) kg +125.65 - -

Processing of pongamia seeds and microalgae solids, per tonne feedstock processed

Oil kg - 286.9 (98.9%) 6 286.9

(78%) 6 44.2

(97.2%) 10 44.2

(50.4%) 10

Phospholipids kg - 22.3 (0.8%) 6 - 13.6

(2.8%) 10 -

Pongamia meal (animal feed) kg - - 531.2 (21.9%) 9 -

Algae meal(animal feed) kg - - - - 318.8 (49.6%) 9

Electricity from biogas kWh - 96.7 (0.08% )7 - 83.6 -

Steam from biogas kg - 928.6 (0.22% )7 - 148.8 -

Lupin (substituting phospholipids) kg - -20.2 8 - -12.3 8 - Lupin (substituting meal) kg - - -398.3 9 - -239.1 9 Fuel production (per tonne substrate - oil or molasses) Aviation fuel kg 53.5 (47.2%) 11 480.3 (47.2%) 11 467.4 (47.2%) 11 Naphtha kg 28.9 (40.8%) 11 259.3 (40.8%) 11 252.3 (40.8%) 11 Diesel kg 8.0 (12%) 11 72.2 (12%) 11 70.3 (12%) 11 Light gases (used internally) GJ 0.85 7.69 7.48 1 All production quantities are based on process modelling by the technoeconomic study.3 6 2 Harvested microalgae slurry contains 30%(wt) microalgae solids. 7 3 Derived from trading prices: raw sugar (2011-2012) A$520/t,59 molasses (2013) A$120/t (Sucrogen, unpublished). 8 4 Bagasse-electricity is assumed to substitute electricity generated from Queensland natural gas (1:1). 9 5 Molasses was assumed to be a substitute for sorghum grain (0.568 kg sorghum / kg molasses, based on calorific values) (USDA 10 Agricultural Research Service (http://ndb.nal.usda.gov/)). 11 6 Derived from estimated economic values of generated products. Pongamia oil value was based on qualified estimates (A$2776/t) 12 (Scott PT, 2013, pers. comm.). Phospholipid value was based on the value of lecithin (A$293/t) (ICIS (www.icis.com)). 13 7 Electricity value was based on wholesale price for electricity in Queensland (A$0.024/kWh) (AEMO (www.aemo.com.ua)). Steam 14 values based on a published estimate (A$10/t).60 15 8 Phospholipids were assumed to substitute lupin grain for both the pongamia and microalgae scenarios (0.905 kg lupins/ kg 16 phospholipids, based on calorific values) (USDA Agricultural Research Service (http://ndb.nal.usda.gov/)). 17 9 When pongamia or microalgae meal are utilised as animal feed (along with the phospholipid product) they were assumed to 18 substitute lupin grain (0.75 kg lupins/ kg meal, based on protein content) (USDA Agricultural Research Service 19 (http://ndb.nal.usda.gov/)). Their economic value as animal feed was based on the economic value of soybean (A$421/t). The 20 economic value of lupins in the ABARES Commodities Statistics is $250 (6-yr average). 21 10 Derived from estimated economic values of generated products. Algae oil value was based on a qualified estimate (A$3,090/t) 22 (Borowitzka MA, 2013, pers. comm.). Phospholipid value based on the value of lecithin (A$293/t) (www.icis.com)). 23 11 Derived from average trading prices (2013): aviation fuel A$987/t (IATA (www.iata.org)), naphtha A$1580/t (Recochem 24 (www.recochem.com.au), diesel A$1674/t (AIP (www.aip.com.au/pricing)). 25

26

24

Table 2 Life cycle impact assessment results for aviation biofuels (base case scenarios) compared with aviation 1 kerosene (per 100 MJ fuel consumed) 2 Impact category Unit System expansion Allocation

Sugarcane Pongamia Microalgae Sugarcane Pongamia Microalgae Kerosene

Fossil energy input MJ 85.2 121.0 134.3 58.7 93.0 99.2 108.6

Global warming potential (GWP) kg CO2eq 8.0 4.7 2.7 2.2 4.7 3.8 8.2

Eutrophication kg PO4eq 0.044 0.012 0.011 0.015 0.009 0.009 0.007

Eco-toxicity -Australian method DAY 51.1E-08 1.1E-09 8.6E-10 1.4E-10 5.2E-10 4.2E-10 4.1E-11

Water use kL 14.7 1.18 1.39 1.56 0.55 0.64 0.001

Land use m2.a 38.9 7.8 7.0 5.1 4.5 6.8 0.003

3

Table 3 Ranking the environmental impacts of the assessed aviation biofuels 4 ■ Lower impact, ■ Moderate impact; ■ Higher impact 5

System expansion Economic allocation

Sugarcane Pongamia Microalgae Sugarcane Pongamia Microalgae

Fossil energy input ■ ■ ■ ■ ■ ■ Global warming potential (GWP) ■ ■ ■ ■ ■ ■ Eutrophication ■ ■ ■ ■ ■ ■ Water use ■ ■ ■ ■ ■ ■ Land use ■ ■ ■ ■ ■ ■ 6