Partnership for AiR Transportation 

Noise and Emissions Reduction

An FAA/NASA/Transport Canadasponsored Center of Excellence

Life Cycle Greenhouse 

Gas Emissions from

Alternative Jet Fuels

PARTNER Project 28 report

Version 1.1

prepared by

Russell W. Stratton, Hsin Min Wong, James I. Hileman

April 2010

REPORT NO. PARTNER-COE-2010-001
Life Cycle Greenhouse Gas Emissions from 

Alternative Jet Fuels

Partnership for Air Transportation Noise and Emissions Reduction

Project 28

Russell&W.&Stratton,&Hsin&Min&Wong&and&James&I.&Hileman

Massachusetts&Institute&of&Technology,&Cambridge,&Massachusetts

PARTNER-COE-2010-001

April 2010

Version 1.1

This work is funded by the U.S. Federal Aviation Administration Office of Environment and

Energy and U.S. Air Force Research Lab under FAA Award Number: 06-C-NE-MIT, Amendment 

Nos. 012 and 021. The project is managed by Warren Gillette of FAA, Tim Edwards, and Bill 

Harrison, both of AFRL.

Any opinions, findings, and conclusions or recommendations expressed in this material are those 

of the authors and do not necessarily reflect the views of the FAA, AFRL, NASA or Transport 

Canada.

The Partnership for AiR Transportation Noise and Emissions Reduction — PARTNER — is a 

cooperative aviation research organization, and an FAA/NASA/Transport Canada-sponsored 

Center of Excellence. PARTNER fosters breakthrough technological, operational, policy, and 

workforce advances for the betterment of mobility, economy, national security, and the 

environment. The organization's operational headquarters is at the Massachusetts Institute of

Technology.

There may be periodic updates provided for this report. Please visit the PARTNER Project 28 

website, http://web.mit.edu/aeroastro/partner/projects/project28.html, to check for the latest 

version.

The Partnership for AiR Transportation Noise and Emissions Reduction

Massachusetts Institute of Technology, 77 Massachusetts Avenue, 33-115

Cambridge, MA 02139 USA

http://www.partner.aero

info@partner.aero
i

Table of Contents

List of Figures .......................................................................................................................................iv

List of Tables .........................................................................................................................................vi

Executive Summary............................................................................................................................ix

Acknowledgements ............................................................................................................................xi

Units, Notation, Abbreviations and Acronyms...................................................................... xii

1 Introduction......................................................................................................................................1

2 Alternative Jet Fuel Pathways..................................................................................................3

3 Procedural Overview of Life Cycle Analysis of GHG Emissions ...............................5

3.1 Life cycle Greenhouse Gas Emissions .......................................................................................... 5

3.2 Analysis Procedure.................................................................................................................................... 8

3.3 Goals and Practices.................................................................................................................................. 9

3.4 Evaluation of Variability.........................................................................................................................10

3.5 Functional Unit: g CO2e/MJ and g CO2e/kg-km......................................................................10

4 Conventional Petroleum Pathways .....................................................................................12

4.1 Crude Oil Recovery and Transportation......................................................................................12

4.2 Conventional Jet Fuel from Crude Oil ..........................................................................................14

4.2.1 Top-Down Approach (Baseline Case) .............................................................................15

4.2.2 Bottom-up Approach (Low and High Emissions Cases)........................................16

4.2.3 Impact of Crude Oil Quality Compared to Processing Technique ....................17

4.2.4 Conventional Jet Fuel Results .............................................................................................18

4.3 Ultra-Low Sulfur Jet Fuel from Conventional Crude.............................................................19

4.3.1 Top-Down Approach (Baseline case) ..............................................................................19

4.3.2 Bottom-up approach (low and high emissions cases).............................................19

4.3.3 ULS Jet Fuel Results ................................................................................................................20

5 Unconventional Petroleum Pathways ................................................................................22

5.1 Conventional Jet Fuel from Oil Sands..........................................................................................22

5.1.1 Bitumen Production through Surface Mining................................................................22

5.1.2 Bitumen Production through In Situ Recovery.............................................................23

5.1.3 Bitumen Upgrading to Syncrude.........................................................................................23

5.1.4 Oil Sands to Jet Fuel Results ...............................................................................................24

5.2 Conventional Jet Fuel from Oil Shale ...........................................................................................26

5.2.1 Analysis Methodology...............................................................................................................27

5.2.2 Oil Shale to Jet Fuel Results.................................................................................................28

6 Fischer-Tropsch Jet Fuel.........................................................................................................30

6.1 Carbon Capture and Sequestration with Fischer-Tropsch Facilities ..........................30

6.2 Fischer-Tropsch Fuel from Natural Gas......................................................................................31

6.2.1 Case Study: Impact of Carbon Capture on GHG Emissions from GTL 

Facilities ...........................................................................................................................................................32

6.3 Fischer-Tropsch Fuel from Coal ......................................................................................................33

6.3.1 CTL without Carbon Capture................................................................................................33

6.3.2 CTL with Carbon Capture.......................................................................................................35

6.4 Fischer-Tropsch Fuel from Biomass .............................................................................................35
ii

6.4.1 Switchgrass....................................................................................................................................36

6.4.2 Corn Stover and Forest Residue ........................................................................................40

6.4.3 Analysis of Biomass Feedstocks ........................................................................................42

6.5 Fischer-Tropsch Fuel from Coal and Biomass........................................................................43

6.5.1 Allocation Methodology............................................................................................................45

6.5.2 Results: F-T Jet Fuel from Coal and Biomass.............................................................46

6.5.3 Case Study: Impact of Carbon Capture on GHG Emissions from CBTL 

Facilities ...........................................................................................................................................................49

7 Hydroprocessed Renewable Jet Fuel from Renewable Oils.....................................50

7.1 Energy Requirements for HRJ from Renewable Oils..........................................................52

7.2 HRJ from Soybean Oil ...........................................................................................................................55

7.2.1 Cultivation of Soybeans...........................................................................................................56

7.2.2 Extraction of Oil from Soybeans..........................................................................................58

7.2.3 Co-Product Usage and Allocation Methodology.........................................................59

7.2.4 Transportation of Soy Oil to HRJ Production Facilities...........................................61

7.2.5 Land Use Change Emissions from Soy Oil Production ..........................................61

7.2.6 Results..............................................................................................................................................62

7.3 HRJ from Palm Oil ...................................................................................................................................63

7.3.1 Cultivation of Palm Fresh Fruit Bunches (FFB) ..........................................................63

7.3.2 Extraction of Oil from Palm FFB..........................................................................................64

7.3.3 Co-Product Usage and Allocation Methodology.........................................................66

7.3.4 Transportation of Palm Oil and Palm Kernel Oil to HRJ Production Facilities

66

7.3.5 Land Use Change Emissions from Palm Oil Production........................................66

7.3.6 Results..............................................................................................................................................67

7.4 HRJ from Rapeseed Oil........................................................................................................................69

7.4.1 Cultivation of Rapeseed ..........................................................................................................69

7.4.2 Extraction of Oil from Rapeseed.........................................................................................72

7.4.3 Co-Product Usage and Allocation Methodology.........................................................73

7.4.4 Transportation of Rapeseed Oil to HRJ Production Facilities .............................73

7.4.5 Land Use Change Emissions from Rapeseed Oil Production.............................74

7.4.6 Results..............................................................................................................................................75

7.5 HRJ from Jatropha Oil ...........................................................................................................................76

7.5.1 Yield and Plant Characterization ........................................................................................76

7.5.2 Cultivation of Jatropha Fruit ..................................................................................................77

7.5.3 Toxicity of Jatropha Fruit and Oil........................................................................................78

7.5.4 Transportation of Jatropha Fruit to Production Facilities .......................................78

7.5.5 Extraction of Oil from Jatropha Fruit .................................................................................79

7.5.6 Co-Product Usage and Allocation Methodology.........................................................80

7.5.7 Results..............................................................................................................................................82

7.6 HRJ from Algae Oil..................................................................................................................................82

7.6.1 Algal Yield and Lipid Content................................................................................................83

7.6.2 System Expansion (Displacement) for Electricity Emissions...............................83

7.6.3 Algal Carbonation Options and Technologies .............................................................85

7.6.4 Cultivation of Algae in Open Ponds ..................................................................................86

7.6.5 Nutrient Recycling via Anaerobic Digestion..................................................................87

7.6.6 Dewatering and Drying.............................................................................................................89

7.6.7 Transportation of Algae to Production Facilities.........................................................92

7.6.8 Extraction of Oil from Algae...................................................................................................92
iii

7.6.9 Sensitivity Analysis and System Design.........................................................................94

7.6.10 Results ...........................................................................................................................................95

7.7 HRJ from Salicornia Oil.........................................................................................................................96

7.7.1 Biomass, Seed and Oil Yield ................................................................................................97

7.7.2 Cultivation of Salicornia ...........................................................................................................97

7.7.3 Transportation of Salicornia Biomass and Seeds to Production Facilities ...99

7.7.4 Oil Extraction from Salicornia Seeds ................................................................................99

7.7.5 Co-Product Usage and Allocation Methodology.........................................................99

7.7.6 Land Use Change Emissions from Salicornia Cultivation .................................. 103

7.7.7 Results........................................................................................................................................... 104

8 Summary of Results ................................................................................................................106

8.1 Life Cycle GHG Emissions...............................................................................................................106

8.2 Life Cycle GHG Intensity...................................................................................................................109

8.3 Biofuel Yield..............................................................................................................................................110

8.4 Biofuel Impact on Domestic Water Resources.....................................................................112

8.5 Invasive Species with Respect to Biofuels .............................................................................114

9 Conclusions................................................................................................................................116

Appendix A – General Feedstock and Fuel Properties ....................................................118

References..........................................................................................................................................119
iv

List of Figures

Figure 1: Aspects of alternative fuels that are being considered for alternative fuel feasibility and 

sustainability. This report has an emphasis on life cycle greenhouse gas emissions 1

Figure 2: Steps considered, in the well-to-wake, life cycle GHG inventory of conventional jet fuel5

Figure 3: Steps considered in the well-to-wake, life cycle GHG inventory of bio-based alternative 

jet fuels 6

Figure 4: Steps considered in the well-to-wake, life cycle GHG inventory of bio-based fuels from 

algae 7

Figure 5: Levels of life cycle GHG studies used to ensure that the adopted practices are 

consistent with the goal of the work 9

Figure 6: Origin specific GHG emissions by species of crude oil entering US refineries in 2005 

(fraction of total imports in parentheses). Based on country profiles published in Skone and 

Gerdes (2008, 2009) 13

Figure 7: Variation in jet fuel processing emissions induced by origin specific crude oil properties 

and processing technique 18

Figure 8: Distribution of reported switchgrass yields across the United States (data from 

Gunderson et al., 2008) 37

Figure 9: The effects of product slate composition on life cycle greenhouse gas emissions from 

F-T jet fuel 46

Figure 10: Sensitivity analysis of operational specifications and configurations of F-T jet fuel from 

coal and biomass 47

Figure 11: Dependence of cumulative life cycle emissions and biomass requirements for varied 

biomass utilization within CBTL 48

Figure 12: Schematic showing the change in hydrocarbon composition between HRD and HRJ 

fuels that results from additional hydroprocessing. 54

Figure 13: Temporal variation in the soy oil and soy meal allocation fractions when using market 

value allocation. Constant dashed lines correspond to default GREET prices 61

Figure 14: Yearly rapeseed yield for France and the UK between 1999 and 2009 70

Figure 15: Utilization of by-products from jatropha cultivation and oil extraction (adapted from 

Reinhardt, 2007) 79

Figure 16: Sensitivity of life cycle emissions of HRJ from jatropha to co-product utilization and 

allocation scheme. Scenarios are described in Table 74 81

Figure 17: System boundary expansion of the algal HRJ fuel pathway 84

Figure 18: Process flows for algae oil HRJ using anaerobic digestion to recover nutrients from 

algae meal 88

Figure 19: Flow chart showing the steps involved in dewatering and drying of algae 89

Figure 20: Energy consumption of a centrifuge secondary harvesting mechanism. Line is a fit 

through four data points 90

Figure 21: An examination of the post-dewatering moisture content impact on energy 

consumption of drying algae to 90% total suspended solids 91

Figure 22: Algal lipid content and post-dewatering moisture content that result in a sustainable 

drying process. Points above each line require additional energy to be added (e.g. from 

natural gas) 92

Figure 23: Life cycle GHG of HRJ production from algae as a function of the extent of 

dewatering. Three different system configurations were explored which are described more 

fully in the figure 95

Figure 24: Sensitivity analysis of operational specifications and configurations of HRJ production 

from algae 95

Figure 25: System boundary definitions for system level energy allocation between HRJ 

production from salicornia oilseeds and electricity generation from salicornia straw biomass

101
v

Figure 26: Comparison of life cycle GHG emissions from the allocation methodologies of Table 

74 based on the production of HRJ from salicornia oil when using the straw biomass coproduct for electricity generation 101

Figure 27: System boundary definitions for system level energy allocation of coupled HRJ and FT fuel production from salicornia oilseeds and straw biomass 102

Figure 28: Life cycle GHG emissions for the alternative jet fuel pathways under consideration. 

Note: CCS denotes carbon capture and sequestration and the land use change (LUC) 

scenarios are defined in Table 96 108

Figure 29: Life cycle GHG emissions for the alternative jet fuel pathways under consideration. 

Uncertainty bars represent the low emissions, baseline, and high emissions scenarios. 

Please note the different scales for the top and bottom portions of the figure. Note: CCS 

denotes Carbon Capture and Storage and Land Use Change (LUC) scenarios are defined 

in Table 96 109

Figure 30: Fuel production potential for various alternative jet fuels that could be derived from 

biomass. This is not an all-encompassing list of alternative jet fuel options; it merely 

represents those examined by the authors as part of their ongoing research 110

Figure 31: Land area requirements to replace conventional jet fuel use within the US with 100% 

SPK and 50/50 blend of SPK with conventional jet fuel. Average US conventional jet fuel 

consumption in 2009 is 1.4 million bbl/day 111

Figure 32: Water consumption and water withdrawals in the US by sector (data adapted from 

DOE, 2006) 112

Figure 33: Water consumption for the production of various fuels (data adapted from DOE, 2006 

and King and Webber, 2008) 113
vi

List of Tables

Table 1: Fuel pathways investigated 4

Table 2: Country specific average waterborne round trip travel distance of crude oil to US 

domestic ports 13

Table 3: Type of process fuel and fuel share in the refining of jet fuel and ULS jet fuel 15

Table 4: Energy requirement in the production of straight-run jet fuel 16

Table 5: Process fuel and fuel shares for the production of straight-run jet fuel 16

Table 6: Energy requirement in the production of jet fuel from hydroprocessing 17

Table 7: Process fuel and fuel shares for the production of jet fuel from hydroprocessing 17

Table 8: Summary of results for jet fuel from conventional crude and a comparison of results to 

the NETL petroleum baseline study 18

Table 9: Energy requirement in the production of straight-run ULS jet fuel 19

Table 10: Process fuel and fuel shares for the production of straight-run ULS jet fuel 20

Table 11: Energy requirement in the production of ULS jet fuel from hydroprocessing 20

Table 12: Process fuel and fuel shares for the production of ULS jet fuel from hydroprocessing 20

Table 13: Summary of results for ULS jet fuel from conventional crude pathway 21

Table 14: Process energy assumptions for the production of bitumen through surface mining for 

the low emissions, baseline and high emissions cases 23

Table 15: Process energy assumptions for the production of bitumen through in situ recovery for 

the low emissions, baseline and high emissions cases 23

Table 16: Assumed parameters in the upgrading of bitumen to syncrude 24

Table 17: Life cycle GHG emissions for jet fuel from Canadian oil sands using surface-mining 

and in-situ recovery 25

Table 18: Life cycle GHG emissions for the high emissions scenario of jet fuel from Canadian oil 

sands with and without carbon capture and sequestration of emissions from asphaltene 

gasification 25

Table 19: Assumptions regarding the use of surface and in situ bitumen recovery in the low 

emissions, baseline, and high emissions scenarios of jet fuel from Canadian oil sands 26

Table 20: Summary of result for jet fuel from Canadian oil sands 26

Table 21: Input assumptions for the production of jet fuel from oil shale for low emissions, 

baseline and high emissions cases 28

Table 22: Summary of results for jet fuel from in situ oil shale pathway 28

Table 23: Summary of inputs and results for F-T jet fuel from natural gas pathway 32

Table 24: Life cycle GHG emissions from natural gas F-T jet fuel with and without carbon capture

33

Table 25: Input assumptions for the production of F-T jet fuel from coal (without carbon capture) 

for low emissions, baseline and high emissions cases 34

Table 26: Summary of results for F-T jet fuel from coal pathway (without carbon capture) 34

Table 27: Input assumptions for the production of F-T jet fuel from coal (with carbon capture) for 

low emissions, baseline and high emissions cases 35

Table 28: Summary of results for F-T jet fuel from coal pathway (with carbon capture) 35

Table 29: Switchgrass yields assumed in the low, baseline and high emissions scenarios 37

Table 30: Reported cultivation inputs for switchgrass 38

Table 31: Cultivation inputs for switchgrass in the low, baseline and high emissions scenarios 39

Table 32: Input parameters for the recovery and transportation of corn stover 41

Table 33: Input parameters for the collection and transportation of forest residue. 41

Table 34: Variation of life cycle GHG emissions of BTL pathway with type of feedstock 42

Table 35: Summary of results for F-T jet fuel from switchgrass with no soil carbon sequestration

43

Table 36: Summary of results for F-T jet fuel from switchgrass with soil carbon sequestration 43

Table 37: Input assumptions for the production of F-T jet fuel from coal and biomass (with carbon 

capture) for low emissions, baseline and high emissions cases 45
vii

Table 38: Summary of results for F-T jet fuel from coal and biomass without soil carbon 

sequestration credit 47

Table 39: Summary of results for F-T jet fuel from coal and switchgrass with soil carbon 

sequestration credit 48

Table 40: Life cycle GHG emissions from F-T jet fuel from coal and switchgrass with and without 

CCS 49

Table 41: Component fatty acid profiles for renewable oils considered in this work 53

Table 42: Experimental and theoretical requirements for the creation of renewable diesel 53

Table 43: Energy requirements for the creation of HRJ 55

Table 44: Farming energy required for average US soybean production 56

Table 45: Farming energy for US soybean production for low emissions, baseline and high 

emissions cases 57

Table 46: US soybeans agrichemical use 57

Table 47: Input assumptions for the cultivation of soybeans for the low emissions, baseline and 

high emissions cases 58

Table 48: Input, output and process energy for extraction of oil from soybeans 58

Table 49: Allocation of GHG emissions between soy oil and soy meal using various 

methodologies 60

Table 50: Land use change emissions arising from soybean production 62

Table 51: Life cycle emissions for the soy oil to HRJ assuming zero land use change emissions

62

Table 52: Life cycle emissions for the soy oil to HRJ assuming conversion of Cerrado grassland 

to soybean field 63

Table 53: Life cycle emissions for the soy oil to HRJ assuming conversion of tropical rainforest 63

Table 54: Assumptions in the cultivation of palm fresh fruit bunches for the low emissions, 

baseline and high emissions cases 64

Table 55: Inputs, outputs and process energy in the extraction of palm oil and palm kernel oil 65

Table 56: Methane emissions from POME treatment 65

Table 57: Assumptions in the transportation of palm oil and palm kernel oil from Southeast Asia 

to the US 66

Table 58: Land use change emissions in the cultivation of palm fresh fruit bunches 67

Table 59: Life cycle emissions for the palm oil to HRJ assuming zero land use change emissions

68

Table 60: Life cycle emissions for the palm oil to HRJ assuming conversion of logged over forest

68

Table 61: Life cycle emissions for the palm oil to HRJ assuming conversion of tropical rainforest

68

Table 62: Life cycle emissions for the palm oil to HRJ assuming conversion of peatland rainforest

69

Table 63: Farming energy, fertilizer and herbicide usage for the production of rapeseed in the low 

emissions, baseline and high emissions scenarios 71

Table 64: Rapeseed drying and storage assumptions in the low emissions, baseline and high 

emissions scenario 72

Table 65: Process inputs for extracting oil from rapeseeds 73

Table 66: Transportation profile of Rapeseed Oil from Europe to the United States 74

Table 67: Land use change emissions from rapeseed cultivation on set aside lands in Europe 75

Table 68: Summary of results from renewable jet fuel production and use from rapeseed 75

Table 69: Life cycle GHG emissions for production and use of renewable jet fuel from rapeseed 

assuming cultivation on set-aside land 76

Table 70: Parts of the jatropha fruit including processed states 77

Table 71: Yields and mass fractions characterizing the jatropha fruit 77

Table 72: Cultivation inputs for the growth of jatropha 78

Table 73: Process inputs for extracting oil from jatropha fruit 80
viii

Table 74: Co-product creation and allocation scenarios from the oil extraction process for 

jatropha capsules. The corresponding life cycle emissions for each scenario are shown in 

Figure 16 81

Table 75: Assumptions used in establishing average US biomass conversion efficiency to 

electricity 81

Table 76: Life cycle emissions from the jatropha oil to HRJ pathway 82

Table 77: Impact of CO2 source and electricity choice on the biomass credit given to algal HRJ84

Table 78: US power generation data as it relates to algae cultivation 85

Table 79: Comparison of energy inputs of direct flue gas injection and MEA extraction 85

Table 80: Cultivation inputs per kilogram of algae for algae growth in an open pond 86

Table 81: Life cycle GHG emissions from the production of nutrients used in algae cultivation 87

Table 82: GHG emissions resulting from fertilizer production within the algae to HRJ pathway for 

the three emissions cases 87

Table 83: Energy consumption and performance specifications for primary algae harvesting 

mechanisms 90

Table 84: Outputs and process energy for N-hexane oil extraction from algae 93

Table 85: Life cycle emissions from the algae oil to HRJ pathway 96

Table 86: Salicornia yield and oil fraction assumptions 97

Table 87: Input assumptions regarding the cultivation of salicornia 98

Table 88: Process inputs for extracting oil from salicornia seeds. All values are in Btu/lb of oil 100

Table 89: Allocation methodologies examined for the production of HRJ from salicornia oil when 

using the straw biomass co-product for electricity generation 101

Table 90: Input assumptions relevant to a BTL facility using salicornia straw biomass as 

feedstock 102

Table 91: Allocation ratios and product slates describing a coupled HRJ and F-T facility 

processing salicornia oilseeds and straw biomass 103

Table 92: Long term soil, root and charcoal carbon sequestration from the cultivation of salicornia 

for fuel production 104

Table 93: Summary of results from renewable and F-T jet fuel production and use from salicornia

104

Table 94: Life cycle GHG emissions for production and use of renewable and F-T jet fuel 

salicornia assuming long-term carbon sequestration 105

Table 95: Baseline life cycle GHG emissions for all fuel pathways studied. Land use change 

scenarios are described in Table 96 107

Table 96: Land use change scenarios considered in this work 108

Table 97: Feedstock and Fuel Properties 118
ix

Executive Summary

The long-term viability and success of a transportation fuel depends on both economic and 

environmental sustainability. These include, but are not limited to, the environmental impacts on 

global climate and air quality, the efficient usage of water and land resources, technical feasibility 

and the economic cost of fuel production. This report focuses on aspects of environmental 

sustainability, with an emphasis on life cycle greenhouse gas emissions as they relate to impacts 

on global climate. Through a life cycle accounting of the GHG emissions starting with the well, 

field, or mine where the fuel feedstock is extracted, and extending to the wake behind the aircraft, 

one can ascertain the change in GHG emissions that result from the use of an alternative fuel. 

The focus of this manuscript is a comparison of the well-to-wake, life cycle GHG emissions from 

select feedstock-to-jet fuel pathways for the United States. Each fuel pathway has been 

considered using a consistent methodology to facilitate equitable comparisons. The emphasis 

throughout is on maximizing the transparency of assumptions and establishing a fundamental 

understanding for the reader of the pivotal factors defining fuel production for each feedstock and 

how these relate to life cycle analysis. As there is considerable variability in the life cycle GHG 

emissions from existing fuel production, and many of the fuel pathways considered in this report 

have not been commercialized, a range of life cycle GHG emissions has been provided for each 

feedstock-to-fuel pathway. In addition to the examination of life cycle GHG emissions, the 

manuscript provides a first order examination of the land and water usage that could accompany 

the development of a biofuel industry for aviation.

The fuel options considered herein are “drop-in” alternatives in that they have the potential to 

serve as a direct replacement for conventional jet fuel, requiring little or no modification to existing 

infrastructure or aircraft. The Greenhouse Gases, Regulated Emissions, and Energy Use in 

Transportation (GREET) framework (versions 1.8b and 1.8a) and its supporting data were the 

primary tool used in the well-to-wake life cycle GHG analysis. The GREET model was developed 

for ground transportation; hence, this analysis involved modifying the underlying data and 

framework of the model to reflect jet fuel production. In cases where a fuel production pathway 

was not preexisting within the GREET framework, the relevant process inputs and production 

characteristics were developed from the open literature. All results are presented in terms of GHG 

emissions (CO2, CH4 and N2O) per unit of energy (lower heating value) consumed by the aircraft.

The fuel pathways considered herein span petroleum based jet fuel from conventional and 

unconventional sources (oil and sands and oil shale), Fisher-Tropsch jet fuel from natural gas, 

coal and biomass and hydroprocessed jet fuels from soy oil, palm oil, rapeseed oil, jatropha oil, 

algae oil, and salicornia oil. In most fuel pathways, the choice of allocation methodology and 

potential for GHG emissions from land use change were found to have the largest impact on the 

results. The scope of this work was limited to only quantifying the impacts of direct land use 

change; emissions from indirect land use change were not considered. Many fuel pathways were 

found to result in life cycle GHG emissions that are either lower or higher than conventional jet 

fuel depending on the specifics of fuel production. For this reason, it is essential not to simply 

assume that biofuels are environmentally beneficial without knowing the specifics of how the fuel 

is produced.

A few of the key results are outlined below: 

o Life cycle GHG emissions are but one of many considerations when evaluating the 

feasibility and sustainability of an alternative fuel option. 

o The data do not include all of the feedstock-to-fuel pathways that could be use to 

create jet fuel. Some interesting options not covered include camelina oil to jet fuel, 

fuels created from pyrolysis oils and advanced fermentation of sugars to 
x

hydrocarbons. These will be addressed as part of the ongoing work and will appear 

in future revisions to this report.

o Of the fuel options considered herein, conventional petroleum has the lowest 

emissions of any jet fuel pathway that that relies exclusively on fossil fuel resources.

o Few biofuels have zero life cycle GHG emissions.

o There is considerable variability in the life cycle GHG emissions; emissions from 

land use change contribute the most to this for the biofuel pathways considered.

o Water availability could be a limiting factor for biofuel production in certain regions of 

the US.

o Biofuel expansion within the U.S. would require a significant increase in crop and 

feedstock production.

o The possibility that non-indigenous species will be introduced into an unprepared 

ecosystem should not be ignored.

o The most significant challenge in developing viable alternative fuels that could 

reduce aviation's GHG emissions lies in developing and commercializing large-scale 

production of next generation biomass feedstocks that could be grown in a

sustainable manner.

As part of the ongoing research that is presented in this report, a more complete assessment of 

land use change emissions that includes indirect effects is being developed. Proper evaluation of 

the indirect effects of alternative fuels within aviation requires modeling of the demand for 

renewable energy resources within the transportation sector, including aviation, as well as the 

demand for renewable energy resources from the energy sector as a whole. Most indirect effects 

are expected to occur on an international scale; hence, domestic analyses, such as those in this 

work, should be done in the context of the global market.

Aviation is not the only potential user of renewable biomass resources, and it will have to 

compete for these limited resources. Furthermore, large land area requirements indicate that it is 

unlikely that a single region could create sufficient biomass to meet worldwide demand for 

biofuels. Hence, it is probable that large-scale implementation of biofuels would arise as a 

superposition of regionally appropriate feedstocks. Current actions with regard to biofuel 

expansion are important in realizing the potential of this industry. 

Biomass feedstocks do need to have the potential to displace large volumes of petroleum fuel to 

have considerable benefit. Any feedstock that is produced in a sustainable manner today could 

not only provide a benefit to those involved in the supply chain of fuel production and use, but it 

would also lead to valuable experience to the biofuels industry providing essential lessons in 

production and processing techniques. This experience should prove invaluable to the 

development of future sustainable feedstocks that could be used to create transportation fuels, 

such as jet fuel, with a minimum of arable land and fresh water. 
xi

Acknowledgements

Over the course of this research study, the authors benefited from the expertise and insights of 

many individuals and organizations. The quality of the underlying research was greatly improved 

through the inputs of Dr. Malcolm Weiss, Mr. Matthew Pearlson, Prof. Ian Waitz, (all from MIT)

and Dr. David Ortiz (RAND Corporation). 

The authors would like to thank the following individuals for their detailed reviews of all or parts of 

this manuscript: Michael Griffin (Carnegie Mellon University), Ray Dums (Chevron), David 

Shonnard (Michigan Technological University), Dennis Bushnell (NASA), Bob Dillmore, Tim 

Skone (both from National Energy Technology Laboratory), Wagner Daraia Rocha, Joanna 

Bauldreay, Trevor Stephenson, and Paul Bogers (all from Shell Global Solutions), Carl Hodges 

(The Seawater Foundation), Tom Kalnes (UOP LLC), and Kristin Lewis (Volpe National 

Transportation Systems Center). Their efforts contributed to a much improved finished product.

The authors gained much insight from participating in regular meetings and workshops of the 

Aviation Fuel Life Cycle Assessment Working Group, led by Bill Harrison, where aspects of this 

work were presented. The input of Maria Vera Morales (University of Cambridge) and Joyce 

Cooper (University of Washington) also contributed greatly to the treatment of algae within this 

work.

The authors benefited from countless interactions with the Commercial Aviation Alternative Fuels 

Initiative (CAAFI) and its leadership, Richard Altman, Nathan Brown, and Kristin Lewis. Through 

this forum, the authors were given deep insight into the aviation industry, including that of 

equipment manufacturers, fuel producers and providers, commercial airlines, environmental 

advocates, and regulators.

Finally, the authors would like to thank Warren Gillette and Lourdes Maurice, of FAA, and Tim 

Edwards and Bill Harrison, both of AFRL, for their leadership in managing this project.
xii

Units, Notation, Abbreviations and Acronyms

Units

Bpd Barrels per day LHV Lower Heating Value

Btu British Thermal Unit Mg Megagram (metric tonne)

Bu Bushel MJ Megajoule

Ha Hectare (10000 m2) mmBtu Million British Thermal Units

HHV Higher Heating Value Ton Imperial Ton (2000 pounds)

kWh Kilowatt-hour Tonne Metric Ton (megagram)

Chemical Species Notation

Cn Hydrocarbon chain of length ‘n’ K2O Potassium Oxide

CH4 Methane N2O Nitrous Oxide

CO Carbon Monoxide NH3 Ammonia

CO2 Carbon Dioxide NOx Nitrogen Oxide

CO2e Carbon Dioxide equivalent P2O5 Phosphorous Pentoxide

H2 Hydrogen Gas SOx Sulfur Oxides

Acronyms and Abbreviations

AFLCAWG Air Force Life Cycle Analysis Working 

Group IPCC Intergovernmental Panel on Climate Change

API American Petroleum Institute ISO International Organization for 

Standardization

ASP Aquatic Species Program LCA Life Cycle Analysis

BTL Biomass to Liquids (via F-T) LPG Liquefied Petroleum Gas

CBTL Coal and Biomass to Liquids (via F-T) LUC Land Use Change

CCS Carbon Capture and Sequestration MEA Monoethanolamine

CRP Conservation Reserve Program NETL National Energy Technology 

Laboratory

CTL Coal to Liquids (via F-T) NREL National Renewable Energy 

Laboratory

DME Dimethyl Ether PARTNER Partnership for Air Transportation 

Noise and Emissions Reduction

DOE Department of Energy (US) PFEI Payload Fuel Energy Intensity

EIA Energy Information Agency PKO Palm Kernel Oil

EISA Energy Independence and Security Act POME Palm Oil Mill Effluent

FAPRI Food and Agricultural Policy Research 

Institute SAGD Steam Assisted gravity Drainage

F-T Fischer Tropsch SOR Steam to Oil Ratio

FFB Fresh Fruit Bunches (from oil palm) SOC Soil Organic Carbon

GHG Greenhouse Gas SPK Synthetic Paraffinic Kerosene

GREET

Greenhouse Gases, Regulated 

Emissions, and Energy Use in 

Transportation

TSS Total Suspended Solids (mass 

percent)

GTL Gas to Liquids (via F-T) TTW Tank to Wake

GWP Global Warming Potential (IPCC) ULS Ultra Low Sulfur

HRD Hydroprocessed Renewable Diesel USDA United States Department of 

Agriculture

HRJ Hydroprocessed Renewable Jet WTT Well to Tank

ICP In-situ Conversion Process (Shell) WTW Well to Wake

IGCC Integrated Gasification Combined Cycle
1 of 129

1 Introduction

Both economic and environmental sustainability are required for any transportation fuel to be 

viable in the long term. An expansion of our energy portfolio to include alternative fuels would 

also result in the desirable consequence of energy diversity. This report presents results from 

ongoing research within the Partnership for AiR Transportation Noise and Emissions Reduction at 

MIT on alternative fuels. As shown by the diagram of Figure 1, the PARTNER alternative fuels 

research portfolio is considering many aspects of alternative fuel sustainability. This report 

focuses on aspects of environmental sustainability, with an emphasis on life cycle greenhouse 

gas emissions as they relate to impacts on global climate.

Figure 1: Aspects of alternative fuels that are being considered for alternative fuel feasibility and 

sustainability. This report has an emphasis on life cycle greenhouse gas emissions

Alternative jet fuels created from renewable resources offer the potential to reduce the 

greenhouse gas (GHG) emissions from aviation. This is not due to a change in fuel composition 

nor is it due to a change in engine efficiency; instead the reduction is due to a change in the GHG 

emissions that result from the extraction, production and combustion of the alternative fuel 

relative to conventional jet fuel. Through a life cycle accounting of the GHG emissions that starts 

with the well, mine, or field where the fuel feedstock is extracted, and extending to the wake 

behind the aircraft, one can ascertain the change in GHG emissions that result from the use of an 

alternative fuel. To emphasize the importance of life cycle analysis for an accurate comparison of 

GHG emissions, consider that the combustion of synthetic fuels, which are covered extensively in 

this work, results in about 4% less CO2 emissions (per unit mass of fuel) as compared to 

conventional jet fuel. However, as will be shown in this report, the life cycle GHG emissions from 

various alternative fuels can vary by two orders of magnitude depending on the feedstock and the 

details of production.

There are many other issues that need to be considered when evaluating the potential of a 

specific alternative fuel. These include, but are not limited to, the efficient usage of water and land 

resources, the environmental impacts on air quality, and the economic cost of fuel production.

This work touches on water and land usage, but not air quality or economic costs. The interested 

reader is directed to Hileman et al. (2009) for an extended discussion of these and other aspects 

of alternative jet fuel feasibility. Furthermore, this work does not consider whether our limited 

biomass resources would provide greater societal benefit if they were used elsewhere, for 

example to create fuels for ground transportation or to generate heat and/or electricity. A recent 

analysis by Hedegaard et al. (2008) indicates that scarce biomass resources could be used more 

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effectively, from perspectives of energy efficiency and CO2 mitigation, for heat and electricity 

rather than ethanol for transportation. These topics are being considered as part of the ongoing 

PARTNER research portfolio.

The focus of this manuscript is a comparison of the well-to-wake, life cycle GHG emissions from 

select feedstock-to-jet fuel pathways for the United States; this is a partial list of potential 

alternative jet fuel options and future revisions to this report will expand upon this list. In some 

cases, details for a given fuel production pathway are expected to differ for other countries. The 

underlying data for each analysis have come from the literature, but each fuel pathways has been 

considered using a consistent methodology. This work is the first of its kind in that that a broad 

range of alternative fuels are analyzed for aviation using consistent methodologies that facilitate 

equitable comparisons. Each pathway is presented in such a way that transparency of 

assumptions is maximized and the reader is able to identify the pivotal factors defining fuel 

production for each feedstock. As there is considerable variability in the life cycle GHG emissions 

from existing fuel production, and many of the fuel pathways considered in this report have not 

been commercialized, a range of life cycle GHG emissions has been provided for each feedstockto-fuel pathway. In addition to the examination of life cycle GHG emissions, the manuscript 

provides a first order examination of the land and water usage that could accompany the 

development of a biofuel industry for aviation.

The contents of the report are structured as follows. Chapter 2 presents the fuel pathways 

considered in this work while Chapter 3 provides background information on creating a life cycle 

GHG emissions inventory. Chapters 4 through 7 present life cycle GHG estimates for 

conventional jet fuel and various other alternative jet fuel pathways. Chapter 4 considers standard 

and Ultra Low Sulfur (ULS) jet fuel from conventional petroleum. Chapter 5 considers 

conventional jet from unconventional petroleum resources of oil sands and oil shale. Chapter 6 

examines Fischer-Tropsch synthesis as a means to create synthetic paraffinic kerosene (SPK) 

fuel from natural gas, coal, and biomass. Chapter 7 presents the analysis of hydroprocessed 

renewable jet fuels (HRJ) from soy, palm, rapeseed, jatropha, algae, and salicornia. Chapter 8 

presents a comparison of the life cycle GHG emissions established in Chapters 4 through 7 with 

a discussion of some constraints that need to be considered to understand the viability of these 

pathways These include potential damages caused by the introduction of invasive species and 

land and water availability. Conclusions from this work are presented in Chapter 9.
3 of 129

2 Alternative Jet Fuel Pathways 

The fuel options considered herein are “drop-in” alternatives in that they have the potential to 

serve as a direct replacement for conventional jet fuel, requiring little or no modification to existing 

infrastructure or aircraft. Alcohols and biodiesel (fatty acid methyl esters) are not considered due 

to both the safety issues and the decrease in fuel efficiency that would accompany their use in 

aircraft operations (Hileman et al., 2009). The fuel compositions analyzed in this work are as 

follows:

• Conventional jet fuel (e.g., Jet A) from conventional petroleum (crude oil) and 

unconventional petroleum (oil sands and oil shale)

• Ultra Low Sulfur (ULS) jet fuel from conventional petroleum

• Synthetic Paraffinic Kerosene (SPK) fuels created via Fischer-Tropsch (F-T) synthesis or 

hydroprocessing of renewable oils.

This should not be viewed an all-encompassing list of potential fuel compositions that could be 

used as a replacement or a blend stock in aviation gas turbine engines. For example, advanced 

fermentation or pyrolysis can both be used to create a jet fuel blendstock,1 but neither of these 

fuels is considered here.

ULS jet fuels are considered as they could reduce aviation’s impact on air quality. The processing 

used to remove sulfur from conventional jet fuel (discussed in detail in Section 4.3) changes the 

fuel properties such that an additive package may be required to improve lubricity. Considerable 

research is ongoing in regards to the analysis of ULS jet fuels (e.g., Miller et al, 2009; Hileman et 

al., 2010; PARTNER Project 27, 2010).

SPK fuels have similar molecular composition to conventional jet fuel with the primary difference 

being a lack of aromatic compounds; conventional jet fuel typically contains 20% aromatic 

compounds. Because certain types of seals require aromatic compounds for proper swelling, 

SPK fuels have been certified in blends up to 50% with conventional jet fuel to maintain a 

minimum aromatic content. 

The fuel production pathways analyzed in this work were jet fuel from conventional crude oil, jet 

fuel from Canadian oil sands, jet fuel from oil shale, Fischer-Tropsch jet fuel from natural gas, 

coal and biomass, and jet fuel created by hydroprocessing soy oil, palm oil, rapeseed oil, algal oil, 

jatropha oil and salicornia oil. As of September 2009, at 50% blend of SPK fuel derived via 

Fischer-Tropsch synthesis with conventional jet fuel was certified for use in commercial aviation 

under ASTM spec D7566-09 (ASTM D7566-09, 2009). The fuel specification and certification 

division of the Commercial Aviation Alternative Fuels Initiative (CAAFI) played a key role in the 

process of establishing D7566 and has outlined plans for a expanding the specification to include 

a 50% HRJ blend with conventional jet by 2010, 100% F-T derived SPK by 2011 and 100% HRJ 

by 2013 (Rumizen, 2010). 

For each pathway, three potential scenarios (low emissions case, baseline case and high 

emissions case) were identified and life cycle GHG emissions were calculated for each of these 

scenarios. The fuel pathways are summarized in Table 1.

 

1 Note that pyrolysis is being examined to create synthetic aromatics that could be blended with SPK fuels to create a 

fully synthetic jet fuel while advanced fermentation is being examined to create a paraffinic fuel for aviation gas turbines.
4 of 129

Table 1: Fuel pathways investigated

Source Feedstock Recovery Processing Final product

Conventional 

crude Crude extraction Crude refining Jet Fuel

Conventional 

crude Crude extraction Crude refining Ultra low Sulfur Jet Fuel

Canadian oil 

sands

Bitumen mining/ 

extraction and 

upgrading

Syncrude refining Jet Fuel

Petroleum

Oil shale In-situ conversion Shale oil refining Jet Fuel

Natural 

gas Natural gas

Natural gas 

extraction and 

processing

Gasification, F-T 

reaction and 

upgrading

F-T Jet Fuel 

(GTL)

Coal Coal Coal mining

Gasification, F-T 

reaction and 

upgrading (with and 

without carbon 

capture)

F-T Jet Fuel 

(CTL)

Coal and 

Biomass Coal and Biomass

Coal mining and 

biomass 

cultivation

Gasification, F-T 

reaction and 

upgrading (with 

carbon capture)

F-T Jet Fuel 

(CBTL)

Biomass Biomass 

cultivation

Gasification, F-T 

reaction and 

upgrading

F-T Jet Fuel

(BTL)

Soy oil

Cultivation and 

extraction of soy 

oils

Hydroprocessing

HRJ Fuel

(Hydroprocess

ed Renewable 

Jet)

Palm oil from 

Southeast Asia

Cultivation and 

extraction of palm 

oils

Hydroprocessing HRJ Fuel

Rapeseed Oil

Cultivation and 

extraction of soy 

oils

Hydroprocessing HRJ Fuel

Algae oil

Cultivation and 

extraction of algae 

oils

Hydroprocessing HRJ Fuel

Jatropha oil

Cultivation and 

extraction of 

jatropha oils

Hydroprocessing HRJ Fuel

Biomass

Salicornia oil and 

solid biomass

Cultivation of 

salicornia and 

extraction of 

salicornia oils

Gasification, F-T 

reaction and 

upgrading (with 

carbon capture); 

Hydroprocessing

F-T Jet Fuel

and HRJ Fuel
5 of 129

3 Procedural Overview of Life Cycle Analysis of GHG 

Emissions 

3.1 Life cycle Greenhouse Gas Emissions

The life cycle analysis of alternative jet fuels encompasses emissions from the complete fuel 

cycle. This includes recovery and transportation of the feedstock from the well, field, or mine to 

the production facility, processing of these materials into fuels, transportation and distribution of 

the fuel to the aircraft tank, and finally, the combustion of the fuel in the aircraft. The steps of such 

a well-to-wake life cycle analysis are shown schematically in Figure 2. These “well-to-wake” 

(WtW) steps can be broadly grouped into fuel production and distribution, “well-to-tank” (WtT), 

and fuel combustion, “tank-to-wake” (TtW). The interested reader is directed to a recent guidance 

document created by a consortium that was assembled by the U.S. Air Force (AFLCAWG, 2009) 

to learn more about the details of creating a life cycle GHG inventory for transportation fuels.

Figure 2: Steps considered, in the well-to-wake, life cycle GHG inventory of conventional jet fuel

For each step of the life cycle, GHG emissions are assessed and reported on the basis of per-unit 

energy consumed by the aircraft (per megajoule). The GHG covered in this analysis are carbon 

dioxide, methane and nitrous oxide using their 100-year global warming potentials (IPCC, 2007). 

This analysis did not cover non-CO2 or non-CO2 equivalent combustion emissions from aircraft -

for example NOX, SOX, soot, and water - that directly or indirectly impact global climate change.

These are currently being considered as an ongoing part of this work and will presented in a 

future report. This study also did not consider energy or GHG emissions associated with the initial 

creation of infrastructure such as extraction equipment, transportation vehicles, farming 

machinery, processing facilities, etc. The impact of such emissions on the total life cycle GHG 

emissions of the pathway is usually relatively small, and within the uncertainty range of the 

analysis. (Hill et al., 2006, Edwards et al., 2007).

Fossil feedstocks such as crude oil, coal or natural gas are created from geologically sequestered 

carbon sources, and the carbon is released as CO2 when the fuel products are burned. Such 

combustion CO2 has to be taken into account in the life cycle analysis (see Figure 2). Biomass 

feedstocks absorb CO2 from the atmosphere when they grow and the CO2 emitted during fuel 

combustion is equal to that absorbed during biomass cultivation. Hence, many biofuels have a 

“biomass credit” that offsets the combustion CO2 in the life cycle analysis (see Figure 3). This 

biomass credit is the primary difference between biomass and fossil fuels in terms of their GHG 

emissions. However, a biofuel does not necessarily have GHG emissions that are below a fossil 

fuel.

GHG = N2O, CH4, CO2

Recovery & 

Extraction 

Raw Material 

Movement 

Jet Fuel 

Production

Jet Fuel 

Transportation 

Jet Fuel 

Combustion 

CO2, PM, NOX, SOX, H2O 

Aviation 

Productivity 

Other Liquid Fuel Products 
6 of 129

Figure 3: Steps considered in the well-to-wake, life cycle GHG inventory of bio-based alternative jet fuels

Biomass feedstocks also have the potential for CO2 emissions or CO2 sequestration from 

changes in land use (see first step of Figure 3). The CO2 emissions or sequestration are due to 

changes in the biomass, soil and organic waste contained on and within the land. In some 

instances, these emissions can dominate the life cycle GHG emissions of the biofuel pathway. 

The land use change can be a direct land conversion, (e.g., tropical rainforest being cleared for 

cropland to grow feedstocks), or it can be an indirect conversion resulting from land being 

converted elsewhere in the world due to economic signals induced by increased demand for 

agricultural products. In either case, it is assumed that a fixed quantity of biomass (e.g., vegetable 

oil) needs to be supplied to global food markets and that additional production (for biofuel 

creation) is met by land that has been converted from some previous use. The magnitude of land 

use change emissions depends primarily on the type of land being converted to cropland and the 

type of crops being grown. For fossil feedstocks, where conversion of land (e.g. forest land, grass 

land) for extraction of fossil resources (e.g. extraction of bitumen) or placement of fuel processing 

facilities (e.g. oil refineries) takes place, land use change emissions are negligible compared to 

other components of the fuel pathway. This is because a large throughput of fuel volume or mass 

(as well as energy) is created per unit area of converted land. 

Only emissions from direct land use changes, where land is converted to facilitate biofuel 

production, were considered in this work. No attempt was made to quantify the magnitude of 

indirect land use change emissions resulting from fluctuations in supply and demand of other 

crops because of increased biofuel production. In order to properly capture these effects, a 

detailed economic model is required which falls beyond the capabilities of GREET and the scope 

of this work. Other academic and government groups have invested a significant amount of effort 

to quantify indirect land use change emissions and the reader is directed to their publications for 

estimates of these effects (EPA, 2010; Melillo et al., 2009). 

For biofuels produced from algae, sufficient growth rates cannot be achieved without the direct 

feeding of CO2 during growth. This is because the atmospheric concentration of CO2 is too dilute 

to support an economically viable growth rate (Putt, 2007). The CO2 used to feed the biomass 

must be abundant and come from a source external to the biofuel production system. In this 

study, fossil energy resource-based electricity generation was chosen to meet these needs. One 

can imagine a coupled system in which the CO2 source and algae facility are linked to one 

another, where a fossil fuel is the primary input and both electricity and algal biofuel are primary 

outputs. This concept is shown schematically in Figure 4 with the system boundary for a 

conventional biofuel pathway expanded to include an outside source of CO2. In addition, direct 

land use changes should be small as compared to crop-based biofuels and indirect land use 

changes should be minimal because the necessary infrastructure can be created in wasteland 

and desert areas. Algae also have the capability to grow in saline water, meaning that fresh water 

is not a pre-requisite. 

Biomass Co-Product 

GHG = N2O, CH4, CO2

Recovery & Extraction 

Raw Material 

Movement 

Jet Fuel 

Production

Jet Fuel 

Transportation 

Jet Fuel 

Combustion 

CO2, PM, NOX, SOX, H2O 

Land 

CO2

Change in 

Land Usage 

Cultivation 

GHG = N2O, CH4, CO2

Aviation 

Productivity 

Other Liquid Fuel Products 
7 of 129

Figure 4: Steps considered in the well-to-wake, life cycle GHG inventory of bio-based fuels from algae

Fuel production generally results in the creation of co-products in addition to the primary fuel 

product. For example, the systems shown in Figures 2-4 generate biomass and liquid fuel coproducts. These co-products have embodied value that can be quantified based on physical

metrics, or their ability to displace some other product elsewhere in the greater marketplace. Four 

methods can be used to assign life cycle GHG emissions between the primary fuel product and 

any co-products that are created:

• Mass allocation

• Energy allocation

• Market-value allocation

• Displacement (or substitution, or system expansion)

The international organization for standardization (ISO) states in ISO 14044:2006(E) that 

processes shared with other product systems shall be identified and dealt with by preferentially 

using process disaggregation, system expansion, allocation by an underlying physical 

relationship and economic value, in this order. Inventories are based on material balances 

between input and output; therefore, allocation procedures should attempt to approximate such 

fundamental input/output relationships and characteristics (ISO, 2006).

The mass and energy allocation approaches distribute the life cycle GHG emissions based on 

either the mass or energy content, respectively, of the co-products and the fuel. In this work, the 

energy allocation method was used to allocate energy and emissions between co-products of the 

Fischer-Tropsch process as well as those in the hydroprocessing of renewable oils to make 

Hydroprocessed Renewable Jet (HRJ); this is because these co-products have utility as energy 

resources.

The market allocation approach apportions emissions based on the market prices of the coproducts and primary fuel product. Unlike the mass or energy allocation approaches, the market 

value allocation can change with time. The sensitivity to market forces could be particularly useful 

when co-products are generated in quantities that stand to flood existing markets and drive the 

co-product price to zero. For example, if a fuel has a co-product that displaces some existing 

product, then the market value method will capture the diminished utility of creating additional coproduct by allocating more of the emissions to the fuel being produced. This is because 

increasing alternative fuel production will not change the price of the alternative fuel as this is set 

by the price of conventional fuel net subsidies and taxes. Co-product creation does, however, 

have the potential to alter the price of similar commodities. In this work, market valuation was 

Electricity 

Biomass Co-Product 

GHG = N2O, CH4, CO2

Raw Material 

Movement 

Jet Fuel 

Production

Jet Fuel 

Transportation 

Jet Fuel 

Combustion 

CO2, PM, NOX, SOX, H2O 

Recovery & 

Extraction 

Recovery & 

Extraction 

Raw Material 

Movement 

Electricity 

Production

CO2

GHG = N2O, CH4, CO2

Aviation 

Productivity 

Other Liquid Fuel Products 
8 of 129

used to allocate emissions between co-products leaving the system after oil extraction within the 

HRJ pathways.

The displacement method assumes that the production of the incidental co-product displaces the 

production of a substitute product. As a result, an emissions credit from the displaced product that 

is no longer produced is applied to the primary product. Although this methodology is desirable 

because it is time-invariant and it could in theory be applied to any co-product, it is hard to 

implement. This is because of difficulties in identifying a suitable product to be displaced, 

calculating the life cycle GHG emissions of that displaced product and determining the 

displacement ratio (Huo et al., 2008). In the case of biofuels, the issue of how to appropriately 

allocate land use change emissions further complicates the application of the displacement 

method. The life cycle analysis of algae in this work applied the displacement method to the fossil 

based electricity used as a CO2 source. 

The use of different allocation methodologies can lead to substantially different results, 

particularly in regards to biofuel pathways where significant quantities of co-products are being 

produced. The appropriate method may depend to a large extent on the question that the life 

cycle analyst attempting to answer. Regardless of which method is applied, it is important that 

those conducting life cycle analyses for any purpose clearly state the allocation approach 

adopted. 

3.2 Analysis Procedure

Analyses of life cycle GHG emissions for several jet fuel production pathways were carried out 

based on available information in the scholarly and technical literature. The Greenhouse Gases, 

Regulated Emissions, and Energy Use in Transportation (GREET) framework (versions 1.8b and 

1.8a) and its supporting data, both developed and maintained by Argonne National Laboratory, 

was the primary tool used in the well-to-wake life cycle GHG analysis.2 A simulation year of 2015 

was used and default GREET assumptions were used in the analysis of the pathways, except 

where more recent data were obtained. For example, the average efficiencies of coal-fired power 

plants (utility boiler) and coal integrated gasification combined cycle (IGCC) plants were assumed 

to be 36 percent and 41.5 percent, respectively, on a lower heating value basis3 (Deutch and 

Moniz, 2007). 

A key limitation of the GREET framework is that it is designed for ground transportation fuels and 

vehicle systems and does not include jet fuel production pathways. Also, not all of the feedstocks 

analyzed in this work are available in GREET (e.g. oil shale). Hence, this work utilized data from 

the literature on jet fuel and jet fuel alternatives where available (e.g. fuel properties, refining 

efficiency) and incorporated them into the GREET framework to derive life cycle GHG emissions. 

Where supporting data are presented in this report, mixed units are used for consistency with 

GREET version 1.8a/1.8b. 

In this work, the GREET framework was primarily used as a database and calculation platform 

where the quality of output energy and emissions values depended on the quality of input 

assumptions such as energy efficiencies, fuel properties, type and share of process fuels, and 

emission allocation method for co-products. Hence, a de novo approach was taken in identifying 

and reviewing key inputs and assumptions for each pathway. Specifically, default GREET input 

assumptions were examined for the fuel pathways available in GREET. Key parameters with a 

 

2 The specific version of GREET used for each pathway within a section is stated at the beginning of the section; 

however, the impact on the results of this work of using GREET version 1.8a or 1.8b is negligible compared to the 

inherent uncertainties of life cycle analysis.

3 From Deutch and Moniz, 2007, the US coal fleet average generating efficiency is about 33% (HHV) and the generating 

efficiency for coal IGCC plants is 38.4% (HHV). Since the difference between HHV and LHV range from 2 to 4%, a 3% 

difference is assumed in this report. Hence, the efficiency of an average coal-fired power plant is assumed to be (33+3) 

36% and the efficiency of a coal IGCC plants is assumed to be (38.4+3) 41.5%. 
9 of 129

significant impact on the life cycle GHG emissions of the pathway were identified. Default GREET 

values for these key parameters were updated wherever necessary using reviews of recent 

information available in the literature. Where a specific pathway was not available in GREET, the 

pathway was built within the GREET framework with all relevant input parameters gathered from 

the open literature. 

The analysis methodology used in this work differs from that adopted by the US Environmental 

Protection Agency (EPA) in their Renewable Fuel Standard (RFS2) recently released in February 

2010. Specifically, life cycle assessments can be categorized as either attributional or 

consequential. As defined by the EPA: 

“An attributional approach to GHG emissions accounting provides information about the GHG 

emitted directly by a product and its life cycle. The product system includes processes that are 

directly linked to the product by material, energy flows or services through the supply-chain. A 

consequential approach to GHG emissions accounting in products provides information about the 

GHG emitted, directly or indirectly, as a consequence of changes in demand for the product. This 

approach typically describes changes in GHG emissions levels from affected processes, which 

are identified by linking causes with effects.” (EPA, 2010)

Attributional and consequential life cycle analyses will tend to yield different results for an 

identical product; hence, comparing results from the two methodologies is inappropriate. This 

work implements an attributional methodology while the EPA has used a consequential analysis 

to more broadly consider the impact of future policy scenarios.

3.3 Goals and Practices

A life cycle analysis should be consistent with the goals of the study. As discussed in the 

“Framework and Guidance for Estimating Greenhouse Gas Footprints of Aviation Fuels”, different 

levels of analysis fidelity originate primarily from robustness of assumptions, data quality and 

level of model completeness (AFLCAWG, 2009). These differences are summarized in Figure 5. 

As indicated, this work focuses on conducting high quality screening-level analyses using 

assumptions that attempt to capture industry averages. 

Figure 5: Levels of life cycle GHG studies used to ensure that the adopted practices are consistent with the 

goal of the work

Although a screening level analysis approach was chosen, uncertainty ranges were established 

using optimistic, nominal and pessimistic scenarios such that the result is presented as a range 

instead of a single point. In most cases, the GHG emissions inventories established for each 

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10 of 129

pathway are not representative of an existing production configuration. As such, site-specific 

examinations of individual fuel pathways are still essential to quantifying specific GHG footprints 

such as would be required to meet Section 526 of EISA 2007. The results of this work do not 

replace such an analysis.

3.4 Evaluation of Variability

To explore the impact of variability in key parameters values, three different scenarios – low GHG 

emissions, baseline or nominal GHG emissions, and high GHG emissions – were envisaged for 

each pathway. Key parameters were identified through examination of the GHG emissions that 

resulted from each of the individual steps of the life cycle (see Figures 1 through 3). The 

engineering judgment of the authors was used to identify parameters that had both variability as 

well as a considerable influence on the life cycle GHG emissions. Input parameters such as 

process efficiency and biomass feedstock yield have both of these qualities in that they exert 

considerable influence on the life cycle GHG emissions of the fuel pathway and their value a 

decade into the future could have considerable variability; hence, these parameters were varied 

as part of the three scenarios. Well defined parameters with a large impact on the life cycle 

emissions (such as the mass of CO2 emitted per unit of fuel consumed by the jet engine) and 

parameters with large variability but only a small impact on the life cycle emissions (such as the 

distance the feedstock needs to travel from the source to the refinery) were generally not 

examined.

By using key parameters to define the low, baseline, and high emissions scenarios, a range of 

GHG emissions, rather than a single value, was derived for each fuel pathway. Appropriate 

values for the key parameters were determined through literature review and consultation with 

relevant experts. In general, industry average values, rather than marginal values, were sought. If 

a marginal value for a key parameter was found that fell outside of typical values and if the 

marginal value indicates a potential industry trend, then the value was examined as a separate 

case study. Variation of the key parameter values across the three scenarios could arise from 

differences in time frame (e.g. historical data versus future projections), different feedstocks (e.g. 

bituminous coal versus sub-bituminous coal), different technologies or changes in process 

designs. While the upper and lower bounds of values found in the literature were generally used 

in the low and high emissions cases, baseline values were usually those which were deemed 

most likely, most frequently occurring, or were the average or mid-point of the range of values 

reported in the literature.

Some of the pathways under consideration result in nitrous oxide emissions that represent more 

than 50% of the total life cycle GHG emissions. This work applied IPCC Tier 1 methodology to 

calculate N2O emissions from each fuel pathway and is therefore subject to the full range of 

uncertainty associated with IPCC correlations. In many cases, the emission factors developed by 

the IPCC have uncertainty ranges in excess of 100% (IPCC, 2006). While the focus of this work 

was not to assess the uncertainties within the IPCC methodology, the reader should be aware of 

their existence and that their impact is of larger consequence for pathways where N2O is a 

significant contributor to the total life cycle GHG emissions.

3.5 Functional Unit: g CO2e/MJ and g CO2e/kg-km

Consistent with (AFLCAWG, 2009), the life cycle GHG emissions are presented using a metric 

that captures the mass of GHG per unit of energy (lower heating value) consumed by the aircraft. 

This is given in units of g CO2e/MJ where 

€

CO2e = CO2 +GWPCH4 ⋅ CH4 +GWPN2O ( ⋅ N2O)well-to-tank+(CO2 )tank -to-wake Equation 1

As discussed in Section 3.1, non-CO2 combustion emissions were not considered in this report.
11 of 129

A metric of life cycle GHG intensity, with units of gCO2e/kg-km, could be generated from this 

metric via multiplication by an energy efficiency metric such as Payload Fuel Energy Intensity 

(PFEI) with units of energy per payload distance flown (Hileman et al., 2008). This metric will be 

considered in Section 8.2.
12 of 129

4 Conventional Petroleum Pathways

Because this work focuses on fuels that could be used in gas turbine powered aircraft, the 

baseline for analysis is jet fuel from conventional petroleum. For the purposes of this report, jet 

fuel could represent JP-8, Jet A, or Jet A-1, which are the fuels in use by the U.S. Air Force, 

commercial aviation in the US, and commercial aviation in Europe as well as much of the rest of 

the world, respectively. Section 4.1 considers the extraction of conventional crude oil for jet fuel 

while Section 4.2 considers petroleum refining to produce jet fuel. Section 4.3 examines an 

ultralow sulfur jet fuel (ULSJ) with a maximum fuel sulfur content of 15 parts per million. ULSJ 

differs from the other alternative fuels considered in this report in that it is derived from 

conventional petroleum and it would be used specifically to reduce aviation’s impact on air quality 

as opposed to diversifying energy sources or reducing greenhouse gas emissions. 

The steps involved in the production of jet fuel from conventional petroleum sources include 

crude oil extraction, transportation of crude oil to U.S. refineries, refining of crude oil to jet fuel, 

and the transportation of jet fuel to the aircraft tank. The GHG emissions resulting from crude oil 

extraction, crude oil transportation and jet fuel transportation were obtained using data from two 

recently published National Energy Technology Laboratory (NETL) studies on the life cycle GHG 

emissions of petroleum-based transportation fuels (Skone and Gerdes, 2008; Skone and Gerdes, 

2009). The emissions that result from crude oil refining were calculated using both a top-down 

and a bottoms-up perspective for conventional and an ultra low sulfur jet (ULSJ) fuel within 

GREET version 1.8a (Sections 4.2 and 4.3, respectively). This work differs from that of Skone 

and Gerdes (2008, 2009) in that the jet fuel pathway considers only jet fuel refined within the US 

and excludes jet fuel made from unconventional petroleum sources, such as oil sands, from the 

final result (jet fuel from unconventional sources is treated individually in section 5). Jet fuel 

refined within the US comprised 88.7% of all domestic jet fuel consumption in 2005 (Skone and 

Gerdes, 2008).

4.1 Crude Oil Recovery and Transportation

The source of crude oil is important in order to properly represent the range of resulting GHG 

emissions. The GHG emissions from crude oil recovery and crude oil transportation are 

designated origin specific GHG emissions. The variation in these emissions by crude oil source is 

primarily due to specific hydrocarbon flaring and venting practices during extraction, the 

emissions resulting from local electricity production, equipment efficiency and the transportation 

distance of crude oil to a US port.

Imported crude oils are on average heavier (lower API gravity4) and contain higher levels of sulfur 

than domestic products (Skone and Gerdes, 2009). The changes in crude oil properties as well 

as processing technique drive a variation in processing emissions of converting crude oil into 

finished fuel products. The GHG emissions associated with the processing of crude oil to jet fuel 

and ULS jet fuel were developed in this study using the GREET framework. These results are 

presented in conjunction with the origin specific GHG emissions and finished fuel transportation 

emissions derived from Skone and Gerdes (2008, 2009) to establish GHG inventories for 

conventional jet fuel and ULS jet fuel produced at US refineries.

Of the crude oil mix fed into US refineries in 2005, only 34% was domestically produced. The 

other 66% was imported from other counties located around the world. When including domestic 

production, over 90% of the crude oil mix came from only 11 countries (Skone and Gerdes, 

2008). Throughout this work, the remaining fraction of imported crude is designated ‘other’ and 

corresponds to the weighted average of all imported crude (excluding Canadian oil sands).

 

4 API gravity is a measure of the density of a petroleum liquid relative to water. An API gravity greater than 10 indicates 

lighter than water while an API gravity less than 10 indicates heavier than water. API gravity = 141.5/SG - 131.5, where 

SG = specific gravity
13 of 129

Table 2: Country specific average waterborne round trip travel distance of crude oil to US domestic ports

Crude Oil 

Source

Country-Specific Average 

Round-Trip Travel 

Distance

(nautical miles)

Fraction of Imported 

Crude Transported 

by Ocean Tanker

Saudi Arabia 24036 100%

Kuwait 25052 100%

Iraq 24740 100%

Venezuela 3578 100%

United States 0 0%

Ecuador 11306 100%

Algeria 8904 100%

Canada 1350 28%

Mexico 2122 100%

Other 20000 100%

Angola 13472 100%

Nigeria 11344 100%

Notes:

1) Skone and Gerdes (2008)

2) Imported crude from Canadian oils sands ignored in this section 

because it is explicitly dealt with in Section 5.1

Figure 6: Origin specific GHG emissions by species of crude oil entering US refineries in 2005 (fraction of 

total imports in parentheses). Based on country profiles published in Skone and Gerdes (2008, 2009)

In their 2008 life cycle GHG analysis of petroleum based fuels, Skone and Gerdes developed 

crude oil extraction profiles, including methane flaring and venting data, for each of these 11 

countries. They also developed a transportation profile for each country by accounting for the 

transport of imported crude oil from its point of extraction to foreign ports, ocean tanker transport 

of waterborne imported crude oil to domestic ports and crude oil transport within the US.

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14 of 129

Domestic crude oil is only subject to transport within the US and 72% of Canadian crude imports 

are transported via pipeline and do not incur waterborne transport emissions; all other imports are 

subject to all three forms of transportation. Listed in Table 2 are the round trip distances used to 

calculate waterborne GHG emissions and the fraction of imports transported by ocean tanker for 

each crude oil source. The interested reader is directed to Skone and Gerdes (2008) for the 

emissions factors for each leg of the transportation process.

The significance of the data reported in Table 2 is that importing crude oil from Saudi Arabia and 

Kuwait results in substantially more GHG emissions from transportation than crude from other 

sources. The recovery and total transportation GHG emissions (by species) from 2005 are given 

for crude oil from each source in Figure 6. Beside the label for each source, the volumetric 

fraction of total crude fed into US refineries imported from that country is given in parentheses. 

The volumetric fractions do not add up to 100% because Canadian oil sands are not listed.

These data highlight that domestically produced crude oil results in lower GHG emissions than 

any other source because of reduced transportation emissions. Even though CO2 emissions 

resulting from domestic oil extraction are higher than Iraq, Kuwait, Venezuela and Saudi Arabia, 

the combination of transportation emissions and methane venting causes these regions to have 

higher origin specific GHG emissions than the US. In the cases of Iraq, Kuwait and Saudi Arabia, 

transportation emissions (primarily ocean transport emissions) represent 47%, 51% and 55% of 

the total origin specific GHG emissions. 

The primary driver for countries with excessive origin-specific GHG emissions is methane 

venting. Methane emissions from Nigerian and Angolan crude extraction exceed all other origin 

specific GHG emissions. Mexican and Canadian crude also have non-negligible methane 

emissions from venting.

The variation in profiles shown in Figure 6 was used to establish the low emissions, baseline and 

high emissions scenarios for jet fuel and ULS jet fuel from conventional crude. The low emissions 

scenario was composed of purely domestic crude oil, the baseline scenario adopted the weighted 

average of all crude oil fed into US refineries, excluding Canadian oil sands, and the high 

emissions scenario used only Nigerian crude. The transportation of jet fuel from US refineries to 

the aircraft tank are independent of the source of crude oil; hence, a single result was used for all 

three emissions scenarios.

4.2 Conventional Jet Fuel from Crude Oil

The conventional jet fuel production pathway forms the baseline against which the life cycle GHG 

emissions of alternative jet fuels are compared. The properties of conventional crude oil were 

based on the projected average crude oil received by U.S. refineries in 2015, obtained using 

historical data provided by the EIA. There is a definite trend for crudes to become heavier and 

more sour (more sulfur) in the future; therefore, a business as usual scenario would likely see jet 

fuel production becoming more energy intensive as more hydroprocessing is required to maintain 

current product quality. This means that the energy intensity of refining may increase beyond the 

values used in this study. Some discussion of the impacts of crude oil quality on GHG emission 

from refining is given section 4.2.3 (EIA, 2008a). 5

The key parameter in analyzing the GHG emissions associated with the production of jet fuel 

from crude oil is fuel-refining efficiency. Two methods were employed in the derivation of jet fuel 

refining efficiency. The first method was a top-down approach, which derived the jet fuel refining 

energy efficiency from the overall U.S. refining energy efficiency. This formed the baseline case. 

 

5 From data on sulfur content and API gravity of average crude oil input to U.S. refineries from 1995 to 2006 given by EIA 

(EIA, 2008a), it was estimated that there was approximately a 2% annual increase in sulfur content and 0.25% annual 

decrease in API gravity. From these trends, the average crude oil quality received by U.S. refineries in 2015 was 

estimated.
15 of 129

The second method was a bottom-up approach, which estimated jet fuel-refining efficiency by 

summing the energy requirements for the individual refining processes. Specifically, two extreme 

cases were examined: straight-run fuel production and hydroprocessing of crude. The refining 

efficiencies obtained using the bottom-up approach were used for the low and high emissions 

cases, respectively, providing a bound on the range of possible values.

4.2.1 Top-Down Approach (Baseline Case)

The overall U.S. refinery efficiency as estimated by Wang (20086) was 90.1% (LHV), based on 

statistics of process fuel use in U.S. refineries, and 2006 refinery fuel inputs and outputs provided 

by the EIA. Overall refinery efficiency is defined as (Wang, 2008):

€

η0 = Eproducts

Einputs

Equation 2

where ηo = petroleum refinery energy efficiency,

Eproducts = energy of all petroleum products, 

Einputs = energy in crude input, other feedstock inputs, and process fuels.

The jet fuel-specific refining efficiency was calculated from the overall refinery efficiency using the 

kerosene relative energy intensity7 and Equation 3, developed by Wang et al. (2004). The relative 

energy intensity of the production of kerosene (jet fuel), XS, based on an energy-content process 

allocation method, is 62.4 percent, and the overall refining efficiency, η0, is 90.1 percent. 

€

ηs = η0

η0 + Xs(1−η0) Equation 3

From Equation 3, the refining energy efficiency of jet fuel is 93.5% (LHV). This refining efficiency 

was used in the baseline case for the life cycle analysis of GHG emissions from the production of 

jet fuel from conventional petroleum.

Based on 2006 U.S. refinery data published by the EIA, Wang (2008) calculated the shares of 

process fuel used in U.S. petroleum refineries (see Table 3). These shares were used as inputs 

to the GREET model for the baseline case of jet fuel production.

Table 3: Type of process fuel and fuel share in the refining of jet fuel and ULS jet fuel

Type of process fuel Process energy share 

(%)

Electricity 3.5

Natural Gas 41.3

Refinery Gas 39.6

Coke 14.3

Residual Oil 1.3

Total 100

 6 This work was used to update the refining efficiency of gasoline, diesel, LPG, residual oil and naphtha in GREET 

(version 1.8b). (GREET, 2008)

7 Relative energy intensity was defined as “the ratio of total energy use share to the mass share of a given fuel.” It 

provides a measure of how energy intensive the production of a particular fuel is relative to the mass share of that fuel 

produced. A relative energy intensity of more than 100% for a particular fuel means that the production of that fuel 

consumes a greater share of overall process energy than the mass share of that fuel produced. The energy intensity of 

the overall refinery is 100%.
16 of 129

4.2.2 Bottom-up Approach (Low and High Emissions Cases)

The jet fuel produced by a refinery may be straight-run, produced from hydroprocessed stocks, or 

a blend of both. The refining efficiency of each production method was estimated by summing the 

energy inputs of individual processes. These two cases formed the low and high emissions 

scenarios, respectively, for the life cycle analysis of GHG emissions in the production of jet fuel 

from conventional petroleum. 

The energy for the processes involved in refining jet fuel was taken from a 2007 Department of 

Energy sponsored report (Pellegrino et al., 2007). The report provided both a range of refining 

process energy use, as well as average energy use. The average energy use data for the 

relevant refining processes were used to calculate the jet fuel refining efficiency. 

Straight-Run Jet Fuel

The main processes involved in the production of straight-run jet fuel are crude desalting and 

atmospheric distillation, followed by chemical treatments (such as the Merox process) to remove 

contaminants like mercaptans and organic acids, etc. The estimated process energy in crude 

desalting and atmospheric distillation is shown in Table 4. As no data were found in the literature 

regarding the energy needed for chemical treatment, it was assumed that the energy needed for 

this process was negligible by comparison.

Table 4: Energy requirement in the production of straight-run jet fuel

Refining process Energy required (J/MJ product)

Crude desalting and atmospheric 

distillation

20,055

Chemical treatment Assumed to be negligible

Total 20,055

Overall refining efficiency (LHV) 98%

Under these assumptions, the refining efficiency of straight-run jet fuel is about 98% (LHV). This 

refining efficiency likely represents the maximum efficiency for the production of jet fuel from 

conventional crude and was used in the low emissions scenario. The corresponding process fuel 

and fuel shares for the production of straight-run jet fuel are shown in Table 5.

Table 5: Process fuel and fuel shares for the production of straight-run jet fuel

Type of process 

fuel

J/MJ of jet fuel Process fuel share 

(%)

Electricity 423 2.1

Natural Gas 5,772 28.8

Refinery Gas 9,669 48.2

Coke 3,548 17.7

Residual Oil 643 3.2

Total 20,055 100

Hydroprocessed Jet Fuel

The refining processes involved in producing jet fuel from hydroprocessing include crude 

desalting, atmospheric and vacuum distillation, and hydrotreating and/or hydrocracking. Since the 

production of this hydroprocessed jet fuel was considered as the high emissions scenario, it was 

assumed that all the above processes were required (including both hydrotreating and 

hydrocracking). The energy needed for the refining processes to produce hydroprocessed jet fuel 

is shown in Table 6.
17 of 129

Table 6: Energy requirement in the production of jet fuel from hydroprocessing

Refining process Energy required (J/MJ product)

Crude desalting and atmospheric 

distillation

20,055

Vacuum distillation 16,379

Hydrotreating (to S content of ~500ppm) 24,368

Hydrocracking 75,092

Total 135,894

Overall refining efficiency (LHV) 88%

The refining efficiency of hydroprocessed jet fuel is approximately 88% (LHV). As mentioned 

earlier, this refining efficiency was assumed in the high emissions scenario. The corresponding 

process fuel shares for the production of hydroprocessed jet fuel are shown in Table 7.

Table 7: Process fuel and fuel shares for the production of jet fuel from hydroprocessing

Type of process 

fuel

J/MJ of jet fuel Process fuel share 

(%)

Electricity 9,137 6.7

Natural Gas 82,683 60.9

Refinery Gas 30,713 22.6

Coke 11,294 8.3

Residual Oil 2,067 1.5

Total 135,894 100

4.2.3 Impact of Crude Oil Quality Compared to Processing Technique 

The scope of section 4.2 thus far has been limited to establishing a range of processing 

emissions for conventional jet fuel while assuming US average crude oil properties. As shown by 

Skone and Gerdes (2009), imported crude oils are on average heavier and contain higher levels 

of sulfur than domestic products. Skone and Gerdes used the API gravity and sulfur content of 

crude oils fed into US refineries to establish origin-specific processing GHG emissions for diesel 

fuel in 2005. Based on these data, the origin-specific processing GHG emissions per barrel of 

crude oil were calculated and subsequently related to origin-specific processing GHG emissions 

for jet fuel.8 The upper and lower bounds on the variation from crude oil quality correspond to 

Mexican crude (API gravity of 23.8 and sulfur content of 3.0%) and Algerian crude (API gravity of 

44.8 and sulfur content of 0.1%), respectively (Skone and Gerdes, 2009).

The variation in processing GHG emissions introduced from crude oil quality and processing 

technique are shown graphically in Figure 7. The variation in processing emissions resulting from 

processing techniques is 70% larger than the variation from crude oil quality. This analysis 

emphasizes that there are two factors that impact the processing emissions of making jet fuel. 

Although they have been considered separately, the impacts of crude oil quality and processing 

technique on processing emissions are not necessarily independent. Namely, the impact of crude 

oil quality on straight run fuel production may not be the same as its impact on hydroprocessed 

jet fuel.

 

8 This calculation was based on the assumption that the difference in processing emissions per barrel of diesel between 

each source and the average is equal to the difference in processing emissions per barrel of jet between each source and 

the average. Average processing emissions for diesel are 9.0 gCO2e/MJ while those for jet are 5.65 gCO2e/MJ.
18 of 129

Figure 7: Variation in jet fuel processing emissions induced by origin specific crude oil properties and 

processing technique

4.2.4 Conventional Jet Fuel Results

The life cycle GHG emissions from the production of jet fuel from conventional crude are shown 

in Table 8. These results incorporate the recovery (crude extraction) and transportation results 

discussed in Section 4.1 to complete the life cycle GHG inventory. A comparison of the domestic 

results from this study with the average results presented by Skone and Gerdes (2008) is shown 

in the far right column of the table. Despite using a different approach to derive the GHG 

emissions in the processing of feedstock in the baseline case (top-down) from that used in the 

NETL study (bottom-up approach), similar results were obtained. These results assume average 

crude oil properties in all three scenarios; hence, they do not include any variation in processing 

emissions from crude oil quality. In addition, the combustion CO2 equivalent emissions used by 

Skone and Gerdes are slightly higher than those calculated in this study. This is due to their 

estimates of CH4 and N2O emissions from jet fuel combustion. These emissions were excluded in 

this study due to the high level of uncertainty associated with their estimation. Overall, the life 

cycle GHG emissions of jet fuel from conventional crude obtained by NETL (88.0 gCO2e/MJ) are 

about 0.7% higher than the baseline results (87.5 gCO2e/MJ) developed herein.

Table 8: Summary of results for jet fuel from conventional crude and a comparison of results to the NETL 

petroleum baseline study

MIT Conventional Jet Fuel NETL

Low Baseline High Baseline

Key Assumptions

Crude oil origin US Average Nigeria n/a

Processing Technique Straight 

Run Average Hydroprocessed n/a

Refining efficiency (LHV) 98.0% 93.5% 88.0% n/a

Life Cycle CO2 Emissions by Stage

Recovery of feedstock (gCO2/MJ) 3.7 4.2 9.4 4.3

Transportation of feedstock (gCO2/MJ) 0.8 1.5 1.8 1.3

Processing of feedstock to fuel 

(gCO2/MJ) 1.6 5.5 11.0 5.5

Transportation of jet fuel (gCO2/MJ) 0.8 0.8 0.8 0.9

Combustion CO2 (gCO2/MJ) 73.2 73.2 73.2 73.7

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) 7.0 11.9 22.9 12.0

WTT CH4 emissions (gCO2e/MJ) 0.5 2.3 13.0 2.3

WTT N2O emissions (gCO2e/MJ) 0.1 0.1 0.1 0.1

Total WTW GHG emissions 

(gCO2e/MJ) 80.7 87.5 109.3 88.0

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.92 1.00 1.25 1.01

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4.3 Ultra-Low Sulfur Jet Fuel from Conventional Crude

The production of ULS jet fuel from conventional crude oil was approached with the same 

techniques and assumptions that were applied to conventional jet fuel. Specifically straight-run 

fuel production and hydroprocessing of crude were considered for the low and high emissions 

scenarios, respectively. The additional hydroprocessing that would be required to desulfurize the 

petroleum stream to create the ULS jet fuel was modeled based on that required for ULS diesel. 

The required hydroprocessing likely exceeds simple hydrotreatment, and it may instead involve 

deep hydrogenation and/or hydrocracking. 

Although the technology to produce a ULS jet fuel is known, it is important to note that the current 

hydroprocessing capacity in the United States is only sufficient to meet ULS diesel needs. It 

would need to be expanded to have sufficient capacity for both ULS jet and ULS diesel fuels. 

Such an expansion would require the construction of additional infrastructure, which was not 

included in the life cycle analysis.

4.3.1 Top-Down Approach (Baseline case)

General Motors et al. (2001) estimated a 2% energy penalty for reducing sulfur content in 

gasoline and diesel fuel from 350ppm to 5ppm. The baseline assumption in this work assumed 

the same 2% energy penalty applies for the production of ULS jet fuel compared to conventional 

jet fuel. Hence, the refining energy efficiency of ULS jet fuel was assumed to be 2% less than that 

of jet fuel, i.e. 91.5 percent (LHV). The process fuel shares outlined for conventional jet fuel 

(Table 3) were also used as inputs to the GREET model for the production of baseline ULS jet 

fuel.

4.3.2 Bottom-up approach (low and high emissions cases)

The bottom-up approach for ULS jet fuel builds upon that from the baseline case wherein the 

energy for the processes involved in refining jet fuel was taken from a 2007 Department of 

Energy sponsored report (Pellegrino et al., 2007). The report provided both a range of refining 

process energy use and average energy use. The average energy use data for the relevant 

refining processes were used to calculate the jet fuel refining efficiency. 

Straight-Run ULS Jet Fuel

The production of straight-run ULS jet fuel requires crude desalting and atmospheric distillation 

followed by hydrotreatment to remove sulfur and other impurities. The estimated energy for these 

processes is shown in Table 9.

Table 9: Energy requirement in the production of straight-run ULS jet fuel

Refining process Energy required (J/MJ product)

Crude desalting and atmospheric 

distillation

20,055

Hydrotreatment (to S content of ~5ppm) 48,184

Total 68,239

Overall refining efficiency (LHV) 93.5%

Hence, the refining efficiency of straight-run ULS jet fuel is about 93.5% (LHV). This refining 

efficiency was used in the ULS jet fuel low emissions scenario. The corresponding process fuel 

shares for the production of straight-run ULS jet fuel are shown in Table 10.
20 of 129

Table 10: Process fuel and fuel shares for the production of straight-run ULS jet fuel

Type of process fuel J/MJ of jet fuel Process fuel share (%)

Electricity 3,806 5.6

Natural Gas 42,570 62.4

Refinery Gas 15,235 22.3

Coke 5,603 8.2

Residual Oil 1,025 1.5

Total 68,239 100

Hydroprocessed ULS Jet Fuel 

The calculation of the process energy required in the production of hydroprocessed ULS jet fuel is 

similar to that of jet fuel, except that additional hydrotreating is required to reduce the sulfur 

content of the fuel from about 500ppm to 5ppm. The energy needed for the refining processes is 

shown in Table 11.

Table 11: Energy requirement in the production of ULS jet fuel from hydroprocessing

Refining process Energy required (J/MJ product)

Crude desalting and atmospheric 

distillation

20,055

Vacuum distillation 16,379

Hydrotreating (to S content of ~5ppm) 47,578

Hydrocracking 75,092

Total 159,104

Overall refining efficiency (LHV) 86%

The refining efficiency of hydroprocessed ULS jet fuel is about 86% (LHV). This refining efficiency 

was assumed in the high emissions scenario for the production of ULS jet fuel from conventional 

crude oil. The corresponding process fuel shares for the production of hydroprocessed ULS jet 

fuel are shown in Table 12.

Table 12: Process fuel and fuel shares for the production of ULS jet fuel from hydroprocessing

Type of process 

fuel

J/MJ of jet fuel Process fuel share 

(%)

Electricity 10767 6.8

Natural Gas 100408 63.1

Refinery Gas 33394 21.0

Coke 12284 7.7

Residual Oil 2251 1.4

Total 159104 100

4.3.3 ULS Jet Fuel Results

The life cycle GHG resulting from the production of ULS jet fuel from conventional crude are 

shown in Table 13. These results incorporate the recovery and transportation results discussed in 

section 4.1 to complete the life cycle GHG inventory.
21 of 129

Table 13: Summary of results for ULS jet fuel from conventional crude pathway

Low Baseline High

Key Assumptions

Crude oil origin US Average Nigeria

Processing Technique Straight 

Run Average Hydroprocessed

Refining efficiency (LHV) 93.5% 91.5% 86%

Life Cycle CO2 Emissions by Stage

Recovery of feedstock (gCO2/MJ) 3.7 4.2 9.4

Transportation of feedstock (gCO2/MJ) 0.8 1.5 1.8

Processing of feedstock to fuel (gCO2/MJ) 5.5 7.3 13.1

Transportation of jet fuel (gCO2/MJ) 0.8 0.8 0.8

Combustion CO2 (gCO2/MJ) 72.9 72.9 72.9

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) 10.9 13.7 25.0

WTT CH4 emissions (gCO2e/MJ) 0.7 2.4 13.2

WTT N2O emissions (gCO2e/MJ) 0.1 0.1 0.1

Total WTW GHG emissions (gCO2e/MJ) 84.6 89.1 111.2

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.97 1.02 1.27

In the baseline case, where a refining energy efficiency penalty of 2% was assumed in the 

production of ULS jet fuel relative to conventional jet fuel, the production of ULS jet fuel results in 

life cycle GHG emissions about 2% greater than those of conventional jet fuel. In the low

emissions case, the life cycle GHG emissions from the production of ULS jet fuel are decreased 

by 3% relative to the baseline conventional jet fuel. This effect arises from reduced origin-specific 

emissions relative to the baseline case and an additional energy intensive hydrotreating step 

compared to the production of straight-run conventional jet fuel. Conversely, the increase in life 

cycle GHG emissions between high emissions scenario ULS jet fuel and baseline conventional jet 

fuel is 27%. The energy penalty in this case results from further hydrotreating the jet fuel from 

about 500ppm to 5ppm (~2% difference in refining efficiency). The main reason driving the 

increase are the methane venting and transportation emissions associated with the Nigerian 

crude oil. In all cases, CO2 emissions from combustion of ULS jet fuel are 0.3 gCO2e/MJ less that 

conventional jet fuel.
22 of 129

5 Unconventional Petroleum Pathways

This manuscript considers two unconventional petroleum resources, oil sands from Alberta, 

Canada and oil shale from the Green River Formation in Colorado, Utah and Wyoming. While 

current production of oil sands is approximately 1.3 million bbl/day, oil shale development is 

presently limited to laboratory and pilot-plant stages (Hileman et al., 2009). Oil sands are deposits 

of tar-like petroleum, known as bitumen, within sand or porous rock while oil shale is a solid 

sedimentary rock that contains kerogen, a mixture of organic compounds, including bitumen, 

which can be refined to oil. Both of these resources require extensive work to convert the raw 

resource to a synthetic crude and this results in increased life cycle GHG emissions relative to 

conventional petroleum. All of the work regarding jet fuel from unconventional crude oil was 

performed with GREET version 1.8a.

5.1 Conventional Jet Fuel from Oil Sands

Alberta’s oil sands resources constitute one of the world’s largest proven oil reserves, second 

only to Saudi Arabia (Lacombe and Parsons, 2007). Bitumen production from oil sands takes 

place primarily by two methods, depending on the depth of the oil sand deposits. Relatively 

shallow deposits (75m or less) are recovered through surface mining, which involves the removal 

of soil and rock overlying the mineral deposit, mining of the oil sands ore and extraction of 

bitumen from oil sands using hot water. Deeper deposits employ in-situ production methods, 

which typically inject steam underground to reduce the viscosity of the bitumen and allow it to be 

pumped to the surface (AEUB, 2007). The bitumen produced can be upgraded to lighter synthetic 

crude oil (syncrude) through hydrogen-addition processes (e.g. hydrocracking) and/or carbonsubtraction processes (e.g. delayed coking). 

The life cycle GHG emissions for the extraction of bitumen by both surface mining and in-situ 

technologies were considered. In particular, steam assisted gravity drainage (SAGD) was the 

main technology assumed for the in-situ production of oil sands. This technique has proven to be 

“technically feasible and economically attractive” for the extraction of bitumen oil sands deposits 

below 200 meters (Isaacs, 2007a). It was further assumed that all bitumen was upgraded to a 

light synthetic crude oil (syncrude).9 Oil sands projects have traditionally used natural gas as the 

main source of process energy for bitumen extraction and upgrading as well as for producing 

hydrogen required for the upgrading process. This stems from the era when natural gas was 

cheap and abundant; however, this practice may not be sustainable as bitumen production 

expands. To reduce the dependence on natural gas, alternative process energy and hydrogen 

sources are being explored. These include coal combustion, coal gasification, nuclear energy, 

and the use of internally generated fuels such as bitumen residues (ACR, 2004). Additional 

energy demands are also being met using diesel, gasoline and grid electricity.

5.1.1 Bitumen Production through Surface Mining

The GHG emissions from bitumen production through surface mining are largely dependent on 

the quantity and source of the process energy. This work explored the use of natural gas and coal 

combustion to generate the steam and electrical power required for bitumen extraction. The total 

process energy data for surface mining were taken from the average values of individual industry 

projects (Deer Creek Energy, 2006; Synenco Energy, 2006; Shell Canada, 2007a). The process 

energy sources and values for the production of bitumen through surface mining for the low 

emissions, baseline and high emissions scenarios are summarized in Table 14.

 

9 It was assumed that about 1,600 standard cubic feet of hydrogen was used per barrel of syncrude produced through 

bitumen upgrading. The quality of syncrude produced (~35-49 °API) is generally lighter than the average crude oil 

received by U.S. refineries (~30 °API). However, syncrude tends to produce lower quality distillate oil due to its low 

hydrogen content and requires more complex refining operations to produce transportation fuels. This report assumed 

that the refining efficiency of jet fuel from the lighter, but lower hydrogen content syncrude was the same as that from 

using heavier crude oil with higher hydrogen content.
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Table 14: Process energy assumptions for the production of bitumen through surface mining for the low 

emissions, baseline and high emissions cases

Parameter Low Baseline High

Process energy for steam and 

power generation (J/MJ 

bitumen)

52058 26029 (NG)

26029 (coal) 52058

Source of process energy NG NG and coal Coal

Grid electricity (J/MJ bitumen) 3213 3213 3213

Diesel (J/MJ bitumen) 10723 10723 10723

Gasoline (J/MJ bitumen) 319 319 319

Total process energy required 

(J/MJ bitumen) 66313 66313 66313

5.1.2 Bitumen Production through In Situ Recovery

The efficiency of bitumen recovery from oil sands using in situ recovery techniques is 

characterized by the steam-to-oil ratio (SOR). SOR is the volume of steam required to extract a 

given volume of bitumen. The SOR of SAGD technology ranges from 2.0 to greater than 3.0, 

(Nieuwenburg, 2006; Lacombe and Parsons, 2007) with a 2006 average of about 2.5 (Canadian 

National Energy Board, 2006). The lower and upper bounds were adopted in the low and high 

emissions scenarios while the average was used in the baseline scenario. These assumptions 

translate to natural gas usages of 840 scf, 1050 scf and 1260 scf per barrel of bitumen in the low 

baseline and high emissions scenarios, respectively.10 Other energy sources, such as coal, can 

also be used to supply an equivalent amount of energy. Electricity used in the in-situ production 

of bitumen was consistent with the average value provided by the National Energy Board (2006). 

In the low and baseline cases, it was assumed that the process energy for steam generation was 

provided by natural gas, and in the high case, by coal. The process energy values for the 

production of bitumen through in-situ production for the three scenarios are summarized in Table 

15.

Table 15: Process energy assumptions for the production of bitumen through in situ recovery for the low 

emissions, baseline and high emissions cases

Parameter Low Baseline High

SOR (steam-to-oil ratio) 2.0 2.5 3.5

Process energy needed to make 

steam (J/MJ bitumen) 138544 173180 242451

Source of steam energy NG NG Coal

Grid electricity (J/MJ bitumen) 5700 5700 5700

5.1.3 Bitumen Upgrading to Syncrude

The parameters analyzed in the upgrading of bitumen to syncrude included the yield of syncrude, 

amount of hydrogen used for the upgrading process, the source of hydrogen and the process 

energy. The yield of syncrude from bitumen upgrading can vary depending on the upgrading 

processes. The average volumetric yield is about 1 barrel of syncrude per 1.16 barrel of bitumen 

input, or 1 MJ of syncrude for every 1.27 MJ of bitumen input (EIA, 2006).11 This yield formed the 

baseline upgrading analysis of this work. In the low scenario, it was assumed that 1.1 MJ of 

bitumen input is required per MJ of syncrude, based on the Shell Scotford upgrading project 

 

10 Assuming that about 420 standard cubic feet of natural gas is needed to make a barrel of steam (Lacombe and 

Parsons, 2007).

11 Assuming that the LHV of bitumen is 39.5 MJ/L (Shell Canada, 2007b), and the LHV of syncrude is 36.1 MJ/L (from 

Table 97).
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(Shell Canada, 2007b). The high scenario assumed that 1 MJ of syncrude was produced from 

1.36 MJ of bitumen input based on the Synenco Energy Northern Lights Upgrading project. 

In the low and baseline cases, it was assumed that 1626 scf of hydrogen was needed per barrel 

of syncrude produced (Isaacs, 2007b, ACR, 2004)12 and that natural gas was the source of the 

hydrogen. In the high case, 1800 scf of hydrogen was assumed to be used per barrel of syncrude 

produced, as predicted by the Alberta Chamber of Resources as the amount needed to produce 

high quality light syncrude in the future (ACR, 2004). It was further assumed that this hydrogen 

was produced from the gasification of asphaltene, a by-product of the bitumen upgrading 

process, with no carbon capture.13 A sensitivity analysis was conducted on the high emissions 

case to examine the impact of carbon capture and sequestration on the CO2 resulting from the 

asphaltene gasification.

14

Natural gas and electricity are the main process energy requirements for the upgrading 

processes. The range of values used for the low to high cases is based on data from the Alberta 

Chamber of Resources (baseline case) and individual industry upgrading projects (Low Case: 

Sturgeon upgrader; High case: Northern Lights upgrader15) (ACR, 2004; Petro-Canada, 2006; 

Synenco Energy, 2006). The parameters used in analyzed in the upgrading of bitumen to 

syncrude are summarized in Table 16.

Table 16: Assumed parameters in the upgrading of bitumen to syncrude

Parameter Low Baseline High

Yield of SCO (MJ bitumen 

per MJ syncrude 

produced)

1.1 1.27 1.36

Amount of H2 needed 

(J/MJ syncrude) 86468 86468 98780

Source of H2 NG NG asphaltene 

gasification

Process energy (NG) 

(J/MJ syncrude) 4747 14400 25207

Electricity (J/MJ syncrude) 8323 4200 34846

Jet fuel refining efficiency 

from syncrude (%) 93.5 93.5 93.5

5.1.4 Oil Sands to Jet Fuel Results

The life cycle GHG emissions from the production of jet fuel from Canadian oil sands using 

surface-mining and in-situ production are shown in Table 17.

Carbon-capture and sequestration (CCS) of emissions from the gasification of asphaltene for 

hydrogen production was examined as a sensitivity study to the high emissions scenario of Table 

 

12 From ACR (2004) ~0.4 volume units of natural gas are needed to produce a volume unit of hydrogen.

13 It is assumed that there are no energy use or emissions associated with the production of asphaltene, which is a byproduct of bitumen upgrading. The efficiency of asphaltene gasification is also assumed to be the same as that of coal 

gasification (default GREET value of 62%).

14 A 90% efficiency in the capture of carbon emitted from the gasification of asphaltene and carbon capture energy 

requirement of 250 kwh/ton C are assumed.

15 In addition to syncrude, the Northern Lights upgrader project also produces butane and excess hydrogen. The 

emission credit for the butane co-product was ignored because it is negligible compared to the other well-to-tank 

emissions (~1%). It was also assumed that the emissions associated with asphaltene gasification to produce any excess 

hydrogen are offset by the emissions credit given when this hydrogen displaces conventional hydrogen on the market. 

The net effect is to ignore the excess hydrogen produced in the process. Such assumptions are deemed appropriate for 

the high emissions scenario. 
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15. Only emissions from the upgrading of bitumen to syncrude are affected by the use of carbon 

capture on the hydrogen production process. The results for the high emissions scenario of 

surface and in situ production of bitumen are shown with and without CCS in Table 18. 

Table 17: Life cycle GHG emissions for jet fuel from Canadian oil sands using surface-mining and in-situ

recovery

Low Baseline High

Surface In situ Surface In situ Surface In situ

Life Cycle CO2 Emissions by Stage

Recovery of feedstock (gCO2/MJ) 14.6 19.8 16.5 23.9 30.1 54.4

Transportation of feedstock (gCO2/MJ) 1.3 1.3 1.3 1.4 1.5 1.7

Processing of feedstock to fuel (gCO2/MJ) 5.5 5.5 5.5 5.5 5.5 5.5

Transportation of jet fuel (gCO2/MJ) 0.5 0.5 0.5 0.6 0.6 0.7

Combustion CO2 (gCO2/MJ) 73.2 73.2 73.2 73.2 73.2 73.2

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) 21.9 27.1 23.8 31.4 37.6 62.3

WTT CH4 emissions (gCO2e/MJ) 2.7 3.1 2.7 3.5 2.5 3.3

WTT N2O emissions (gCO2e/MJ) 0.1 0.1 0.1 0.1 0.1 0.2

Total WTW GHG emissions (gCO2e/MJ) 97.9 103.6 99.8 108.2 113.5 139.0

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 1.12 1.18 1.14 1.24 1.30 1.59

Table 18: Life cycle GHG emissions for the high emissions scenario of jet fuel from Canadian oil sands with 

and without carbon capture and sequestration of emissions from asphaltene gasification

Surface Mining In situ recovery

High Emissions Scenario: w/o 

CCS

w/ CCS w/o 

CCS

w/ CCS

Life Cycle CO2 Emissions by Stage

Recovery of feedstock (gCO2/MJ) 30.1 19.4 54.4 43.8

Transportation of feedstock (gCO2/MJ) 1.5 1.3 1.7 1.6

Processing of feedstock to fuel 

(gCO2/MJ) 5.5 5.5 5.5 5.5

Transportation of jet fuel (gCO2/MJ) 0.6 0.5 0.7 0.7

Combustion CO2 (gCO2/MJ) 73.2 73.2 73.2 73.2

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) 37.6 26.7 62.3 51.5

WTT CH4 emissions (gCO2e/MJ) 2.5 2.5 3.3 3.3

WTT N2O emissions (gCO2e/MJ) 0.1 0.1 0.2 0.2

Total WTW GHG emissions 

(gCO2e/MJ) 113.5 102.6 139.0 128.2

Life Cycle GHG Emissions Relative 

to Baseline Conventional Jet Fuel 1.30 1.17 1.59 1.47

In developing a single value for each of the scenarios, the proportion of bitumen extracted 

through surface-mined oil sands versus that of bitumen extracted through in-situ production for 

processing into jet fuel was varied. Specifically, the low case assumes that all bitumen was 

extracted from surface-mined oil sands and the high case assumes all bitumen was extracted 

entirely through in-situ processes. The Alberta Energy and Utilities Board (AEUB) forecast that in 

2015, about 56.5% of crude bitumen production will come from surface mining and the remaining 

43.5% from in-situ production (AEUB, 2007). These proportions were adopted in the baseline 

case. The inputs for the scenario analysis are summarized in Table 19. 
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Table 19: Assumptions regarding the use of surface and in situ bitumen recovery in the low emissions, 

baseline, and high emissions scenarios of jet fuel from Canadian oil sands

Low Baseline High

Proportion of bitumen used in jet fuel 

production from surface-mining processes

100% 56.5% 0%

Proportion of bitumen used in jet fuel 

production from in-situ processes 0% 43.5% 100%

The corresponding life cycle GHG emissions from producing jet fuel from Canadian oil sands are 

shown in Table 20. Recall that the low emissions value represents the low case for surface 

mining while the high emissions value represents the high case for in-situ recovery.

Table 20: Summary of result for jet fuel from Canadian oil sands

Low Baseline High

Life Cycle CO2 Emissions by Stage

Recovery of feedstock (gCO2/MJ) 14.6 19.7 54.4

Transportation of feedstock (gCO2/MJ) 1.3 1.3 1.7

Processing of feedstock to fuel (gCO2/MJ) 5.5 5.5 5.5

Transportation of jet fuel (gCO2/MJ) 0.5 0.5 0.7

Combustion CO2 (gCO2/MJ) 73.2 73.2 73.2

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) 21.9 27.1 62.3

WTT CH4 emissions (gCO2e/MJ) 2.7 3.0 3.3

WTT N2O emissions (gCO2e/MJ) 0.1 0.1 0.2

Total WTW GHG emissions (gCO2e/MJ) 97.9 103.4 139.0

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 1.12 1.18 1.59

The life cycle GHG emissions of the production and use of jet fuel from Canadian oil sands range 

from 1.12 to 1.59 times higher than those from the production and use of conventional jet fuel.

This analysis has only considered the use of natural gas as a process fuel and source of 

hydrogen. Although natural gas is currently the main source of energy for oil sands production, its 

use may not be sustainable in the long term. To reduce dependence on natural gas, coal and 

asphaltene (a bitumen residue) were considered as an alternative energy sources. Though 

abundant, the use of coal and bitumen upgrading residues can result in greater GHG emissions 

compared to the use of natural gas. Specifically, using coal energy to power bitumen production 

and asphaltene gasification to provide hydrogen for bitumen upgrading, the life cycle GHG 

emissions from surface-mining and in-situ production are 1.3 times and 1.6 times greater than 

those of conventional jet fuel, respectively. Even when carbon capture is used to reduce 

emissions from the gasification of asphaltene, the life cycle GHG emissions from surface-mining 

and in-situ production are still about 1.2 times and 1.5 times greater than those of conventional jet 

fuel, respectively.

5.2 Conventional Jet Fuel from Oil Shale

Oil shale is a sedimentary rock formation of mostly carbonate and silicate minerals containing a 

solid organic material called kerogen. This analysis of jet fuel production from oil shale is based 

on the Shell in situ conversion process (ICP), in which oil shale is heated while still in the earth 

using electrically heated wells for a prolonged period (two to three years). The heat releases a 

combination shale oil and shale gas from the kerogen in the oil shale.16 Shale oil is a light crude 

 

16 Note that oil shale is the geologic rock formation that contains kerogen, whereas shale oil is the light crude that is 

released from the oil shale.
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composed largely of middle distillates while shale gas is mixture of natural gas, propane and 

butane. The yield relationship between the two products is estimated as two-thirds shale oil and 

one-third shale gas on an energy basis. The area surrounding the extraction site is frozen to form 

an impermeable barrier, preventing groundwater from disturbing the heating process and 

preventing products from escaping (Bartis et al., 2005; Mut, 2005). 

Ex situ conversion is the alternative to the Shell ICP. Ex situ heating of the kerogen occurs above 

ground and requires the shale to be mined before it is processed (Brandt, 2008). The conversion 

of oil shale to shale oil and shale gas on the surface takes place at higher temperatures than the 

in situ technique. The higher temperatures could result in the decomposition of the carbonate 

minerals contained within the oil shale, potentially releasing considerably more CO2 than the 

Shell ICP process.17

5.2.1 Analysis Methodology

The pathway for the extraction of shale oil from oil shale using the Shell ICP is not available in 

GREET and was analyzed based on incorporating information available from the literature into 

the GREET framework. Specifically, the process energy for the production of shale oil from oil 

shale, as well as the yield of oil and gas products using the Shell ICP, were adopted from 

Brandt’s analysis of the Green River Formation in Colorado, Utah, and Wyoming (Brandt, 2008). 

This analysis was specifically focused on the Green River Formation; however, many other oil 

shale resources are dispersed around the world.18

Only major process energy demands, namely the in situ heating energy and the energy to 

maintain the frozen wall (about one order of magnitude less than the in situ heating energy) were 

considered.19 The effects of different amounts and sources of in-situ heating energy on the 

overall GHG emissions of this pathway were explored with the low emissions, baseline and high 

emissions scenarios. The low and baseline cases assumed that 25% of the in situ heating energy 

needed was provided through the recycling of waste heat (as was assumed in the low carbon 

case in Brandt’s analysis), while no recycling of waste heat was assumed in the high case. As a 

large amount of electrical energy is needed to provide in-situ heating for the Shell ICP, dedicated 

coal-based power generation facilities were assumed to be near the extraction site due to the 

vast abundance of coal resources in the Green River Formation. The locality of electricity was 

assumed to reduce transmission losses to 5% from the GREET default value of 8%. For the low 

case, the use of coal IGCC electricity with carbon capture (90% efficiency) was assumed;20 while 

the baseline and high cases employ traditional pulverized coal-fired electricity without carbon 

capture. 

Brandt (2008) provided low and high estimates of the electrical energy required for in situ heating, 

the amount of shale oil output and the amount of shale gas co-produced. There is no natural gas 

consumption within the process; therefore, energy and emission credits were given to the natural 

 

17 Typical oil shale from the Green River Formation is composed of 23% dolomite (calcium-magnesium bicarbonate) and 

16% calcite (calcium carbonate). Surface conversion processes require up to 750°C while the decomposition of dolomite 

and calcite occur around 575°C and 650°C, respectively. The Shell ICP process takes place at temperatures between 

340°C and 400°C. Because of this increased temperature, production of shale oil using surface retorting could result in 

between 1.2 to 3 times more GHG emissions than if the production had been performed using the Shell ICP process. 

(Hileman et al., 2009)

18 There are known oil shale formations in Australia, Brazil, Canada, China, Estonia, Israel, Jordan, Morocco, Russia, 

Sweden, Syria, Thailand, Turkey and the United States (Geology.com, 2009)

19 Other minor process energy requirements like drilling and pumping energy (< 1% of retorting energy), as well as 

energy needed for infrastructural construction, are ignored in this study.

20 From Deutch and Moniz (2007), the estimated efficiency of an IGCC plant with 90% carbon capture is 31.2% (HHV) or 

about 34% (31.2+3) on a LHV basis. The efficiency of 34% (LHV) was adopted in this study.
28 of 129

gas co-produced using the displacement method.21 The low emissions scenario used the low 

value for in situ heating energy with high values for shale oil and shale gas production; the 

baseline scenario used mean values; and the high emissions scenario used high value for in situ 

heating energy with low values for shale oil and shale gas production. In addition, shale oil is 

much lighter and contains almost no heavy ends compared to traditional crude oil; hence, the 

refining efficiency of processing shale oil to jet fuel was assumed to be higher (~2-3%) than that 

of refining traditional crude oil.22

The key processes and assumptions involved in the production of jet fuel from oil shale using the 

Shell ICP are summarized in Table 21.

Table 21: Input assumptions for the production of jet fuel from oil shale for low emissions, baseline and high 

emissions cases

Low Baseline High

Process conditions

Use 25% recycled 

heat for retorting; use 

coal IGCC electricity 

with CCS; capture 

efficiency of 90%

Use 25% recycled 

heat for retorting; 

use 100% coalfired electricity; no 

carbon capture

No recycled heat 

used; 100% 

coal-fired 

electricity; no 

carbon capture

Electrical energy input 

(J/MJ shale oil produced) 134600 148100 211900

Natural gas co-produced 

(J/MJ shale oil produced) 

(LHV)

223700 189600 152500

Refining efficiency (LHV) 96% 96% 96%

5.2.2 Oil Shale to Jet Fuel Results

The GHG emissions from the production and use of jet fuel from oil shale using the Shell ICP are 

shown in Table 22.

Table 22: Summary of results for jet fuel from in situ oil shale pathway

Low Baseline High

Life Cycle CO2 Emissions by Stage

Recovery of feedstock (gCO2/MJ) 3.6 41.2 59.7

Transportation of feedstock (gCO2/MJ) 0.5 0.6 0.7

Processing of feedstock to fuel (gCO2/MJ) 3.3 3.3 3.3

Transportation of jet fuel (gCO2/MJ) 0.5 0.6 0.7

Combustion CO2 (gCO2/MJ) 73.2 73.2 73.2

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) 7.8 45.8 64.4

WTT CH4 emissions (gCO2e/MJ) 2.4 2.5 3.2

WTT N2O emissions (gCO2e/MJ) 0.6 0.2 0.2

Total WTW GHG emissions (gCO2e/MJ) 84.1 121.6 141.0

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.96 1.39 1.61

 

21 The displacement (system expansion) method was used to account for energy and emission credits to natural gas, i.e., 

the natural gas produced was assumed to displace the recovery and processing of conventional petroleum based natural 

gas in a separate, independent facility.

22 Jim Bartis, interview with Hsin Min Wong, July 16, 2007.
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With carbon capture (low emissions case), the life cycle GHG emissions of jet fuel from oil shale 

are reduced to slightly less than baseline conventional jet fuel. Without the capture of carbon 

dioxide from coal-based electricity plants providing the in situ heating energy, the production of jet 

fuel from oil shale produces life cycle GHG emissions 1.4 to 1.6 times greater than baseline 

conventional jet fuel. 

In addition to increased GHG emissions, oil shale development also presents other adverse 

impacts to the environment. Though less intrusive to the surface topography than ex situ 

processes and not requiring the disposal of spent shale, in-situ conversion will still cause 

displacement of all other land uses in the area and disruptions to the local ecological community 

(Bartis et al., 2005). In situ methods also have the potential to cause ground water contamination. 

Though the freeze wall protects groundwater during production, contamination may occur postproduction. As the Green River formation lies within the Colorado River drainage basin, water 

contamination could impact millions of downstream users (Gruenwald, 2006). 
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6 Fischer-Tropsch Jet Fuel 

The Fischer Tropsch (F-T) process first involves the steam reforming or gasification of any carbon 

containing feedstock (e.g. natural gas, coal or biomass) to synthesis gas (syngas), which is a 

mixture of hydrogen and carbon monoxide. The syngas is subsequently converted to paraffinic 

hydrocarbons in the presence of an iron- or cobalt-based catalyst (Fischer-Tropsch synthesis). A 

third upgrading step cracks the longer hydrocarbon chains to maximize the production of 

synthetic paraffinic liquid fuels like diesel and jet fuel. Syngas must be cleaned before FischerTropsch synthesis step to remove contaminants, particularly sulfur, to avoid poisoning the 

catalyst. Hence, the resultant Fischer-Tropsch liquid fuels are virtually free of contaminants and 

the jet fuel fraction of the product slate falls into the category of synthetic paraffinic fuels. 

All jet fuels produced using F-T synthesis have similar characteristics, independent of feedstock 

type. Any small variations in fuel properties are primarily associated with the operating conditions 

(e.g., catalyst, temperature, and pressure) within the synthesis reactors and how the direct 

products of the synthesis are treated and processed. All jet fuels produced using the F-T process 

share common characteristics with regard to compatibility with existing infrastructure and aircraft, 

combustion emissions, and their relative merit for use in aviation. Feedstock choice, however, 

does have a strong influence on fuel production capacity, production cost, life cycle greenhouse 

gas emissions, and technology readiness (Hileman et al., 2009). 

Fischer-Tropsch fuels created from natural gas, coal, biomass and combinations of coal and 

biomass were analyzed in this work. Gas-to-Liquid (GTL) production is currently limited to 

Malaysia where Shell has been producing approximately 15,000 bbl/day since 1993. Sasol as 

well as Shell in collaboration with Qatar Petroleum are both constructing GTL facilities in Qatar 

with design capacities of 34,000 bbl/day and 140,000 bbl/day, respectively. Existing coal-toliquids (CTL) capacity is limited to Sasol in South Africa where a production capacity of 160,000 

bbl/day of oil equivalent has been consistently maintained. There is no commercial scale 

production of F-T fuels using biomass as the feedstock (BTL). This technology is still in the 

development phase; however, a German firm, CHOREN, began start-up operations of a 300 

bbl/day facility in 2008 and Solena Group, with Rentech, announced plans for a 1,800 bbl/day 

BTL facility located in Gilroy, California. Experience with simultaneously gasifying a combination 

of coal and biomass in a single gasifier is presently limited to successful tests at an IGCC plant in 

the Netherlands (Hileman et al., 2009). The analysis of F-T jet fuel from coal and natural gas was 

conducted using GREET version 1.8a while F-T jet fuel from biomass and the combination of coal 

and biomass was considered using GREET version 1.8b.

6.1 Carbon Capture and Sequestration with Fischer-Tropsch 

Facilities

By comparison to conventional power plants, F-T plants are well suited for the implementation of 

carbon capture and sequestration (CCS). This opportunity occurs because the F-T process 

results in a relatively pure stream of CO2 upstream of the F-T synthesis step, whereas coal power 

plants produce flue gas, which must be scrubbed to obtain a pure CO2 stream. 

This work considered cases with CCS implemented on a gas-to-liquids plant, a coal-to-liquids 

plant and a coal and biomass-to-liquids plant. Carbon dioxide is already captured within F-T 

plants as part of the process; therefore, the only difference for standard CCS implementation is 

the addition of CO2 compression, transport and storage capital and operating costs (Tarka, 2009). 

It was assumed that the energy needed for CO2 compression was provided by electricity 

internally generated within the F-T process. This results in a reduction of the overall process 

efficiency because some of the energy from the feedstock is used to generate this additional 

energy. The electrical energy needed to compress the captured carbon dioxide to a pipeline 
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ready pressure of about 15 MPa was assumed to be 250 kWh/ton carbon23 (GREET, 2008; 

Kreutz et al., 2008). The transportation of compressed carbon dioxide and the energy required for 

sequestration in a storage site were not considered.

The amount of carbon available for capture is equal to the difference between the carbon present 

in the feedstock and the carbon present in the final products. The term carbon capture efficiency 

is used to define the percentage of available carbon that is actually captured. Capture efficiencies 

of 80% (Kreutz et al., 2008), 85% (SSEB, 2005) and 90% (Bartis et al., 2008; Deutch and Moniz, 

2007; Tarka, 2009) were assumed for the high emissions, baseline and low emissions cases, 

respectively. Recent analyses at National Energy and Technology Laboratory (NETL) have 

indicated that capital expenditures associated with systems designed to capture CO2 at the 80% 

level would not vary significantly from those capturing at the 90% level (Dilmore and Skone, 

2009). A capture efficiency of 85% was chosen for the baseline case in this work to reflect the 

lack of commercial experience surrounding widespread implementation of CCS. Carbon emitted 

from the combustion of process fuels is not captured.

6.2 Fischer-Tropsch Fuel from Natural Gas

The life cycle GHG emissions for the production of F-T fuels using natural gas sourced outside 

North America were considered. The Gas to Liquids (GTL) pathway is available in GREET and 

well-documented based on current industry data (Wang, 1999; Wang and Huang, 1999; Wang, 

2002). Specifically, this analysis assumed non-North American natural gas was supplied to an 

internationally located stand-alone F-T liquid fuels plant designed to maximize liquid fuels 

production (e.g. through recycling of tail gas from F-T reactors). Sufficient electrical energy was 

produced to fuel all internal processes, with negligible excess electricity produced for export. The 

process included the upgrading (hydroprocessing) of long-chain liquids to a final product slate of 

diesel (C18), jet fuel (C12) and naphtha (C4-C6). The energy allocation method was adopted for 

assigning energy and emissions to various liquid products. Default GREET assumptions were 

adopted regarding the transportation profiles of F-T fuel from international plants to the US (5000 

miles by ocean tanker plus domestic transport via pipeline, barge and rail).

Most studies in the literature focus on F-T reactor designs and conditions that produce diesel and 

naphtha. To produce jet fuel instead of diesel, additional hydrocracking and greater syngas 

recycle are needed, resulting in a small increase in hydrogen and power requirements for the 

plant. Furthermore, a moderate decrease in the CO2 associated to jet fuel compared to diesel 

would ensue due to changes in the allocation fractions. As these additional energy requirements 

do not lead to substantial increases in CO2 emissions from the facility (Gray et al., 2007), they 

were ignored in this analysis, (i.e. the production of F-T jet fuel is assumed to have the same 

emissions as the production of F-T diesel). This assumption applies to all of the F-T jet fuel 

pathways analyzed in this study, namely the production of F-T jet fuel from natural gas, coal and 

biomass. Although F-T jet fuel can be made without added burdens, it is not possible to have a 

product slate of 100% F-T jet fuel24 (a value of 25% is taken as the preferred value in these 

studies, and a sensitivity study is shown for a co-fed coal and biomass F-T facility).

The F-T process efficiency is a key parameter affecting the life cycle GHG emissions of the 

production of F-T jet fuel from natural gas. Equation 4 defines process efficiency for a general F-T 

facility. GTL process efficiencies from the literature vary from 60 to 65% (Edwards et al., 2007; 

 

23 Kreutz et al. assume 90.5 kWh per tonne of CO2, which converts to 300 kWh per ton of carbon. GREET default value 

in 2010 is 300 kWh per ton of carbon but an improved efficiency of 250 kWh per ton of carbon is assumed for 2015.

24 An F-T plant configured to produce 70% diesel and 30% naphtha should theoretically be able to undergo 

modifications such that it could yield 60% jet fuel and 40% naphtha (Gray et al., 2007). Sasol is developing the ability to 

produce a joint Battlefield-Use Fuel of the Future (BUFF) using F-T synthesis. This fuel could be used in place of JP-8 in 

military aircraft and they report a yield of ~30% that conforms to the freezing point standards of JP-8. The rest of the 

product slate is composed of “heavy” diesel and naphtha (Lamprecht, 2007).
32 of 129

Green Car Congress, 2006a; Wang, 2002). The low emissions, baseline and high emission 

scenarios assumed LHV process efficiencies of 65%, 63% and 60%, respectively.

€

Process Efficiency = 1 MJfuel

1 MJfeedstock +Process Energy Equation 4

The important assumptions and corresponding life cycle GHG emissions in the production and 

use of F-T jet fuel from natural gas are shown in Table 23.

Table 23: Summary of inputs and results for F-T jet fuel from natural gas pathway

Low Baseline High

Key Assumptions

Process efficiency (LHV) 65% 63% 60%

Life Cycle CO2 Emissions by Stage

Recovery of feedstock (gCO2/MJ) 4.6 4.6 4.6

Transportation of feedstock (gCO2/MJ) 0.0 0.0 0.0

Processing of feedstock to fuel (gCO2/MJ) 19.4 20.2 21.4

Transportation of jet fuel (gCO2/MJ) 1.2 1.2 1.2

Combustion CO2 (gCO2/MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) 25.2 26.0 27.2

WTT CH4 emissions (gCO2e/MJ) 4.4 4.6 4.8

WTT N2O emissions (gCO2e/MJ) 0.0 0.0 0.0

Total WTW GHG emissions (gCO2e/MJ) 100.1 101.0 102.4

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 1.14 1.15 1.17

The life cycle GHG emissions of the production and use of F-T jet fuel made from natural gas 

range from 1.14 times to 1.17 times higher than those from conventional petroleum-based jet fuel.

6.2.1 Case Study: Impact of Carbon Capture on GHG Emissions from GTL Facilities

A distinguishing factor of GTL facilities is the reduced CO2 emitted as compared to CTL and BTL. 

From the perspective of the operator this could potentially lead to higher capture costs per ton of 

carbon; hence the discussion of CCS with a GTL plant is often overlooked. Nonetheless, this 

work explored the effect of capturing CO2 produced from GTL plants in the baseline scenario 

configuration. A capture efficiency of 85% was assumed for consistency with the baseline 

assumptions in the other F-T pathways.

A comparison of results from the baseline scenario with and without CCS is given in Table 24. 

CCS could reduce the processing GHG emissions of F-T jet fuel from natural gas by 15 gCO2/MJ, 

such that the WTT emissions from the GTL pathway become comparable to those from 

conventional jet fuel. As is the case with all fossil based fuels, combustion emissions dominate 

the life cycle GHG inventory, making it difficult to improve upon conventional jet fuel.
33 of 129

Table 24: Life cycle GHG emissions from natural gas F-T jet fuel with and without carbon capture

Baseline Baseline with 

CCS

Key Assumptions

Carbon capture efficiency -- 85%

Process Efficiency (LHV) 63%

62.7%

(63% without 

CCS)

Life Cycle CO2 Emissions by Stage

Recovery of feedstock (gCO2/MJ) 4.6 4.6

Transportation of feedstock (gCO2/MJ) 0.0 0.0

Processing of feedstock to fuel (gCO2/MJ) 20.2 5.4

Transportation of jet fuel (gCO2/MJ) 1.2 1.2

Combustion CO2 (gCO2/MJ) 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) 26.0 11.2

WTT CH4 emissions (gCO2e/MJ) 4.6 4.6

WTT N2O emissions (gCO2e/MJ) 0.0 0.0

Total WTW GHG emissions (gCO2e/MJ) 101.0 86.2

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 1.15 0.99

6.3 Fischer-Tropsch Fuel from Coal

This examination of life cycle GHG performance of F-T jet fuel production from coal was 

conducted for scenarios both with and without carbon capture and sequestration. Similarly to GTL 

plants, a stand-alone F-T liquid fuels plant designed to maximize liquid fuels production with no 

excess electricity was examined. The upgrading (hydroprocessing) of long-chain liquid products 

to desired fuel products was also included. Energy allocation was used to assign energy and 

emissions among liquid fuel products.

6.3.1 CTL without Carbon Capture

Process efficiency and coal type were judged to be key parameters having a significant impact on 

the overall GHG emissions of the Coal to Liquids (CTL) pathway. Both bituminous and subbituminous coal based processes were examined.25 In 2007, almost 65% of bituminous coal 

production in the U.S. occurred via underground mining while the remainder was surface-mined. 

On the other hand, sub-bituminous coals are almost exclusively surface-mined. (EIA, 2008b) 

Underground mining results in substantially greater methane emissions compared to surface 

mining processes. Methane emissions arising from the production of underground-mined and 

surface-mined coals were estimated using aggregate coal mining methane emissions data (EIA, 

2007; Bartis et al., 2008).26

 

25 Lignite coal can possibly be used as a feedstock to CTL plants but lignite production in the US is much lower (~7% of 

total coal production in 2007, EIA 2008c) compared to bituminous and sub-bituminous coal production (46.7% and 46.3% 

of total coal production in 2007, respectively, EIA 2008c). Reliable data on methane emissions associated with the mining 

of lignite coal are not available. For these reasons, lignite coal is not analyzed in this work.

26 Bartis et al., 2008 estimated methane emissions of 338 pounds of carbon dioxide equivalent per ton of undergroundmined coal and methane emissions of 42.4 pounds of carbon dioxide equivalent per ton of surface-mined coal based on 

EIA data (EIA, 2008b). The methane emissions per MJ of coal production were calculated from the lower heating values 

of bituminous coal and sub-bituminous coal. 
34 of 129

The low emissions case assumed a lower heating value process efficiency of 53% with surfacemined bituminous coal.27 In the baseline case, a process efficiency of 50% was used (Bartis et 

al., 2008; Deutch and Moniz, 2007; Van Bibber et al., 2007; Marano and Ciferno, 2002) with the 

2007 average US coal mix of underground-mined and surface-mined bituminous coal and 

surface-mined sub-bituminous coal28; the use of anthracite or lignite coal was not considered in 

this analysis. The high emissions case assumed a process efficiency of 47% and undergroundmined bituminous coal (based on case 1 of Southern States Energy Board CTL study, SSEB, 

2005). For comparison, the first Sasol CTL plant built in the 1950s had process efficiencies under 

40% (UK DTI, 1999; Gray and Tomlinson, 2001).

The inputs assumed for the three scenarios are summarized in Table 25.

Table 25: Input assumptions for the production of F-T jet fuel from coal (without carbon capture) for low 

emissions, baseline and high emissions cases

Low Baseline High

Process efficiency (LHV) 53% 50% 47%

Coal input Surface-mined 

bituminous coal

Average 

bituminous and 

sub-bituminous 

coal mix29

Undergroundmined bituminous 

coal

Coal mining methane 

emissions (g CO2e/MJ 

coal)

0.80 2.8 6.4

The life cycle GHG emissions from the production and use of F-T jet fuel from a CTL plant without 

carbon capture and sequestration are shown in Table 26.

Table 26: Summary of results for F-T jet fuel from coal pathway (without carbon capture)

Low Baseline High

Life Cycle CO2 Emissions by Stage

Recovery of feedstock (gCO2/MJ) 0.8 0.8 0.8

Transportation of feedstock (gCO2/MJ) 0.1 0.1 0.0

Processing of feedstock to fuel (gCO2/MJ) 100.5 117.2 122.4

Transportation of jet fuel (gCO2/MJ) 0.6 0.6 0.6

Combustion CO2 (gCO2/MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) 102.1 118.8 123.9

WTT CH4 emissions (gCO2e/MJ) 1.5 5.7 13.7

WTT N2O emissions (gCO2e/MJ) 0.0 0.0 0.0

Total WTW GHG emissions (gCO2e/MJ) 174.0 194.9 208.0

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 1.99 2.23 2.38

 

27 Thomas Tarka, interview with David Ortiz, October 3, 2008.

28 Compared to 2007, the coal production mix in 2017 is projected to comprise a larger proportion of surface-mined subbituminous coal from Western coal production, particularly the Powder River Basin. (EIA, 2008d)

29 From EIA (2008b) about 49% of total bituminous and sub-bituminous coal production in 2007 is made up of surfacemined sub-bituminous coal, 18% is made up of surface-mined bituminous coal, and the remaining 33% is made up of 

underground-mined bituminous coal. Coal methane emissions, lower heating value and carbon content of the average 

coal mix used in the baseline case are based on weighted average values.
35 of 129

6.3.2 CTL with Carbon Capture

As discussed in Section 6.1, carbon capture efficiencies of 80%, 85% and 90% were assumed for 

the high emissions, baseline and low emissions cases, respectively. These input assumptions are 

summarized in Table 27.

Table 27: Input assumptions for the production of F-T jet fuel from coal (with carbon capture) for low 

emissions, baseline and high emissions cases

Low Baseline High

Carbon compression 

energy

250 kWh/ton C 250 kWh/ton C 250 kWh/ton C

Process efficiency 

(LHV)

51.3%

(53% without 

CCS)

48.4%

(50% without CCS)

45.6%

(47% without CCS)

Coal input Surface-mined 

bituminous coal

Average 

bituminous and 

sub-bituminous 

coal mix

Undergroundmined bituminous 

coal

Carbon capture 

efficiency 90% 85% 80%

The life cycle GHG emissions of the production and use of F-T jet fuel from a CTL plant with 

carbon capture and sequestration are shown in Table 28.

Table 28: Summary of results for F-T jet fuel from coal pathway (with carbon capture)

Low Baseline High

Life Cycle CO2 Emissions by Stage

Recovery of feedstock (gCO2/MJ) 0.8 0.8 0.8

Transportation of feedstock (gCO2/MJ) 0.1 0.1 0.1

Processing of feedstock to fuel (gCO2/MJ) 11.4 19.4 26.6

Transportation of jet fuel (gCO2/MJ) 0.6 0.6 0.6

Combustion CO2 (gCO2/MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) 12.9 20.9 28.1

WTT CH4 emissions (gCO2e/MJ) 1.6 5.9 14.1

WTT N2O emissions (gCO2e/MJ) 0.0 0.0 0.0

Total WTW GHG emissions (gCO2e/MJ) 84.9 97.2 112.6

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.97 1.11 1.29

The production of F-T jet fuel from a CTL plant without carbon capture results in life cycle GHG 

emissions that are between 2.0 (low emissions case) to 2.4 (high emissions case) times higher 

than conventional jet fuel. When carbon sequestration is implemented, the life cycle GHG 

emissions relative to those of conventional jet fuel range from almost 1.0 to 1.3. The use of 

carbon capture and sequestration can allow for an approximate 50% reduction in GHG emissions 

for a coal-to-liquids facility.

6.4 Fischer-Tropsch Fuel from Biomass

A self-sufficient BTL plant was assumed for the GHG analysis presented here. This assumes that 

biomass feedstock is used to meet internal process energy needs, with little or no excess 

electricity produced for export. Similar to the other F-T plants analyzed above, the BTL plant was 

assumed to produce commercial quality liquid fuels like diesel and jet fuel, and the energy 

allocation method was adopted for assigning energy and emissions between liquid fuel products. 
36 of 129

The biomass feedstocks examined include waste biomass (e.g. forest residue, agricultural 

residue) and non-food energy crops (e.g. herbaceous biomass) that were assumed to be grown 

on land that would not incur adverse direct or indirect land use change emissions (e.g. idle or 

abandoned cropland). Specifically, three types of biomass feedstocks were considered: 

switchgrass, corn stover and forest residue. Switchgrass was considered as the primary biomass 

feedstock in all cases of the scenario analysis (low emissions, baseline and high emissions) 

because of its better yield and improved scalability relative to waste products. Furthermore, if 

waste products were used on a large scale then they would become a market commodity, as they 

are no longer waste. Such a change in classification leads to questions as to where system 

boundaries should be drawn with respect to the crop that initially produced the waste. Such 

issues were not considered in the scope of this report.

6.4.1 Switchgrass

Switchgrass is a perennial warm season grass native to North America, found in remnant prairies, 

native grass pastures, and naturalized along roadsides. Other forms of herbaceous biomass 

include mixed prairie grasses, wheat, hay and leaves, among others. As a replacement for annual 

crops, warm season grasses have also been shown to provide important habitat for wildlife, 

including game birds and other species threatened by the loss of tall grass prairie habitat 

(McLaughlin et al., 2002). The assumptions regarding the yield, energy and emissions associated 

with switchgrass cultivation were based on a survey of existing cultivation data from the literature. 

Also considered is the potential for long term changes in carbon contained within the soil on 

which the switchgrass is grown. The greatest potential for long-term soil carbon sequestration is 

in those situations where agricultural practices have led to a progressive and historical decline in 

soil carbon stocks (McLaughlin el al., 2002). The properties (e.g. lower heating value, carbon 

content) of switchgrass used in this work are reported in Table 97

6.4.1.1 Switchgrass Yield And Cultivation Trends

The approach used in this work is similar to that taken by the National Academies in their 2009 

report on Liquid Fuels from Coal and Biomass. Figure 8 shows a distribution of annual 

switchgrass yields taken from Gunderson et al. (2008) that were used to establish predictive 

maps of potential yields across the continental United States. The data set comprises 

approximately 1400 observations with a mean of approximately 4.9 tons/acre/yr. For each data 

point, the specific cultivar, crop management information, ecotype, precipitation and temperature 

in the long-term climate record were documented. Using their model, Gunderson et al. (2008) 

predicted yields in excess of 8.9 tons/acre/yr for lowland ecotypes30 in the Appalachian region 

and 5.4-6.2 tons/acre/yr in the Nebraska/South Dakota region. Similarly, yields for upland 

ecotypes in the Appalachian region were predicted to be greater than 6.2 tons/acre/yr and 3.1-4.5 

tons/acre/yr in the Nebraska/South Dakota region. Gunderson et al. (2008) openly discuss that 

their model predicts the theoretical maximum yield for a given set of input conditions; hence, 

experimental yields for these regions will most likely be lower in practice. 

Other studies have focused on establishing estimates for a national average yield. Heaton et al. 

(2004) found an average switchgrass yield of 4.6 tons/acre/yr (+/- 0.3 tons/acre/yr) and 

McLaughlin et al. (2002) projected a national average annual yield of 4.2 tons/acre/yr. Vadas et 

al. (2008) adopted a nominal yield of 4.0 tons/acre/yr and an optimistic yield of 5.8 tons/acre/yr 

based on data from large field plots in southern Wisconsin while Adler et al. (2007) simulated 

switchgrass production in Pennsylvania as 4.3 tons/acre/yr using DAYCENT.31 Finally, the 

GREET herbaceous biomass production pathway assumes a yield of 6.0 tons/acre/yr. 

 

30 Lowland and upland ecotypes are defined by position relative to the level where water flows or where flooding occurs

31 DAYCENT is the daily time-step version of the CENTURY biogeochemical model. From weather (daily maximum and 

minimum air temperature, precipitation), soil-texture class, and land-use inputs, DAYCENT simulates fluxes of carbon (C) 

and nitrogen (N) between the atmosphere, vegetation, and soil while predicting crop production, soil organic-matter 

changes, and trace-gas fluxes.
37 of 129

Figure 8: Distribution of reported switchgrass yields across the United States (data from Gunderson et al., 

2008)

While numerous studies have estimated the potential yield of switchgrass, many of these results 

are based on small plots (less than 5m2) and the results are not necessarily indicative of what can 

be expected of farm-scale production (National Academies, 2009). Schmer et al. (2008) managed 

switchgrass as a biomass energy crop in field trials of 7.4 to 22.2 acres on marginal cropland 

from 10 farms across a wide precipitation and temperature gradient in the mid-continental US. 

The actual farm-scale production resulted in harvested yields about 35 to 50 percent lower than 

those of small-scale plots. It is possible that the lower yields from large-scale production can be 

attributed to farmers’ inexperience with the cropping system or differences in cropland quality; 

however, farmers worked closely with the researchers in collecting this data and the land had 

been in active crop production before being converted to switchgrass production (National 

Academies, 2009). Actual yield data from Schmer et al. (2008) ranged from 2.3 to 5.0 tons/acre/yr 

with a mean of 3.2 tons/acre/yr.

In their analysis, Gunderson et al. (2008) concluded that switchgrass yield is most influenced by 

ecotype (upland or lowland) and the relationship of precipitation and temperature. Lower yields 

were attributed to factors that were not quantified across the data set, such as soil pH, inherent 

soil fertility, total solar radiation (vs. long periods of cloud cover) and others that are artifacts of 

each individual growing site and cannot be aggregated for a generalized result. The specific rate 

of nitrogen application was found not to have a significant influence on yield. Very high levels of 

fertilization certainly did not guarantee increased biomass production, and in many cases, cases 

the zero fertilizer plantings did as well as any fertilized stands. Based on these conclusions, the 

yields adopted for the low emissions, baseline and high emissions scenarios (as shown in Table 

29) were assumed to be independent input parameters from other cultivation inputs (on a per ton 

basis). 

Table 29: Switchgrass yields assumed in the low, baseline and high emissions scenarios

Low1 Baseline2 High3

Yield 

(tons/acre/yr)

5.8 4.6 3.2

Notes:

1) Optimistic yield from Vadas et al. (2008) based on large field plots in 

southern Wisconsin

2) Projected national average from McLaughlin et al. (2002)

3) Average farm-scale yield from Schmer et al. (2008) based on midcontinental US

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38 of 129

6.4.1.2 Cultivation and Transportation of Switchgrass

The key inputs for switchgrass production are the process fuels and electricity used in farming, 

fertilizer inputs and herbicide usage. These parameters have been identified in several studies 

but are the subject of much uncertainty. Although it was determined that these inputs do not have 

a substantial impact on yield, they are essential for estimating the GHG emissions associated 

with switchgrass production. A summary of the available data is given in Table 30. 

These data show considerable variation in the application rates of non-nitrogen fertilizers and 

herbicides. The phosphorous and potassium application rates quoted by Vadas et al. (2008) are 

an order of magnitude larger than those given by Adler, which are in turn an order of magnitude 

larger than the GREET default values. 

Using the same arguments, which were previously made in considering yield as an independent 

parameter, the process fuel usage, nitrogen fertilizer application and other fertilizer and herbicide 

application were decoupled from their respective data sets. Hence, they were also considered as 

independent parameters for the purposes of the scenario analysis. The input parameters used for 

the low emissions, baseline and high emissions scenario are outlined in Table 31. The GREET 

default parameters were not used at all in this work because they are a decade older than the 

other results and they are not consistent with the 2015 timeframe of this study.

Default GREET transportation and distribution assumptions were adopted for switchgrass. 

Specifically that bailed switchgrass is transported 40 miles by truck from the field to an F-T 

processing facility in loads of 24 tons.

Table 30: Reported cultivation inputs for switchgrass

Adler et al. 

(2007)1

Vadas et 

al. (2008)2

Schmer et 

al. (2008)3

GREET 

(2008)4

Process Fuels (Btu/ton)

Diesel 82874 113046 107533 201589

Gasoline 0 22609 0 0

Electricity 0 7536 0 15641

Crop Management 

(g/ton)

Nitrogen 5218 11348 7701 10635

P2O5 1236 10387 0 142

K2O 2488 24607 0 226

Limestone 9491 0 0 0

Herbicides 6.4 0 185 28

Notes:

1) Actual fertilizer application rates were only given for nitrogen. All others were 

given in terms of CO2e with application rates calculated using production 

emissions from GREET 1.8b.

2) Actual phosphorous and potassium usage was given in terms of elemental 

weight and converted to P2O5 and K2O using molar mass fractions. Lube oil 

consumed is expressed in terms of diesel equivalent on an energy basis.

3) Data is the average of 10 field scale plots in Nebraska, North Dakota and South 

Dakota.

4) From Oak Ridge National Laboratory in 1998.
39 of 129

Table 31: Cultivation inputs for switchgrass in the low, baseline and high emissions scenarios

Low Baseline High

Process Fuels (Btu/ton)

Diesel 82874 107533 113046

Gasoline 0 0 22609

Electricity 0 0 7536

Nitrogen Fertilizer (g/ton) 5218 7701 11348

Other Fertilizers (g/ton)

P2O5 0 1236 10387

K2O 0 2488 24607

Limestone 0 9491 0

Herbicides 185 6.4 0

6.4.1.3 Nitrous Oxide Emissions

Nitrous oxide emissions can either be estimated using specialty software or through simple IPCC 

emissions factors. Estimates from Adler et al. (2007) using DAYCENT included both direct 

emissions of N2O through nitrification and denitrification processes in the soil and indirect 

emissions of N2O through soil nitrogen losses in forms other than N2O (e.g. NOX, NH3, NO3), 

which were subsequently converted to N2O elsewhere. Conversely, the GREET method employs 

the IPCC 2006 conversion factor for direct and indirect N2O emissions from switchgrass 

production, as shown in using Equation 5.

€

N2O Emissions gN2O

ton

"

#

$ %

&

' = gnitrogen fertilizer

ton

"

#

$

%

&

'⋅ 0.01325⋅ 44gN2O

28gN

"

#

$

%

&

' Equation 5

The N2O emissions from Adler et al. (2007) were found to be 43% higher than those predicted by 

Equation 5 from the same application rate. The principle reason for this discrepancy is that

annual nitrogen in crop residues (above-ground and below-ground) was not included. Estimates 

of the nitrogen deposited on the soil in the form of crop residues was obtained using the IPCC 

Tier 1 methodology for perennial grasses (IPCC, 2006) and implemented through Equation 6. 

€

N2O Emissions gN2O

ton

"

#

$ %

&

' = gnitrogen fertilizer

ton + 6025 "

#

$

%

&

'⋅ 0.01direct + gnitrogen fertilizer

ton

"

#

$

%

&

'⋅ 0.001indirect

"

#

$

%

&

'⋅ 44gN2O

28gN

"

#

$

%

&

'

 Equation 6

where 6025 is the nitrogen in crop residues, 0.01direct is the emissions factor for N2O from nitrogen 

and 0.001indirect is the emissions factor for volatilized NH3 and NOx from synthetic nitrogen 

converted to N2O. The N2O emissions as calculated using the new IPCC methodology are within 

2% of those estimated by Adler et al. (2007) for the same nitrogen application rate. As such, 

Equation 6 was used for all N2O calculations within the switchgrass production pathway.

6.4.1.4 Long Term Soil Carbon Sequestration from Switchgrass

Soil carbon sequestration is a potential strategy for offsetting CO2 emissions to the atmosphere. 

The capacity of perennial energy crops to offset CO2 emissions through soil carbon sequestration 

depends on the rate of soil carbon additions, the long-term capacity of soil for carbon storage, 

and the stability of sequestered soil carbon over time. The greatest potential for long-term soil 

carbon sequestration is in those situations where agricultural practices have led to a progressive 

and historical decline in soil carbon stocks (McLaughlin el al., 2002). Two land use change 

scenarios were considered for switchgrass production. The first is switchgrass grown on 

Conservation Reserve Program (CRP) land and second is switchgrass grown on carbon-depleted 

soils. The carbon contained within soil can become depleted over time as a result of farming 

traditional row crops with conventional tilling practices. The CRP compensates farmers for 

removing lands from crop production for environmental and economic reasons (National 

Academies, 2009). After stopping crop production, the land naturally re-establishes its carbon 

content due to the growth of native perennial grasses; hence, switchgrass grown on CRP land 
40 of 129

was assumed to cause no net change of carbon in the soil. The change in soil carbon resulting 

from switchgrass grown on carbon-depleted soils was based on results from McLaughlin et al. 

(2002) and Adler et al. (2007). 

McLaughlin et al. (2002) developed an empirically derived soil carbon dynamics model that 

estimates soil carbon accumulation rates for contrasting soil types and climates. After accounting 

for regional soil carbon gains by prior cropping history and climatic region, they found the average 

soil carbon sequestration level of traditional cropland converted to switchgrass production to be 

0.21 Mg of C/acre/yr over a 30-year period. These sequestration rates are based on annual rates 

of aboveground production over a 6-year period, ranging from 5.3 to 7.9 tons/acre/yr. The 

variation between individual sites in the fist decade ranged from a minimum of -0.2 Mg of 

C/acre/yr for southeastern pastures to a maximum of 0.57 Mg of C/acre/yr for south-central 

croplands (McLaughlin et al., 2002). Therefore, increased soil carbon from switchgrass cultivation 

is not guaranteed and in some cases a carbon release could occur. 

Adler et al. (2007) also estimated the net change in soil carbon associated with the cultivation of 

switchgrass. As was the case with McLaughlin et al. (2002), the land use change estimates made 

by Adler et al. (2007) were sensitive to initial soil carbon levels, which are influenced by previous 

vegetation cover and land management. To acquire realistic modern soil carbon levels, 1800 

years of native vegetation followed by tree clearing, plowing, and 200 years of cropping were 

simulated. The consequence of this effort is that the initial conditions included legacy effects of 

215 years of conventional tillage cropping. The result of 15-year switchgrass cycle in DAYCENT 

was an average carbon sequestration of approximately 0.17 Mg of C/acre/yr (Adler et al., 2007). 

Changes in soil carbon arising from changes in land use can constitute a major component of the 

life cycle GHG emissions; however, their quantification involves high levels of uncertainty. 

Therefore, a single value was chosen to illustrate the potential impact of land use change 

emissions on this pathway. Specifically, the estimate of 0.17 Mg of C/acre/year presented by 

Adler et al. (2007) was adopted as it represents a conservative value with respect the work of 

McLaughlin et al. (2008).

6.4.2 Corn Stover and Forest Residue

Input assumptions on the energy and emissions associated with the recovery and transportation 

of corn stover and forest residues were based on default GREET assumptions (GREET, 2008). 

Default GREET assumptions were also adopted for the energy and carbon content of corn stover 

and forest residues32 (see Table 97. While forest residues require no fertilizer and their removal is 

assumed to have negligible impact on the surrounding environment, corn stover is usually left on 

the field to replenish soil nutrients and mitigate erosion; however, there is little consensus 

regarding the quantification of the impacts on productivity, soil structure, and nutrient cycling of 

removing stover from the field. In addition to maintaining soil carbon levels, stover is a source of 

nitrogen; hence, synthetic fertilizer would be needed to supply the incremental amount of 

nutrients needed for corn cultivation when stover is removed (GREET, 2007). 

The yield attainable for corn stover is directly proportional to the fraction of stover that must be left 

on the field to mitigate these aforementioned negative impacts. The quantity of available stover 

can then be calculated as the difference between the total stover yield and quantity left on the 

field. The National Academies reported an average yield of corn stover across the United States 

in 2007 of 151 bu/acre/yr33, or 4.24 tons/acre/yr. This yield is based on the assumption of a 1:1 

ratio of dry weight of corn grain to stover (National Academies, 2009). Results from Wilhelm et al. 

(2007) have shown that the stover needed to maintain soil carbon, and thus productivity, is a 

greater constraint to an environmentally sustainable harvest than that needed to control water 

 

32 A significant variation of forest residue properties can be found in the literature, due to different assumptions of the 

type of biomass (e.g. hard wood vs. soft wood), which constitute the forest residues.

33 A bushel of corn or corn stover is defined as 56 lbs at 15.5% moisture content by mass.
41 of 129

and wind erosion. They predicted that 3.4 tons/acre/yr are required to mitigate erosion and soil 

carbon loss with moldboard plowing while only 2.3 tons/acre/yr are required if no-till practices 

were employed. Sheehan et al. (2004) considered the USDA’s regional tolerable soil-loss limits as 

the constraint for leaving stover on the field. They found that 2.2 tons/acre/yr are required with 

conventional tillage while only 1.1 tons/acre/yr are required if no-till practices are employed. 

The corn stover yield adopted in this work was based on the total yield from the National 

Academies (2009) and the constraints for soil carbon and erosion under no-till practices from 

Wilhelm et al. (2007). These resulted in a yield of 1.65 dry tons/acre/yr (equivalent to 1.94 

tons/acre/yr at 15% moisture). The key input assumptions for the recovery and transportation of 

corn stover and forest residues are shown in Tables 32 and 33, respectively.

Although the corn stover yields adopted in this work were chosen to prevent a reduction of the 

natural carbon stock of the soil, an examination of the magnitude of potential land use change 

emissions from improperly harvested corn stover is still relevant. The analysis from Sheehan et 

al. (2004) imposed no constraint on maintaining soil carbon levels and the evolution of carbon 

stored in the soil was a central part of their results. Under maximum stover removal conditions 

subject to maintaining USDA’s tolerable soil constraints, an average emissions rate of 13 kg of 

C/acre/yr was calculated over the first 30 years. The emissions profile over that time period is 

strongly positive for the first 10 years but becomes negative after year 20 as the soil begins to 

approach a new equilibrium state.

Table 32: Input parameters for the recovery and transportation of corn stover

Input parameter1 Value

Yield 1.65 dry ton/acre

Collection Energy 254,190 Btu/dry ton 

(100% diesel fuel use)

Fertilizer Use

Nitrogen

Phosphorus

Potassium

4.50 kg/dry ton

1.63 kg/dry ton

8.35 kg/dry ton

N2O emissions2 13.3 g/dry ton

Transportation distance 30 miles

Truck payload 24 ton

Notes:

1) All input parameters were based on GREET 1.8b (GREET, 2008) 

simulation year of 2015.

2) Includes N2O emissions from nitrification and denitrification in the soil, 

as well as N2O credit from corn stover removal based on GREET 

defaults.34

Table 33: Input parameters for the collection and transportation of forest residue.

Input parameter1 Value

Diesel fuel consumption 3.4 gal/dry ton 

Collection energy 459,200 Btu/dry ton 

Transportation distance 75 miles

Truck payload 17 ton

Note:

1) These input parameters were based on GREET 1.8b (GREET, 2008) 

 

34 Incremental fertilizer use (including nitrogen fertilizer) is accounted for to make up for the loss in soil nutrients from the 

removal of corn stover from the field. On the other hand, if left on the field, a fraction of the nitrogen in corn stover will be 

converted to N2O and emitted from the soil. This N2O emission is avoided when corn stover is removed from the field. In 

this case, the N2O credit from the removal of corn stover slightly outweighs the N2O emissions resulting from the 

incremental application of nitrogen fertilizers, resulting in net negative N2O emissions.
42 of 129

6.4.3 Analysis of Biomass Feedstocks

A sensitivity analysis was carried out to investigate the effect of varying feedstock type 

(switchgrass, corn stover and forest residue) and the influence of changes in soil carbon due to 

switchgrass growth or corn stover removal on the overall life cycle GHG emissions of the BTL 

pathway. A process efficiency of 45% was assumed in all the cases. The results are summarized 

in Table 34.

The sensitivity study shows that the life cycle GHG emissions from the use of switchgrass, 

without considering soil carbon change credit, are the highest of the three feedstocks. This is 

largely due to the large N2O emissions arising from the use of nitrogen fertilizers. However, when 

a soil carbon change credit is included, the life cycle GHG emissions are -0.02 times those of 

conventional jet fuel. Hence, the pathway becomes carbon negative because the CO2

sequestration arising from land use change can dominate the GHG emissions of the BTL 

pathway. Overall, the use of all three types of feedstock in a biomass to liquids facility results in 

significant reductions (~80-100% reduction) of life cycle GHG emissions compared to 

conventional jet fuel.

Table 34: Variation of life cycle GHG emissions of BTL pathway with type of feedstock

Feedstock

Life cycle 

CO2 

emissions 

(gCO2/MJ)

Life cycle 

N2O 

emissions 

(gCO2e/MJ)

Life cycle 

GHG 

emissions 

(gCO2e/MJ)

Compared to 

baseline 

conventional 

jet fuel

Corn stover (without 

soil carbon change 

emissions)

7.8 1.0 9.0 0.10

Corn stover (with soil 

carbon change 

emissions)

12.4 1.0 13.6 0.16

Forest residue 11.7 0.1 12.2 0.14

Switchgrass (without 

soil carbon change 

credit)

7.3 10.3 17.7 0.20

Switchgrass (with soil 

carbon change credit) -12.5 10.3 -2.0 -0.02

Note:

1) Process efficiency of 45% was assumed in all cases.

For the low emissions case, a process efficiency of 52% was assumed, based on the analysis by 

Kreutz et al. (2008) of a 4400 bpd BTL plant. The baseline case assumes a process efficiency of 

45% from the Choren process based on the “self-sufficient basis scenario” where all required 

process energy is provided by biomass feedstock (Baitz et al., 2004). Choren currently operates 

the only commercial-scale BTL plant in the world, producing 300 bpd of F-T liquids. This is further 

confirmed by estimates of roughly a 5% efficiency drop for BTL plants compared to CTL plants 

due to additional processing energy for biomass grinding and drying.35 The high emissions case 

directly applies a 5% drop in process efficiency compared to 47% assumed in the CTL case, 

resulting in a process efficiency of 42%. 

The input assumptions and life cycle GHG emissions for the production and use of F-T jet fuel

from switchgrass with and without a soil carbon sequestration credit are shown in Tables 35 and 

36 respectively. The ‘biomass credit’ represents the CO2 that is absorbed from the atmosphere 

during biomass growth. Note that the CO2 emitted during the combustion of process fuels and the 

F-T fuel is approximately equal to the CO2 absorbed from the atmosphere during growth of the 

biomass feedstock.

 

35 Thomas Tarka, interview with David Ortiz, October 3, 2008.
43 of 129

Table 35: Summary of results for F-T jet fuel from switchgrass with no soil carbon sequestration

Land Use Change Scenario B0 Low Baseline High

Key Assumptions

Feedstock Switchgrass Switchgrass Switchgrass

Process Efficiency (LHV) 0.52 0.45 0.42

Life Cycle CO2 Emissions by Stage

Biomass Credit (gCO2/MJ) -192.7 -222.7 -238.6

Recovery of feedstock (gCO2/MJ) 3.8 6.4 11.4

Transportation of feedstock (gCO2/MJ) 0.6 0.6 0.6

Processing of feedstock to fuel (gCO2/MJ) 122.1 152.1 168.0

Transportation of jet fuel (gCO2/MJ) 0.5 0.5 0.5

Combustion CO2 (gCO2/MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) -65.8 -63.1 -58.2

WTT CH4 emissions (gCO2e/MJ) 0.1 0.2 0.5

WTT N2O emissions (gCO2e/MJ) 7.2 10.3 13.3

Total WTW GHG emissions (gCO2e/MJ) 11.9 17.7 26.0

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.14 0.20 0.30

Table 36: Summary of results for F-T jet fuel from switchgrass with soil carbon sequestration

Land Use Change Scenario B1 Low1 Baseline1 High1

Land use change emissions (gCO2/MJ) -13.6 -19.8 -30.5

WTW CO2 emissions (gCO2/MJ) -9.0 -12.5 -18.2

Total WTW GHG emissions (gCO2e/MJ) -1.7 -2.0 -4.4

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel -0.02 -0.02 -0.05

Notes: 

1) All other input assumptions (cultivation of switchgrass, F-T processing and carbon 

capture efficiency) are based on those in the B0 emissions case of the 

corresponding scenario.

The life cycle GHG emissions of the BTL pathway using switchgrass as a feedstock are 0.14 

times to 0.3 times those of conventional jet fuel with no soil carbon sequestration. When the soil 

carbon sequestration credit is included, the emissions from the pathway range from -0.02 to -0.05 

times those of conventional jet fuel. The larger value for land use change emissions in the high 

scenario occurs due to the lower assumed yield per acre.

Nitrous oxide emissions represent more than 50% of the total life cycle GHG emissions from the 

switchgrass to F-T jet pathway. As a result, life cycle GHG emissions for this pathway are 

strongly influenced by any uncertainty associated with the IPCC correlations that were used in the 

nitrous oxide estimates. The reader should be aware of these inherent uncertainties when 

comparing different pathways for GHG reduction potential in Sections 8.1 and 8.2. 

6.5 Fischer-Tropsch Fuel from Coal and Biomass

While both CTL and BTL hold promise as alternative jet fuels, they also have flaws. Even with 

85% carbon capture, a CTL plant has life cycle GHG emissions over 110% of conventional jet 

fuel (i.e., the life cycle GHG emissions are 10% higher than conventional jet fuel); without carbon 

capture, CTL has 220% of the emissions of conventional jet fuel. If the goal is to reduce GHG 

emissions, then coal alone appears to be a poor choice. Biomass-to-liquids plants without carbon 
44 of 129

capture have life cycle GHG emissions that are less than 10% of conventional jet fuel; however, 

considerable logistical challenges exist in obtaining sufficient biomass to operate at large scales 

because of the relatively low energy density of biomass. Because current F-T plant designs are 

capital intensive, it is not economically feasible to build many small plants that are dispersed 

among the regions where biomass is being grown. Biomass must therefore be accumulated from 

considerable areas and transported to large central plants; hence, the infrastructure to move the 

biomass becomes a limiting factor. Emissions from the transportation of the biomass to the 

processing facility are included in the life cycle analysis, but represent a negligible fraction of the 

total emissions. Since both biomass and coal are processed into an F-T fuel using similar 

technology, they could be processed at a single F-T plant. The biomass offsets the high 

emissions from coal and coal offsets the low energy density and production limitations of 

biomass. A coal and biomass to liquid (CBTL) plant with carbon capture and storage (CCS) was 

considered in this analysis. A configuration without CCS was examined as a case study; 

however, the focus herein is on a configuration with CCS as the primary goal is to reduce GHG 

emissions.

The coal and biomass can be gasified either in parallel with the syngas streams being mixed 

afterwards, or in the same unit (co-gasification). Since the parallel configuration is a superposition 

of the results of sections 6.3 and 6.4, this section examines co-gasification. Parallel processing 

also requires additional infrastructure, as separate gasifiers are needed for each feedstock. Cogasification was assumed to occur in an entrained flow gasifier. Such technology is already 

commercially available for large scale processing of coal and biomass (van der Drift et al., 2004). 

Before entering the gasifier, biomass must be milled down to particles of diameter 1mm or less.

Currently, the most energy efficient method of milling the biomass is via torrefaction pretreatment, which is a mild thermal treatment where the biomass is heated to ~250°C yielding a 

solid uniform product with lower moisture content and higher energy content. Studies have shown 

that torrified wood can be milled to the required size using only 10-20 kWelectricity/MWbiomass and 

that capacity expansion factors between 2 and 6.5 can be achieved (van der Drift et al., 2004; 

Bergman et al., 2005). Moreover, torrified particles can be pneumatically transportable, which is 

considered impossible for a bed of untreated biomass particles. Efficiencies for torrefaction range 

from 85% to 97%, with 90% assumed in the baseline case (Bergman et al., 2005).

Because of the pre-processing of biomass, the overall F-T plant efficiency depends on the weight 

percent of biomass that is being co-gasified. This study explored a range from 10% to 40% 

biomass feed with 40%, 25% and 10% chosen for the low, baseline and high emissions cases, 

respectively. Due to the CO to H2 ratio resulting from gasification, feeds with biomass weight 

fractions below 50% lead to syngas favorable for methanol and hydrocarbon fuels whereas 

biomass weight fractions above 50% lead to syngas favorable for dimethyl ether (DME) 

synthesis. Additionally, higher biomass feeding rates lead to the formation of less char and tars 

(Kumabe et al., 2007). CTL plant efficiencies were modified to account for the extra power 

consumption of pre-processing the biomass; hence, the reduction of the CTL plant efficiency, 

ηCTL, depends on both the fraction of biomass feed and the torrefaction efficiency. The plant is 

assumed to be self sufficient in terms of electricity production with no extra power exported to the 

grid. Higher efficiencies could be possible if additional electricity were generated for grid export. 

The implementation for CCS comes at the cost of 250kWh per ton of carbon captured, as was the 

case with CTL. This energy requirement is also accounted for in the CBTL process efficiency 

plant. In total, the CBTL plant efficiency was examined as a function of CTL efficiency, biomass 

weight percentage, torrefaction efficiency, biomass grinding energy and CCS efficiency, as 

expressed in Equation 7.

€

ηCBTL = f ηCTL, biomass wt%, ηtorrefaction ( , biomass grinding energy, ηCCS ) Equation 7

Assumptions about feedstock type are similar to those made when coal and biomass were 

considered separately. Specifically, surface-mined sub-bituminous coal and switchgrass were 

assumed in the low emissions case; average US coal from 2007 (mix of underground-mined and 
45 of 129

surface-mined bituminous coal and surface-mined sub-bituminous coal36; anthracite or lignite coal 

is not considered) and switchgrass were assumed in the baseline case and underground-mined 

bituminous coal (from case 1 of Southern States Energy Board CTL study, SSEB, 2005) and 

switchgrass were assumed in the high case. Sub-bituminous coal is used in the low emissions 

case because it reduces GHG emissions by increasing the fraction of input energy provided by 

biomass for a given biomass/coal mass ratio. Input assumptions for the combined coal and 

biomass pathway are summarized in Table 37.

Table 37: Input assumptions for the production of F-T jet fuel from coal and biomass (with carbon capture) 

for low emissions, baseline and high emissions cases

Low Baseline High

CTL Process Efficiency 

(LHV) 53% 50% 47%

Biomass Weight Fraction 40% 25% 10%

Carbon Capture Efficiency 90% 85% 80%

Carbon Compression Energy 250 kWh/ton C 250 kWh/ton C 250 kWh/ton C

Torrefaction Efficiency 97% 90% 85%

Coal Input

Surface-mined 

sub-bituminous 

coal

Average bituminous 

and sub-bituminous 

coal mix

Undergroundmined bituminous 

coal

Coal Mining Methane 

Emissions (gCO2e/MJCoal) 0.8 2.8 6.4

Biomass Type Switchgrass Switchgrass Switchgrass

6.5.1 Allocation Methodology 

As previously stated, an F-T facility can produce a wide product slate. For example, the study 

carried out by NETL on F-T diesel from CBTL assumed an output of 70% diesel and 30% 

naphtha (Tarka, 2009). In this analysis, F-T jet fuel is the product of interest but does not consist 

of more than roughly a third of the total plant output to prevent excessive naphtha production.

Since other F-T fuels are made as a result of producing F-T jet fuel, the emissions from 

processing and all other upstream activities must be allocated among the fuels being produced.

Even when configured to make jet fuel, diesel fuel will likely be the primary output of the F-T 

facility due to economic drivers. For this reason, it is sensible to allocate emissions among the 

liquid products (i.e., jet fuel, diesel fuel, and naphtha) on the basis of their respective energy 

content.

Figure 8 demonstrates problems that could result from using the displacement method to assign 

biomass ‘credits’ to a fuel that is not the primary product. When the yield of jet fuel is 25% by 

volume, there are 3 liters of other F-T fuels produced for every 1 liter of jet fuel. When the yield of 

jet fuel is reduced to 5% by volume, there are 19 liters of other F-T fuels produced for every 1 liter 

of jet fuel. In the limit where the yield of jet fuel approaches zero, the quantity of other F-T fuels 

asymptotically approaches infinity. If the other F-T fuels represent a reduction in emissions as 

compared to their petroleum equivalents then the displacement method attributes these 

emissions reductions to the jet fuel; therefore, the displacement methodology results in a jet fuel 

which appears to have life cycle emissions that approach negative infinity as the yield of jet fuel is 

reduced towards zero. The results from Figure 9 show that an energy allocation scheme prevents 

a product that is responsible for a third, or less, of total output from the facility receiving the 

emissions ‘credit’ from the biomass used as feedstock for the entire product slate.

 

36 Compared to 2007, the coal production mix in 2017 is projected to comprise a larger proportion of surface-mined subbituminous coal from Western coal production, particularly the Powder River Basin. (EIA, 2008d)
46 of 129

Figure 9: The effects of product slate composition on life cycle greenhouse gas emissions from F-T jet fuel

The diverging effect resulting from use of the displacement method is unavoidable because jet 

fuel is not the primary product from the F-T plant. This highlights the important conclusion that life 

cycle GHG emissions from a given fuel are as much a function of subjective choices of the 

analyst/operator regarding allocation methodologies, as they are of specific production 

characteristics and process inputs. In order to maintain consistent results, which cannot be 

skewed by choices, such as what is the product slate that is being selected, energy allocation 

was chosen. The results using this method come with the caveat that for each unit of jet fuel 

produced, substantial volumes of other fuels are also produced, such as diesel and naphtha, 

which could also carry environmental benefits. The specific product slate considered in this 

analysis was 25% F-T jet fuel, 55% F-T diesel and 20% F-T naphtha.

6.5.2 Results: F-T Jet Fuel from Coal and Biomass

Local sensitivity analysis was conducted on the feedstock type, the quantity of GHG emissions 

from land use change, CTL process efficiency, biomass weight percentage, torrefaction 

efficiency, CCS efficiency and CCS compression energy. Each parameter was varied with all 

others held at their baseline values with the impact on life cycle GHG emissions quantified as a 

percent change from the baseline value. Figure 10 presents this information in a manner that 

allows the magnitude of each change to be seen in comparison to the others. The biomass feed 

rate has the dominant influence. Since the biomass feed rate is a parameter that is chosen by the 

operator, the emissions from CBTL facilities will be dictated by practical, as opposed to 

technological, limitations. Overall, the choice of feedstock and the potential for soil carbon 

sequestration were found to have a larger impact on life cycle GHG emissions than the process 

efficiencies.

When examining the impacts of changing the plant efficiency (this can be achieved either directly 

or by changing the energy consumption for biomass pre-processing or carbon dioxide 

compression) it was found that lower efficiencies lead to lower net emissions. This counterintuitive result occurs because the plants were chosen to be self-sufficient and CCS is used to 

capture emissions from gasifying additional biomass to supply syngas for process fuel. The 

capture and sequestration of carbon contained in the biomass leads to a net carbon removal from 

the atmosphere and hence having less biomass converted to fuel results in more of the carbon in 

the biomass going to sequestration. 

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Figure 10: Sensitivity analysis of operational specifications and configurations of F-T jet fuel from coal and 

biomass

The results for the low emissions, baseline and high emissions scenarios for CBTL using 

switchgrass as the biomass feedstock are shown in Tables 38 and 39. Table 38 gives the results 

when no soil carbon sequestration credit is given to the switchgrass while Table 39 gives the 

results when the soil carbon sequestration credit is included. The ‘biomass credit’ represents the 

CO2 that is absorbed from the atmosphere during biomass growth. Increasing biomass credit 

reflects larger amounts of biomass being used as feedstock.

Table 38: Summary of results for F-T jet fuel from coal and biomass without soil carbon sequestration credit

Land Use Change Scenario B0 Low Baseline High

Key Assumptions

Biomass Weight Fraction 40% 25% 10%

Biomass Input Switchgrass Switchgrass Switchgrass

Carbon Capture Efficiency 90% 85% 80%

CBTL Process Efficiency (LHV)

48.9%

(53% without 

CCS or 

biomass 

processing)

46.0%

(50% without 

CCS or 

biomass 

processing)

44.1%

(47% without 

CCS or 

biomass 

processing)

Life Cycle CO2 Emissions by Stage

Biomass Credit (gCO2/MJ) -78.6 -44.3 -15.3

Recovery of feedstock (gCO2/MJ) 1.2 1.2 1.1

Transportation of feedstock (gCO2/MJ) 0.3 0.2 0.1

Processing of feedstock to fuel (gCO2/MJ) 14.7 21.9 28.6

Transportation of jet fuel (gCO2/MJ) 0.5 0.5 0.5

Combustion CO2 (gCO2/MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) -62.0 -20.5 14.9

WTT CH4 emissions (gCO2e/MJ) 1.1 4.9 13.6

WTT N2O emissions (gCO2e/MJ) 2.9 2.0 0.9

Total WTW GHG emissions (gCO2e/MJ) 12.4 56.9 99.8

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.14 0.65 1.14

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48 of 129

Table 39: Summary of results for F-T jet fuel from coal and switchgrass with soil carbon sequestration credit

Land Use Change Scenario B1 Low1 Baseline1 High1

Land use change emissions (gCO2/MJ) -5.5 -3.9 -2.0

WTW CO2 emissions (gCO2/MJ) 2.9 46.0 83.4

Total WTW GHG emissions (gCO2e/MJ) 6.9 53.0 97.8

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.08 0.61 1.12

Notes: 

1) All other input assumptions (cultivation of switchgrass, F-T processing and carbon capture 

efficiency) are based on those in the B0 emissions case of the corresponding scenario.

The life cycle GHG emissions of the CBTL pathway range from 0.14 to 1.14 times those of 

conventional jet fuel when no soil carbon sequestration credit is given. The emissions for this 

pathway range from 0.08 to 1.12 times those of conventional jet when the soil carbon 

sequestration credit is included. The large range of this pathway is primarily driven by the 

variation in biomass weight fraction of the feedstock.

In their assessment of F-T diesel production, Tarka (2009) used a displacement (system 

expansion) scheme instead of energy-based allocation to account for the benefit of making a 

reduced carbon, biomass-based F-T naphtha in addition to the F-T diesel. As such, their results 

differ from those given here. When a common allocation approach and productions assumptions 

are implemented, both analyses yield similar results. 

Figure 11: Dependence of cumulative life cycle emissions and biomass requirements for varied biomass

utilization within CBTL

Figure 11 presents the implication of varying biomass weight over a range of 5% to 45%. Life 

cycle GHG emissions can be reduced to a fraction of conventional jet fuel with considerable 

biomass usage. For example, provided sufficient CCS is available, a CBTL jet fuel created from 

45% biomass could have life cycle GHG emissions that are only 20% of conventional jet fuel; 

however, roughly 245 railroad cars of biomass would be needed every day to create sufficient 

CBTL jet fuel to power the aircraft at Boston Logan airport.37 This large amount of biomass 

 

37 6,500 tonnes of biomass would be needed per day to provide 25,000 barrels per day of jet fuel at a biomass feed rate 

of 45% (this is roughly the consumption of Boston Logan Airport). A typical railroad car can carry 26.5 tonnes of biomass 

(Mahmudi, 2006).

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highlights the importance of considering GHG reductions for high biomass weight percentages in 

conjunction with biomass feeding requirements; it also points to lower biomass percentages being 

more realistic. Future work will consider the economics of CBTL fuels.

6.5.3 Case Study: Impact of Carbon Capture on GHG Emissions from CBTL Facilities

The analysis presented above assumed the use of CCS to reduce GHG emissions. Two 

additional cases were examined to emphasize the importance of using CCS to reduce GHG

emissions from CBTL facilities. The first is the baseline CBTL case without CCS and without soil 

carbon sequestration. The second is the baseline CBTL case without CCS where the biomass 

feed rate has been adjusted such that the life cycle GHG emissions are on parity with 

conventional jet fuel.

A comparison of results from the baseline scenario with and without CCS is given in Table 40. 

Not having CCS leads to an increase in GHG emissions of 106.1 gCO2/MJ, such that the WTW 

emissions from the CBTL pathway become 186% of conventional jet fuel. Without CCS, one 

needs to use 70% biomass, by weight, for F-T fuel from coal and switchgrass to reach GHG 

parity with conventional jet fuel. This understates the importance of CCS to getting reduced GHG 

emissions from CBTL fuels.

Table 40: Life cycle GHG emissions from F-T jet fuel from coal and switchgrass with and without CCS

Baseline Baseline

(w/o CCS)

Biomass for 

GHG Parity (w/o

CCS)

Key Assumptions

Biomass Weight Fraction 25% 25% 70%

Biomass Input Switchgrass Switchgrass Switchgrass

Carbon capture efficiency 85% 0% 0%

Life Cycle CO2 Emissions by Stage

Biomass credit (gCO2/MJ) -44.3 -42.0 -141.1

Recovery of feedstock (gCO2/MJ) 1.2 1.2 1.9

Transportation of feedstock (gCO2/MJ) 0.2 0.2 0.4

Processing of feedstock to fuel (gCO2/MJ) 21.9 126.1 146.5

Transportation of jet fuel (gCO2/MJ) 0.5 0.5 0.5

Combustion CO2 (gCO2/MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) -20.5 86.0 8.3

WTT CH4 emissions (gCO2e/MJ) 4.9 4.7 2.3

WTT N2O emissions (gCO2e/MJ) 2.0 1.9 6.5

Total WTW GHG emissions (gCO2e/MJ) 56.9 163.0 87.5

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.65 1.86 1.00
50 of 129

7 Hydroprocessed Renewable Jet Fuel from 

Renewable Oils

Renewable oils can be processed into a fuel that has properties similar to those of F-T fuels. The 

processing involves hydrotreatment to deoxygenate the oil with subsequent hydrocracking to 

create hydrocarbons that fill the distillation range of jet fuel (Hileman et al., 2009). This work 

examined the life cycle GHG emissions from the production and use of Hydroprocessed 

Renewable Jet fuel (HRJ) from soybean oil, palm oil, rapeseed oil, algae oil, jatropha oil and 

salicornia oil. As of the writing of this report, the production of hydroprocessed jet fuel from any oil 

feedstock is still limited to quantities suitable for flight-testing. Numerous flight tests have been 

successfully conducted of fuel composed of 50% HRJ from mixes of jatropha, algae and camelina 

blended with conventional jet fuel. 

In all cases, HRJ will have to compete with hydroprocessed renewable diesel (HRD) and 

biodiesel for feedstock availability. Biodiesel is currently the only biofuel produced at commercial 

scales from renewable oils; however, facilities to hydroprocess renewable oils are being built 

worldwide with a production capacity of nearly 60,000 bbl/day (Hileman et al., 2009). While 

biodiesel is not appropriate for use in gas turbines at any blending ratio, biodiesel production can 

be used to understand the resource potential of renewable oils for biofuel because it is made from 

the same feedstock. Soy and palm oil were examined using GREET version 1.8a whereas all 

other HRJ pathways used GREET version 1.8b.

Soybean oil is of interest as it is used extensively in the US and Europe for biodiesel production. 

The US and Europe used a total of 48,700 bbl/day (2.6 million metric tons per year) of soy oil in 

2009 for biodiesel, which represents only 20% of total soy oil use in these regions. Similarly, 

rapeseed oil is the main feedstock used for biodiesel production in Europe. Approximately 35% of 

all rapeseed oil used in Europe went to biodiesel, amounting to 58,000 bbl/day (3.1 million metric 

tons per year). Palm oil, on the other hand, has grown to become the most produced oil in the 

world. Its production has increased rapidly in the past 20 years and the production of palm oil 

surpassed soy oil for the first time in 2006. Almost 90% of global palm oil production occurs in 

Indonesia and Malaysia (FAPRI, 2010). Biodiesel production is expanding based on the palm oil 

resources in this area. Malaysia has issued approval of 91 companies to build domestic palm oil 

based biodiesel plants; however, total planned capacity of the entire region is still less than

30,000 bbl/day (1.5 million metric tons per year) (Planet Arc, 2007; Forbes.com, 2007; Mission 

Biofuels Limited, 2007). 

Soybeans, palm and rapeseed are edible food crops requiring fertile cropland for cultivation. The 

use of these crops in fuel production could result in direct or indirect land use change emissions. 

As noted previously in the text, large uncertainties exist in estimating the GHG emissions from 

both direct and indirect land use change. This work only quantifies direct land use change 

because the economic models required to analyze indirect effects are beyond the scope of this

effort. Similar to the treatment of switchgrass in Sections 6.3 and 6.4, the following sections 

consider a range of direct land use change scenarios using multiple land conversion scenarios to 

establish upper and lower bounds for these values. These scenarios were created based upon 

existing data from the literature. Since indirect land use change emissions are the integrated 

impact of direct land use change resulting from increased crop prices, the range of direct land use 

change emissions presented herein should bracket any potential indirect land use change 

emissions occurring because of these feedstocks.

Jatropha curcus is a small tree or large shrub, up to 5-7 meters tall, which can grow without 

irrigation in a broad spectrum of rainfall regimes. All subsequent references to jatropha are with 

regard to jatropha curcas. Under normal conditions, the jatropha plant flowers only once a year 

during the rainy season; however, in permanently humid regions or under irrigated conditions it 

can be made to flower almost all year round (Achten et al., 2008). The resulting fruit is composed 
51 of 129

of an outer capsule containing two or three seeds. Each seed has a shell and a kernel, which 

contains oil. Jatropha plants have higher oil yields than many other oil yielding crops; however, 

the husk and the seed shells result in more co-product per unit mass of jatropha oil than both 

algae and palm fresh fruit bunches.

The potential for jatropha was recently demonstrated by successful gas turbine test flights using a 

hydroprocessed mix of jatropha and other oils. Jatropha cultivation is ideal for regions of the 

world with the highest rates of poverty and plenty of hot, dry land because it is well adapted to the 

tropics and subtropics. India has been recently pushing to expand biodiesel production from 

jatropha oil; the government has announced plans to subsidize an intensive program to plant 

jatropha for biofuels on 27 million acres of “wastelands”. Despite these recent efforts, global 

production in 2008 was limited to 242 cultivation projects, amounting to only 2.2 million acres. 

Furthermore, most jatropha grown for biofuels is cultivated locally on plots of less than 12 acres

(Yale Environment 360, 2009). The major limitations to expanding from local cultivation to large 

scale production is that farmers are in poverty and only allow jatropha to grow on their land 

because of its ability to grow in the arid conditions. In areas where jatropha is not naturally 

occurring, farmers do not wish to take the initiative to restructure their farms to prevent other 

revenue generating species from dominating the natural growth of jatropha. New plantations 

takes 3-5 years for jatropha production mature such that it can be grown commercially and 

farmers don’t have the capital to invest in projects where the potential for revenue is several 

years away. Due to the general poverty of the regions where jatropha is considered promising, 

there is little to no loan availability or presence of long term contracts to guarantee a return on 

their investment (Mani, 2010). While it appears that jatropha may be an appropriate solution to 

provide fuel to the villages in which it is grown, there is currently minimal evidence to indicate that 

jatropha will expand beyond the village level to become an energy resource on the global scale. 

Algae were first examined as a biofuel feedstock by the Department of Energy during the Aquatic 

Species Program (ASP) from 1978 to 1996. The ASP focused most of its attention on identifying 

a specific factor that would stimulate the algae to have a high lipid weight fraction. Much 

discussion still surrounds the possibility of genetically modifying certain strains of algae to 

produce more oils; however, the present analysis focuses only on previously documented strains 

that currently exist. Furthermore, it is important to differentiate between micro-algae and macroalgae. Microalgae, as the name suggests, are tiny organisms which grow in water with 

concentrations ~0.2-0.4g/L and have the appearance of tinting the water green; these are the 

algae considered in this work. Macro algae are the classical long strands that grow on the bottom 

of ponds and lakes, (a.k.a. seaweed). While some research has been conducted using macroalgae as a fuel source, it is not considered further in this work; hereafter, algae will refer to 

microalgae.

No company is currently producing commercial quantities of algal oil for use as a transportation 

fuel; instead they are producing quantities that are appropriate for various stages of research and 

development. Furthermore, the industry is not sufficiently defined to have well defined best 

practices for cultivating and extracting the oil from the algal cells. This report examines relatively 

conventional methods of oil extraction wherein the algae are dried prior to chemical treatment; 

however, several research efforts are ongoing to evaluate whether the oil could be extracted via 

electrical pulses or if the algae can be genetically modified to naturally excrete oil. Although no 

commercial scale production of algae currently exists, significant investments are being made into 

developing it as a feedstock for alternative fuels. For example, the Department of Defense 

recently ordered 20,000 gallons of algae fuel from Solazyme at a cost of $8.5 million USD ($425 

per gallon) for advanced testing as F-76 Naval Distillate (Green Car Congress, 2009). This is a 

relatively small sum compared to the 600 million US$ that Exxon Mobil has invested in research 

and development of algal transportation fuels (New York Times, 2009). 

Halophytes are considered an oil seed crop for fuel production because they prosper in sea or 

brackish water on marginal lands. Other benefits of halophyte agriculture include freeing up 

arable land for freshwater resources, cleansing the environment, decontaminating soils, 
52 of 129

desalinating brackish waters and soil carbon sequestration. Considering that 43% of the earth’s 

landmass is arid or semi arid and 97% of the earth’s water is seawater (Hendricks and Bushnell, 

2008), halophytes are an attractive option for large-scale production. From the halophyte family, 

Salicornia bigelovii emerged as an oilseed crop from a screening of wild halophytes and was 

selected for seawater trials due to its seed yield and oil fraction (Anwar et al., 2002). All further 

references to halophyte or salicornia refer specifically to Salicornia bigelovii.

The development of salicornia for fuel production is still in the experimental stage; no fuel testing 

has been conducted on fuel produced from the salicornia plant. Despite our inexperience, a major 

UNFCCC project in the Sonoran desert has been cultivating salicornia on 30 hectares of coastal 

land since 1996 with the goal of developing cost-effective cultivation processes on a commercial 

basis, and optimizing agronomic methods for irrigation and harvesting (UNFCCC, 1998). The 

GCC (Cooperation Council for the Arab States of the Gulf) countries have also established 

research programs for halophytes in the form of an experimental 200-hectare integrated seawater 

agricultural system near Abu Dhabi. The Masdar Institute of Science and Technology (MIST) is 

collaborating with UOP, Boeing and Etihad Airways to realize this project (Green Car Congress, 

2010).

7.1 Energy Requirements for HRJ from Renewable Oils

The hydrotreatment process for the production of HRJ from renewable oils was based on the 

UOP hydrodeoxygenation process, which primarily produces “green diesel” (Marker et al., 2005; 

Huo et al., 2008). Other techniques are available to produce hydrocarbon fuels from renewable 

oils beyond the approach developed by UOP; however, the UOP process is currently the most 

established for jet fuel production. Similar to F-T fuels, additional hydroprocessing is needed for 

the production of jet fuel instead of diesel, resulting in increased hydrogen and power 

requirements. The assumption that additional processing requirements for F-T jet fuel relative to 

diesel are negligible is justified in the literature (Gray et al., 2007); however, in the case of HRJ, 

using diesel as a surrogate for jet is only appropriate for crude estimates.

The UOP process used for the creation of Hydroprocessed Renewable Diesel (HRD) is described 

by the following chemical reaction:

€

CnH2n +1COOH + 3H2 →Cn +1H2n +4 +2H2O Equation 8

where n is the carbon chain length of the fatty acids within the triglyceride molecules used as a 

feedstock for the process. Triglycerides are formed from a single molecule of glycerol, combined 

with three fatty acids. This simplified analysis assumed that all renewable oils, regardless of type 

(soy, pal, rapeseed, jatropha, algae or salicornia) are identical and contain only fatty acids with a 

carbon chain length of 18. The error introduced by this assumption is likely small compared to the 

uncertainty in quantifying cultivation inputs for each feedstock; however, not all oils are chemically 

equal and this analysis does not reflect the physical properties of any single oil type.

Table 41 presents the actual distributions of fatty acid carbon chain lengths for most of the oils 

considered in this work.38 An example of a shortcoming of the assumption used in this analysis is 

the treatment of oils containing unsaturated carbon chains. The presence of double bonds within 

a carbon chain would lead to additional hydrogen consumption during the deoxygenation process 

in order to saturate the molecule, however, such effects are ignored herein.

 

38 The notation for denoting carbon chain length and number of double bonds is (chain length):(number of double bonds). 

For example, a chain length of 18 with 2 double bonds is expressed as 18:2.
53 of 129

Table 41: Component fatty acid profiles for renewable oils considered in this work

Fatty Acid Components 

(weight %)

Caprylic (8:0)Capric (10:0)Lauric (12:0)Myristic (14:0)Palmitic (16:0)Palmitoleic (16:1)Stearic (18:0)Oleic (18:1)Linoleic (18:2)Linolenic (18:3)Arachidic (20:0)Gadoleic (20:1)Behenic (22:0)Erucic (22:1)

Soybean — — — — 11 — 4 22 53 8 — — — —

Palm — — — — 44 — 4 39 11 — — — — —

Palm Kernel 3 7 47 14 9 — 1 19 1 — — — — —

Rapeseed (B. campestris) — — — — 4 — 2 33 18 9 — 12 — 22

Rapeseed (B. napus) — — — — 3 — 1 17 14 9 — 11 — 45

Jatropha curcas3 — — — — 13 — 8 45 34 — — — — 1

Salicornia bigevolii4 — — — — 7 — 3 18 73 — — — — —

Notes:

1) Unless otherwise indicated, this information comes from: DeMan et al. (1999)

2) Additional data for fatty acid components of coconut oil, canola oil, sunflower oil, 

cottonseed oil, peanut oil, olive oil, mustard seed, lard, yellow grease is also available 

from DeMan et al. (1999)

3) From Shweta et al. (2004)

4) Salicornia oil is similar to safflower oil in fatty acid composition (Glenn et al., 1998). This 

profile is averaged values for safflower oil from: Coşge et al. (2007)

Equation 8 can be re-written in a mass balance form for easier comparison to experimental data.

1.12 lb Oil + 0.024 lb Hydrogen → 1.00 lb HRD + 0.14 lb Water

The feedstock, key products and process energy needed per pound of HRD are summarized in 

Table 42. 

Table 42: Experimental and theoretical requirements for the creation of renewable diesel

Experimental

Feedstock (lb) Low Baselin

e High

Theoretica

l

Oil 100 100 100 100

H2 (51586 Btu/lb) 1.5 2.72 3.8 2.14

Key Products (lb)

HRD (18908 Btu/lb) 83.0 84.19 86 89.28

Propane Mix Gas (18568 Btu/lb) 2.0 4.75 5 0.00

Process Energy (Btu)

Electricity 5785 6942 8099 --

Natural Gas 8950 8950 8950 --

Notes:

1) Steam is assumed to be produced from natural gas at 80% efficiency

2) Energy contents are taken from GREET (2008)

3) Experimental data taken from Appendix 2 of Huo et al. (2008) with 

modifications per recommendations from UOP (Kalnes, 2009)

Further refinement of HRD is required for the creation of HRJ. The strategy to estimate the 

process requirements of HRJ was to use the experimental data for the creation of HRD and 

subsequently estimate the additional requirements to convert the HRD into HRJ. For this 

analysis, HRD and HRJ are assumed to be symmetrical distributions of straight chains 
54 of 129

hydrocarbons centered on C12 and C18 respectively. As shown schematically in Figure 11, the 

cracking from diesel fuel to jet fuel was assumed to occur via the addition of gaseous hydrogen.

Figure 12: Schematic showing the change in hydrocarbon composition between HRD and HRJ fuels that 

results from additional hydroprocessing.

The mechanism by which hydrocarbon chains crack is through smaller molecules, (e.g., pentane 

(C5H12) and hexane (C6H14)) breaking off the end. The dominant effect that takes place is the 

reduction of C18 to C13 by cracking pentane and C12 by cracking hexane (Kalnes, 2009). Other 

reactions are also occurring where molecules from the distribution about C18 crack to those from 

the distribution about C12. To account fully for this effect would entail including the statistical 

nature by which chemical reactions are more likely to take place. In keeping with the level of 

detail required of this analysis, the aforementioned effects were assumed to cancel out if the two 

distributions have the same shape about their mean.

Making use of these arguments simplifies the analysis to two chemical reactions converting diesel 

fuel to jet fuel:

€

C18H38 + H2 →C12H26 +C6H14

C18H38 + H2 →C13H28 +C5H12

2C18H38 + 2H2 →C12H26 +C13H28 +C5H12 +C6H14

 Equation 9

Written in mass balance form and normalized for one lb of HRD, the overall equation governing 

the formation of HRJ from renewable oil can be expressed as:

1.00 lb HRD + 0.0079 lb Hydrogen → 0.697 lb HRJ + 0.311 lb Naphtha

Although variations in carbon chain length are not captured in this analysis, fatty acids with 

carbon chain length distributions around 12 are better suited for use as feedstock for jet fuel 

because higher blending percentages can be used without the need for hydrocracking. 

Furthermore, carbon chain lengths closer to 12 would result in a higher yield of jet fuel per unit 

mass of oil input. 

Naphtha in this case is a combination of 46 percent C5H12 and 54 percent C6H14 by mass. Using 

these ratios of HRD to HRJ, the process energies from Table 42 were modified to reflect the 

energy requirements to create HRJ as shown in Table 43. Based on discussions with experts at 

UOP (Kalnes, 2009), it is assumed that total process energies (natural gas and electricity) will 

increase by 10 to 30 percent per pound of renewable feedstock when including the hydrocracking 

required for the formation of HRJ. The total hydrogen consumption is the sum of that needed to 

first make HRD and then to crack it to HRJ. In all cases where renewable oils are processed into 

finished fuel products, energy and emissions were allocated based on energy content.
55 of 129

Table 43: Energy requirements for the creation of HRJ

Emissions Scenarios

Feedstock (lb)

Low Baselin

e

High

Oil 100 100 100

H2 (51586 Btu/lb) 2.15 3.38 4.48

Key Products (lb)

HRJ (18950 Btu/lb) 57.8 58.7 59.9

Naphtha (19215 Btu/lb) 25.8 26.2 26.8

Propane Mix Gas (18568 Btu/lb) 2.0 4.8 5.0

Process Energy (Btu)

Electricity 6364 8330 10529

Natural Gas 9845 10740 11635

The hydroprocessing step of converting renewable oil into HRD has a mass yield of 84% and 

results in 8.7 gCO2/MJ. After making the aforementioned changes, hydroprocessing renewable oil 

into HRJ has a mass yield below 60% and results in 10.3 gCO2/MJ. The emissions associated 

with hydrogen production in this work are representative of steam reforming natural gas and are 

consistent with default GREET assumptions. While these results provide a first approximation of 

mass and energy inputs, they do not reflect the impact of oil composition on process inputs or 

differences in real world production scenarios. 

The decrease in yield is accompanied by an increase in naphtha production. Although naphtha is 

used as a blending stock in gasoline, it has a lower economic value than HRD, which is a high 

performance diesel fuel. Because petroleum refineries have on-site hydrogen production to meet 

internal demands that could be expanded to supply the hydrogen for hydroprocessing renewable 

oils, hydroprocessing facilities would likely have lower emissions if they were integrated into 

existing petroleum refineries. With such a configuration, excess naphtha resulting from HRJ 

production can be integrated into the refinery naphtha stream and catalytically reformed to high 

value hydrocarbons or steam reformed to supplement the internal hydrogen needs of the 

process. However, if the value of the HRJ and naphtha stream is less than the HRD stream, a 

fuel producer would likely focus on HRD production unless they could charge a premium for HRJ 

fuel. Under such a situation, the aviation community could consider using HRD as a dilute blend 

stock in gas turbine engines (~10%). This option would require further research to ensure viability 

with the current fleet of aircraft. Further research is being devoted to understand the tradeoffs 

among HRD, HRJ, and FAME production.

7.2 HRJ from Soybean Oil 

The production of HRD from soybean oil is available in GREET (as documented in Huo et al., 

2008). Default assumptions on farming energy, fertilizer use, yield, etc, were updated where 

necessary using data from the literature (mainly the U.S. Department of Agriculture (USDA) and 

Hill et al., 2006). The key parameters chosen for establishing the low emissions, baseline, and 

high emissions scenarios were yield per hectare, N2O emissions, liming emissions and the 

processing emissions whereby soy oil is converted into HRJ (from Section 7.1). While one case 

assumed zero land use change emissions, two other independent land use change scenarios 

were examined; within which each of the aforementioned key parameters were varied.
56 of 129

7.2.1 Cultivation of Soybeans

The farming energy data used was based on 2002 survey results provided by USDA’s Economic 

Research Service.39 These data are shown in Table 44.

Table 44: Farming energy required for average US soybean production

Fuel type

Farming 

Fuels

(gal/ha)

Farming 

energy 

(Btu/ha)2

Fuel share 

(%)

Diesel 11.91 1523500 70.1

Gasoline 3.2 372900 17.1

LPG 1.0 83960 3.9

Electricity 19.3 (kWh/ha) 65780 3.0

NG 3.7 (m3/ha) 127500 5.9

Total 2174000 100

Notes:

1) Diesel use included custom work of 1.7 gal/acre (Hill et al., 

2006; Sheehan et al., 1998a).

2) LHV values of fuels (except electricity) were obtained from 

GREET (GREET, 2008).

Soybean production and yield40 data were based on historical and projected U.S. data. The 

baseline case assumes a projected soybean yield in 2015 of 110 bu/ha41 (FAPRI, 2008). The low 

and high emissions scenarios were based on analysis of historical soybean yield data from 1987 

through 2007 (USDA, 2008a). The variation between the lowest yield and the mean was -28% 

while that between the highest yield and the mean was +17%.42 Based on these historical data, it 

was assumed that the yield in some future drought year could be 28% lower than in the baseline 

case, corresponding to the high emissions case of 79.5 bu/ha. Similarly, a yield in some future 

ideal growing year could be 17% higher than in the baseline case, corresponding to the low 

emissions case of 129 bu/ha. 

Using the farming energy per hectare and the soybean yield per hectare, the farming energy per 

bushel of soybean was determined as shown in Table 45. The high emissions case includes the 

energy used to grow and process the soybean seed (i.e. seeding energy) as estimated by Hill et 

al. (2006) to be an additional 4.5% of total energy inputs (including farming and fertilizer energy).

The data on fertilizer (nitrogen, phosphorus and potassium), herbicide and insecticide use in 

soybean cultivation from recent years was obtained from the USDA (USDA, 2008b). Glyphosate 

was the most commonly applied herbicide in soybean cultivation. The energy data for the 

manufacture of glyphosate are not available in GREET, so the energy requirements provided by 

Hill et al. (2006) (475 MJ/kg) were used. Default GREET assumptions for herbicide energy use 

were applied for the remaining proportion of non-glyphosate herbicide use. Insecticide and 

fungicide are also applied in soybean cultivation, but in negligible amounts (e.g. 0.4 g/bu of 

insecticide and 0.1 g/bu of fungicide were applied in 2006) compared to fertilizer and herbicide 

application. Hence, these were not considered here. The average agrichemical use from 2002, 

2004 and 2006 was adopted in all cases (see Table 46).

 

39 Via e-mail from Kathleen Kassel to Hsin Min Wong on February 28, 2008.

40 Yield is defined as total production divided by total planted area.

41 Based on average of projections in the years 2014/15 and 2015/16. One bushel of soybeans is defined as 60lbs.

42 The average yield between 1987 and 2007 was 90.2 bu/ha; the lowest and highest yields within this time period were 

65.0 bu/ha and 105.0 bu/ha, respectively.
57 of 129

Table 45: Farming energy for US soybean production for low emissions, baseline and high emissions cases

Low Baseline High1

Soybean yield 

(bu/ha) 129 110 79.5

Diesel 11,789 13,793 20,009

Gasoline 2,886 3,376 4,898

LPG 650 760 1,103

Electricity 509 596 864

NG 987 1154 1,675

Farming fuel energy 

(Btu/bu)

Total 16,820 19,680 28,549

Notes:

1) Includes an additional 4.5% energy to grow and process seeds.

Table 46: US soybeans agrichemical use

Year Agrichemical use (g/bu) 2002 2004 2006 Average 

Nitrogen 52 64 31 49

Phosphorus 154 196 114 155

Potash 311 310 214 278

Herbicide

(Percentage of glyphosate 

in herbicide use)

15

(73.6%)

13 

(83.1%)

15

(88.8%)

14

(81.8%)

Emissions from N2O were estimated using a combination of GREET and IPCC Tier 1 

methodology (IPCC, 2006) in the low and baseline scenarios and from Hill et al. (2006) in the 

high emissions scenario. Specifically, GREET calculated the total amount of nitrogen (in the form 

of soybean biomass) left on the field as 200.7 g/bushel of soybeans harvested. The IPCC Tier 1 

methodology estimates the combined direct and indirect conversion rate for nitrogen from 

synthetic fertilizers as 1.325% and nitrogen from crop residues as 1.225%. These rates include 

the atmospheric deposition of nitrogen volatilized from managed soils as well as nitrogen from 

leaching and runoff. The formula for calculating N2O emissions from soybean cultivation is given 

by:

€

N2O Emissions gN2O

bu

"

#

$ %

&

' = gnitrogen fertilizer

bu

"

#

$

%

&

'⋅ 0.01325 + 200.7 gN-crop residue

bu

"

#

$

%

&

'⋅ 0.01225

"

#

$

%

&

'⋅ 44gN2O

28gN

"

#

$

%

&

' 

Equation 10

Assuming a nitrogen fertilizer application rate of 49 g/bu, total N2O emissions are 4.9 g/bu. Hill et 

al. (2006) assumed N2O emissions to be an all-encompassing number of 1760 g/ha based on 

conventional tillage and rotation of corn, soybean and wheat. Under a soybean yield of 110 

bu/ha, emissions from N2O would be 16 g/bu, roughly 3 times higher than those estimated by 

GREET.

The scenario analysis adopts zero nitrogen fertilizer use in the low emissions case,43 the 

estimates of GREET in the baseline case and those from Hill et al. in the high emissions case. 

Hence, N2O emissions were 3.9 g/bu in the low case, 4.9 g/bu in the baseline case and 22 g/bu in 

the high emissions case (the high emissions case assumes a soybean yield of 79.5 bu/ha).

Lime (composed primarily of calcium carbonate) can be added to soil to reduce acidity and 

increase nutrient uptake. However, the use of lime must be accounted for in the overall life cycle 

 

43 Growing condition for soybeans around the US vary substantially. While the average nitrogen usage across the US is 

49g/bu, it is uncommon for soybeans to respond to pre-plant fertilizer N applications in the central US soybean belt 

(Franzen, 1999). In many cases, soybeans fix nitrogen into the soil and are planted prior to corn to increase soil nitrogen 

in soy/corn/wheat rotation farms.
58 of 129

analysis of soybean farming emissions. GREET does not account for lime use in soybean 

cultivation. Hill et al. (2006) estimate 230 kg/ha of CO2 from liming. The energy and emissions 

associated with the manufacture of lime were assumed negligible.44

The low emission case assumes zero liming and the high emissions case uses the full CO2

emissions from Hill et al. (230 kg/ha); the baseline case uses the average of these extremes (115 

kg/ha). The soybean yields from Table 45 result in 1045 gCO2/bu (using a yield of 110 bu/ha) in 

the baseline case and 2893 gCO2/bu (using a yield of 79.5 bu/ha) in the high emissions case. A 

summary of the parameters used in the cultivation of soybeans is given in Table 47.

Table 47: Input assumptions for the cultivation of soybeans for the low emissions, baseline and high 

emissions cases

Input parameter Low Baseline High 

Soybean yield (bu/ha) 129 110 79.5

Farming energy (Btu/bu) 16,820 19,680 28,549

Fertilizer inputs (g/bu)

Nitrogen

Phosphorus

Potash

0

155

278

49

155

278

n/a

155

278

Herbicide (g/bu) 14 14 14

Percentage of glyphosate in 

herbicide use (%) 81.8 81.8 81.8

N2O emissions (g N2O/bu) 3.9 4.9 22

Liming emissions (g/bu) 0 1,045 2,893

7.2.2 Extraction of Oil from Soybeans

The energy needed for the crushing and extraction of oil from soybeans was based on GREET 

default assumptions (~5,800 Btu/lb soy oil produced). GREET data is based on the study by 

Sheehan et al. (1998a), but adjusted to reflect potential technological improvements (about 10% 

less total process energy than the value estimated by Sheehan et al., 1998a). The inputs, outputs 

and process energy needed per lb of processed soy oil are summarized in Table 48.

Table 48: Input, output and process energy for extraction of oil from soybeans

Input (lb)

Soybean 5.70

Output (lb)

Soy oil 1

Soy bean meal 4.48

Process energy (Btu) 

Electricity 551

Natural Gas 5134

N-hexane 182

Total 58671

Note:

1) GREET default value of 5867 Btu for simulation year 2010 

was adjusted to 5800 Btu for the simulation year 2015 and 

used here. 

 

44 The errors introduced by this assumption are likely small because the energy and emissions that arise from the 

manufacture of lime are small compared to those of other fertilizers and herbicides, (e.g., nitrogen fertilizer manufacture 

energy and CO2 emissions are 42 mmBtu/ton and 2.5 Mg/ton, respectively, compared to 7 mmBtu/ton and 0.5 Mg/ton for 

lime).
59 of 129

7.2.3 Co-Product Usage and Allocation Methodology

In the extraction of soy oil from soybeans, soybean meal is produced in large quantities (by mass 

relative to soy oil) as a co-product. Four methods were explored to allocate the energy and 

emissions of soybean farming and soy oil extraction between soy oil and soybean meal. These 

were allocation by mass, energy, market value, and the displacement (system expansion) 

method. The resultant allocation of GHG emissions between soy oil and soybean meal based on 

the four methods are given in Table 49. 

Soybean meal is primarily used as an animal feed and could potentially displace barley, corn, and 

soybean.

The amount of product that soybean meal would displace was calculated on a protein 

equivalence basis (i.e. matching of protein content). Soybean meal was assumed to have protein 

content of ~48% (Ahmed et al., 1994). Avoidance of land use change emissions associated with 

the cultivation of the displaced crops was taken into account in the analysis for illustrative 

purposes.

7.2.3.1 Displacement of Barley

The protein content of barley was assumed to be 12% (USDA, 2008c; OMAFRA, 2003; Kennelly 

et al., 1995). Hence, 1 lb of soy meal displaces 4 lbs of barley on a protein equivalence basis. As 

0.38 lbs of soy meal is produced per MJ of fuel, about 1.5 lbs of barley is displaced per MJ of fuel. 

From the life cycle assessment of GHG emissions of barley cultivation conducted by Lechón et al. 

(2005), the CO2 credit is 71.5 g/lb of barley displaced (71.3 gCO2/MJ fuel) and N2O credit is 0.36 

g/lb of barley displaced (107.0 gCO2e/MJ fuel). Lechón et al. did not include land use change 

emissions associated with the farming of barley. This work assumed the land use change credit 

associated with the displaced barley was the avoidance of the conversion of Brazilian Cerrado 

grassland to cropland (~2,833,333 gCO2/ha when emissions were amortized over 30 years, 

Fargione et al, 2008). Assuming a barley yield of 5940 lb/ha (Lechón et al., 2005), the resultant 

land use change credit was 477 gCO2/lb of barley displaced (476g CO2/MJ).

7.2.3.2 Displacement of Corn

Corn is a widely used animal feed that could be displaced by soy meal. The protein content of 

corn is about 9% (OMAFRA, 2003; Parish, 2007; Kennelly et al., 1995). Hence, 1 lb of soybean 

meal displaces 5.3 lbs of corn on a protein equivalence basis. As 0.38 lbs of soybean meal is 

produced per MJ of fuel, about 2.0 lbs of corn is displaced per MJ of fuel. The life cycle GHG 

emissions for corn farming were analyzed in GREET using default GREET values (version 1.8b, 

GREET, 2008). Specifically, the CO2, CH4 and N2O credits per lb of corn displaced were 100.6 

g/lb (133.3 gCO2/MJ fuel), 0.15 g/lb (5.0 gCO2e/MJ fuel) and 0.22 g/lb (86.9 gCO2e/MJ fuel), 

respectively. In the case of corn displacement, land use change credit was assumed to result 

from the avoidance of worldwide conversion of non-cropland to cropland for corn cultivation as 

simulated by Searchinger et al. (2008) (11.7 Mg CO2/ha when emissions were spread over 30 

years). Assuming a corn yield of 150 bu/acre (average yield from 2003-2007, USDA, 2008a), the 

land use change credit was estimated as 1393 g/lb of corn displaced (1846.0 gCO2/MJ fuel).

7.2.3.3 Displacement of Soybeans

Soybean is the default product assumed to be displaced by soybean meal in GREET. The protein 

content of soybeans was assumed to be 40% (GREET, 2008). Hence, 1 lb of soybean meal 

displaces 1.2 lbs of soybean on a protein equivalence basis. As 0.38 lbs of soybean meal is 

produced per MJ of fuel, 0.46 lbs of soybean is displaced per MJ of fuel produced. Based on the 

assumptions of the baseline scenario, the CO2, CH4 and N2O credits per lb of soybeans displaced 

were 71.0 g/lb (21.3 gCO2/MJ fuel), 0.08 g/lb (0.57 gCO2e/MJ fuel) and 0.09 g/lb (7.9 gCO2e/MJ 

fuel), respectively. In considering land use change emissions, worldwide conversion of noncropland to cropland for corn cultivation as simulated by Searchinger et al. (2008) was used (11.7 

Mg CO2/ha when emissions were spread over 30 years). This scenario was assumed to broadly 

apply to the case of soybean farming. Assuming a soybean yield of 110 bu/ha (as in the baseline 

case described above), the land use change credit was estimated to be 1773 g/lb of soybean 

displaced (530 gCO2/MJ fuel).
60 of 129

7.2.3.4 Results

Table 48 shows six different approaches to allocation between soy oil and soy meal. The first 

three rows show allocation by mass, market value and energy while the rest shows the 

displacement calculation described above.

Table 49: Allocation of GHG emissions between soy oil and soy meal using various methodologies

Allocation Approach Soy Oil (%) Soy Meal 

(%)

Mass 18.2 81.8

Market Value1 44.7 55.3

Energy2 33.9 66.1

LUC included for soy meal 

and barley -5.7 105.7

LUC included for soy meal 

but not barley 71.2 28.8 Displacement of barley

no LUC -205.8 305.8

LUC included for soy meal 

and corn -234.5 334.5

LUC included for soy meal 

but not corn 63.6 36.4 Displacement of corn

no LUC -286.2 386.2

LUC included for both soy 

meal and displaced 

soybeans

9.6 90.4

LUC included for soy meal 

but not displaced soybeans 95.2 4.8

Displacement of 

soybean

no LUC 48.8 51.1

Notes:

1) Market value of soy oil and soy meal are $1.05/kg and $0.29/kg (FAPRI, 2009)

2) Energy content of soy oil and soy meal are 37.2 MJ/kg and 16.2 MJ/kg 

(GREET, 2008)

3) Life cycle GHG emissions from soybean production, soy oil extraction and land 

use change are 31.5 gCO2e/MJ, 26.9 gCO2e/MJ, 599.0 gCO2e/MJ of fuel 

produced

4) Conversion ratio used is 0.587 lbHRJ/lboil and energy allocation to HRJ after 

hydroprocessing is 65.3%

5) LUC refers to land use change

7.2.3.5 Discussion

The use of different co-product allocation approaches can result in substantially different results, 

particularly when significant amounts of co-product are produced and different allocation 

approaches are appropriate for different scenarios and pathways. The displacement method, as 

applied here, is not appropriate because of the large variation in life cycle GHG emissions that 

would result from the choice of feedstock (i.e., barley, corn, and soybean) displaced by soy meal 

and how land use change are addressed. A more appropriate use of the displacement method 

would be achieved by modeling worldwide agriculture; however, that is beyond the scope of the 

analysis presented here.

In this case, the mass or energy allocation method may not be most appropriate as soy meal is 

not valued based on its mass, or sold as a commercial energy product. Further, allocation by 

mass would benefit a fuel producer, as it will result in most of the emissions being assigned to the 

meal instead of the oil. As discussed with regard to the product slate from a Fischer Tropsch 

facility, a general shortcoming in the displacement approach arises when a large quantity of coproduct is generated relative to the primary product. This causes an overestimation of credits for 

the co-product and may even lead to net negative emissions. Because the high protein content of 

soy meal leads to commercial value as an animal feed, market value allocation was adopted here 
61 of 129

and in all cases where oil extraction from an oilseed results in meal leaving the system and an 

independent product. Market value allocation is further explored in the next section. 

7.2.3.6 Market Value Allocation with Soy Oil to HRJ

In using the market value allocation approach, temporal fluctuations in the market prices of coproducts could cause temporal changes in the allocation values. The sensitivity to market forces 

is apt in this case as it allocates shares of the energy and emissions from production based on 

the current utility (of which price is the most reasonable measure) for each product. If a market 

becomes saturated with a co-product then the utility and market value will both approach zero. 

Market fluctuations make getting a consistent result challenging. Figure 13 shows the temporal 

variation in the allocation fraction between soy oil and soy meal from 2005 through 2015. Price 

projections are based on the database maintained by FAPRI (FAPRI, 2009) and physical 

quantities are based on Table 48. The dashed lines correspond to the constant values used 

within GREET (GREET, 2008).

Figure 13: Temporal variation in the soy oil and soy meal allocation fractions when using market value 

allocation. Constant dashed lines correspond to default GREET prices

Although the GREET default values turn out to be a reasonable mean of actual prices over time, 

this study used the 2015 FAPRI projects prices of $1.05/kg and $0.29/kg for soy oil and soybean 

meal, respectively (FAPRI, 2009).

7.2.4 Transportation of Soy Oil to HRJ Production Facilities

The estimation of GHG emissions arising from transportation (of feedstock and fuel) is based on 

default GREET assumptions.

7.2.5 Land Use Change Emissions from Soy Oil Production

The use of food crops as a source of renewable oils for fuel production may lead to emissions 

from land use change. The magnitude of land use change emissions can depend on the type of 

converted land, the type of crops grown and the farming practices employed. In addition to a 

scenario where no land use change emissions were incurred, two scenarios of land use change 

were chosen to explore the range of magnitudes of GHG emissions due to land use change. The 

goal of this section is to provide the reader an understanding of how land use change emissions 

compare to the emissions from the other five life cycle stages. It is not intended to explicitly 

quantify the specific land use change emissions that would result from expanded soy oil 

production. 

The first scenario considers the conversion of Cerrado grassland in Brazil to soybean fields and 

the second scenario considers converting tropical rainforests in Brazil to soybean fields (Fargione 

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62 of 129

et al., 2008). Using these Brazilian estimates with the present analysis of soybean production 

implies there is little difference between soybean cultivation in Brazil and soybean cultivation in 

the US. It further implies a transportation profile for the transportation of soy oil from Brazil to the 

US; however, the magnitude of the GHG emissions resulting from this additional transportation 

were assumed to be negligible relative to the GHG emissions from either LUC scenario. In all 

scenarios, the impact of varying soybean yield was explored on land use change emissions 

amortized over 30 years. The impacts per bushel of soybeans are shown in Table 50. 

Table 50: Land use change emissions arising from soybean production

Scenario

Land use 

change 

emissions 

(Mg CO2/ha)

Soybean 

yield 

(bu/ha)

Land use 

change 

emissions 

(g CO2/bu)3

Grassland 

conversion1 852

79.5

110

129

35,600

25,800

22,000

Tropical rainforest 

conversion1 7372

79.5

110

129

309,000

223,300

190,400

Notes:

1) Soybean yields in Brazil were assumed to be similar to soybean 

yields in the U.S. (FAPRI, 2008) 

2) From Fargione et al., 2008

3) Amortized over 30 years (no discounting)

7.2.6 Results

The life cycle GHG emissions from the production and use of HRJ from soy oil for each land use 

change option are summarized Tables 51-53. Without land use change emissions, the life cycle 

GHG emissions of the production of HRJ from soy oil range from 0.31 to 0.68 times those of 

conventional jet fuel. However, as illustrated in the various land use change scenarios, land use 

change emissions can dramatically increase the overall life cycle GHG emissions of the fuel to 

several times that of conventional jet fuel. Specifically, the land use change scenarios 

investigated in this work result in life cycle GHG emissions ranging from 0.9 to nearly 9 times 

those of conventional jet fuel.

Table 51: Life cycle emissions for the soy oil to HRJ assuming zero land use change emissions

Land Use Change Scenario S0 Low Baseline High

Key Assumptions

Soybean yield (bu/ha) 129 110 79.5

Life Cycle CO2 Emissions by Stage

Biomass Credit (gCO2/MJ) -73.7 -70.5 -68.9

Recovery of feedstock (gCO2/MJ) 17.5 20.1 25.9

Transportation of feedstock (gCO2/MJ) 1.2 1.2 1.1

Processing of feedstock to fuel (gCO2/MJ) 7.1 10.3 13.2

Transportation of jet fuel (gCO2/MJ) 0.6 0.6 0.6

Combustion CO2 (gCO2/MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) -47.3 -38.4 -28.1

WTT CH4 emissions (gCO2e/MJ) 1.2 1.3 1.5

WTT N2O emissions (gCO2e/MJ) 3.0 3.6 15.4

Total WTW GHG emissions (gCO2e/MJ) 27.3 37.0 59.2

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.31 0.42 0.68
63 of 129

Table 52: Life cycle emissions for the soy oil to HRJ assuming conversion of Cerrado grassland to soybean 

field

Land Use Change Scenario S1 Low1 Baseline1 High1

Land use change emissions (gCO2/MJ) 54.4 60.8 82.5

WTW CO2 emissions (gCO2/MJ) 77.4 92.8 124.9

Total WTW GHG emissions (gCO2e/MJ) 81.7 97.8 141.7

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.93 1.12 1.62

Notes: 

1) All other input assumptions (cultivation of soybeans, extraction of soy oil, 

processing of soy oil to HRJ) are based on those in the S0 emissions case of the 

corresponding scenario.

Table 53: Life cycle emissions for the soy oil to HRJ assuming conversion of tropical rainforest

Land Use Change Scenario S2 Low1 Baseline1 High1

Land use change emissions (gCO2/MJ) 471.5 527.2 715.5

WTW CO2 emissions (gCO2/MJ) 494.5 559.2 757.8

Total WTW GHG emissions (gCO2e/MJ) 498.8 564.2 774.7

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 5.70 6.45 8.85

Notes: 

1) All other input assumptions (cultivation of soybeans, extraction of soy oil, 

processing of soy oil to HRJ) are based on those in the S0 emissions case of the 

corresponding scenario.

7.3 HRJ from Palm Oil 

A palm oil to HRJ pathway was created within the GREET framework using information on 

farming energy, fertilizer use, yield, oil extraction energy, etc., obtained from the literature, mainly 

from the Malaysian Palm Oil Board and Wicke et al. (2007).45 The extraction of oil from palm 

fresh fruit bunches (FFB) yields two types of oil: palm oil and palm kernel oil. Both oil types were 

assumed to be main products in the FFB extraction process and used in the production of HRJ.46

The only co-product from the process is palm kernel expeller, which can be used as an animal 

feed. The key parameters chosen for establishing the low emissions, baseline, and high 

emissions cases were palm fresh fruit bunch yield per acre, farming energy, methane emissions 

from palm oil mill effluent treatment and processing of palm oil into HRJ47 (from Section 7.1). In 

addition to a scenario where land use change emissions were assumed zero, three land use 

change scenarios were examined where the aforementioned key parameters were varied.

7.3.1 Cultivation of Palm Fresh Fruit Bunches (FFB)

Using a process similar to that of soybean-to-HRJ, the yields of palm FFB were derived from 

historical data spanning 1987 through 2007 in Malaysia (MPOB, 2008a). The data was 

extrapolated using a linear fit to estimate the yield in 2015.48 The resultant yield of 21.3 ton/ha (all 

 

45 Palm oil is not a preexisting pathway within GREET. As such, a new pathway was built within the GREET framework 

using the soy oil to renewable diesel pathway as a guide.

46 This is in contrast to other studies where diesel is the desired product from renewable oil processing and palm kernel 

oil, whose carbon number distribution centered on C12 is outside the diesel range, is considered a by-product instead of a 

main product in the FFB extraction process.

47 Although palm kernel oil has a shorter carbon length (mostly 12 carbons in length) than palm oil (mostly 16 and 18 in 

length), for the purposes of this study, they are both assumed to be 18 carbon atoms in length. This was discussed briefly 

in Section 7.1.

48 From the linear fit, the yield was estimated to increase at a rate of about 0.0435 tonnes per year from 18.08 tonnes in 

1987.
64 of 129

references in this section to tons refer to short tons) was adopted in the baseline case. The 

percentage variations in the data between the lowest yield and mean, and the highest yield and 

mean were found to be -14% and +9%, respectively. The high emissions case assumed a yield 

14% lower than the baseline case, corresponding to 18.2 ton/ha. The low emissions case 

assumed a yield 9% higher than the baseline case, corresponding to 23.1 ton/ha.

In Southeast Asian oil plantations, most of the cultivation and harvest of the palm FFB are done 

manually with the help of animals. The main fossil energy used is diesel fuel for powering farm 

machinery and equipment. This work assumed that 3.0 mmBtu/ha of diesel was consumed in the 

low emissions and baseline cases, while 4.9 mmBtu/ha was used in the high emissions case 

(Wicke et al., 2007). The farming energy per ton of FFB harvested was estimated by combining 

the farming energy per hectare with the yield of palm FFB (see Table 54).

Only nitrogen fertilizer was considered in this study as the GHG emissions contributions from the 

production of phosphate and potash fertilizers, as well as herbicides, were negligible compared to 

those of nitrogen fertilizers (Wicke et al., 2007). The application of organic fertilizers (e.g. empty 

fruit bunches) was not considered because their emissions are carbon-neutral. The nitrogen 

fertilizer application rate adopted in all three cases (low, baseline and high) was 139 kg/ha (Wicke 

et al., 2007). The corresponding nitrogen fertilizer application in terms of grams of nitrogen per 

ton of FFB was calculated based on the yields in each scenario.

Emissions from N2O were estimated using IPCC Tier 1 methodology (IPCC, 2006). Due to the 

lack of data, N2O emissions from nitrogen in above and below ground crop residues were not 

accounted for in this analysis. The IPCC Tier 1 methodology estimates the combined direct and 

indirect conversion rate for nitrogen from synthetic fertilizers as 1.325%. This rate includes the 

atmospheric deposition of nitrogen volatilized from managed soils as well as nitrogen from 

leaching and runoff. The formula for calculating N2O emissions from the cultivation of palm fresh 

fruit bunches is given by:

€

N2O Emissions gN2O

tonFFB

"

#

$

%

&

' = gnitrogen fertilizer

tonFFB

"

#

$

%

&

'⋅ 0.01325

"

#

$

%

&

'⋅ 44gN2O

28gN

"

#

$

%

&

' Equation 11

A summary of the parameters assumed in the cultivation of palm FFB discussed above is given in 

Table 54.

Table 54: Assumptions in the cultivation of palm fresh fruit bunches for the low emissions, baseline and high 

emissions cases

Input parameter Low Baseline High

FFB yield (ton/ha) 23.1 21.2 18.2

Farming energy 

(Btu/ton FFB) 131,300 143,100 270,800

Nitrogen fertilizer inputs

(g/ton FFB) 6,020 6,560 7,640

N2O emissions 

(g N2O/ton FFB) 125.3 136.6 159.1

7.3.2 Extraction of Oil from Palm FFB

Palm oil and palm kernel oil are produced from palm FFB at a processing mill. During the 

extraction process, palm kernel shells (PKS), fiber, empty fruit bunches (EFB) and kernels are 

also produced. Palm Kernel Effluent and fiber are burnt to generate electricity to fuel mill 

processes, while EFB are used as organic fertilizers. Palm kernels can be further processed to 

produce palm kernel oil and palm kernel expeller, which can be used as an animal feed. A waste 

stream called palm oil mill effluent (POME) is also produced from the extraction of oil from FFB. 

POME treatment (through anaerobic digestion) results in the emission of CH4. 
65 of 129

The palm oil extraction rate, kernel recovery rate, palm kernel oil extraction rate, palm kernel 

expeller extraction rate and the energy inputs needed for the crushing and extraction of oil from 

palm FFB and palm kernels were derived from data from the Malaysian Palm Oil Board (MPOB, 

2008b) and Wicke et al. (2007). The input parameters, energy consumption and product yield are 

shown in Table 55. The total process energy of 199.5 Btu/lb oil was used in all the three 

scenarios (low emissions, baseline and high emissions cases).

The treatment of palm oil mill effluent results in methane production. For every ton of FFB 

processed, between 1.5 to 2.2 kg of CH4 is emitted from POME treated in an open digesting tank 

(Reijnders and Huijbregts, 2008). This work assumed methane emissions of 1.5 kg CH4/ton FFB 

in the low emissions case, 1.85kg CH4/ton FFB (average value) in the baseline case, and 2.2 kg 

CH4/ton FFB in the high emissions case. The resultant CH4 emissions per pound of oil are given 

in Table 56.

Table 55: Inputs, outputs and process energy in the extraction of palm oil and palm kernel oil

Extraction of palm oil from FFB

Palm oil extraction rate (ton palm oil/ton FFB)1 0.20

Amount of kernels produced (ton kernels/ton palm 

oil)1

0.25

Energy input 

Diesel (litre/ton FFB)

2,3 1.36

Extraction of palm kernel oil from kernels

Palm kernel oil extraction rate (ton PKO/ton kernel)1 0.45

Palm kernel expeller extraction rate (ton PKE/ton 

kernel)1

0.51

Energy input2

Electricity (kWh/ton kernel)

Diesel (litres/ton kernel)

77.3

17.3

Overall energy use and product yields

Input 

FFB (lb) 4.5

Output

Palm oil and palm kernel oil (lb)

Palm kernel expeller (lb)

1

0.11

Process energy

Electricity (Btu/lb oil)

Diesel (Btu/lb oil)

Total (Btu/lb oil)

29.7

169.8

199.5

Notes:

1) MPOB, 2008b.

2) Wicke et al., 2007. 

3) The amount of diesel energy needed for electricity generation is 

reduced by the use of palm kernel shell and fiber (by-products of 

palm oil extraction) for electricity production.

Table 56: Methane emissions from POME treatment

Low Baseline High

CH4 emissions from 

POME treatment (kg 

CH4/ton FFB)

1.5 1.85 2.2

CH4 emissions from 

POME treatment (g 

CH4/lb oil)

3.32 4.15 4.98
66 of 129

7.3.3 Co-Product Usage and Allocation Methodology

The market allocation method was used to apportion emissions among palm oil, palm kernel oil 

and palm kernel expeller49 to be consistent with the approach taken in the soy oil pathway. The 

market prices assumed were $0.98/kg for palm oil, $1.22/kg for palm kernel oil and $0.14/kg for 

PKE, based on average export prices between January and October 2008 (MPOB, 2008b).

7.3.4 Transportation of Palm Oil and Palm Kernel Oil to HRJ Production Facilities

It is assumed that palm oil and palm kernel oil are produced in facilities near palm plantations in 

Southeast Asia and then transported to the United States for hydroprocessing to HRJ. This 

transportation pathway is not available in GREET. The key assumptions used in this work are 

summarized in Table 57.

Table 57: Assumptions in the transportation of palm oil and palm kernel oil from Southeast Asia to the US

Transportation of palm oil and PKO from mill to harbor in Southeast Asia

Mode (%)

Truck 100

Distance (miles) 631

Transportation of palm oil and PKO from Southeast Asia to U.S. ports2

Mode (%)

Ocean tanker 100

Distance (miles)

Gulf Coast (50%)

East Coast (25%)

West Coast (25%)

Average

13500

12300

7700

11750

Transportation of palm oil and PKO from U.S. ports to HRJ production 

facilities3

Mode (%)

Truck 

Rail 

50

50

Distance (miles)

By truck

By rail

100

500

Notes:

1) Wicke et al., 2007.

2) Shipping distances from 

http://www.asaim.org.tw/Ocean%20Shipping%20Distances.pdf

3) Authors’ own estimates.

7.3.5 Land Use Change Emissions from Palm Oil Production

Like soy oil, palm oil requires the use of arable land for growth and its use in fuel production may 

lead to land use change emissions. In addition to a scenario where no land use change 

emissions were incurred, three scenarios of land use change were considered using available 

data in the literature to illustrate the possible range of land use change emissions. The scenarios 

are regionally appropriate for Southeast Asia and are thus consistent with all the present analysis 

of palm oil production. The goal of this section is to provide an understanding for the reader of 

how land use change emissions compare the emissions from the other five life cycle stages. It is 

 49 In the extraction of palm oil from FFB and palm kernel oil from kernels, co-products such as palm kernel shell, fiber, 

empty fruit bunches and palm kernel expeller are formed. As palm kernel shell and fiber are used for electricity production 

in the mill and empty fruit bunches as fertilizers in palm FFB cultivation to reduce energy requirement and fertilizer input, 

respectively, they are recycled streams within the process rather than co-products. In addition, the emissions generated 

by the use of these products are considered carbon-neutral as they were previously absorbed from the atmosphere during 

growth. Hence, palm kernel expeller is the only co-product considered in the allocation of process energy and emissions. 
67 of 129

not intended to explicitly quantify the specific land use change emissions that would result from 

expanded palm oil production. 

The first case assumed direct land use change emissions from the conversion of previously 

logged forest to palm plantations (Wicke et al., 2007). The second and third cases assumed land 

use change emissions resulting from the conversion of tropical rainforest and peat land rainforest 

in Southeast Asia, respectively, to palm plantations (Fargione et al., 2008). In all scenarios, the 

total land use change emissions were uniformly allocated over 30 years with no discounting,

roughly the life-span of a palm plantation. In addition, the impact of varying palm FFB yield was 

explored in each land use change scenario. The resultant land use change emissions per ton of 

FFB are shown in Table 58.

Table 58: Land use change emissions in the cultivation of palm fresh fruit bunches

Scenario

Land use 

change 

Emissions

(Mg CO2/ha)

Assumed 

palm FFB 

yield

(ton/ha)

Land use change 

emissions

(g CO2/ton FFB)1

Conversion of 

previously logged over 

forest 

-- -- 78,4002

Conversion of tropical 

rainforest 7023

18.2

21.2

23.1

1,285,700

1,103,800

1,013,000

Conversion of peat 

land rainforest 3,4523

18.2

21.2

23.1

6,322,300

5,427,700

4,981,200

Notes:

1) Assumed to be amortized over 30 years 

2) Derived from data in Wicke et al., 2007, assuming crude palm oil energy 

content of 36 MJ/kg and that 1 lb of FFB produces 0.2 lbs of crude palm oil.

3) Fargione et al., 2008

7.3.6 Results

The key assumptions and corresponding life cycle GHG emissions in the production and use of 

HRJ from palm oil with and without land use change emissions are outlined in Tables 59-62. 

Excluding land use change emissions, the life cycle GHG emissions of the production of HRJ 

from palm oil and palm kernel oil range from 0.2 to 0.4 times those of conventional jet fuel. 

However, when including land use change emissions, they can dominate GHG emissions of the 

entire pathway. Specifically, the land use change scenarios investigated in this work result in life 

cycle GHG emissions ranging from 0.4 to over 9 times those of conventional jet fuel.
68 of 129

Table 59: Life cycle emissions for the palm oil to HRJ assuming zero land use change emissions

Land Use Change Scenario P0 Low Baseline High

Key Assumptions

Palm FFB yield (ton/ha) 23.1 21.2 18.2

Life Cycle CO2 Emissions by Stage

Biomass Credit (gCO2/MJ) -73.7 -70.5 -68.9

Recovery of feedstock (gCO2/MJ) 4.8 4.9 6.6

Transportation of feedstock (gCO2/MJ) 3.3 3.1 3.1

Processing of feedstock to fuel (gCO2/MJ) 7.1 10.3 13.2

Transportation of jet fuel (gCO2/MJ) 0.6 0.6 0.6

Combustion CO2 (gCO2/MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) -58.0 -51.6 -45.5

WTT CH4 emissions (gCO2e/MJ) 5.2 6.3 7.4

WTT N2O emissions (gCO2e/MJ) 4.9 5.1 5.8

Total WTW GHG emissions (gCO2e/MJ) 22.5 30.1 38.1

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.26 0.34 0.44

Table 60: Life cycle emissions for the palm oil to HRJ assuming conversion of logged over forest

Land Use Change Scenario P1 Low1 Baseline1 High1

Land use change emissions (gCO2/MJ) 10.1 9.6 9.5

WTW CO2 emissions (gCO2/MJ) 22.5 28.5 34.4

Total WTW GHG emissions (gCO2e/MJ) 32.6 39.8 47.6

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.37 0.45 0.54

Notes: 

1) All other input assumptions (cultivation of palm FFB, extraction of palm oils, 

processing of palm oils to HRJ) are based on those in P0 emissions case of 

corresponding scenario.

Table 61: Life cycle emissions for the palm oil to HRJ assuming conversion of tropical rainforest

Land Use Change Scenario P2 Low1 Baseline1 High1

Land use change emissions (gCO2/MJ) 130.7 135.8 155.2

WTW CO2 emissions (gCO2/MJ) 143.1 154.6 180.1

Total WTW GHG emissions (gCO2e/MJ) 153.2 166.0 193.3

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 1.75 1.90 2.21

Notes: 

1) All other input assumptions (cultivation of palm FFB, extraction of palm oils, 

processing of palm oils to HRJ) are based on those in P0 emissions case of 

corresponding scenario.
69 of 129

Table 62: Life cycle emissions for the palm oil to HRJ assuming conversion of peatland rainforest

Land Use Change Scenario P3 Low1 Baseline1 High1

Land use change emissions (gCO2/MJ) 642.8 667.9 763.1

WTW CO2 emissions (gCO2/MJ) 655.2 686.7 788.0

Total WTW GHG emissions (gCO2e/MJ) 665.3 698.0 801.2

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 7.60 7.98 9.16

Notes: 

1) All other input assumptions (cultivation of palm FFB, extraction of palm oils, 

processing of palm oils to HRJ) are based on those in P0 emissions case of 

corresponding scenario.

7.4 HRJ from Rapeseed Oil

The production of HRJ from rapeseed oil was analyzed with the GREET framework using 

cultivation and processing data from the literature.50 Rapeseed has been grown for the production 

of animal feed and vegetable oils for both human consumption and biofuel production. The 

leading producers of rapeseed are currently China, Canada, India and the European Union (FAO, 

2010). The use of rapeseed oil as a feedstock for biofuels is of particular interest in Europe,

where Rapeseed Methyl Ester is one of the two main biofuels under consideration (CONCAWE, 

2002). This analysis assumes that rapeseed oil is produced in Europe and subsequently imported 

to the United States to be hydroprocessed into HRJ. The key parameters used to form the low, 

baseline and high emissions scenario were the rapeseed yield, oil content, farming energy, 

fertilizer application, transportation distance and oilseed drying energy requirements. In addition 

to a scenario where land use change emissions were assumed zero, a scenario where rapeseed 

is grown on set-aside land51 was examined; the aforementioned key parameters were varied 

within each land use change scenario.

7.4.1 Cultivation of Rapeseed

Rapeseed cultivation was assumed to take place predominantly in the United Kingdom (UK) and 

France based on data from Mortimer and Elsayed (2006), Edwards et al. (2007), Richards (2000) 

and Prieur et al. (2008). The analysis was supplemented by additional data from Sweden and 

Denmark from Bernesson et al. (2004) and Schmidt (2007). This was deemed appropriate due to 

the relative similarity in climate among southern Sweden, Denmark, France and the UK. 

Rapeseed yield was estimated using data for the UK and France from 1999 through 2009. The 

baseline scenario adopted a projected rapeseed yield in 2015 of 3.35 Mg/ha (Eurostat, 2010) 

using linear regression on the historical data. The low and high emissions scenarios were 

developed using the same method employed to estimate soybean and palm yields. Specifically, 

based on historical rapeseed yield data from 1999 through 2009 (Eurostat, 2010), the variation 

between the lowest yield and the line of best fit was -16.8% (UK in 2001) while that between the 

highest yield and the line of best fit was +15.7% (France in 2009). Based on these historical data, 

it was assumed that yield fluctuations in some future year could be 16.8% lower than in the 

baseline case, corresponding to the high emissions case of 2.79 Mg/ha. Similarly, a yield in some 

 

50 Rapeseed oil is not a preexisting pathway within GREET. As such, a new pathway was built within the GREET 

framework using the soy oil to renewable diesel pathway as a guide.

51 From 1988 through 2009, the EU government compensated farmers to remove 10%-15% of their land from production 

to deliver some environmental benefits following considerable damage to agricultural ecosystems and wildlife as a result 

of the intensification of agriculture. The program has since changed such that the set aside system is on a voluntary basis 

with no compensation. Land that was set aside by farmers could be available for increased rapeseed cultivation (The 

Telegraph, 2009).
70 of 129

future ideal growing year could be 15.7% higher than in the baseline case, corresponding to the 

low emissions case of 3.89 Mg/acre. 

Yearly data from both France and the UK is shown in Figure 14. The weighted average 

corresponds to the ratio of total harvested weight to total planted area from both countries. 

Although there is substantial fluctuation in yield from year to year, the underlying trend is 

increasing over time at a rate of 23.7 kg/ha/year.

Figure 14: Yearly rapeseed yield for France and the UK between 1999 and 2009

The oil fraction of rapeseed ranges from 40% to 45% by mass based on the sources used to 

model cultivation. The oil yield per kilogram of rapeseed is higher than any other renewable oil 

feedstock considered in this work. In this analysis, oil fractions of 45% (Prieur et al., 2008; 

Bernesson et al., 2004), 44% (Schmidt, 2007) and 41% (Richards, 2000; Mortimer and Elsayed, 

2006) were assumed in the low, baseline and high emissions scenarios.

Inputs to rapeseed cultivation are fuels for farming operations, nitrogen fertilizers, phosphate, 

potash and herbicides. The energy consumption of seed crop growth and processing of seeds is 

less than 1% of all energy used in cultivation and harvesting and was considered negligible for 

the purposes of this analysis (Richards, 2000). The usage per hectare of each of these resources 

in the low, baseline and high emissions scenario is given in Table 63. Nitrogen fertilizer 

application was assumed to occur in the form of 50% ammonia and nitrogen solutions and 50% 

ammonium nitrate and ammonium sulfate. Defualt GREET assumptions for soybeans were 

adopted regarding the types of herbicides applied during rapeseed cultivation. The largest 

variation was found in the use of diesel fuel on the farm. The low emissions scenario was based 

on data from Mortimer and Elsayed (2006) whose estimate for North East England was by far the 

most optimistic. The baseline scenario was based on French data from Prieur et al. (2008), 

although Bernesson (2004) gave a similar estimate in his analysis of rapeseed production in 

Sweden. The high emissions scenario employs data from Richards (2000) for arable lands in 

England, which is confirmed by Schmidt (2007) with his farming energy estimates of rapeseed 

production in Denmark.

The corresponding production inputs per Mg of rapeseed for each scenario were calculated by 

combining the production inputs and yields per hectare.

The production of rapeseed oil results in straw biomass production. The average ratio of oilseed 

production to straw is approximately 0.96:1.52 This analysis assumed that the straw was ploughed 

back into the fields after harvest. This leads to minimized depletion of soil nutrients and fertilizer 

 

52 The straw and oilseed production from Richards (2000) were 4 Mg/ha and 4.08 Mg/ha, respectively (ratio of 0.98:1). 

The straw and oilseed production from Schmidt (2007) were 2.93 Mg/ha and 3.13 Mg/ha, respectively (ratio of 0.94:1). 

!"#$

!"%$

&"#$

&"%$

'"#$

'"%$

("#$

!))*$ &###$ &##&$ &##($ &##+$ &##*$ &#!#$

!"#$%$$&'()$*&'+,-./"0'

,-./01$ 23$ 45/1.-$-167-16658/$
71 of 129

savings, which were accounted for in the cultivation inputs (Prieur et al., 2008). Where there is a 

nearby heat or power generation facility that is outfitted to accommodate biomass feedstocks, the 

straw represents a potential energy source (Richards, 2000); however, straw from rapeseed is 

rarely harvested because of burning problems with the newer varieties and lower yields than 

grasses or wheat. Lower yield makes rapeseed straw more expensive to harvest so it is simply 

tilled back into the soil by most farmers (Bernesson et al., 2004).

Table 63: Farming energy, fertilizer and herbicide usage for the production of rapeseed in the low 

emissions, baseline and high emissions scenarios

Low Baseline High

Rapeseed Yield (Mg/ha) 2.79 3.35 3.89

Rapeseed Oil Fraction (mass) 45% 44% 41%

Fuel Usage

Diesel (MJ/ha)1 1857 2310 3934

Fertilizer Usage

Nitrogen(kg-N/ha)2 140 164 180

Phosphate (kg-P2O5/ha)3 34 47 56

Potash (kg-K2O/ha)4 35 43 82

Herbicides5 1.8 2.3 2.8

Notes:

1) Mortimer and Elsayed (2006) – Low Case; Prieur et al. (2008) – Baseline 

Case; Richards (2000) –High Case

2) Bernesson et al. (2004) and Schmidt (2007) – Low Case; Prieur et al. (2008) 

– Baseline Case; Edwards et al. (2007) – High Case

3) Bernesson et al. (2004) – Low Case; Prieur et al. (2008) – Baseline Case; 

Mortimer and Elsayed (2006) and Schmidt (2007) – High Case

4) Prieur et al. (2008) – Low Case; Richards (2000) and Bernesson et al. (2004) 

– Baseline Case; Mortimer and Elsayed (2006) and Schmidt (2007) – High 

Case

5) Richards (2000) – Low Case; Prieur et al. (2008) – Baseline Case; Mortimer 

and Elsayed (2006) – High Case

Emissions from N2O were estimated using IPCC Tier 1 methodology (IPCC, 2006). Nitrogen in 

above and below ground crop residues was estimated by applying the aforementioned crop 

residue production ratio to estimate the amount of straw tilled back into the soil. Rapeseed straw 

has been characterized as 0.75% nitrogen by mass (Karaosmanoglu et al., 1999) leading to 7125 g 

of nitrogen reapplied to the field in the form of straw biomass per megagram of oilseed production. 

The IPCC Tier 1 methodology estimates the combined direct and indirect conversion rate for 

nitrogen from synthetic fertilizers as 1.325% and nitrogen from crop residues as 1.225%. These 

rates include the atmospheric deposition of nitrogen volatilized from managed soils as well as 

nitrogen from leaching and runoff. The formula for calculating N2O emissions from rapeseed

cultivation is given by:

€

N2O Emissions gN2O

Mg

"

#

$

%

&

' = gnitrogen fertilizer

Mg

"

#

$

%

&

'⋅ 0.01325 + 7125 gN-crop residue

Mg

"

#

$

%

&

'⋅ 0.01225

"

#

$ %

&

'⋅

44gN2O

22gN

Equation 12

The least defined aspects of rapeseed production are the drying and storage practices. In 

practice, there is little consensus on the oilseed moisture content at harvest, which has 

implications for energy consumption during drying. A recent survey of current harvesting, drying 

and storage practices of oilseed rape in the UK found that most farmers harvest above 12% 

moisture content, which is the threshold for Ochratoxin B production (Armitage et al., 2005).53

 53 Ochratoxin B is the most abundant food contaminating mycotoxin in the world. Human exposure occurs primarily 

through consumption of improperly stored food products (Armitage et al., 2005)
72 of 129

After harvest the rapeseed must be dried to a moisture content of 9% for storage (Prieur et al., 

2008; Richards, 2000; Schmidt, 2007). Most rapeseed is stored for about 3 months before being 

sold. This tendency is driven mainly by market strategy and cash flow. Longer storage periods 

may require lower moisture contents to minimize mite infestation and deterioration through 

rancidity. Mites are the greatest problem faced by rapeseed famers and were observed on more 

than 25% of sites. From the perspective of seed crushers, moisture content (high or low) and 

admixture of stores were the most common reason for rejection or price reductions (Armitage et 

al., 2005). This analysis assumed moisture contents of 13%, 14% and 15% at harvest and 9% 

after drying in the low emissions, baseline and high emissions scenario, respectively. Losses due 

to mites, rancidity and admixture were neglected due to lack of quantitative data but these could 

become important for ill-maintained rapeseed stored over long periods of time.

Table 64: Rapeseed drying and storage assumptions in the low emissions, baseline and high emissions 

scenario

Low Baseline High

Moisture Content

Harvest1 13% 14% 15%

Storage2 9% 9% 9%

Drying Energy Consumption3

Diesel (MJ/Levaporated) n/a 4.7652 6

Grid Electricity (MJ/Levaporated) n/a 0 3.6

Diesel (MJ/Mg) 165.0 277.0 423.5

Grid Electricity (MJ/Mg) 0.0 0.0 254.1

Notes:

1) Mortimer and Elsayed (2006) – Low Case; Average of low and high 

cases – Baseline case; Bernesson et al. (2004) – High Case

2) Prieur et al. (2008), Richards (2000), Mortimer and Elsayed (2006), 

Schmidt (2007)

3) Mortimer and Elsayed (2006) – Low Case; Bernesson et al. (2004) –

Baseline case; Schmidt (2007) – High Case

Approximately one third of farmers surveyed in the UK use ambient air drying to reduce the 

moisture content of their seed from the value at harvest to that required for storage. Drying using 

ambient air can take from 2 to 4 weeks, although 2 weeks is the most common. The other two 

thirds of farmers use hot air dryers to reduce drying times at the expense of increased energy 

consumption (Armitage et al., 2005). This work assumed the use of hot air drying according to 

energy consumption estimates from Mortimer and Elsayed (2006), Bernesson et al. (2004) and 

Schmidt (2007) in the low, baseline and high emissions scenarios, respectively. The assumptions 

regarding the drying and storage of rapeseed used in this work are summarized in Table 64.

7.4.2 Extraction of Oil from Rapeseed

A modified version of the process for oil extraction from soybeans established by Sheehan et al. 

(1998a), including only the processes relevant to rapeseed in an N-hexane extraction facility, was 

used to model the process inputs to extracting oil from rapeseed. Ozata et al. (2009) also used 

this approach in their analysis of biodiesel from rapeseed. The changes to the data from Sheehan 

et al. (1998a) were limited to removing the energy demands for drying as this has been explicitly 

quantified for rapeseed in Table 64. The process energies were converted from energy per unit 

mass of oilseed to energy per unit mass of oil using the oil fractions from the low emissions, 

baseline and high emissions scenarios. The outputs and energy consumption assumed in the 

extraction of oil from rapeseed are shown in Table 65.
73 of 129

Table 65: Process inputs for extracting oil from rapeseeds

Low1 Baseline1 High1

Receiving and Storage

Electricity 18.60 19.02 20.41

Rapeseed Preparation

Electricity 68.76 70.32 75.46

Steam 153.35 156.83 168.31

Oil Extraction

Electricity 11.46 11.73 12.58

N-hexane 94.15 96.29 103.34

Meal Processing

Electricity 63.56 65.01 69.77

Steam 492.54 503.74 540.59

Oil Recovery

Electricity 1.21 1.24 1.33

Steam 77.04 78.79 84.55

Solvent Recovery

Electricity 1.66 1.69 1.82

Steam 0.00 0.00 0.00

Oil Degumming

Electricity 5.38 5.50 5.91

Steam 60.53 61.91 66.44

Waste Treatment

Electricity 1.82 1.86 1.99

Steam 32.56 33.30 35.74

Totals

Electricity 172.45 176.37 189.27

Natural Gas2 1020.03 1043.21 1119.54

N-hexane3 94.15 96.29 103.34

Notes: 

1) All values are in Btu per pound of oil

2) Steam is generated from natural gas with an efficiency of 80%.

3) GREET uses Liquefied Petroleum Gas (LPG) as a surrogate for Nhexane when calculating emissions

7.4.3 Co-Product Usage and Allocation Methodology

In the extraction of oil from rapeseed, rapeseed meal is produced in large quantities as a coproduct (1.22-1.44 kg of meal per kg of oil). This is similar to the extraction of oil from soybeans; 

hence, the same challenges with respect to allocation arise. Rapeseed meal is primarily used as 

an animal feed and could potentially displace barley, corn, and soybean meal. One kg of 

rapeseed meal is equivalent to 0.87 kg of soybean meal on a protein basis (Prieur et al., 2008).

Based on the results from section 7.2.3, the displacement method, as applied here, is not 

appropriate because of the large variation in life cycle GHG emissions that will result from the 

choice of feedstocks (i.e., barley, corn, and soybean meal) displaced by rapeseed meal. The 

mass or energy allocation method may not be most appropriate as rapeseed meal is not valued 

based on its mass, or sold as a commercial energy product. The commercial value of the protein 

in rapeseed meal for animal feed resulted in market value allocation being adopted in this work. 

This is internally consistent with the other pathways considered in this work as well as with Prieur 

et al. (2008), Mortimer and Elsayed (2006) and Ozata et al. (2009).

7.4.4 Transportation of Rapeseed Oil to HRJ Production Facilities

Rapeseed cultivation, harvesting and oil extraction were assumed to occur primarily in France 

and the UK. The one-way distance from the farm to the oil extraction facility was assumed to be 
74 of 129

115 km (Prieur et al., 2008).54 It was then assumed that the United States imports this oil to a 

domestic hydroprocessing facility where it is converted to jet fuel. This transportation profile is not 

available in GREET and was created using the data from Table 66. Default GREET assumptions 

were used for the details of each transportation mode.

Table 66: Transportation profile of Rapeseed Oil from Europe to the United States

Transportation of Rapeseed Oil to European Shipping Ports1

Mode (%)

Truck 100

Distance (kilometers) 150

Transportation of Rapeseed Oil from European Ports to U.S. ports2

Mode (%)

Ocean tanker 100

Distance (kilometers)

Western UK to Eastern US (50%)

Western France to Eastern US (25%)

Southern France to Western US (25%)

Average

5520

5780

7170

6000

Transportation of Rapeseed Oil from U.S. ports to HRJ production facilities1

Mode (%)

Truck 

Rail 

50

50

Distance (miles)

By truck

By rail

160

800

Notes:

1) Author’s own estimates

2) Shipping distances from http://www.searates.com/reference/portdistance/

7.4.5 Land Use Change Emissions from Rapeseed Oil Production

Two scenarios of land usage were considered for the cultivation of rapeseed in France and the 

UK. Rapeseed is an established crop in these areas where over 2 million hectares were 

harvested in 2008 (Eurostat, 2010). The first scenario represents established rapeseed 

production with an assumption that land use change emissions were zero. A second scenario 

resulting in positive GHG emissions was envisioned where rapeseed production is expanded for 

biofuel production on set aside land thus resulting in land use change emissions. Set aside land 

is land that was removed from agricultural production as a result of government mandates to reestablish some environmental benefits to agricultural ecosystems and wildlife. This is similar to 

the Conservation Reserve Program (CRP) in the United States. While removed from production, 

the natural carbon and nitrogen stocks of the land are replenished. Participation by farmers in the 

set aside system has recently changed to voluntary and these lands could be available for 

rapeseed cultivation (The Telegraph, 2009). A return to crop production would cause the 

accumulated carbon and nitrogen in the soils to be depleted over time. The goal of this section is 

to provide an understanding for the reader of how land use change emissions compare to 

emissions from the other five life cycle stages. It is not intended to explicitly quantify the specific 

land use change emissions that would result from expanded palm oil production. 

The estimate of GHG emissions resulting from the conversion of set aside land to rapeseed 

cultivation was based on Schmidt (2007). The total land use change emissions were amortized 

over 30 years with no discounting. The corresponding land use change emissions per Mg of 

rapeseed were calculated by combining the GHG estimate with the yield per hectare; this is 

presented in Table 67.

 

54 Richards (2000) and Mortimer and Elsayed (2006) estimated one-way transport distances of 90 km and 130 km,

respectively.
75 of 129

Table 67: Land use change emissions from rapeseed cultivation on set aside lands in Europe

Scenario

Land use 

change 

Emissions

(Mg CO2e/ha)

Assumed 

Rapeseed yield

(Mg/ha)

Land use 

change 

emissions

(g CO2e/Mg)1

Conversion of 

set-aside land2 94.6

2.79

3.35

3.89

1,129,700

940,800

810,200

Notes:

1) Assumed to be amortized over 30 years with no discounting

2) Estimate from Schmidt (2007)

7.4.6 Results 

The life cycle GHG emissions from the production and use of HRJ fuel from rapeseed oil are 

given in Tables 68 and 69. When no land use change emissions are present, the life cycle GHG 

emissions range from 0.45 to 0.87 times those of conventional jet fuel; however, nitrous oxide 

emissions represent between 39% and 44% of the total. Edwards et al. (2007) found nitrous 

oxide emissions from rapeseed production to be of similar magnitude using an independent, wellvalidated soil chemistry model (DNDC, version 82N). When rapeseed is grown on set aside land, 

the life cycle GHG emissions range from 0.87 to 1.47 times those of conventional jet fuel. The 

variation in the biomass credit is due to minor changes in the allocation scheme through the 

pathway. The transportation of oil across the Atlantic is responsible for only 0.6 g CO2e/MJ; these 

emissions would not have been incurred had the fuel been processed and used within Europe.

Nitrous oxide emissions represent more than approximately 40% of the total life cycle GHG 

emissions from the rapeseed to HRJ pathway. As such, the consequences of the uncertainty 

associated with IPCC correlations are more important for this pathway. Although the magnitude of 

N2O emissions in this work compare favorably with the detailed model used by Edwards et al. 

(2007), the reader should be aware of these inherent uncertainties when comparing different 

pathways for GHG reduction potential in sections 8.1 and 8.2.

Table 68: Summary of results from renewable jet fuel production and use from rapeseed

Land Use Change Scenario R0 Low Baseline High

Key Assumptions

Total Biomass Yield (Mg/ha/yr) 2.79 3.35 3.89

Seed Oil Fraction 45% 44% 41%

Life Cycle CO2 Emissions by Stage

Biomass Credit (gCO2/MJ) -73.7 -70.5 -68.9

Recovery of feedstock (gCO2/MJ) 13.6 17.2 26.4

Transportation of feedstock (gCO2/MJ) 3.2 3.1 3.1

Processing of feedstock to fuel (gCO2/MJ) 7.1 10.3 13.2

Transportation of jet fuel (gCO2/MJ) 0.6 0.6 0.6

Combustion CO2 (gCO2/MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) -49.2 -39.2 -25.7

WTT CH4 emissions (gCO2e/MJ) 1.0 1.3 1.7

WTT N2O emissions (gCO2e/MJ) 17.6 22.4 29.5

Total WTW GHG emissions (gCO2e/MJ) 39.8 54.9 75.9

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.45 0.63 0.87
76 of 129

Table 69: Life cycle GHG emissions for production and use of renewable jet fuel from rapeseed assuming 

cultivation on set-aside land

Land Use Change Scenario R1 Low1 Baseline1 High1

Land use change emissions (gCO2/MJ) 38.4 43.0 52.6

WTW CO2 emissions (gCO2/MJ) 35.8 40.0 48.9

WTW N2O emissions (gCO2e/MJ) 2.7 3.0 3.6

Total WTW GHG emissions 

(gCO2e/MJ) 78.2 97.9 128.5

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.89 1.12 1.47

Notes: 

1) All other input assumptions (rapeseed cultivation, extraction of oil, processing 

of oil to HRJ) are based on those in the R0 emissions case of the 

corresponding scenario.

7.5 HRJ from Jatropha Oil 

The creation of HRJ from jatropha oil is not a pathway available in GREET; hence, supporting 

information was obtained from the literature and a pathway was constructed within the GREET 

framework. The jatropha fruit is composed of an outer capsule containing two or three seeds.

Each seed has a shell and a kernel, which contains oil. This structure differs from other oil seed 

crops discussed thus far because additional co-products beyond meal result from the oil 

extraction process.

There are also concerns with jatropha cultivation for biofuels that do not apply to soy, palm, or 

rapeseed. An overarching concern of jatropha cultivation for fuel production is that the biomass 

co-products are toxic to both humans and animals. Further, there are questions regarding the 

introduction of a non-native invasive species to the North American ecosystem. The toxicology of 

jatropha oil is discussed later in this section while more details on the impacts of invasive species 

within the North American ecosystem are provided in Section 8.4.

7.5.1 Yield and Plant Characterization

Jatropha plants are well adapted to semi-arid conditions, although more humid environments are 

shown to result in higher crop yields. The plant can tolerate high temperatures but it does not 

tolerate frost, which causes immediate damage. Upon removal of the outer capsule, the primary 

products are seeds and the co-products are husks; at this stage, the seeds can be processed 

directly or the shells can be removed through decortication.55 If the seeds are left intact, oil is 

extracted leaving de-oiled cake as the co-product. If the shells are removed before oil extraction, 

oil and meal are created from the kernel and the shells are considered a separate co-product. 

The parts of the jatropha fruit are characterized in Table 70.

Upon examination of 28 growth sites worldwide, a correlation of 0.22 was found between the 

quantity of precipitation and seed yield (Achten et al., 2008). This means that although more 

precipitation is moderately connected to higher seed yields, there are many cases where 

excellent yields have been realized in dry conditions and poor yields realized in wet conditions. 

The majority of data used in this work came from cultivation details and physical characterization 

of jatropha by Reinhardt et al. (2008) and Achten et al (2008). 

Based on these data, it was concluded that an average yield of 2500kg/ha/yr of dry seeds is a 

representative estimate while 5000kg/ha/yr could be realized under optimal management 

practices; 1000kg/ha/yr appears to be a reasonable lower bound (Achten et al., 2008; Reinhardt 

et al., 2008). These values provide the bounds on yield for the low and high emissions cases.

 

55 Decortication is a procedure involving the removal of a surface layer, membrane or fibrous cover. In the case of 

jatropha, this refers to the removal of the shells from the kernels.
77 of 129

Note that yields are quoted in terms of seed weight. To fully characterize the fruit, mass ratios of 

oil to seed, husk to seed and kernel to seed were developed for the low emissions, baseline and 

high emissions cases. These are shown Table 71 in conjunction with yield assumptions.

Table 70: Parts of the jatropha fruit including processed states

Product

Energy 

Content

(MJ/kg)

Subfractions Description

Capsule -- Husk + 

seeds Entire fruit

Husk 15.5 --

Outer core of fruit, surrounding seeds; 

green and ‘fleshy’ in fresh state, later 

brown and dry

Seed -- Shell + 

kernel

Compact unit inside fruit, consists of shell 

and kernel, usually 2-3 per capsule

Shell 19.0 -- Brown or black shell surrounding the 

kernel

Kernel -- -- White compact nucleus of seed, actual 

oil-containing part of the fruit

Oily cake 19.5 Seeds 

(processed)

Leftovers from the mechanical oil

extraction from seeds (incl. shells), 

contains residual oils

De-oiled 

cake 17.5 Seeds (processed)

Leftovers from the mechanical and 

solvent-aided oil extraction from seeds

Meal 18.0 Kernels 

(processed)

Leftovers from the mechanical and 

solvent-aided oil extraction from seeds

Notes:

1) Reinhardt et al (2007 and 2008)

Table 71: Yields and mass fractions characterizing the jatropha fruit

Low Baseline High

Jatropha Yield 

(kgseed/ha/yr)

5000 2500 1000

Oil Ratio (kgoil/kgseed) 0.37 0.35 0.34

Husk Ratio (kghusk/kgseed) 0.48 0.60 0.60

Kernel Ratio (kgkernel/kgseed) 0.67 0.63 0.63

7.5.2 Cultivation of Jatropha Fruit

The input assumptions surrounding jatropha cultivation are based on a 30-hectare test plot in 

India documented by Reinhardt et al. (2008). The inputs required for growth are seedlings, 

irrigation water (first three years only), diesel fuel (for tractor and irrigation pump) and mineral 

fertilizers in the form of nitrogen, phosphorous pentoxide (P2O5) and potassium oxide (K2O). 

Since irrigation water is needed for only the first three years of growth, this analysis does not 

include the diesel fuel required for pumping irrigation water in calculating life cycle GHG 

emissions of HRJ from jatropha. The cultivation assumptions adopted in this work are listed in 

Table 72.
78 of 129

Table 72: Cultivation inputs for the growth of jatropha

Low Baseline High

Cultivation Diesel 

(Btu/kgseed) 1163 1320 1419

Pesticides (g/kgseed) 0 0 0

Nitrogen (g/kgseed) 31.8 34.0 35.3

P2O5 (g/kgseed) 12.6 13.0 13.4

K2O (g/kgseed) 31.3 37.4 37.4

Nitrous oxide emissions from jatropha cultivation were estimated using the IPCC Tier 1 

methodology (IPCC, 2006). Due to the lack of data, N2O emissions from nitrogen in above and 

below ground crop residues were not accounted for in this analysis. The IPCC Tier 1 

methodology estimates the combined direct and indirect conversion rate of nitrogen from 

synthetic fertilizers to N2O emissions as 1.1%. These rates include the atmospheric deposition of

nitrogen volatilized from managed soils; however, nitrogen from leaching and runoff was 

assumed to be negligible, in terms of N2O conversion, as jatropha is ideally grown in well-drained 

or gravelly soils56 (Achten et al., 2008). The formula for calculating N2O emissions from jatropha 

cultivation is given by:

€

N2O Emissions gN2O

kgseed

"

#

$ %

&

' = gnitrogen fertilizer

kgseed

"

#

$ %

&

'⋅ 0.011

"

#

$ %

&

'⋅ 44gN2O

28gN

"

#

$ %

&

' Equation 13

7.5.3 Toxicity of Jatropha Fruit and Oil

Due to the toxicity of jatropha fruit and oils, attention is warranted to the impacts on human health 

and work environment. The fruits contain irritants affecting pickers and manual dehuskers. In 

addition, accidental consumption of the seeds or oils can lead to severe digestion problems. For 

these reasons, intercropping edible crops with jatropha is only recommended during the period 

before any fruit is borne (Achten et al., 2008). Gandhi et al. (1995) evaluated the crude oil in vivo 

and in vitro for toxicity, skin irritation and haemolytic activity. The toxic fraction containing phorbol 

esters was subsequently isolated from the oil. Locals use the oil as a cathartic purgative and for 

treating skin ailments. The seeds are also used for the treatment of dropsy, gout, paralysis and 

rheumatism. Upon administering tests on mice, rats and rabbits, jatropha oil was found to be 

acutely toxic after oral administration, leading to diarrhea, bloodshot eyes and inflammation of the 

gastro-intestinal tract. Topical application not only had an irritant effect but also caused diarrhea 

and mortality in the animals. Gandhi et al. (1995) summarize the impacts of ingestion and contact 

with jatropha oil as a severe health hazard to livestock and humans. The phorbol esters in the oil 

are irritants of skin and mucous membranes produce haemolysis of red blood cells and are 

reported to be tumor promoters (Gandhi et al., 1995). These results lead to the conclusion that 

complete removal of toxins is essential before any industrial applications are considered for 

jatropha oil. The removal of toxins was not modeled as a source of GHG emissions within this 

analysis.

7.5.4 Transportation of Jatropha Fruit to Production Facilities

Even though there are questions regarding the wisdom of introducing potentially invasive species 

such as jatropha to non-native environments, the production of jatropha in this analysis is 

assumed to take place in the southwestern United States. If the oil were produced overseas, then 

there would be an increase in the GHG emissions from transportation, comparable to that 

discussed in regards to rapeseed oil (see Section 7.4.3). After harvesting, assumptions of the 

transportation mechanisms and distances are consistent with GREET defaults for the soybeans 

to HRJ pathway. Specifically, the transportation of capsules is by truck to a local storage area and 

 

56 Nitrous oxide emissions from leaching and run off only apply to soils where the soil water-holding capacity is exceeded 

(IPCC, 2006)
79 of 129

subsequently to a local oil extraction facility. The capsule processing, oil extraction and 

hydroprocessing of oil to HRJ are assumed to occur at the same location with on site capabilities 

for power generation using biomass co-products. As was the case in other HRJ pathways, 

transportation elements of the life cycle GHG emissions are sufficiently small to be within the 

margin of error; therefore, assumptions in this area carry little consequence.

7.5.5 Extraction of Oil from Jatropha Fruit

Extracting oil from the jatropha fruit has a large influence on the life cycle emissions. Before 

beginning oil extraction, a dehusker must remove the husks and obtain the seeds. In small-scale 

production facilities, the seeds are then crushed in a screw press to extract the oil. This method 

can only obtain up to 80% of the oil so larger production facilities mill the seeds into small 

particles and N-hexane chemical solvent is used to obtain up to 99% of the oils (Achten et al., 

2008). The deterministic factor of this life cycle is how the co-products are used (husks, shells, 

meal). It is not necessary to remove the shells from the kernels before the solvent treatment, but 

it is more energy efficient to do so because the additional burden of processing the added 

material from the shells through the chemical solvent is more than the burden of removing the 

shells beforehand. If the shells are removed prior to milling, they are obtained independently of 

the meal and the oil. If the shells were not removed, the seeds could be split into a de-oiled cake 

and the oil itself. Regardless of whether the shells are removed, the resultant product is not 

suitable for animal and human consumption because of its toxicity (see Section 7.5.3).

The co-products from oil extraction can be used in a multitude of capacities. Figure 15 shows the 

products resulting from this process and potential uses for each.

Figure 15: Utilization of by-products from jatropha cultivation and oil extraction (adapted from Reinhardt, 

2007)

When using the co-products from jatropha, caution must be maintained due to its toxicity. For 

example, the use of shells and meal as fertilizer in edible crop production raises bio-safety 

questions and warnings have been issued to the serious lack of information surrounding the 

health effects of burning the oil in closed quarters (Achten et al., 2008).

To estimate the emissions from oil extraction, this work combined the process inputs from 

Reinhardt et al. (2008) with modified processes for oil extraction from soybeans established by 

Sheehan et al. (1998a) to model oil extraction from jatropha fruit in an N-hexane extraction 

facility. The assumed process energies are summarized Table 73.

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80 of 129

Table 73: Process inputs for extracting oil from jatropha fruit

Low1,3 Baseline1 High1

Receiving and Storage

Electricity 33.5 38.3 39.4

Jatropha Preparation

Electricity (briquetting) 93.2 122.8 126.6

Electricity (decorticator) 23.2 24.6 25.3

Electricity (dehusking) 51.6 59.0 60.7

Electricity (cleaning) 3.0 2.9 3.0

Steam (cleaning) 125.9 123.2 127.0

Oil Extraction

Electricity 9.4 9.2 9.5

N-hexane4 77.3 75.7 77.9

Meal Processing

Electricity 38.1 37.3 38.4

Steam 368.7 361.0 371.9

Oil Recovery

Electricity 1.0 1.0 1.0

Steam 63.2 61.9 63.8

Solvent Recovery

Electricity 1.4 1.3 1.4

Steam 0.0 0.0 0.0

Oil Degumming

Electricity 4.4 4.3 4.5

Steam 49.7 48.6 50.1

Waste Treatment

Electricity 1.5 1.5 1.5

Steam 26.7 26.2 27.0

Totals

Electricity 260.3 302.1 311.2

Natural Gas2 792.8 776.3 799.6

N-hexane4 77.3 75.7 77.9

Notes: 

1) All values are in Btu per pound of oil

2) Steam is generated from natural gas with an efficiency of 80%.

3) The low emissions case appears to have higher process energy 

than the baseline; however, this arises due a larger fraction of the 

seed weight going through the extraction processes (see Table 71)

and is counteracted in the fuel production stage.

4) GREET uses Liquefied Petroleum Gas (LPG) as a surrogate for Nhexane when calculating emissions

7.5.6 Co-Product Usage and Allocation Methodology

To understand the influence of co-product type and usage on the life cycle emissions of the fuel, 

four scenarios, outlined in Table 74, were considered. These scenarios examined how the life 

cycle GHG emissions of HRJ from jatropha oil change depending on the use of co-products for 

animal feed, fertilizer or electricity production. The electricity production was further broken down 

to examine displacement of US average grid electricity and energy allocation between oil and 

electricity. The life cycle GHG emissions resulting from each scenario are summarized in Figure 

16. 

As explained below, Scenario 1 was chosen for this analysis. Using the products for power 

generation seems a more logical choice for the biomass co-product because the displacement of 
81 of 129

fertilizer on large scales with a product that is toxic to humans was deemed undesirable due to 

the potential for ground water contamination (this eliminates Scenario 2). Energy allocation was 

chosen over displacement due to the large quantities of co-product generated per unit of oil 

production (this eliminates Scenarios 3 and 4). As shown in Figure 16, the displacement of fossil 

based electricity results in the unrealistic result of negative life cycle GHG emissions for the fuel. 

It is important to understand that the allocation method used in scenario 4 attributes energy and 

emissions based on the electricity produced from the biomass co-products and the energy 

contained in the oil. This is equivalent to expanding the system boundary of the oil extraction 

stage to include the power generation unit. Assumptions regarding the generation of renewable 

electricity from biomass are outlined in Table 75.

Table 74: Co-product creation and allocation scenarios from the oil extraction process for jatropha capsules.

The corresponding life cycle emissions for each scenario are shown in Figure 16

Scenario Co-Product Use Emissions Allocation Method

1 Husks, shells 

and meal Burned for electricity

Energy allocation via energy of 

electricity produced and energy 

contained in the oil

2 Husks, shells 

and meal Fertilizer

Displacement method (system 

expansion) where fertilizers are 

displaced in subsequent cultivation 

practices

Husks and 

shells Burned for electricity

Displacement method (system 

expansion) where electricity from US 

3 grid is displaced

Meal Detoxified and sold 

for animal feed

Market value allocation between oil and 

meal. 

4 Husks, shells 

and meal Burned for electricity

Displacement method (system 

expansion) where electricity from US 

grid is displaced

Figure 16: Sensitivity of life cycle emissions of HRJ from jatropha to co-product utilization and allocation 

scheme. Scenarios are described in Table 74

Table 75: Assumptions used in establishing average US biomass conversion efficiency to electricity

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Generation Method Production 

Share

Generation 

Efficiency

Biomass Utility Boiler 99% 32.1%

Biomass IGCC 1% 43.0%

Note: 

1) Transmission losses of 8% were also included in the overall 

efficiency of generation
82 of 129

7.5.7 Results

The life cycle emissions from the production and use of HRJ fuel from jatropha oil are given in 

Table 76. The most noticeable difference in life cycle GHG emissions of jatropha HRJ in 

comparison to other renewable oil plants is the larger nitrous oxide emissions arising from 

nitrogen fertilizer requirements for plant growth. The life cycle GHG emissions resulting from the 

production and use of jatropha HRJ range from 0.36 to 0.52 times those from conventional jet 

fuel. The variation in the biomass credit is due to minor changes in the allocation scheme through 

the pathway. Because marginal land was assumed and no estimates of root carbon sequestration 

from jatropha were available, the GHG emissions from land use change were assumed to be zero 

and no other land use change scenarios were created.

Table 76: Life cycle emissions from the jatropha oil to HRJ pathway

Low Baseline High

Key Assumptions

Jatropha Seed Yield (kg/ha/yr) 1000 2500 5000

Jatropha Seed Oil Fraction 0.34 0.35 0.37

Life Cycle CO2 Emissions by Stage

Biomass Credit (gCO2/MJ) -73.7 -70.5 -68.9

Recovery of feedstock (gCO2/MJ) 16.1 16.7 17.6

Transportation of feedstock (gCO2/MJ) 1.5 1.5 1.5

Processing of feedstock to fuel (gCO2/MJ) 7.1 10.3 13.2

Transportation of jet fuel (gCO2/MJ) 0.6 0.6 0.6

Combustion CO2 (gCO2/MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) -48.5 -41.3 -36.1

WTT CH4 emissions (gCO2e/MJ) 1.0 1.2 1.4

WTT N2O emissions (gCO2e/MJ) 8.9 9.1 9.4

Total WTW GHG emissions (gCO2e/MJ) 31.8 39.4 45.1

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.36 0.45 0.52

Nitrous oxide emissions represent more than 20% of the total life cycle GHG emissions from the 

jatropha to HRJ pathway. As such, the consequences of the uncertainty associated with IPCC 

correlations are more pronounced for this pathway. The reader should be aware of these inherent 

uncertainties when comparing different pathways for GHG reduction potential in sections 8.1 and 

8.2. 

7.6 HRJ from Algae Oil

The creation of HRJ from algae oil is not a pathway available in GREET; hence, supporting 

information was obtained from the literature.57 Algae can be grown in an open pond, a controlled 

bioreactor, or a combination of both. A typical open pond approach consists of a raceway (oval) 

shaped pond using a paddlewheel to circulate the water and mix the algae for even light 

exposure and growth. In bioreactors, the algae are grown in sheets or tubes, allowing for much 

higher growth rates per unit area than open ponds. Bioreactors shield the algae from weather 

variations and facilitate growth in vertical geometries thereby reducing land requirements; 

however, these designs are cost intensive relative to open ponds. Only open pond technologies 

were examined in this analysis because of their reduced capital costs, the relative abundance of 

experimental documentation, and their increased technological readiness (relative to bioreactors).

Given time, the capital costs of bioreactors could decrease as technological advances are made; 

 

57 Algae oil is not a preexisting pathway within GREET. As such, a new pathway was built within the GREET framework 

using the soy oil to renewable diesel pathway as a guide.
83 of 129

these concepts will be examined further as a part of this continuing research effort. Furthermore, 

only conventional means of oil extraction using chemical solvents were considered in this report. 

As was discussed in the introduction of Chapter 7, there is currently much research devoted to 

other methods of oil extraction including genetic modification of the algae such that they naturally 

excrete oil and using electric shocks to open the algal cells. Such methods were not considered 

here.

7.6.1 Algal Yield and Lipid Content

The two defining characteristics for algae as a biofuel are its growth rate (generally given in 

grams/m2/day) and lipid content (generally given as a weight percent of total biomass). Both 

quantities vary greatly within the literature as they depend on variables including algae type and 

weather conditions, among many others. Generally higher algal growth rates reported in the 

literature represent bioreactor technology and not open ponds. Some recent presentations have 

employed yields equivalent to 140g/m2/day at 25% lipids content; an estimate over 550% of the 

yields adopted in this study (Daggett, 2007). The current analysis is based on the engineering 

judgment of the authors gained from their literature review of open ponds. During peak periods of 

growth, 50g/m2/day could be achieved, but a yearly average of 20 g/m2/day appears to be more 

reasonable (Ben-Amotz, 2008; Sheehan et al., 1998b). A survey of algal strains also returned a 

range of lipid contents up to 40% (Becker, 2007). Assuming that technology will improve by the 

simulation year of 2015, 50 g/m2/day and 40% lipids by weight was adopted for the low emissions 

case, 25 g/m2/day and 25% lipids by weight was adopted for the baseline case and 20 g/m2/day 

at 15% lipids by weight was adopted for the high emissions case.

7.6.2 System Expansion (Displacement) for Electricity Emissions

While much of the methodology for the analysis of algal HRJ is similar to that discussed for other 

HRJ pathways, the life cycle is complicated by the need to feed CO2 to the algae to sustain 

acceptable growth rates. As schematically shown in Figure 17, system expansion (displacement 

method) was used instead of energy allocation when apportioning emissions between the fuel 

and any electricity generated in providing the CO2 required for growth. Justification for this choice 

is rooted in the argument that electricity used for CO2 production operates independently of the 

algae cultivation and hence, should be treated differently than co-products physically created 

from the cultivation itself. The system boundary was expanded to include both the electricity and 

emissions from a power plant producing equivalent electricity to that within the original system 

boundary. The expanded system had both HRJ and biomass co-products leaving the system 

boundary, but zero net electricity exiting the expanded system. Thus, the expanded system could 

be treated in a similar fashion as the other HRJ pathways.

The life cycle GHG emissions from the type of electricity used to supply CO2 for algal growth and 

the type of electricity being displaced both impact the life cycle emissions of the algal fuel. This is 

due to the variation that could exist in the emissions per kilowatt-hour of different power 

generation technologies (e.g. a coal utility boiler has higher emissions than an integrated 

gasification combined cycle which, in turn, has higher emissions than a nuclear power plant). The 

options in Table 77 outline the effects on the life cycle emissions that these choices could cause.

When CO2 created from ‘dirty’ electricity is used to feed algal growth, but ‘clean’ electricity is 

included in the expanded system then the CO2 emissions credit is less than the CO2 used to grow 

the algae; this results in a fuel that appears to be ‘dirty’ in terms of higher CO2 emissions. When 

CO2 created from ‘clean’ electricity is used to feed the algae but ‘dirty’ electricity is included in the 

expanded system then the CO2 emissions credit is greater than the CO2 used to grow the algae; 

this results in a fuel that appears to be ’clean’ in terms of lower CO2 emissions. In this analysis, 

the electricity used to provide the CO2 was assumed to be the same as that in the expanded 

system. For this configuration, the CO2 emissions credit was approximately equal to the biofuel 

combustion emissions, which is the assumption used for biofuels that have not been ‘fed’ CO2 for 

enhanced growth.
84 of 129

Figure 17: System boundary expansion of the algal HRJ fuel pathway

Table 77: Impact of CO2 source and electricity choice on the biomass credit given to algal HRJ

CO2 Source for 

Algae Growth

Electricity displaced in 

expanded system 

boundary

CO2 “Biomass Credit” 

allocated to the algal fuel

Conventional Coal Biomass Credit ≈ Combustion 

CO2

US Average Grid Biomass Credit < Combustion 

CO2

Conventional Coal 

Electricity

Nuclear (zero CO2) Biomass Credit ≈ 0

Conventional Coal Biomass Credit > Combustion 

CO2

US Average Grid Biomass Credit ≈ Combustion 

CO2

US Average Grid 

Electricity

Nuclear (zero CO2) Biomass Credit ≈ 0

Carbon dioxide usage in algae cultivation can vary depending on the lipid content and meal 

carbon content of the algae. A simpler approach was adopted in this analysis where data from the 

literature were used to conclude that algae as a whole is approximately 60% carbon (Weissman 

and Goebel, 1987; Kadam, 2001). The supporting literature indicates that CO2 requirements 

range from 2.16-2.2 kg per kg of algae grown. Emissions factors for power plant technologies 

from GREET are listed in Table 78 (these are not the full life cycle GHG emissions from power 

generation, rather they are just the CO2 available to be captured and used from the power plant 

itself). Depending on the power plant, between 0.23 and 0.5 kg of algae could ideally be grown 

from the CO2 resulting from each kWh of electricity. This includes electricity generated from 

biomass.

Electricity 

Biomass Co-Product 

GHG = N2O, CH4, CO2

Raw Material 

Movement 

Jet Fuel 

Production

Jet Fuel 

Transportation 

Jet Fuel 

Combustion 

CO2, PM, NOX, SOX, H2O 

Recovery & 

Extraction 

Recovery & 

Extraction 

Raw Material 

Movement 

Electricity 

Production

CO2

GHG = N2O, CH4, CO2

Electricity 

Production 

GHG = N2O, CH4, CO2

? 

Electricity 

Aviation 

Productivity 

Original System Boundary 

Expanded System Boundary 

Other Liquid Fuel Products 
85 of 129

Table 78: US power generation data as it relates to algae cultivation 

Generation Technology

Fraction of 

US Mix

(%)

Power 

Plant 

Efficiency

(%)

Power Plant 

Emissions

(gCO2/kWh)

Plant Capacity 

for Algae 

Cultivation

(kgalgae/kWh)

Oil-Fired 2.6 34.8 834 0.38

NG-Fired 22.5 43.5 495 0.23

Coal-Fired 48.7 36.1 1027 0.47

Biomass Fired 1.3 32.2 1084 0.50

Nuclear 17.6 -- 0 0

Other (hydro, wind, etc.) 7.3 -- 0 0

Notes:

1) All data from GREET (2008)

2) Based on 2.18 kg CO2 being required per kg of algal growth

7.6.3 Algal Carbonation Options and Technologies

Algae cultivation using an open pond configuration can be supplied CO2 as a part of flue gas 

either from an adjacent power plant or from a purified CO2 stream. Flue gas is approximately 14% 

CO2 (Kadam, 2001) but can be purified to more than 99% CO2 using the recyclable solvent 

monoethanolamine (MEA). The process known as MEA extraction is specifically targeted to 

remove large amounts of CO2 from flue gas. The greatest advantage of MEA extraction is its 

relatively high carrying capacity in terms of the amount of CO2 absorbed per unit volume of 

solvent. Less solvent circulation is required for a given system performance specification, leading 

to lower capital and operating costs than other solvent extraction methods (Herzog et al., 1991).

Little data exists documenting the benefits, if any, of using pure CO2 compared to flue gas as a 

carbon source for algae cultivation. The assumption here is that both methods result in the same 

algae yield. The disadvantage of flue gas manifests from the compression and transportation of 

over seven times more gas volume for the same quantity of CO2. For this reason, flue gas usage 

is only considered an option for algae cultivation facilities co-located with their CO2 source. The 

disadvantage of pure CO2 is that flue gas must be scrubbed to remove all non-CO2 elements and 

subsequently compressed for transportation; flue gas requires only a simple blower. Scrubbing 

flue gas using the MEA process requires substantial quantities of steam and energy intensive 

compression. The compression can represent between 13 and 30 percent of the total process 

energy consumption (Herzog et al., 1991). Table 79 compares the energy usage of direct flue gas 

injection with two studies estimating energy usage for the MEA process. In both MEA studies, 

98% of the energy results from producing steam to strip CO2 from the amine solution. The 

remainder of the MEA process energy, and all energy for direct injection, comes from electricity.

Table 79: Comparison of energy inputs of direct flue gas injection and MEA extraction

Total Energy

(MJ/tCO2)

Total 

Energy

(MJ/kgalgae)

Emissions

(gCO2/kgalgae)

Direct Injection

Kadam et al. (2001) 80 0.18 35

MEA

Kadam (2001) 6650 14.63 911

Herzog et al. (1991) 6097-10248 13.41-22.55 835-1404

Note: 

1) Steam production is assumed to occur using an 80% efficient industrial 

boiler

The emissions to create a pure CO2 stream are over 25 times higher than those from using flue 

gas from a co-located source. For this reason, the low and baseline scenarios adopted the 

assumption that algae cultivation occurs adjacent to a power plant, thus using flue gas as the 
86 of 129

CO2 source. The high emissions scenario assumed that CO2 must be scrubbed from the flue gas 

using MEA extraction prior to compression for transportation and storage, according to the 

estimate by Kadam (2001). This assumption is appropriate for facilities that are not located in the 

immediate vicinity of a CO2 source. Furthermore, transportation energy is not included in the 

estimates of Table 79 and would be a function of distance between the CO2 source and algae 

cultivation facility.

7.6.4 Cultivation of Algae in Open Ponds

The process energy and nutrient requirements for algae cultivation were established from two 

sources: (1) a simulated open pond algae farm that was used to capture flue gas from an 

adjacent fossil power plant and (2) design specifications for an open pond system used for fuel 

production. The simulated open pond algae farm parameters were obtained for a 1000-hectare 

algal production system based on primary bench scale data and process modeling (Kadam, 

2001). Weissman and Goebel generated the open pond design specifications for a 192-hectare 

system in 1987 (Weissman and Goebel, 1987). While Kadam presents all energy inputs of 

cultivation and harvesting as a single value, Weissman and Goebel show the inputs for the 

individual steps of mixing, pumping, primary harvesting and secondary harvesting. The data from 

both studies are in good agreement and were amalgamated to form the inputs to this analysis.

Since both studies quote similar electrical inputs, all electricity estimates were taken from 

Weissman and Goebel due to their higher degree of detail. In their design specifications, electric 

motors were assumed to operate with a drive efficiency of 70%. Drive efficiency encompassed 

the motor, an in-line speed reducer and a two-stage chain and sprocket reduction. This study 

considers a simulation year of 2015; hence, modern motors and gearing mechanisms could be 

incorporated. A modern three-phase (variable speed) electric motor meeting similar cost and 

performance requirements is rated as up to 92.4% efficient (Marathon Electric, 2009). The 

mechanical efficiency of a chain and sprocket has been found to reach 98.6% in ideal conditions 

and 81% in poor conditions (Spicer et al., 2001). Assuming both a motor and connection 

efficiency of 90%, the drive efficiency climbs to 81%, corresponding to an improvement of 15.7% 

above Weissman and Goebel’s estimates. This efficiency improvement was applied to all process 

inputs related to electric motors or electric pumps. 

The cultivation inputs used for the low, baseline and high emissions scenarios are shown in Table 

80 (normalized per kilogram of algae) while harvesting and drying are dealt with in subsequent 

sections.

Table 80: Cultivation inputs per kilogram of algae for algae growth in an open pond

Low Baseline High

Nutrients

CO2 (kg) 2.16 2.2 2.2

Nitrogen (kg) 0.044 0.053 0.053

Superphosphate (kg) 0.019 0.029 0.039

Potassium Sulfate (kg) 0.030 0.030 0.030

Process Energy

Mixing (Btu) 281.8 281.8 281.8

Water Supply (Btu) 229.8 229.8 229.8

Nutrient Supply (Btu) 16.2 16.2 16.2

Nitrogen from fertilizer use was assumed to come only from ammonia. As a well-established 

fertilizer in the farming industry, ammonia has a production pathway within the GREET 

framework. The production pathways of superphosphate and potassium sulfate are not 

established within GREET. The process energies required for the production of superphosphate 

were adopted from Anderi Silva and Alexandre Kuley (2003). Specific inputs for potassium sulfate 

were not available in the literature. A final emissions inventory from potassium sulfate production 
87 of 129

was taken from Kadam (2001). The emissions resulting from the production of nitrogen fertilizer, 

superphosphate and potassium sulfate are summarized in Table 81.

Table 81: Life cycle GHG emissions from the production of nutrients used in algae cultivation

Nitrogen Superphosphate Potassium 

Sulfate1

CO2 2537 235 -3701

CH4 62.5 7.5 1.5

N2O 5.6 1.3 1.5

Total 2605 244 -367

Notes:

1) Negative process energy flows occur due to displacement 

allocation being used in their life cycle assessment

2) Units are g CO2e/kg of nutrient

Nitrous oxide emissions from algae cultivation were estimated using IPCC Tier 1 methodology for 

flooded rice fields (IPCC, 2006). It was assumed that the conversion rate of nitrogen contained in 

a flooded rice field is similar to the conversion rate of nitrogen from an open pond. Open ponds 

were the only source of N2O emissions from algae considered in this work. The IPCC Tier 1 

methodology for flooded rice fields estimates the direct conversion rate of nitrogen from synthetic 

fertilizers as 0.3%. No mechanisms for indirect emissions were considered due to a lack of 

information. The formula used in this study for calculating N2O emissions from algae cultivation is 

given by:

€

N2O Emissions gN2O

kg

"

#

$

%

&

' = gnitrogen fertilizer

kg

"

#

$ %

&

'⋅ 0.003

"

#

$

%

&

'⋅ 44gN2O

28gN

"

#

$

%

&

' Equation 14

7.6.5 Nutrient Recycling via Anaerobic Digestion

Fertilizer production is a non-negligible GHG source in the algae to HRJ pathway. As can be seen 

from the data in Table 82, which were derived from Tables 80 and 81, the nitrogen fertilizer 

emissions are comparable to those from the baseline soybean to HRJ pathway. High quantities of 

fertilizer in any algal effluent leaving the system can also lead to eutrophication of natural waters. 

The additional fertilizer can over-stimulate the production of organic compounds leading to 

negative environmental effects such as hypoxia and reductions in water quality, fish, and other 

animal populations. If not properly contained, the environmental impacts of this process include 

decreased biodiversity, changes in species composition and dominance, and toxicity. These 

considerations warrant an investigation into nutrient recycling systems to reduce fertilizer 

demand. The analysis that follows assumes that all nitrogen and phosphorous is contained in the 

meal after oil extraction.

Table 82: GHG emissions resulting from fertilizer production within the algae to HRJ pathway for the three 

emissions cases

Low Baseline High

Nitrogen (gCO2e/kgalgae) 114.6 130.3 138.1

Superphosphate (gCO2e/kgalgae) 4.6 7.1 9.5

Potassium Sulfate (gCO2e/kgalgae) -11.0 -11.0 -11.0

One means of nutrient recycling is anaerobic digestion, wherein microorganisms break down 

carbon-based matter in the absence of oxygen. It is widely used as a renewable energy source 

because the process produces a methane-rich biogas. Other products of anaerobic digestion are 

a liquid effluent and a solid digestate. Of interest here is the nutrient-rich liquid effluent that can 

be used to supplement fertilizers. A conventional reactor is maintained at an operating 

temperature of approximately 35°C. Retention times for conversion range from 20 to 30 days 

where about 60% of organic carbon is converted to biogas. The biogas composition is typically 

60% methane and 40% CO2 with traces of hydrogen sulfide and water vapor (Chynoweth et al, 
88 of 129

2001). By using the algal meal co-products to feed a digester, a substantial nutrient fraction could 

be recovered. Such systems would also allow for the recycle of carbon contained in the meal, 

thus reducing the demand on an external CO2 source.

Several factors affect the rate of digestion and biogas production. The most important is 

temperature. Anaerobic bacteria communities can endure temperatures ranging from below 

freezing to above 57°C, but they thrive best at temperatures between 37°C and 54°C. Bacteria 

activity, and thus biogas production, falls off gradually from 35°C to 0°C (DOE, 2008). In some 

cases, the conventional design discussed above is being replaced by more innovative designs 

influenced primarily by the suspended solids content of the feed. Designs for feed with 

intermediate solids contents (such as sewage sludge or aquatic plants) involve recycling the 

solids following settling within the digester. Such designs have increased loading rates 20-fold 

and improved process stability. Furthermore, the biodegradability of certain feedstocks has been 

seen to exceed 90% (Chynoweth et al., 2001).

The process flows for an algae system that includes an anaerobic digester are shown in Figure 

18. It is assumed that the algae are being grown in areas with sufficient heat and sunshine to 

sustain the required temperature of 35°C and the energy required to maintain a suitable 

temperature is negligible from the life cycle analysis standpoint. Furthermore, any additional 

heating requirements would be low-grade and could be met using flue gas before injection into 

the pond or exhaust gas from the drying facility.

Figure 18: Process flows for algae oil HRJ using anaerobic digestion to recover nutrients from algae meal

Weissman and Goebel explored the potential for nutrient recovery from algae meal using 

anaerobic digestion. They projected that 50% of carbon not converted to biogas, 75% of all 

nitrogen and 50% of all phosphorous could be recovered in the liquid effluent. The remainders 

would be contained in the digestate, which is considered a waste of zero value. 

The carbon content of algae meal can vary depending on the total carbon content and lipid 

faction of the algae. It is also directly proportional to the methane production potential from 

anaerobic digestion. Algae was assumed to have a constant carbon content of 60% while lipids 

were assumed to have a constant carbon content of 76.1% (based on their molecular formula). 

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Under these assumptions, the carbon content of meal, and thus the methane production 

potential, is a function of lipid fraction.58

When digested, the resulting biogas can be used directly to meet internal heat demands or to fuel 

any other heat engine. Methane within the biogas can be upgraded to the same standards as 

fossil natural gas. If allowed, the biogas may be utilized within the local gas distribution networks. 

Gas must be cleaned to reach pipeline quality, and be of the correct composition for the local 

distribution network to accept it. These restrictions on raw biogas usage outside the system lead 

to the conclusion that on-site usage is the most practical use for biogas produced from anaerobic 

digestion. 

7.6.6 Dewatering and Drying

After cultivation, algae can represent as little as 1 part in 2000 in water (Kadam, 2001; Weissman 

and Goebel, 1987). Conventional chemical oil extraction technologies require a feedstock that is 

approximately 90% dry. The task of extracting and drying the solid algae is the most energy 

intensive step of the cultivation process. As shown schematically in Figure 19, the algae must first 

be harvested, then dewatered and finally dried to reach the desired concentration. The 

applicability of algae as an environmentally beneficial biofuel when using conventional oil 

extraction facilities can be highly dependent on the extent of dewatering and the method of 

drying. 

Figure 19: Flow chart showing the steps involved in dewatering and drying of algae

Generally, harvesting and initial dewatering is performed by mechanical or gravitational force 

while drying relies on direct heating to evaporate the remaining water. Due to the high latent heat 

of water, dewatering and drying have considerable impact on the life cycle GHG emissions of the 

pathway. Additional dewatering and less drying generally lead to reduced emissions. Solar, 

natural gas, algae meal and biogas from the anaerobic digestion of algae meal were considered 

as options for providing the drying energy. Common types of dewatering procedures are 

flocculation59 or settling, filtering with a vacuum or press and centrifugation. The performance of 

each method is characterized by the total suspended solids (TSS) of the initial feed (given as a 

percentage by mass), the concentration factor,60 energy usage per m3 of throughput and energy 

usage per kg of processed solids. The initial TSS relates the energy usage per kg of processed 

solids to the energy usage per m3 of throughout. 

Weissman and Goebel consider three different two-stage harvesting and dewatering

configurations. Each begins with a solids concentration of 0.075% and is comprised of primary 

harvesting using a micro strainer, vacuum belt filter or settling pond followed by secondary 

dewatering using a centrifuge to achieve 10% solids. The centrifuge dominates energy 

consumption from this step; hence, a higher concentration factor in the primary harvesting step 

reduces energy consumption in the centrifuge. Specifications for a belt-filter, micro-strainer and 

settling pond are given in Table 83 (Shelef et al., 1984). Notice that the concentration factor of the 

 

58 This assumption is most likely only valid for lipid contents between 0 and 50%; however, that is irrelevant because 

algae strains considered in this work do not exceed 50% lipids.

59 Flocculation is a process where particles come out of suspension in the form of floc or flakes. The action differs from 

precipitation in that, prior to flocculation, the particles are merely suspended in a liquid and not actually dissolved in a 

solution.

60 Concentration factor is defined as the ratio of initial feed volume to concentrated volume after separation.

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belt filter and settling pond are higher than that of the micro-strainer. In all cases, flocculent must 

be used to aid in the harvesting process; however, the environmental impacts of the flocculent 

were assumed to be negligible in terms of the life cycle GHG emissions.

Table 83: Energy consumption and performance specifications for primary algae harvesting mechanisms

Micro-strainer Vacuum Belt Filter Settling pond

Electricity

(Btu/kg)

Concentration 

Factor

Electricity

(Btu/kg)

Concentration 

Factor

Electricity

(Btu/kg)

Concentration 

Factor

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It is desirable to extend the centrifuge energy consumption estimates to explore the impacts of 

varying the %TSS after dewatering. The result of combining the energy consumed per m3 of 

throughput with the initial TSS is the energy consumed per kg of processed solids. The 

relationship between the energy consumed per kg of processed solids and concentration factor is 

plotted in Figure 18 and was subsequently used to define a continuous function for the centrifuge 

energy consumption. Data points originate from the three configurations described by Weissman 

and Goebel and independent average data for a solids ejecting disc centrifuge from Shelef et al. 

(1984). The advantage of this type of centrifuge for algae harvesting is its ability to reliably 

dewater to 12-25% solids, although, solids finer than algae may be retained in the overflow 

stream. Based on a review of the technology survey conducted by Molina Grima et al. (2003) and 

the conclusions of Shelef et al. (1984), even the best centrifuges appear to have a concentration 

limit of 25% TSS. More common results place the final TSS level around 15 percent. While 

following the energy consumption mapped in Figure 20, this analysis assumed that dewatering 

can occur to a maximum of 25% TSS. 

Figure 20: Energy consumption of a centrifuge secondary harvesting mechanism. Line is a fit through four 

data points

The values in Figure 20 are based on historical studies and may be pessimistic for a simulation 

year of 2015. As such, the efficiency improvement of 15.7% discussed with regards to cultivation 

was applied to all electrical inputs of harvesting and dewatering. 

Each method must be considered within the context of reliability and scalability. The advantages 

of micro-strainers are their operation, low energy consumption, and high filtration ratios. Their 

problems include incomplete solids removal and the buildup of bacterial and algae slime on the 

micro fabric (Shelef et al., 1984). Regardless of their merits, micro-strainers result in high-energy 

consumption at the centrifugation stage due to their low concentration factor, making them an 

undesirable choice. Belt filters and settling ponds are both promising from the perspective of 

energy consumption and they have been assessed as a reliable method for harvesting (given the 

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use of a flocculent). Furthermore, the concentration factors estimated by Weissman and Goebel 

are reasonable when compared to data from Shelef at al. (1984). Because some experiments 

have indicated that belt filters may be less successful at harvesting small algae such as Chlorella

(Weissman and Goebel, 1987), settling ponds were chosen for the low, baseline and 

highemissions scenarios.

Figure 21: An examination of the post-dewatering moisture content impact on energy consumption of drying 

algae to 90% total suspended solids

The impacts of dewatering on drying energy requirements are demonstrated in Figure 21. As the 

extent of dewatering increases, there is a sharp reduction in the energy required for drying to 

90% TSS. Drying energy must be in the form of direct heat in order to evaporate water from the 

algae slurry. Solar drying is considered to be an optimistic choice but is not out of the realm of 

possibility, with some reports estimating a drying area only 12% of that required for growing 

(Shelef et al., 1984). The low emissions scenario therefore adopted solar drying while the 

baseline and high emissions scenarios require the combustion of a process fuel for heat. The 

process fuel may be natural gas, algae meal or biogas from the anaerobic digestion of algae 

meal.

As discussed in the previous section, an opportunity for system integration is available by using 

energy contained in the algae meal to supply internal energy demands. This energy can be 

obtained by burning the meal directly in a boiler or by combusting the biogas produced from 

anaerobic digestion. Depending on the quantity of meal produced per kg of algae, some or all of 

the drying energy can be supplied by these methods. This threshold point, where all drying 

energy is supplied by the meal, is fundamentally related to the lipid content and the postdewatering %TSS content. Designing a sustainable drying technique that does not rely on the 

combustion of non-renewable fuels is essential in creating a sustainable fuel. 

The threshold lipid content is also dependent on whether the meal is burned directly or whether 

anaerobic digestion is used to obtain biogas. These relationships are explored in Figure 22 where 

two different biomass conversion factors from anaerobic digestion are compared to directly 

burning the meal in a boiler. While both methods result in zero net GHG emissions from the 

drying process, nutrient recovery is not possible with meal combustion. The opportunity to 

recover nutrients leads to the conclusion that an algae cultivation facility concerned with 

minimizing GHG emissions would be equipped with an anaerobic digester. Therefore, the low and 

baseline emissions scenario assumed the use of an anaerobic digestion system with biomass 

conversion factors of 80% and 70% respectively. The high emissions scenario assumed the meal 

is sold as animal feed.

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Figure 22: Algal lipid content and post-dewatering moisture content that result in a sustainable drying 

process. Points above each line require additional energy to be added (e.g. from natural gas)

For an algae strain comprised of 25% oil by mass, sufficient meal is produced to supply the 

energy to dry other algae from a post-dewatering %TSS content of 19% via direct burning and 

from 17% to 21% via anaerobic digestion (depending on anaerobic digester efficiency). If the 

post-dewatering %TSS content were above these threshold values then the algae meal would 

need to be supplemented by natural gas to supply sufficient energy for drying to 90% TSS. 

The decision to use algae meal or natural gas as a drying fuel must be made in the context of the 

economic, technological and energy usage limitations of dewatering. The trade-offs between 

these factors and other system parameters can only be understood by considering the entire 

algal production system. 

7.6.7 Transportation of Algae to Production Facilities

The production of algae in this analysis is assumed to take place in the southwestern United 

States. After harvesting, assumptions about the transportation mechanisms and distances have 

been modified from GREET defaults for the soybeans to HRJ pathway. When meal is used as an 

energy source, oil extraction must be collocated with the cultivation facility to enable the recycling 

and integration of energy flows. In these configurations, only the oil is transported from the 

cultivation site to a hydroprocessing facility. Specifically, the transportation is by tanker truck in 

25-ton shipments; raw algae transportation would occur in truckloads of only 15 tons. In cases 

where meal is sold as a supplement to animal feed, standard GREET transportation assumptions 

were adopted. As was the case in every other pathway considered, the contribution from 

transportation elements in the life cycle analysis are sufficiently small to be within the margin of 

error; therefore, the importance of these assumptions is minimal. As such, the geographic 

location of the facility is of secondary importance to the overall results. However, as was 

discussed in Section 7.6.3, the relative location of the algae facility to its CO2 source is very 

important to the life cycle GHG emissions.

7.6.8 Extraction of Oil from Algae

The extraction of oil from algae is currently an area of considerable research. This analysis used 

a modified version of the process for oil extraction from soybeans established by Sheehan et al. 

(1998a) that includes only the processes relevant to algae in an N-hexane extraction facility. The 

process energies from Sheehan et al. were converted from energy per unit mass of biomass to 

energy per unit mass of oil using the oil fractions from the low emissions, baseline and high 

emissions scenarios. The outputs and energy consumption assumed in the extraction of oil from 

algae are shown in Table 84.

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Table 84: Outputs and process energy for N-hexane oil extraction from algae

Low1 Baseline1 High1

Algae Lipid Content 40% 25% 15%

Meal Produced (lbmeal/lboil) 1.5 3 5.67

Receiving and Storage

Electricity 20.9 33.5 55.8

Algae Preparation

Electricity 4.0 6.5 10.8

Steam 172.5 276.0 460.0

Oil Extraction

Electricity 12.9 20.6 34.4

N-hexane3 105.9 169.5 282.5

Meal Processing

Electricity 52.2 83.5 139.1

Steam 505.4 808.6 1347.7

Oil Recovery

Electricity 1.4 2.2 3.6

Steam 86.7 138.7 231.1

Solvent Recovery

Electricity 1.9 3.0 5.0

Steam 0.0 0.0 0.0

Oil Degumming

Electricity 6.1 9.7 16.1

Steam 68.1 109.0 181.6

Waste Treatment

Electricity 2.0 3.3 5.4

Steam 36.6 58.6 97.7

Total

Electricity 101.4 162.2 270.3

Natural Gas2 1086.6 1738.6 2897.6

N-hexane3 105.9 169.5 282.5

Notes:

1) All values are in Btu per pound of oil

2) Steam is assumed to be generated from natural gas with an 

efficiency of 80%.

3) GREET uses Liquefied Petroleum Gas (LPG) as a surrogate for Nhexane when calculating emissions

As discussed previously, algae meal resulting from oil extraction can be burned in a utility boiler, 

used as a feed to an anaerobic digester or sold as a substitute product to soy meal consumed as 

animal feed. When burned in a boiler, the direct heat can be used to fuel the drying process. If 

there is excess meal after drying is completed, the rest is sold as animal feed; if meal is unable to 

provide all the drying energy then natural gas could be used to supply the difference. When 

digested, the resulting biogas can be used directly to fuel the drying process or any other heat 

engine. Methane within the biogas can be upgraded to the same standards as fossil natural gas. 

If allowed, the biogas may be utilized within the local gas distribution networks. Gas must be 

cleaned to reach pipeline quality, and be of the correct composition for the local distribution 

network to accept it. These restrictions on raw biogas usage outside the system lead to the 

conclusion that on-site electricity generation is the most practical use for biogas not consumed for 

drying. Any electricity generated over and above the internal demand of cultivation is considered 

exportable to the grid and emissions from cultivation and oil extraction were allocated between oil 

and exported electricity based on energy. If insufficient biogas is produced to provide all the 

drying energy then natural gas could be used to supply the difference. 
94 of 129

When sold as an independent product, market value allocation was used in partitioning emissions 

between algae oil and meal. As discussed with regards to soy oil to HRJ, using market value 

allocation captures temporal changes in the allocation fractions between products. Since no 

established market price exists for algae oil, or algae meal, they were assumed related to the 

prices of soy oil and soy meal. Prices assumed for soy oil and soybean meal are the 2015 

projections made by FAPRI (FAPRI, 2009). Algae oil was assumed to have the same market 

price as soy oil, $1.05/kg, while algae meal was assumed to have a price related to that of soy 

meal based on protein equivalency. Protein content serves as a common denominator because 

both meals would be used as animal feed. Soybean meal was assumed to have a protein content 

of 48% (Ahmed et al., 1994) and algae meal to have an average protein content of 52% (Becker, 

2007).61 Based on these protein contents, a price of $0.31/kg was calculated for algae meal.

7.6.9 Sensitivity Analysis and System Design

For algal facilities yielding a fixed lipid content, the life cycle GHG emissions of fuel production 

are most sensitive to the extent of dewatering. Using the relationships and assumptions

discussed in the previous section, life cycle GHG emissions were explored for a range of postdewatering %TSS contents. Using assumptions consistent with the baseline case, the results are 

plotted in Figure 23 for three different system configurations. It can be seen that maximum 

dewatering is always optimum for minimizing life cycle GHG emissions, regardless of the source 

of drying energy and use of meal. If the post-dewatering %TSS content is less than 15%, algal 

HRJ results in higher GHG emissions on a life cycle basis than jet fuel from conventional 

petroleum. Recall that dewatering to 25% TSS content was assumed as the maximum 

dewatering achievable with current technology. By comparing the two natural gas drying cases, 

one can see the benefit of nutrient recycling as cases using fertilizer to meet all nutrient demands 

resulted in approximately twice the GHG emissions, for higher post-dewatering %TSS contents, 

as those where nutrients were recycled. The lowest life cycle GHG emissions were achieved by 

using biogas from the anaerobic digester and recycled nutrients.

The low emissions, baseline and high emissions scenarios assumed post-dewatering %TSS 

contents of 25%, 20% and 15% respectively. While the high emissions scenario assumed natural 

gas as the fuel used for drying, the baseline scenario was constructed with an anaerobic digester 

producing biogas configured to supply the drying energy. The low scenario assumed solar drying 

with an anaerobic digester producing biogas, which would then be used for on-site electricity 

generation to meet internal demands with excess power being exported to the gird. 

Local sensitivity analysis was conducted on the anaerobic digestion biomass conversion 

efficiency, lipid content and CO2 injection method. Each parameter was varied with all others held 

at their baseline values with the impacts quantified as a percent change from the baseline value. 

Figure 24 presents this information in a manner that allows the magnitude of each change to be 

seen in comparison to the others. Section 7.6.3 discussed the use of MEA extraction to create a 

pure CO2 stream from flue gas. The importance of collocating the cultivation facility to its CO2

source is further emphasized by these results. The unexpected result that life cycle GHG 

emissions are reduced by using algae with lower lipid contents can be understood by considering 

that additional meal, and hence more biogas and by extension more energy, is available per kg of 

oil if the algae has lower lipid contents. Finally, the conversion efficiency is directly proportional to 

the quantity of energy available from the meal, making it the second most sensitive operational 

specification of the system (second to post-dewatering %TSS content).

 

61 The meal is assumed to be the only oil-containing portion of algae. This hypothesis is verified by considering soybeans 

and soy meal. Soybeans have a protein content of 40% (GREET, 2008) and an oil fraction of 18.25% (Sheehan et al., 

1998a) leading to a soy meal protein content of 48.9%. The average protein and lipid contents of the strains documented 

by Becker (2007) were 45.1% and 13.2%, respectively.
95 of 129

Figure 23: Life cycle GHG of HRJ production from algae as a function of the extent of dewatering. Three 

different system configurations were explored which are described more fully in the figure

Figure 24: Sensitivity analysis of operational specifications and configurations of HRJ production from algae

7.6.10 Results

The key assumptions and corresponding life cycle GHG emissions in the production and use of 

HRJ from algae oil are outlined in Table 85. It should be noted that although water usage was not 

quantified in this section, it is a part of ongoing research efforts. The most notable differences 

between the cases lie in the recovery step and the WTT CH4. The recovery step includes 

emissions from CO2 injection, dewatering and drying. The dominant factors of each life cycle 

stage were described in previous sections and are the driving sources of variation between 

scenarios. The increased WTT CH4 in the high emissions scenario is a result of natural gas 

usage for MEA extraction and drying. 

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The life cycle GHG emissions resulting from the production and use of HRJ from renewable algae 

oil range from 0.16 to 2.2 times those from conventional jet fuel. Variation in the biomass credit is 

due to minor changes in the allocation schemes used throughout the pathway.

Table 85: Life cycle emissions from the algae oil to HRJ pathway

Low Baseline High

Key Assumptions

Algae Yield (g/m2/day) 40 25 20

Algal Lipid Content 40% 25% 15%

Anaerobic Conversion Efficiency 80% 70% n/a

Life Cycle CO2 Emissions by Stage

Biomass Credit (gCO2/MJ) -73.7 -70.5 -68.9

Recovery of feedstock (gCO2/MJ) 5.4 29.6 143.1

Transportation of feedstock (gCO2/MJ) 0.3 0.3 1.2

Processing of feedstock to fuel (gCO2/MJ) 7.1 10.3 13.2

Transportation of jet fuel (gCO2/MJ) 0.6 0.6 0.6

Combustion CO2 (gCO2/MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) -60.3 -29.7 89.2

WTT CH4 emissions (gCO2e/MJ) 0.7 1.8 27.7

WTT N2O emissions (gCO2e/MJ) 3.3 8.1 5.8

Total WTW GHG emissions (gCO2e/MJ) 14.1 50.7 193.2

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.16 0.58 2.21

The uncertainty surrounding N2O emissions from algae cultivation is larger that other pathways 

due to the assumption that algae ponds have the same emission factor as flooded rice fields. 

While there is little existing information on N2O formation from algae ponds, the results from 

Table 85 indicate that N2O represents less than 16% of the total life cycle GHG emissions. The 

reader should be aware of the additional uncertainty with respect to N2O emissions from algae 

cultivation when comparing different pathways for GHG reduction potential in sections 8.1 and 

8.2. 

7.7 HRJ from Salicornia Oil 

The creation of HRJ from salicornia oil is not a pathway available in GREET; hence, supporting 

information was obtained from the literature and a pathway was created within the GREET 

framework. Salicornia is both a wild and cultivated annual shrub that germinates, grows and 

reproduces in areas of high salinity such as coastal shorelines, marshes or inland lakes. The 

plant itself is leafless with green jointed and succulent stems that form terminal fruiting spikes in 

which seeds are borne. In subtropical regions, salicornia can grow up to 50cm in height with most 

of the seed spikes on the upper one third of the plant (Anwar et al., 2002). Since oil containing 

seeds represent only a small fraction of the total plant, the deterministic factor in the life cycle 

analysis of jet fuel from Salicornia is the usage of non-oil containing biomass. The cultivation of 

halophytes on arid or semi-arid land, where there is little or no carbon stock naturally present in 

the soil, can lead to substantial net long-term carbon storage. Salicornia falls into the category of 

coastal halophytes because of its ability to grow in saltwater.

This work considers the production of HRJ from Salicornia oil while varying the crop productivity, 

oil yield, cultivation inputs and nutrient usage to establish the low emissions, baseline and high 

emissions scenarios. Due to the nature of Salicornia growth, the combination of either electricity 

generation or Fischer-Tropsch synthesis with an HRJ facility were explored. Long-term carbon 

sequestration potential is quantified through scenarios that vary the beneficial GHG credit from 

land use change.
97 of 129

7.7.1 Biomass, Seed and Oil Yield

In a similar manner to jatropha, additional products are created while growing seeds for oil 

production. When considering the yield of salicornia, three yield parameters define productivity: 

total biomass yield per hectare, seed yield per kilogram of total biomass and seed oil fraction. 

Total biomass is defined as seeds and straw biomass (similar to other forms of herbaceous 

biomass considered for BTL facilities). Data used to establish growth and oil yields was taken 

from field trials conducted at Puerto Penasco, Sonora, Mexico in an extreme coastal desert 

environment at the northern Gulf of California (Glenn et al., 1991). The ranges for the emissions 

scenarios were established using data from multiple plots taken from 1982 through 1988. The 

baseline total biomass yield and seed yield were defined by taking a weighted average (weighted 

by the number of plots) of the data from Glenn et al. (1991). The low emissions and high 

emissions data were defined as plus and minus one standard deviation from the weighted mean, 

respectively (standard deviation also weighted by the number of plots). The oil fractions adopted 

were taken directly from the range quoted by Glenn et al. (1991). The assumptions regarding total 

biomass yield, seed yield and oil fractions for Salicornia are given in Table 86.

The total biomass yield is almost an order of magnitude higher than the seed yield (per hectare).

In the baseline case, this translates to 7.22 kg of straw biomass for every kilogram of seeds, 25.6 

kg of straw biomass per kilogram of oil and 43.5 kg of straw biomass per kilogram of HRJ. The 

most important point highlighted by these numbers is that the production of salicornia will not be 

driven by the demand for its seeds, but rather the demand for its straw biomass. Were salicornia 

grown to use its straw biomass for either electricity production or as feedstock to a FischerTropsch facility, the production of hydroprocessed renewable fuels from its oil seeds would be a 

high value co-product from the process.

Table 86: Salicornia yield and oil fraction assumptions

Low Baseline High

Total Biomass Yield (kg/ha/yr) 17614 16247 14880

Seed production (gseed/kgtotal biomass) 142 122 101

Seed Yield (kg/ha/yr) 2506 1977 1504

Seed Oil Fraction 33% 28.2% 26%

In section 7.7.5, a system is considered where straw biomass is used for renewable power 

generation and seeds are used for HRJ production. The heating value of salicornia biomass 

varies with the species, the chief variable being the ash content, but in general salicornia biomass 

falls in the range of Lignite A or B coals (Glenn et al., 1992).

In section 7.7.6, a system is considered where straw biomass is used as a feedstock for a BTL 

facility and seeds are used for HRJ production. The BTL plant is similar to that discussed in 

section 6.4; namely, that biomass feedstock is used to meet internal process energy needs, with 

little or no excess electricity produced for export, and commercial quality middle distillate fuels 

such as diesel and jet fuel are produced.

7.7.2 Cultivation of Salicornia

The cultivation of salicornia is generally motivated by the opportunity to directly sequester carbon 

from the atmosphere. This occurs by reversing the trend towards desertification through 

cultivation of salicornia on saline lands and re-vegetation of degraded rangelands (Glenn et al., 

1993). For this reason, process energy requirements for the cultivation of salicornia are 

sometimes presented in the form of fossil carbon emitted per carbon absorbed during growth.

Glenn et al. (1992) calculated the carbon costs of salicornia assuming that all farm operations, 

from pumping water to planting, harvesting, baling and hauling the crop were performed using 

diesel fuel. They found that salicornia required 225kg to 300kg of fossil carbon for each 1000kg of 

atmospheric carbon fixed. The low emissions and high emissions case were defined as the lower 

and upper extremes, respectively, while the mean is assumed in the baseline case. All 

calculations assumed diesel fuel composed of 85.6% carbon (see Table 87) and salicornia 

composed of 24.7% carbon (Glenn et al., 1992).
98 of 129

The use of fertilizers in the cultivation of salicornia is a subject of debate. The test plots created 

by Glenn et al. in the Sonoran Desert were irrigated solely with hyper saline seawater that had 

first been used in a shrimp aquaculture facility (O’Leary et al., 1985), which added nitrogen and 

other nutrients to the water. In later trials, fields irrigated with un-enriched seawater received 

fertilizer additions equivalent to 200kg of nitrogen per hectare as urea, di-ammonium phosphate 

or ammonium nitrate (Glenn et al., 1991). The degree to which fertilizer must be added is a 

function of the coupling between aquaculture farms and salicornia farms. Large-scale coastal 

shrimp farms have caused algal blooms and disease problems in rivers and basins that receive 

their nutrient-rich effluent. The same problems can be expected to occur from the large volume of 

highly saline drainage water containing unused fertilizer discharging from salicornia farms (Glenn 

et al. 1998). When coupled together, salicornia farms could help mitigate the problem if 

aquaculture effluent is recycled onto a salicornia farm instead of being discharged directly to the 

sea; hence, any assumptions made regarding fertilizer use carry a degree of uncertainty. The 

assumptions adopted in this work to establish a reasonable range deal with varying the fraction of 

irrigation water that comes from aquaculture facilities. The low emissions case assumed 100% of 

the irrigation water was pre-enriched from an aquaculture facility; the baseline case assumed 

50% was pre-enriched, leading to 100kg of nitrogen usage per hectare, and the high emissions 

case assumed the full 200kg of nitrogen usage per hectare.

Irrigation is the single most expensive production cost in growing salicornia. In general, seawater 

irrigation requires copious and frequent, sometimes even daily, irrigation to prevent salt from 

building up in the root zone. Partially offsetting this effect is that increased salinity of irrigation 

water can lead to higher water usage efficiencies; therefore, higher salinity leads to less water 

being required to produce a kilogram of dry biomass (Glenn et al., 1992). During cultivation 

experiments in the Sonoran Desert, it was determined that salicornia can thrive when water 

salinity exceeds 100ppm (three times the normal ocean level); however, the volume of seawater 

required for irrigation is 35% more than the value that would be required if freshwater were being 

used. The additional volume is needed to control salt levels in the soil (Glenn et al., 1998). 

Although more volume is required for salicornia cultivation, seawater farms often require less 

water lifting than conventional farms, which may lift water from wells far deeper than 100 meters. 

This difference results in seawater farms using less energy for water pumping than freshwater 

farms. The life cycle GHG emissions calculated in this work include the emissions from pumping 

irrigation water but secondary effects of salt-water irrigation were not quantified. Glenn et al. 

(1998) showed that normal farm and irrigation equipment could be modified such that it is 

protected from salt damage from seawater.

The input assumptions discussed above are summarized in Table 87.

Table 87: Input assumptions regarding the cultivation of salicornia

Low Baseline High

Process Fuels

Diesel (kg/kgC Fixed) 225 262.5 300

Diesel (Btu/kgseed)

1 18557 25316 34831

Fertilizer Use

Nitrogen (kg/ha/yr) 0 100 200

Nitrogen (g/kgseed)

1 0 50.6 133.0

Notes:

1) Calculated using the yield assumptions from Table 86

Emissions from N2O were estimated using IPCC Tier 1 methodology (IPCC, 2006). Due to the 

lack of data, N2O emissions from nitrogen in above and below ground crop residues were not 

accounted for in this analysis. The IPCC Tier 1 methodology estimates the combined direct and 

indirect conversion rate for nitrogen from synthetic fertilizers as 1.325%. These rates include the 

atmospheric deposition of nitrogen volatilized from managed soils as well as nitrogen from 

leaching and runoff. The formula for calculating N2O emissions from salicornia cultivation is given 

by:
99 of 129

€

N2O Emissions gN2O

kgseed

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7.7.3 Transportation of Salicornia Biomass and Seeds to Production Facilities

The production of salicornia biomass and seeds was assumed to take place on the southwestern 

coast of the United States. After harvesting, GREET default values from the soybeans to HRJ 

pathway were assumed in regards to transportation mechanisms and distances travelled for the 

seeds. The assumptions of transportation mechanisms and distances for straw biomass are 

consistent with GREET defaults for herbaceous biomass in the BTL pathway. Specifically, the 

transportation of seeds is by truck to a local storage area and subsequently to a local oil 

extraction facility. The transportation of straw biomass is also by truck directly to an F-T or 

renewable power generation facility assumed to be the same distance as the HRJ facility. The 

difference between seed and straw biomass transportation is that seeds are loose and moved in 

loads of 15 short tons while straw biomass is baled and moved in loads of 24 short tons. Even 

after accounting for transportation of both seeds and straw biomass, the transportation elements 

of the life cycle GHG emissions are sufficiently small to be within the margin of error; therefore, 

assumptions in this area carry little consequence.

7.7.4 Oil Extraction from Salicornia Seeds

As there is no commercially available process for extracting oil from salicornia seeds, the 

extraction process was modeled with the soybean oil extraction process described by Sheehan et 

al. (1998a). Glenn et al. (1998) classify salicornia oil as highly poly-unsaturated and similar to 

safflower oil in fatty-acid composition (as presented in Table 41). They also conclude that it can 

be extracted from the seed and refined using conventional oilseed equipment. This same method 

was applied to both algae and jatropha oil extraction. Salicornia seeds more closely resemble 

soybeans than either algae or jatropha; hence, less modification to the soybean process was 

required. Specifically, pre-drying of seeds was eliminated and seed shell removal energy was 

included. Field drying of the seeds, prior to harvest was assumed and the energy for removing 

shells from jatropha seeds was applied to salicornia seeds. The process inputs assumed in this 

analysis are summarized Table 88.

7.7.5 Co-Product Usage and Allocation Methodology

Based on the yields adopted in the baseline case, 7.22 kg of straw biomass is produced for each 

kilogram of seed. Although salicornia could be grown for its seeds with the straw biomass tilled 

back into the field to facilitate higher rates of long-term carbon sequestration in the soil, it is 

doubtful that this would be done in practice. In an economy where 85% of all energy is derived 

from fossil carbon sources, the waste biomass could be used as a renewable energy source thus 

displacing fossil-based electricity or heat generation. Assuming the energy content of straw 

biomass is 16.3 MJ/kg, within the range of lignite A and B coals, the energy contained in straw 

biomass created per pound of oil is 10.5 times that contained in the oil itself.

7.7.5.1 Electricity Production from Biomass Co-products

The use of straw biomass co-product for electricity production was modeled with the assumption 

that to the electricity production occurred at the same location as the oil extraction and HRJ 

production. Three allocation methodologies were envisaged for this configuration. A summary of 

each is given in Table 89. The first methodology, which is the most straightforward, would 

displace average US grid electricity with the result being the lowest life cycle emissions for HRJ. 

The second methodology, system level energy allocation of recovery and feedstock 

transportation emissions, is the most complicated and is shown schematically in Figure 25. The 

unallocated cultivation and transportation emissions are first divided between HRJ production and 

electricity production based on the energy of the final products (39.9% and 60.1% respectively); 

the emissions associated to HRJ production are then allocated between oil and meal based on 

market value (61.5% and 39.5% respectively); both are then summed back together and 

allocated based on energy between HRJ and all other energy products from the system (15.4% 
100 of 129

and 84.6% respectively). The other energy products from the system are comprised of renewable 

naphtha, mixed propane gas and electricity. The third allocation methodology involves initially 

allocating only the cultivation emissions between the seeds and straw biomass based on market 

valuation (67.2% and 32.8% respectively).62 The life cycle GHG emissions resulting from each of 

the aforementioned systems are compared in Figure 26.

Table 88: Process inputs for extracting oil from salicornia seeds. All values are in Btu/lb of oil

Low1 Baseline1 High1

Receiving and Storage

Electricity 20.9 33.5 55.8

Salicornia Preparation

Electricity 4.0 6.5 10.8

Steam 172.5 276.0 460.0

Oil Extraction

Electricity 12.9 20.6 34.4

N-hexane 105.9 169.5 282.5

Meal Processing

Electricity 52.2 83.5 139.1

Steam3 505.4 808.6 1347.7

Oil Recovery

Electricity 1.4 2.2 3.6

Steam 86.7 138.7 231.1

Solvent Recovery

Electricity 1.9 3.0 5.0

Steam 0.0 0.0 0.0

Oil Degumming

Electricity 6.1 9.7 16.1

Steam 68.1 109.0 181.6

Waste Treatment

Electricity 2.0 3.3 5.4

Steam 36.6 58.6 97.7

Totals

Electricity 101.4 162.2 270.3

Natural Gas2 1086.6 1738.6 2897.6

N-hexane3 105.9 169.5 282.5

Notes:

1) All values are in Btu per pound of oil

2) Steam is assumed to be generated from natural gas with an 

efficiency of 80%.

3) GREET uses Liquefied Petroleum Gas (LPG) as a surrogate 

for N-hexane when calculating emissions

 62 Market value of seeds is the sum of market value of oil and meal individually. Value of salicornia oil was taken as 

equivalent to soy oil while the value of salicornia meal was found by scaling the value of soy meal by the ratio of protein 

contents. Soy meal has an average protein content of 48% (Ahmed et al., 1994) while salicornia meal has a protein 

content of 42% (Glenn et al., 1992). The market value of straw biomass was assumed to be proportional to the cost of 

coal based on energy value. The average cost of coal for electric utilities in 2008 was $2.07/mmBtu (EIA, 2009a).
101 of 129

Figure 25: System boundary definitions for system level energy allocation between HRJ production from 

salicornia oilseeds and electricity generation from salicornia straw biomass

Table 89: Allocation methodologies examined for the production of HRJ from salicornia oil when using the 

straw biomass co-product for electricity generation

Scenario CoProductUse Emissions Allocation Method

1 Straw 

biomass

Burned for 

electricity 

production

Displacement method (system expansion) where 

electricity from US grid is displaced. The corresponding 

emissions credits are given to the fuel production 

process.

2 Straw 

biomass

Burned for 

electricity 

production

Electricity production considered a parallel process to 

HRJ production. Cultivation and feedstock 

transportation emissions are allocated using a system 

level energy allocation scheme between all energy 

products from the system.

3 Straw 

biomass

Burned for 

electricity 

production

Cultivation and feedstock transportation emissions are 

allocated prior to any electricity production based on 

market valuation of the unprocessed seeds and straw 

biomass.

Figure 26: Comparison of life cycle GHG emissions from the allocation methodologies of Table 74 based on 

the production of HRJ from salicornia oil when using the straw biomass co-product for electricity generation

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As was observed for other fuel pathways, different allocation schemes result in varied emissions 

within the same pathway. The most important point to realize from this configuration is that there 

are 5 MJ of electricity generated for each MJ of HRJ. Hence, giving a liquid fuel credit for this 

electricity will reduce its emissions considerably. Additionally, when only the oilseeds are used for 

fuel production, the yield of HRJ per hectare is small, being less than even soybeans. This 

underscores the importance of the straw biomass as the driving renewable energy resource from 

salicornia cultivation. 

7.7.5.2 Fischer-Tropsch Fuel Production from Biomass Co-products

If the straw biomass co-product from salicornia oilseeds is used for electricity production then jet 

fuel represents only 15% of the total energy products created. This is a smaller fraction than any 

other oilseed crops considered in this work and is not conducive to being a part of a large-scale 

biofuel production network. In section 6.4, jet fuel from solid terrestrial biomass via the FischerTropsch process was discussed with switchgrass as the feedstock. Any carbon containing matter 

could theoretically be used to create syngas, which contains primarily H2, CO and CO2. This 

section discusses the implementation of a fuel production configuration where salicornia oilseeds 

are processed through an HRJ facility and all of the straw biomass is used as feedstock to a BTL 

facility. 

By incorporating a BTL facility producing 25% jet fuel, the total yield of jet fuel grows to be three 

times larger than that from HRJ alone for the same quantity of feedstock consumed. Furthermore, 

F-T diesel and naphtha are also created which contribute to GHG mitigation efforts in other 

energy consuming sectors. For these reasons, this work focused on a coupled HRJ and F-T 

facility. All assumptions discussed thus far were applied to the HRJ section, while input 

assumptions pertinent specifically to the BTL section are summarized in Table 90.

Table 90: Input assumptions relevant to a BTL facility using salicornia straw biomass as feedstock

Low Baseline High

BTL Plant Efficiency 52% 45% 42%

F-T Jet Fuel Yield (fraction of all F-T fuel products) 30% 25% 20%

Straw Biomass Energy Content (MJ/kg) 17.4 16.3 15.1

Figure 27: System boundary definitions for system level energy allocation of coupled HRJ and F-T fuel 

production from salicornia oilseeds and straw biomass

As was the case when electricity was produced, the allocation methodology is an important 

choice in determining the final life cycle GHG emissions of the jet fuel. Figure 27 outlines how the 

coupling of an HRJ facility with an F-T facility was arranged for this analysis. Straight black lines 

correspond to emissions flows following one or more products. This methodology allocates 

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103 of 129

energy and emissions common to both processes on a pseudo-system wide level by energy 

content and attributes energy and emissions coming from a specific process to the products from 

the process. The unallocated cultivation and transportation emissions are first divided between 

HRJ production and F-T fuel production based on the final energy products (allocation ratio 1); 

those associated to HRJ production are then allocated between oil and meal based on market 

value (allocation ratio 2); the HRJ and F-T emissions are then summed back together and 

allocated based on energy between jet fuel and all other energy products from the system 

(allocation ratio 3). The other energy products from the system are comprised of F-T diesel, F-T 

naphtha, hydroprocessed renewable naphtha and mixed propane gas. The allocation ratios and 

combined facility product slate resulting in the low emissions, baseline and high emissions 

scenarios are presented in Table 91.

Table 91: Allocation ratios and product slates describing a coupled HRJ and F-T facility processing 

salicornia oilseeds and straw biomass

Low Baseline High

Product Slate (MJ/MJJet)

F-T Diesel 1.2 1.5 1.8

F-T Naphtha 0.30 0.49 0.75

Renewable Naphtha 0.16 0.16 0.16

Mixed Propane Gas 0.012 0.027 0.028

Salicornia Meal 0.52 0.64 0.71

Allocation Ratios

Allocation Ratio 1 32.1% HRJ

67.9% F-T

30.9% HRJ

69.1% F-T

27.8% HRJ

72.2% F-T

Allocation Ratio 2 66.7% Oil

33.3% Meal

61.5% Oil

38.5% Meal

58.8% Oil

41.2% Meal

Allocation Ratio 3 37.1% Jet Fuel

62.9% Other Fuels

31.9% Jet Fuel

68.1% Other Fuels

26.6% Jet Fuel

73.4% Other Fuels

7.7.6 Land Use Change Emissions from Salicornia Cultivation

The degree to which carbon can be sequestered through salicornia cultivation depends on the 

initial state of the soil in which they are grown and the penetration of the roots in this soil. Dry land 

soils are typically low in organic carbon and could conceivably hold greater carbon under restored 

conditions (Glenn et al., 1993). 

Estimates of soil organic carbon, root carbon and charcoal carbon storage of semi-permanent 

cultures conducted by Sommer et al. (1999) have been previously used to estimate potential 

carbon sequestration from halophyte cultivation (Hendricks, 2008; Hendricks and Bushnell, 

2008). These estimates are for deep soils under small farmer land use systems in the Eastern 

Amazon region that contain conventional vegetation and crops, not salicornia. They were 

intended to quantify current carbon stock; hence, using the carbon storage values from Sommer 

et al. as a sequestration potential implicitly assumes similar soil carbon storage capacity and zero 

soil carbon prior to any halophyte cultivation. Soil carbon stored for semi-permanent secondary 

vegetation range in the Eastern Amazon region range from 146 to 167 tC ha-1, where 

approximately 90% is soil organic carbon, 5% is root carbon and the remaining 5% is charcoal in 

the soil. Furthermore, although this range applies to the top 6m of soil, an average of 67% of the 

carbon is stored in the top 1.2 meters (Sommer et al., 1999). 

A second independent analysis of carbon sequestration from salicornia was carried out for a joint 

project between the United States and Mexico. The project is called “Project Salicornia: 

Halophyte Cultivation in Sonora” and is operated under the United Nation Framework convention 

for Climate Change. Project developers have a preliminary estimate that the cultivation area 

would reach a steady state soil carbon content of 49 tC ha-1 after 100 years (UNFCCC, 1998). 

This value happens to correspond to half the carbon stock in the top 1.2 meters of soil as 

estimated by Sommer et al. (1999), namely 48.7 to 55.7 tC ha-1. The UNFCCC estimate was 

adopted for this study as a land use change scenario.
104 of 129

Under the optimistic assumption that all of the soil carbon sequestration occurs in the first 30 

years, the impact of long-term soil carbon sequestration on life cycle GHG emissions are given in 

Table 92. The sequestered carbon was amortized over 30 years with no discounting. The values 

are on the basis of carbon sequestered per MJ of jet fuel created from salicornia oil and straw 

biomass

Table 92: Long term soil, root and charcoal carbon sequestration from the cultivation of salicornia for fuel 

production

Long Term Carbon Long Term Carbon Sequestration (gCO2/MJ)

Sequestration (tC ha-1) Low Baseline High

49 49.7 41.9 33.9

Note:

1) Since values represent carbon sequestration, larger values 

correspond to lower emissions.

Soil carbon sequestration estimates lead to emissions credits ranging from 0.39 to 0.57 times the 

life cycle GHG emissions from the production and use of conventional jet fuel. Recall that these

estimates assume that the carbon content of the soil used for cultivation is depleted prior to 

cultivation occurring. Were salicornia grown on land with non-depleted carbon stocks then it is 

unlikely that any long-term soil carbon sequestration would result.

7.7.7 Results

The life cycle GHG emissions resulting from the production and use of HRJ and F-T jet fuel from 

salicornia seeds and straw biomass are summarized in Table 93. Results incorporating long-term 

carbon sequestration are given in Table 94.

These results reflect the sum of emissions from HRJ production and F-T jet production for one 

total megajoule of jet fuel. Based on the input assumption adopted in this work and the allocation 

methodologies discussed above, the life cycle emissions of the salicornia to HRJ and F-T jet 

pathway range from 0.35 to 0.76 times those from the production and use of conventional jet fuel. 

If long-term carbon sequestration occurs because of the salicornia growth, the life cycle GHG 

emissions range between -0.2 to 0.37 times those from conventional jet fuel.

Table 93: Summary of results from renewable and F-T jet fuel production and use from salicornia

Land Use Change Scenario H0 Low Baseline High

Key Assumptions

Total Biomass Yield (kg/ha/yr) 17614 16247 14880

Seed production (gseed/kgtotal biomass) 142 122 101

Seed Yield (kg/ha/yr) 2506 1977 1504

Seed Oil Fraction 33% 28.2% 26%

Life Cycle CO2 Emissions by Stage

Biomass Credit (gCO2/MJ) -90.9 -105.3 -116.9

Recovery of feedstock (gCO2/MJ) 26.7 36.8 47.1

Transportation of feedstock (gCO2/MJ) 1.0 1.1 1.2

Processing of feedstock to fuel (gCO2/MJ) 21.6 38.3 51.5

Transportation of jet fuel (gCO2/MJ) 0.5 0.5 0.5

Combustion CO2 (gCO2/MJ) 70.4 70.4 70.4

WTT GHG Emissions by Species

WTT CO2 emissions (gCO2/MJ) -41.0 -28.6 -16.5

WTT CH4 emissions (gCO2e/MJ) 1.0 1.3 1.7

WTT N2O emissions (gCO2e/MJ) 0.2 4.6 10.5

Total WTW GHG emissions (gCO2e/MJ) 30.5 47.7 66.1

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel 0.35 0.55 0.76
105 of 129

Table 94: Life cycle GHG emissions for production and use of renewable and F-T jet fuel salicornia 

assuming long-term carbon sequestration

Land Use Change Scenario H1 Low1 Baseline1 High1

Land use change emissions (gCO2/MJ) -49.7 -41.9 -33.9

WTW CO2 emissions (gCO2/MJ) -20.3 -0.1 20.0

Total WTW GHG emissions (gCO2e/MJ) -19.2 5.8 32.2

Life Cycle GHG Emissions Relative to 

Baseline Conventional Jet Fuel -0.22 0.07 0.37

Notes: 

1) All other input assumptions (salicornia cultivation, extraction of oil, processing 

of oil to HRJ and straw biomass to F-T jet) are based on those in the H0 

emissions case of the corresponding scenario.

Without the opportunity to obtain nutrients from the recycled streams of aquaculture farms, 

substantial increases in nitrous oxide emissions are likely to occur. An increase in N2O emissions 

translates to more pronounced consequences from the uncertainty associated with the IPCC 

correlations. The reader should be aware of the potential for these inherent uncertainties when 

comparing different pathways for GHG reduction potential in sections 8.1 and 8.2. 
106 of 129

8 Summary of Results

8.1 Life Cycle GHG Emissions

Table 95 summarizes the results of the life cycle GHG emissions for the baseline scenario of the 

fuel pathways considered in this work. Figure 25 presents the results of the analysis in graphical 

form. It is important to note that this plot does not show cumulative totals, but rather it displays the 

contribution of emissions from each step in the fuel life cycle. The impact of the land use change 

scenarios, which are summarized in Table 96, is included in the form of two pathways for 

switchgrass fed F-T jet, three pathways for soy oil to HRJ, four pathways for palm oil HRJ, two 

pathways for rapeseed to HRJ and two pathways for salicornia oil HRJ. These results highlight 

the need to avoid land usage changes that result in positive GHG emissions. This method of 

presentation displays the ‘biomass credits’ that are given to biofuels from the CO2 absorbed 

during biomass growth; these credits are largely the reason why these fuels offer the potential for 

reduced GHG emissions. With the exception of BTL and CBTL, the biofuel pathways all have 

similar ‘biomass credits’ and the magnitude of these credits is approximately63 the magnitude of 

the combustion emissions. The ‘biomass credit’ for CBTL is smaller because the fuel is created 

from a combination of coal and biomass. The ‘biomass credit’ for BTL is larger because biomass 

is used to power the entire fuel production process.

The baseline life cycle GHG emissions values in Figure 25 were combined with the low and high 

emissions scenario values to create Figure 26. The uncertainty bars represent the range of 

emissions as given by the low and high emissions cases. Many pathways have baseline life cycle 

GHG emissions that are lower than conventional jet fuel but have the potential to have GHG 

emissions that are higher than conventional jet fuel. For this reason, it is essential not to simply 

assume that biofuels are environmentally beneficial without knowing the specifics of how the fuel 

is produced.

A few of the key results are outlined below: 

o Life cycle GHG emissions are but one of many considerations when evaluating the 

feasibility and sustainability of an alternative fuel option. 

o The data do not include all of the feedstock-to-fuel pathways that could be use to 

create jet fuel. Some interesting options not covered include camelina oil to jet fuel, 

fuels created from pyrolysis oils and advanced fermentation of sugars to 

hydrocarbons. These will be addressed as part of the ongoing work and will appear 

in future revisions to this report.

o Of the fuel options considered herein, conventional petroleum has the lowest 

emissions of any jet fuel pathway that that relies exclusively on fossil fuel resources.

o Few biofuels have zero life cycle GHG emissions.

o There is considerable variability in the life cycle GHG emissions; emissions from 

land use change contribute the most to this for the biofuel pathways considered.

 

63 In some of the high and low emissions scenarios, the ‘biomass credit’ is not quite equal to the combustion emissions 

due to variations in the allocation ratios throughout the pathway. Since the biomass credit’ is given in the first life cycle 

stage (feedstock recovery), it is subject to all allocation ratios.
107 of 129

Table 95: Baseline life cycle GHG emissions for all fuel pathways studied. Land use change scenarios are 

described in Table 96

Biomass 

Credit

Recovery

Feedstock

Transport

Processing

Fuel

Transport

Combustion

WTT N

O2

WTT CH4

Land Use

Change

Total

Crude to 

conventional jet fuel 0.0 4.2 1.5 5.5 0.8 73.2 0.1 2.3 0.0 87.5

Crude to ULS jet fuel 0.0 4.2 1.5 7.3 0.8 72.9 0.1 2.4 0.0 89.1

Oil sands to jet fuel 0.0 19.0 1.3 5.5 0.5 73.2 0.1 3.1 0.0 102.7

Oil shale to jet fuel 0.0 41.2 0.6 3.3 0.6 73.2 0.2 2.5 0.0 121.5

Natural gas to F-T 

fuel 0.0 4.6 0.0 20.2 1.2 70.4 0.0 4.6 0.0 101.0

Coal to F-T fuel 

(no carbon capture) 0.0 0.8 0.1 117.2 0.6 70.4 0.0 5.7 0.0 194.8

Coal to F-T fuel 

(with carbon capture) 0.0 0.8 0.1 19.4 0.6 70.4 0.0 5.9 0.0 97.2

Switchgrass to F-T 

fuel

(LUC-B0)

-222.7 6.4 0.6 152.1 0.5 70.4 10.3 0.2 0.0 17.7

Switchgrass to F-T 

fuel

(LUC-B1)

-222.7 6.4 0.6 152.1 0.5 70.4 10.3 0.2 -19.8 -2.0

Coal and 

Switchgrass to F-T 

fuel with CCS 

(LUC-B0)

-44.3 1.2 0.2 21.9 0.5 70.4 2.0 4.9 0.0 56.9

Coal and 

Switchgrass to F-T 

fuel w/o CCS 

(LUC-B1)

-44.3 1.2 0.2 21.9 0.5 70.4 2.0 4.9 -3.9 53.0

Soy oil to HRJ 

(LUC-S0) -70.5 20.1 1.2 10.3 0.6 70.4 3.6 1.3 0.0 37.0

Soy oil to HRJ 

(LUC-S1) -70.5 20.1 1.2 10.3 0.6 70.4 3.6 1.3 60.8 97.8

Soy oil to HRJ

(LUC-S2) -70.5 20.1 1.2 10.3 0.6 70.4 3.6 1.3 527.2 564.2

Palm oils to HRJ

(LUC-P0) -70.5 4.9 3.1 10.3 0.6 70.4 5.1 6.3 0.0 30.1

Palm oils to HRJ 

(LUC-P1) -70.5 4.9 3.1 10.3 0.6 70.4 5.1 6.3 9.6 39.8

Palm oils to HRJ 

(LUC-P2) -70.5 4.9 3.1 10.3 0.6 70.4 5.1 6.3 135.8 166.0

Palm oils to HRJ 

(LUC-P3) -70.5 4.9 3.1 10.3 0.6 70.4 5.1 6.3 667.9 698.0

Rapeseed oil to HRJ

(LUC-R0) -70.5 17.2 3.1 10.3 0.6 70.4 22.4 1.3 0.0 54.9

Rapeseed oil to HRJ

(LUC-R1) -70.5 17.2 3.1 10.3 0.6 70.4 22.4 1.3 43.0 97.9

Jatropha oil to HRJ -70.5 16.7 1.5 10.3 0.6 70.4 9.1 1.2 0.0 39.4

Algae oil to HRJ -70.5 29.6 0.3 10.3 0.6 70.4 8.1 1.8 0.0 50.7

Salicornia to HRJ 

and 

F-T Fuel (LUC-H0)

-105.3 36.8 1.1 38.3 0.5 70.4 4.6 1.3 0.0 47.7

Salicornia to HRJ 

and 

F-T Fuel (LUC-H1)

-105.3 36.8 1.1 38.3 0.5 70.4 4.6 1.3 -41.9 5.8
108 of 129

Table 96: Land use change scenarios considered in this work

Land use 

change Scenario 0 Scenario 1 Scenario 2 Scenario 3

Switchgrass None

Carbon depleted 

soils converted to 

switchgrass 

cultivation

n/a n/a

Soy oil None

Grassland 

conversion to 

soybean field

Tropical rainforest 

conversion to 

soybean field

n/a

Palm oil None

Logged over forest 

conversion to palm 

plantation field

Tropical rainforest 

conversion to 

palm plantation 

field

Peat land

rainforest 

conversion to 

palm plantation 

field

Rapeseed 

oil None

Set-aside land 

converted to 

rapeseed cultivation

n/a n/a

Salicornia None

Desert land 

converted to 

salicornia cultivation 

field

n/a n/a

Figure 28: Life cycle GHG emissions for the alternative jet fuel pathways under consideration. Note: CCS 

denotes carbon capture and sequestration and the land use change (LUC) scenarios are defined in Table 96

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Figure 29: Life cycle GHG emissions for the alternative jet fuel pathways under consideration. Uncertainty 

bars represent the low emissions, baseline, and high emissions scenarios. Please note the different scales 

for the top and bottom portions of the figure. Note: CCS denotes Carbon Capture and Storage and Land Use 

Change (LUC) scenarios are defined in Table 96

8.2 Life Cycle GHG Intensity

As was discussed in Section 3.6, the life cycle GHG emissions from Table 95 as well as Figures 

28 and 29 can be combined with an aircraft energy efficiency to yield a metric of life cycle GHG 

intensity. With a Payload Fuel Energy Intensity (PFEI) of 0.015 MJ / kg-km, the U.S. commercial 

fleet had a life cycle GHG intensity of 1.3 g CO2e/kg-km in 2007.64 Because SPK fuels have 

increased specific energy (energy per unit mass) relative to conventional jet fuel, the fleet wide 

PFEI will decrease by 0.3% (Hileman et al., 2010). Despite the decrease in energy usage, there 

would be a 3% increase in fuel volume relative to conventional jet fuel. The change in energy with 

the use of SPK fuels is sufficiently small relative to the change in life cycle GHG emissions that 

have been discussed in this document that a life cycle GHG intensity value could be obtained for

any of the fuels considered in this document by multiplying the life cycle GHG emissions by the 

PFEI. Other fuel efficiency metrics could similarly be converted to life cycle GHG metrics using 

the same method.

 

64 This value was calculated based on calculations performed by Hileman et al. (2008) using the mean energy content of 

JP-8 and 2007 data from the Bureau of Transportation Statistics (1.17 x 1011 revenue ton miles flown and 1.99 x 1010

gallons of jet fuel consumed, http://www.transtats.bts.gov/).

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8.3 Biofuel Yield

The focus of this work was to establish life cycle GHG emissions inventories for a variety of 

alternative jet fuels and to determine the scales at which these fuels would need to be 

implemented in order to achieve emissions reduction targets. The former is covered in the results 

from Section 8.1 while the latter requires additional analysis of biofuel yield.

Figure 30 summarizes the fuel production potential for all of the biofuel pathways presented in 

Figures 28 and 29. The range in yields of fuel per kilogram of oilseed feedstock arises from the 

both the crop yield per acre of land and the variation in oil fraction. Rapeseed seed yields the 

most oil per kilogram (44%) followed by jatropha seeds (35%), algae (25%), palm kernels (22%) 

and finally soybeans (18%). Recall that there can be considerable variability in biomass oil yields, 

(e.g., potential algal yields up to 390% of those used in this work have been quoted by 

stakeholders in the industry). The fuel production from salicornia is the result of the combined jet 

fuel production from HRJ and BTL facilities. When configured in this manner, each megajoule of 

jet fuel is composed of 65% F-T jet and 35% HRJ.

Figure 30: Fuel production potential for various alternative jet fuels that could be derived from biomass. This 

is not an all-encompassing list of alternative jet fuel options; it merely represents those examined by the 

authors as part of their ongoing research

A subtle but important point surrounding the F-T jet fuel results is that only 25% of the fuel output 

from the F-T fuel facility was assumed to be jet fuel. Using switchgrass CBTL as an example, 

there would be 12,000 (3000 × 4) liters of liquid hydrocarbons produced for every hectare of 

switchgrass, only 3000 liters of which would be jet fuel (the rest could be fuels such as diesel, 

gasoline, and naphtha). All 12000 liters of fuel produced carry an environmental benefit; 

therefore, all liquid fuel products must be considered to capture the total CO2 mitigation. The 

switchgrass (or other biomass) used as feedstock for pure BTL plants have very low energy 

densities. This causes a large quantity to be required in order to make a relatively small quantity 

of jet fuel (which has a high energy density). Although the same feedstock could be used for both 

pure BTL and CBTL, when supplemented by coal, the same fuel output can be obtained from a 

smaller quantity of biomass. This increase is only due to supplementing the biomass feedstock 

with coal and not to an increase in biomass usage efficiency. Corn stover or forest residue can 

also be used as a feedstock while maintaining life cycle GHG emissions within 9% of the value 

found using switchgrass.

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The life cycle GHG emissions from Figure 29 and production potentials from Figure 30 can be 

combined to select fuel pathways that hold the most potential for reducing aviation’s GHG 

emissions. This combination is needed to reduce aviation’s GHG emissions because fuel 

pathways having both low life cycle GHG emissions as well as large fuel production potential are 

required. A graphical representation of the land requirements to supply the entire 2009 US jet fuel 

market with 100% SPK and a 50/50 blend of SPK and conventional jet fuel is shown in Figure 

31.65 Three representative fuel yields were chosen to span the relevant range from Figure 30.

Figure 31: Land area requirements to replace conventional jet fuel use within the US with 100% SPK and 

50/50 blend of SPK with conventional jet fuel. Average US conventional jet fuel consumption in 2009 is 1.4 

million bbl/day

Fossil-to-jet fuel pathways have large production potential, but they have comparable or higher 

emissions than conventional jet fuel; therefore, their use will not reduce GHG emissions. BTL 

fuels have low GHG emissions, but they also have limited fuel production potential due to the 

large capital costs for F-T production facilities. With the use of excess rapeseed, palm or soy 

(available after food needs are met) for HRJ production, rapeseed to HRJ, soy to HRJ and palm 

to HRJ have low life cycle GHG emissions; however, there is little excess available and new 

cropland is required for additional production. Current global production of soy, palm and 

rapeseed oil translate to only 34%, 43% and 18% of US jet fuel demand, respectively (FAPRI, 

2009). As such, expanded production of soy oil and palm oil for large-scale HRJ production could 

result in significant GHG emissions from land use change. Because of its toxic characteristics and 

low yield, jatropha is likely limited to small regional applications making it inappropriate to replace 

considerable quantities of conventional jet fuel. Hence, BTL fuels as well as HRJ fuels from soy, 

palm, and jatropha have limited potential for reducing GHG emissions. The production potential of 

CBTL is largely dependent on the biomass weight fraction of the feedstock. Switchgrass was 

found to yield three times more volume of jet per hectare than corn stover when fed with a weight 

fraction of 25%. Salicornia holds promise if it is used to make both HRJ and BTL (or CBTL) fuels 

as it could reduce life cycle GHG emissions by 25% compared to conventional jet fuel and has a 

production potential equivalent to one third of palm. The most obvious opportunity for large-scale 

production is using algae to make HRJ, which explains the recent wave of investments in the 

algae industry.

 

65 Average US consumption of jet fuel thus far in 2009 has been roughly 1.4 million barrels per day (EIA, 2009b)

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8.4 Biofuel Impact on Domestic Water Resources

Water plays an essential role in developing and utilizing energy resources. It is used in energyresource extraction, refining and processing, and transportation. The dependence of biofuels on 

water extends even further to include water used for feedstock growth. This work has thus far 

only focused on life cycle GHG emissions and yield as the bounding factors to large-scale biofuel 

production. Water requirements and regional availability serve as a third bound to the trade space 

of biofuel development.

It is important to distinguish between water withdrawal and water consumption. Water withdrawal 

involves withdrawing water from its source and subsequently returning it after use. The quality of 

the returned water may not be the same as when it was originally removed. Electricity generation 

is the best example where water use is dominated by withdrawn water used for cooling (some 

cooling water is lost to evaporation and this fraction is considered consumed). Water 

consumption involves withdrawing water from its source and not returning it after use. Crop 

irrigation represents the largest fraction of water consumption within the US. Figure 32 shows 

water withdrawals and water consumption by sector for the US in 2000 and 1995 respectively. 

Notice that the volume of consumed water is only 30% of that withdrawn.

Figure 32: Water consumption and water withdrawals in the US by sector (data adapted from DOE, 2006)

The water required to refine one liter of petroleum fuels was found to be between 1 and 2.5 liters 

(King and Webber, 2008). Jet fuel consumption in the US is currently at 1.4 million barrels per 

day, or 222 million liters per day (EIA, 2009b). This means that the aviation industry alone is 

responsible for consuming between 222 and 556 million liters of water each day. Most alternative 

fuels will require at least the quantity of water that refineries currently consume and have the 

potential to consume several orders of magnitude more (DOE, 2006). This fact leads us to define 

the life cycle water consumption in fuel production using a similar metric to that used to quantify 

life cycle GHG emissions. The units of liters consumed per MJ of fuel energy delivered to the tank 

are used to encompass the water consumed during the recovery and transportation of the 

feedstock from the well, field, or mine to the production facility, processing of these materials into 

fuels, transportation and distribution of the fuel to the aircraft tank, and combustion of the fuel in 

the aircraft. Without considering any specific examples, water consumption from feedstock 

recovery and processing will dominate the life cycle water consumption while water consumption 

from feedstock and fuel transportation will be indirect through consuming fuels that required water 

for their production. Water consumption from the combustion stage is zero. 

The primary focus of this work was on life cycle GHG emissions; hence no new data has been 

generated concerning water usage within fuel production pathways. While this is an active part of 

our ongoing research, this work makes use of a study concerning energy demand on water 

resources from the US Department of Energy (2006) and a study on the water intensity of 

transportation from King and Webber (2008). The relevant data from both papers is presented in 

Figure 33. Notice that corn and soybeans requiring irrigation consume 3 orders of magnitude 

more water than refining of conventional petroleum based fuels. The irrigation needed for crop 

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production varies greatly depending on the region. For example, water use for irrigated soy 

production varies from 600,000 liters per hectare for Pennsylvania to about 4.3 million liters per 

hectare for Colorado, with a national average of 2.4 million liters per hectare (DOE, 2006). The 

overall average is deceiving because many areas use no irrigation while others have much higher 

demands. 

Few new reservoirs have been built since 1980, and fresh surface-water withdrawals have 

peaked at about just over 1 trillion liters per day. Many regions depend on groundwater to meet 

increasing water demands, but declining groundwater tables could severely limit future water 

availability. Some regions have seen groundwater levels drop as much as 300 to 900 feet over 

the past 50 years from pumping water from aquifers at a rate faster than the natural rate of 

recharge (DOE, 2006). The expansion of biofuel production could exacerbate non-uniform 

regional water stresses between states; hence, the impact of biofuels on water consumption will 

be regionally dependent and cannot be summarized by a single value. 

If feedstocks can be found that require no additional water above the status quo, their processing 

to jet fuel will still place additional strain on the current system. Converting natural gas and coal to 

F-T diesel are 5 and 7 times more water intensive than refining of conventional petroleum, 

respectively. Similarly, water consumption from steam reforming of natural gas to make hydrogen 

for hydroprocessing renewable oils to HRJ will be at least comparable to refining conventional 

petroleum. 

Figure 33: Water consumption for the production of various fuels (data adapted from DOE, 2006 and King 

and Webber, 2008)

Depending on the water quality needs for particular applications, freshwater supplies can be 

augmented with degraded or brackish water. This applies to the algae and salicornia pathways 

discussed in sections 7.6 and 7.7. Algae grown in open ponds have the additional complication of 

evaporation from the pond surface. Algae are most effectively grown in shallow, high surface area 

ponds. Assuming an evaporative rate of 1.5 cm/day (Weissman and Goebel, 1987), 100 liters of 

water per hectare need to be replenished each minute. This constraint will limit the location of 

algae farms to being adjacent to a plentiful source of degraded or brackish water.

As previously discussed, biofuel production would aggravate regional strains on freshwater 

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supply and local infrastructure. Further insight as to where these strains will be felt can be gained 

by considering the data from DOE (2006) in conjunction with the regions within the US where 

biofuel feedstock cultivation expansion is likely. In their 2006 report on the interdependency of 

energy and water, the US Department of Energy (DOE) showed that freshwater withdrawal in 

most regions of the US exceeds the available precipitation (precipitation minus 

evapotranspiration66). While the ratio of freshwater withdrawals to available precipitation is less 

than 30 for most of the northeast, midwest and southeast, values between 100 and 500 are not 

uncommon for western states. The magnitude of this ratio indicates the degree to which current 

water demands are being met with groundwater pumping or transport of surface water from other 

regions (DOE, 2006).

Locations most conducive to next generation biofuel feedstock cultivation are the Southwest and 

Southeast for algae, Southwest for salicornia, and Midwest and Southeast for switchgrass. With 

the exception of Midwest and part of the southeast, these are regions where groundwater 

withdrawal is well in excess of the available precipitation each year. The southwest should 

receive particular attention when discussing the expansion of domestically produced biofuels 

because of its large water constraints. 

These data demonstrate that water availability could be a limiting factor for biofuel production in 

certain regions of the US. Water usage is part of this ongoing work and will be addressed further 

in subsequent reports.

8.5 Invasive Species with Respect to Biofuels 

Crops ideal for large-scale biofuel production are those with high yield that do not require fresh 

water irrigation and can maintain high yields when grown on marginal lands. These are also 

general traits that describe invasive species. More specifically, the National Invasive Species 

Information Center (NISIC) defines the following characteristics as some of the traits of species 

likely to be invaders (NISIC, 2006). 

• Rapid growth and short life cycle: go from seed to producing seed very rapidly –

sometimes within a few weeks.

• Able to grow in a wide range of habitats

• High number of seeds produced

• Long seed dormancy and staggered germination

• Efficient method of seed dispersal

• Benefit from allelopathy, which is the release of chemicals into the surrounding soil that 

prohibit the growth of other plants.

As defined by most government organizations, invasive species are non-indigenous and 

adversely affect the habitats they invade economically, environmentally or ecologically. There are 

approximately 50,000 non-indigenous species in the United States. Some of these are 

noninvasive and beneficial to society, such as corn, wheat and rice. Others have caused major 

economic losses in agriculture and forestry and have irreversibly damaged the ecosystems to 

which they were introduced (Pimentel et al., 2000). 

Historically most plant introductions, with the exception of agricultural weeds, have been 

intentional while microbe introductions have been accidental. Regardless of initial rationale, an 

additional 700,000 hectares of US wildlife habitat are invaded each year by non-indigenous 

weeds. As an example, 4 million hectares of grassland in northern California have been lost to 

yellow star thistle (Campbell, 1994). These are lands that may otherwise have been used for 

energy crop cultivation such as switchgrass. Invaded lands can also become predisposed to fires. 

Cheatgrass growth in the Great Basin in Idaho has increased the frequency of fires by more than 

an order of magnitude (Whisenant, 1990).

 

66 Describes the sum of evaporation and plant transpiration from the Earth’s land surface to the atmosphere.
115 of 129

The accidental introduction of weeds and microbes often occurs via crop seeds and other parts of 

host plants. The agriculture industry suffers an overall reduction of 12% in crop yields due to 

weeds with each 1% decrease in crop yield being accompanied by a 4.5% increase in crop cost 

to the farmer. Ecologists agree that non-indigenous weeds are a greater risk than native ones. 

Plant pathogens carried by foreign microbes result in crop losses of approximately 65% those of 

weeds. Similarly, pathogens of forest plants cause the loss of 9% of forest products each year 

(Pimentel et al., 1997). 

The majority of the aforementioned impacts occurred because of intentional introduction of 

foreign plants to the North American ecosystem. Many of the feedstocks considered in this work 

have never before been grown at large scale and/or require deliberate introduction to an 

ecosystem. The financial burden of losses and control due to invasive plants and microbes has 

been estimated as several tens of billions of dollars each year (Pimentel et al., 2000). While the 

authors are not attempting to imply that the expansion of biofuels could result in such significant 

economic losses, it is important to understand that controlling invasive plants is not a negligible 

consideration in the decision making process. The awareness that invasive species can be a 

direct financial burden highlights the potentially negative economic consequences of introducing 

new species to the environment. Maintaining environmentally sustainable feedstock production 

that meets food and energy demands is essential to the economic success of meeting large-scale 

biofuel demands. 

The expansion of biofuel consumption within the U.S. will require a significant increase in crop 

and feedstock production and this presents the possibility that non-indigenous species will be 

introduced into an unprepared ecosystem. Ironically, the impact of these invasives could be to 

inhibit crop production, which could hurt the industry that was responsible for their introduction. 
116 of 129

9 Conclusions

As part of continuing research on alternative jet fuels, a screening level life cycle assessment of a 

wide range of potential drop-in alternative jet fuels was conducted. Three scenarios were 

developed for each pathway corresponding to optimistic, nominal and pessimistic assumptions 

regarding specific process inputs and production characteristics. In most fuel pathways, the 

choice of allocation methodology and potential for GHG emissions from land use change were 

found to have the largest impact on the results.

Consistent allocation methodologies were implemented across multiple pathways to facilitate 

equitable comparisons of different alterative fuels. In all analyses, the use of displacement or 

system expansion was minimized to reduce the variability of the results to subjective 

assumptions. The displacement method was only implemented where assumptions could be 

made that minimize the impact of allocation on the result (e.g. algae electricity). Energy allocation 

was used for any process that resulted in a product slate of liquid hydrocarbons (e.g., jet, diesel 

and naphtha from F-T synthesis). Market value allocation was used for processes resulting in a 

combination of oil and meal (e.g. separation of soybeans into soy oil and soy meal). In all such 

cases, the oil price was assumed equal to that of soy oil and the price of meal assumed relative 

to soy meal based on relative protein content. The use of mass or volume allocation was avoided.

The treatment of land use change emissions (both positive and negative) in this work was 

developed to provide an understanding for the reader of how land use change emissions 

compare with the emissions from the other five life cycle stages. It was not intended to explicitly 

quantify the specific land use change emissions that would result from expanded production of 

any given feedstock. The scope of this work was limited to only quantifying the impacts of direct 

land use change; emissions from indirect land use change were not considered. Multiple 

scenarios were used to explore the range of magnitudes of GHG emissions due to land use 

change; these included a scenario where no land use change emissions were incurred. This 

approach allowed the impacts of different land use change scenarios to be isolated from the other 

emissions from fuel production.

As part of ongoing research, a more complete assessment of land use change emissions that 

includes indirect effects is being developed. Proper evaluation of the indirect effects of alternative 

fuels within aviation requires modeling of the demand for renewable energy resources within the 

transportation sector, including aviation, as well as the demand for renewable energy resources 

from the energy sector as a whole. Most indirect effects are expected to occur on an international 

scale; hence, domestic analyses, such as those in this work, need to be done in the context of the 

global market.

Given their reduced life cycle GHG emissions relative to conventional jet fuel, some alternative 

fuels could play a central role in mitigating aviation's contribution to climate change, including 

helping aviation to achieve carbon-neutral growth when combined with improved technology and 

operations that are more efficient. If appropriate renewable feedstocks were used, both FischerTropsch (F-T) fuels and Hydroprocessed Renewable Jet (HRJ) fuel could provide aviation with 

modest (~10%) to large (~50%) reductions in emissions that contribute to global climate change. 

If projections of soil carbon sequestration prove valid, a salicornia-based biofuel could have a 

100% reduction in life cycle GHG emissions.

Aviation is not the only potential user of renewable biomass resources, and it will have to 

compete for these limited resources. Furthermore, large land area requirements indicate that it is 

unlikely that a single region could create sufficient biomass to supply the entire planet with 

biofuels. Hence, it is probable that large-scale implementation of biofuels would arise as a 

superposition of regionally appropriate feedstocks. It is critical to continue to examine feedstocks 

that could be used to create transportation fuels, such as jet fuel, without the need for arable land, 

with a minimum of fresh water, and with large yields per hectare. 
117 of 129

Although this analysis has identified several shortcomings that could prevent biofuels from being 

a complete environmental solution for the aviation industry, they could still be an important part of 

the aviation industry’s strategy for reducing life cycle GHG emissions. Current actions with regard 

to biofuel expansions are important in realizing the potential of this industry. Not all feedstocks 

need to have the potential to displace large volumes of petroleum fuel. Any feedstock produced 

today can lead to valuable experience through benefitting the local economy and providing 

essential lessons in production and processing techniques. This experience would be invaluable 

should a higher yield crop, such as algae, become commercially viable.

The most significant challenge is not in developing viable alternative fuels that could reduce 

aviation's GHG emissions -- the technology exists; rather, the challenge lies in developing and 

commercializing the large scale production of next generation of biomass feedstocks that could 

be grown in a sustainable manner.
118 of 129

Appendix A – General Feedstock and Fuel Properties

The properties of the main feedstocks and fuels used in this analysis are given in Table 97. In 

most cases, these properties represent only a single value within the typical range of each 

characteristic. While these data are appropriate for the level of detail of this work, sample specific 

data should be used (where available) in conducting analyses of individual production 

configurations.

Table 97: Feedstock and Fuel Properties

Feedstock or Fuel

LHV 

(MJ/kg)

Densit

y 

(kg/L)

Carbon 

content (wt 

%)

Sulfur 

content

(wt ppm)

Source(s)

Crude oil or syncrude 41.31 0.8782 84.61 16,9002 EIA, 2008a

Conventional Jet A 43.2 0.802 86.2 600 Hileman et al., 

2010

ULS Jet A 43.3 0.791 85.9 15 Hileman et al., 

2010

F-T Jet Fuel / HRJ 44.1 0.76 84.7 0 Hileman et al., 

2010

F-T Diesel / HRD 44.0 0.78 87.1 0 GREET, 2008

F-T Naphtha / HR 

Naphtha 44.4 0.70 84.2 0 GREET, 2008

Coal (U.S. average)

4 22.7 - 59.0 11,100 GREET 2007;

EIA, 2006

Bituminous coal4 26.4 - 64.8 29,400 SSEB, 2006

Sub-bituminous coal4 18.4 - 49.2 3,500 SSEB, 2006

Petroleum coke5 33.2 - 92.3 68,000 EIA, 2006;

GREET, 2008

Biomass (forest 

residue)6 15.4 - 51.7 0 GREET, 2008

Biomass (corn stover)6 16.3 - 44.5 0 GREET, 2008

Biomass (switchgrass)6 17.6 - 47.0 900 GREET, 2008

Natural gas 47.1 0.0007

8 72.4 6 GREET, 2007

Hydrogen 120 0.0000

9 0 0 GREET, 2007

Notes:

1) Energy content of crude oil assumed to be 5.8 million Btu per barrel (HHV); carbon content 

calculated from formula: percent carbon = 76.99 + (10.19×Specific Gravity) + (-0.76×Sulfur 

Content). (EIA, 1999)

2) Density and sulfur content derived using historical data (1995-2006) provided in EIA, 2008a

3) As source of process energy (e.g. electricity generation). LHV and sulfur content from GREET, 

2007; carbon content derived from coal HHV and U.S. average coal carbon emission factor of 

26.0 million metric tons per quadrillion Btu for the electric power sector in 2004. (EIA, 2006) 

4) For Coal-To-Liquids (CTL) process.

5) Used as a source of process energy in the refining of jet fuel. LHV and carbon content from EIA, 

2006, sulfur content from GREET, 2008.

6) For Biomass-To-Liquids (BTL) process. 
119 of 129

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