Meeting Emissions Reduction Targets: A Probabilistic ...

Meeting Emissions Reduction Targets: A Probabilistic Lifecycle Assessment of the Production of Alternative Jet Fuels Alexia P. Payan1, Michelle Kirby2, Cedric Y. Justin3, and Dimitri N. Mavris4 School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0150 In 2009, the aviation industry announced its commitment to address aviation’s environmental footprint by defining a four-pillar strategy to reduce greenhouse gas emissions. One of these pillars concerns the development of more efficient technologies for aircraft and engines, and the replacement of fossil carbon-based energy sources by renewable and sustainable solutions. Novel fuel-efficient technologies with promising emission reduction potential are currently being developed around the world. For instance, new lighter materials, new engine architectures, and futuristic aircraft concepts are studied to decrease fuel consumption and thus improve the carbon footprint of the future worldwide fleet. However, the projected 5% average annual growth in air transportation will most likely offset the expected carbon savings from new technologies alone. Another option for the aviation industry is to reduce its carbon footprint by considering the use of bio-jet fuels produced from renewable biomass feedstocks. Cost is however a barrier to large-scale commercial deployment of alternative fuels in aviation. In addition, selecting sustainable biomass options for the production of alternative jet fuels is challenging due to uncertain social, economic, environmental, and climatic factors. In this paper, we first examine the potential of new aircraft and engine technologies to reduce aviation-related carbon emissions. We show that without additional measures, technology infusions are not sufficient to meet the ambitious carbon emissions reduction goal set forth by IATA. Next, we study how changing the fuel source by introducing biofuels has potential to alleviate the environmental footprint of aviation. In this analysis, we account for the uncertainties associated with the selection of biomass feedstock options and their corresponding refining processes to produce suitable “drop-in” bio-jet fuels. A probabilistic analysis encompassing likely scenarios is carried out and a visualization interface is proposed to substantiate and facilitate decision making by aviation industry stakeholders.

Nomenclature ACRP ASTM ATJ CAAFI EIA EPA FAA F-T 1

= = = = = = = =

Airport Cooperative Research Program American Society for Testing and Materials Alcohol-to-Jet Commercial Aviation Alternative Fuels Initiative Energy Information Administration Environmental Protection Agency Federal Aviation Administration Fischer-Tropsch

Postdoctorate Fellow, Aerospace Systems Design Laboratory, School of Aerospace Engineering, 270 Ferst Drive, Atlanta, GA, 30332-0150, AIAA Member. 2 Research Engineer II, Aerospace Systems Design Laboratory, School of Aerospace Engineering, 270 Ferst Dr. Atlanta, GA, 30332-0150, AIAA Member. 3 PhD Candidate, Aerospace Systems Design Laboratory, School of Aerospace Engineering, 270 Ferst Dr. Atlanta, GA, 30332-0150, AIAA Member. 4 Boeing Professor of Advanced Aerospace Systems Analysis, School of Aerospace Engineering, Director Aerospace Systems Design Laboratory, 270 Ferst Dr. Atlanta, GA, 30332-1050, AIAA Associate Fellow. 1 American Institute of Aeronautics and Astronautics

GHG HEFA HFC IATA ICAO IEA MIT NASA PFC PIP SESAR SPK SRWC TIM TOPSIS UF UK UN UNFCC U.S.D.A U.S. D.O.E. U.S. D.O.T. UT

= = = = = = = = = = = = = = = = = = = = = = =

Greenhouse Gas Hydroprocessed Esters and Fatty Acids Hydrofluorocarbons International Air Transport Association International Civil Aviation Organization International Energy Agency Massachusetts Institute of Technology National Aeronautics and Space Administration Perfluorocarbons Performance Improvement Package Single European Sky ATM Research Synthetic Paraffinic Kerosene Short Rotation Woody Crops Technology Impact Matrix Technique for Order Preference by Similarity to Ideal Solution University of Florida University of Kentucky University of Nevada United Nations Framework Convention on Climate Change U.S. Department of Agriculture U.S. Department of Energy U.S. Department of Transportation University of Tennessee

I. Introduction

M

ITIGATING the environmental impacts of aviation has always been a key challenge and main driver for research and technology developments. While significant emphasis has been given to aviation noise and pollutant emissions reduction, greenhouse gas (GHG) emissions are now a predominant environmental concern. These include water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (NOx), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6). The worldwide aviation sector accounts for approximately 3% of the yearly global anthropogenic greenhouse gas emissions1,2,3. Assuming a continuous increase in the air traffic volume by an average of 4 to 5% per year, this contribution is expected to rise to 5% in 2050 if no further action is taken4. In order to mitigate this, airlines, manufacturers, airports, and air navigation service providers have committed to continuously improve fuel efficiency by 1.5% per year from 2009 to 2020, to adopt a carbon-neutral growth from 2020, and to reduce the world’s aviation carbon footprint by 50% by the year 2050 5. In order to meet these ambitious emission reduction goals, the International Air Transport Association (IATA) coordinated with the International Civil Aviation Organization (ICAO) 6,7 and the United Nations Framework Convention on Climate Change (UNFCC)8 to adopt a four-pillar strategy. These four pillars include investment in new technologies to develop more efficient engines and airframes, as well as sustainable and renewable alternative jet fuels; development of more efficient operations and infrastructures to improve airport and air traffic management procedures; and implementation of economic measures favoring the reduction of carbon emissions5. This is depicted in Figure 1. In the near term, adopting navigation systems and air traffic control practices that optimize flight routes using real-time weather data, reduce time idling on congested taxiways, and reduce holding time around airports could immediately decrease fuel burn and consequently emissions. In the United States, the NextGen initiative uses satellites to track aircraft and improve their routes in order to shorten travel distances and reduce airport congestion9. Similar efforts in Europe include the Single European Sky ATM Research (SESAR) project whose mission is to reduce the environmental footprint of aviation while ensuring the safety and fluidity of air transportation10. In the long term, aviation-related greenhouse gas emissions may be further decreased by the development of advanced propulsion systems, the use of lightweight materials, and the improvement of aircraft aerodynamics. Airlines regularly retire older, less efficient aircraft from their fleet to introduce new generations of more efficient aircraft. Such state-of-the-art aircraft concepts include the Boeing 787 and the Airbus A350. Both designs combine lightweight materials and advanced propulsion systems to achieve greater fuel efficiency11,12. Airlines can also 2 American Institute of Aeronautics and Astronautics

improve the aerodynamic performance of their existing fleet by retrofitting fuel efficient technologies such as winglets or laminar flow control surfaces and performance improvement packages (PIP). More radical innovations include blended wing bodies13, which not only would reduce drag and thus fuel consumption and therefore carbon emissions, but also increase the lifting surface to the entire aircraft. Nevertheless, aviation emission savings through technological improvements alone are projected to be offset by the growing demand for air travel and the increased use of more environmentally-friendly fuels like ethanol and biodiesel in ground vehicles5.

Figure 1: Schematic Aviation Emissions Reduction Roadmap (IATA)5 In addition to building more efficient aircraft and engines, changing the type of fuel might be a promising option for the aviation industry to reduce emissions of both greenhouse gas and particulate matter. Lower-carbon alternative fuels or biofuels have the potential to not only cut down greenhouse gas and harmful emissions, but also decrease the dependence on fossil fuels. They also respond to the desire for a distributed and renewable fuel production infrastructure less dependent on foreign imports. As time goes by, more and more airlines are testing blends of renewable jet fuels to power their engines on commercial routes, sending a strong signal to producers of bio-jet fuels that the aviation industry is ready to accept them as “drop-in” alternatives14,15. However, cost remains the biggest barrier to large-scale commercial deployment of alternative fuels in aviation. This is because the technical challenges involved in the selection and transformation of biomass options into usable feedstocks, and in the production and refining of biofuels are substantial. In this paper, we first look at new aircraft and engine technologies and their impacts on carbon emissions. Then, we look at how the production of lower-carbon alternative jet fuels helps bridge the gap between the technological reduction in aircraft carbon emissions and the carbon reduction goal of 50% by 2050 set forth by the aviation industry.

II. New Technologies One natural venue for the reduction of fuel consumption and thus carbon emissions in the atmosphere is the design of new aircraft with lower empty weights, improved aerodynamics, and more efficient engines. These improvements are made possible thanks to the use of new technologies which impact the overall structure (new lightweight materials such as composites), the aerodynamics (winglets, raked wingtips, scimitar wingtips, refined wing to body fairings, …) or the powerplants (increased bypass ratio using geared turbofans, composite fans, new alloys for the high pressure turbine, …). In its June 2013 Technology Roadmap, IATA has identified a list of 24 technologies that are promising for future aircraft developments. This list of technologies is presented in Table 1.

3 American Institute of Aeronautics and Astronautics

Table 1: IATA Identification of Most Relevant Technologies for Reducing CO2 Emissions5 Aerodynamics

Wing

Fuselage

Empennage

Natural Laminar Flow Control Hybrid Laminar Flow Control Active Load Alleviation

Fiber Metal Laminate (CentrAl) Composite Primary Structures Adv. Aluminum Aerostructures



Variable Camber

Al-Li Alloys

Al-Li Alloys

Al-Li Alloys

Engine Advanced Turbofan Geared Turbofan

Additional Technologies Winglet Structural Health Monitoring Lightweight Cabin Interiors Fuel Cells for Secondary Power

Riblets Laminar Flow Drag Coatings

Table 2: Technology Impact Matrix In order to assess the effects of these Geared Composite Winglet Technology technologies on the vehicle emissions, a surrogate Turbofan Primary Impact Factors Best Worst Best Worst Best Worst model of a notional 160-passenger aircraft Wing Weight +0.02 +0.05 -0.45 -0.1 operating a typical 1000 nautical miles mission is first constructed16. The surrogate model is used in Fuselage Weight -0.3 -0.1 place of a physics-based model in order to speed Empennage -0.1 0.0 Weight up the estimations of carbon dioxide emissions. It uses the physical characteristics of the aircraft to Engine Weight -0.05 +0.04 evaluate carbon emissions. Next, a Technology CDi -0.07 -0.04 Impact Matrix (TIM) is constructed using data SFC -0.12 -0.05 from the IATA Technology Roadmap document to evaluate the impact of infusing technologies into the notional aircraft. Both a best case and a worst case scenario are investigated. A preliminary analysis of the uncertain impacts of the geared turbofan, the winglet and the composite primary technologies is given in Table 2 as an example. Table 3: CO2 Emissions Reduction at Vehicle Level16 From this analysis, it is apparent that infusing CO2 Emissions CO2 Emissions Technology Name some of these technologies yield substantial (kg/pax.km) Reduction (%) improvements in terms of fuel burn and associated Baseline Aircraft (No technology) 0.0739 0.0% carbon emissions. This is summarized in Table 3. Natural Laminar Flow Control 0.0726 1.8% For instance, the replacement of current engine Hybrid Laminar Flow Control 0.0726 1.8% designs by an advanced turbofan would reduce CO2 emissions by 18%. If we were to implement a Active Load Alleviation 0.0736 0.5% compatible portfolio of technologies such as the CentrAl 0.0700 5.3% one highlighted in grey in Table 3, the resulting Composite Primary Structures 0.0680 7.9% reduction in CO2 emissions would be ~39%. Variable Camber 0.0721 2.4% Nevertheless, it is very unlikely that this entire Fuel Cells for Secondary Power 0.0715 3.3% technology portfolio will be infused into a single Advanced Aluminum Aerostructures 0.0719 2.7% aircraft design due to the risks involved. Therefore, Lightweight Cabin Interiors 0.0735 0.6% the end-result still falls short of the ambitious goal Al-LI Alloys 0.0730 1.3% of reducing aviation CO2 emissions by 50% by Riblets 0.0733 0.8% 2050. Furthermore, expected growth in the aviation Laminar Flow Drag Coatings 0.0735 0.6% sector will only exacerbate this shortcoming. Advanced Turbofan 0.0606 18.0% Relying exclusively on technology advancements Geared Turbofan 0.0669 9.5% to meet these goals is therefore not sufficient and a Winglet 0.0733 0.9% more radical approach to cut emissions is Structural Health Monitoring 0.0718 2.9% warranted to supplement the improvements yielded by new technology adoptions. While a large number of studies have focused on quantifying the effects of new technologies, operations, infrastructures, and economic measures on aircraft GHG emissions, only a few have looked at the potential of alternative fuels to reduce the GHG footprint of aviation. 4 American Institute of Aeronautics and Astronautics

III. Alternative Fuels Another way to achieve the aviation industry goal of a 50% net reduction in CO2 emissions by 2050 is to progressively change the source of jet fuel from fossil-based to biomass-based. Commercial aviation has traditionally relied on fossil kerosene and concerns about securing fuels are arising as supplies of petroleum-based fuels may decline. Furthermore, while some research is being conducted to produce aircraft fuels from sun energy and carbon dioxide, such radical concepts are not mature and will not be operational for several decades17. Aviation industry stakeholders are seeking a cost-effective and significant supply of jet fuel that may be used in current and future fleets. They also want the assurance that it does not require any major modification to the aircraft and its subsystems. Alternative jet fuels may be a solution. Several programs have been undertaken around the world to study the environmental, social, and economic potential of alternative fuels in aviation. Various airlines, aircraft and engine manufacturers, and other industry participants have also been partnering with biofuel suppliers to test blends of renewable jet fuels in existing engines, both on the ground and in flight18. Over the past five years, several commercial routes have been flown using blends of biofuels and conventional jet fuel. For instance, in 2008, Air New Zealand flew a Boeing 747 from Auckland to Wellington with one of its four engine fuelled with a blend of 50% hydro-processed jatropha seeds and 50% Jet A119. In late 2011, KLM launched a series of 100 flights from Amsterdam to Paris powered by a 50% blend of camelina-derived biofuel, followed by 100 more flights in February 2012 using cooking oil-derived biofuel. Similarly, between July and December 2012, Lufthansa performed a long term evaluation of Hydroprocessed Esters and Fatty Acids (HEFA) kerosene on 1187 flights from Hamburg to Frankfurt. Biofuels may theoretically be produced from any type of biomass, i.e. renewable living organism utilizing carbon as a food source. However, the production of biofuels mostly comes from the processing of grasses, plant seeds, and non-edible tree fruits. As a matter of fact, contrary to fossil sources, biomass sources absorb carbon dioxide as they grow in a proportion equivalent to that produced when the fuel is burnt in the jet engine. As a consequence, when the extraction of fossil fuels releases large amounts of CO2 in the atmosphere, the growth of biomass feedstocks absorbs large amounts of CO2. This absorption sometimes more than compensates the remaining lifecycle CO2 emissions associated with the production of biofuels. This is depicted in Figure 2. (a) (b)

CO2

CO2 CO2

Flight Distribution at Airports

CO2

Refining CO2

CO2

CO2 CO2

Distribution at Airports

Flight

Transport

Transport

CO2

CO2

CO2

Refining

Extraction

Transport

CO2

Feedstock Growth

Harvest

Figure 2: Comparison of the Carbon Dioxide Emissions for the Production of Jet Fuel From (a) Fossil Sources and (b) Biomass Sources There exist two common types of biofuels: first-generation and second-generation. First-generation biofuels, such as ethanol and biodiesel, are derived from vegetable oils, sugar-rich and starch-rich food crops such as rapeseed, sugarcane and corn. While first-generation biofuels are widely used for ground transportation, home heating, power generation and cooking, they are not suitable for use in aircraft. Indeed, they do not meet the required performance and safety specifications for use in modern jet engines20,21. In addition, the production of firstgeneration biofuel sources may raise concerns about agricultural land-use changes, food prices, pesticides and fertilizers utilization, as well as water quality and water resources for irrigation. Second-generation biofuels are 5 American Institute of Aeronautics and Astronautics

derived from processed or chemically converted non-food oil-rich plants, grasses, wastes, and fruit trees such as camelina, jatropha, miscanthus, waste fat, halophytes, and algae. Second-generation crops do not typically require large quantities of pesticides, fertilizers or water for irrigation as they have the potential to grow on marginal lands and resist extreme environmental conditions while still delivering large quantities of biomass feedstocks22. Therefore, the production of second-generation biofuel sources does not compete for valuable land and water resources traditionally used for food supplies. They further provide socio-economic value to local communities that have large amounts of unviable land for food crops. In order to determine the potential of alternative jet fuels for GHG emissions reduction, it is necessary to account for the entire lifecycle of emissions released during growth and harvest of the crop, transport and processing of raw materials, refining of bio-fuel, and fuel distribution at airports. When all this is taken into account, it is still expected that biofuels will provide significant reductions in their overall CO2 lifecycle footprint compared to fossil fuels18,2325 . The aviation industry is interested in developing alternative fuels that can be mass-produced from fast-growing and high-yield crops, but that do not compete with the production of food products. In order for biofuels to fulfill this promise, it is essential to consider sustainable biomass feedstocks and production processes. In this context, sustainability refers to the ability of a biofuel to conserve an ecological balance between productivity, biodiversity and usage of natural resources. This is why aviation industry stakeholders have been focusing on second- or nextgeneration biomass feedstocks for producing bio-jet fuels. Several “drop-in” fuels26-28, with similar qualities and characteristics as conventional Jet A or Jet A-1, produced from biomass feedstocks that have the potential to yield large amounts of greener fuels at more stable prices have been investigated. Due to the ability of second-generation feedstocks to be grown on various types of soils and under various climatic conditions, it is likely that aircraft will be powered by blends of conventional jet fuel and alternative fuels produced from different types of biomass sources. While a number of technologies exist to produce alternative fuels, it is unclear which technologies may prove viable in the long term. Currently, only two types of alternative jet fuels have been certified for use in aviation when blended with at least 50% of conventional kerosene: the Fischer-Tropsch (F-T) hydroprocessed Synthesized Paraffinic Kerosene (SPK) and the synthesized paraffinic kerosene from Hydroprocessed Esters and Fatty Acids (HEFA)26. It has been shown that HEFA fuels have the potential to reduce lifecycle greenhouse gas emissions by 60% compared to petroleum-based fuels29. The aviation industry has also been exploring the use of less traditional feedstock options to produce alternative jet fuels such as lignocellulosic materials, industrial and agricultural wastes, animal and fish fats, sewage, and other types of paper, food, or wood residues. Several biofuel alternatives produced from some of those unconventional feedstocks are currently being developed or tested against stringent specification standards to determine their suitability for use in existing aircraft engines18. While alternative jet fuels are now widely accepted by aviation industry stakeholders, some uncertainties remain regarding their technical and economic performance, as well as their social and environmental impacts. The reliability, sustainability, renewability, and availability of the feedstocks from which alternative fuels are produced are key to success. Production alone is not enough. A large share of the studies on alternative fuels for aviation has focused only on lifecycle assessments of complete alternative fuel pathways and has overlooked their inherent uncertainty. In this context, most of them have led to the development of deterministic decision support tools to quantify the lifecycle GHG emissions of various alternative fuel pathways, and to assess their effects across several dimensions, including technical, social, economic, environmental, and climatic30-37. However, such decision-support tools lack the capability to account for the inherent uncertainty associated with the production of alternative fuels from a wide variety of feedstock types, growing under different soil, atmospheric and climatic conditions. In this work, we therefore develop a transparent methodology for the selection of sustainable feedstock options while taking into account the technical, social, economic, environmental, and climatic uncertainty associated with the production of alternative jet fuels.

IV. Methodology for the Selection of Sustainable Biomass Feedstocks The objective of this paper is to develop a probabilistic assessment of the lifecycle emissions associated with the production of sustainable and renewable alternative jet fuels from various feedstock options, based on the general principles of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Originally, 6 American Institute of Aeronautics and Astronautics

TOPSIS is a deterministic multi-criteria decision analysis method38-40 which compares a set of competing alternatives according to a set of decision criteria. These criteria may be qualitative or quantitative and are to be maximized or minimized depending on the nature of the problem. Scenarios are generated by attributing importance weights to the selection criteria. Each scenario yields a matrix of scores for the alternatives to be compared. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (defined as the set of maximum scores for criteria requiring maximization and minimum scores for criteria requiring minimization) and the longest geometric distance from the negative ideal solution (defined as the opposite of the positive ideal solution). In most applications of TOPSIS, the competing alternatives are ranked according to a deterministic set of criteria values. In the context of the selection of feedstock options for the production of bio-jet fuels, the uncertainty associated with each decision criteria must be accounted for in the decision-making process. Thus, a probabilistic TOPSIS analysis is proposed to enable the construction of a structured methodology for rigorously downselecting promising feedstock options according to various economic, social, environmental, and technical scenarios. A. Biomass Sources and Selection Criteria In a first step, several first- and second-generation feedstocks are identified as potential options to efficiently produce alternative jet fuels while reducing lifecycle carbon emissions. These include corn stover, sweet sorghum, rapeseed (canola) oil, camelina and jatropha seeds, waste fat, wood residues, short rotation woody crops (SRWC), miscanthus, switchgrass, and algae. They are depicted in Figure 3. Corn Stover

Sorghum

treepower.org

westernfarmpress.com

Miscanthus

heritagemiscanthus.com

Camelina

betalabservices.com

Switchgrass

ethanolproducer.com

Jatropha Tree

betalabservices.com

Wood Residues

renewableenergyfocus.com

Canola Plant

theguardian.com

Algae

thinkprogress.org

Figure 3: Alternative Biomass Feedstock Options Three types of refining processes are then identified to process and convert bio-derived feedstock sources into bio-jet fuels: Fischer-Tropsch (F-T) process, Alcohol-To-Jet (ATJ) process, and fast pyrolysis with hydroprocessing of esters and fatty acids (HEFA) process. The Fischer-Tropsch process belongs to the family of gas-to-liquids technology. It features a series of chemical reactions that converts a mixture of carbon dioxide and hydrogen, obtained from the burning of biomass feedstocks, into synthetic liquid hydrocarbons41-43. The Alcohol-to-Jet (ATJ) 7 American Institute of Aeronautics and Astronautics

process transforms sugars or alcohols derived from fermented sugar-rich or starchy biomass into liquid jet fuel through a suite of catalytic reactions44. Hydroprocessing is a catalytic process through which fast pyrolysis triglyceride oils derived from oil-rich plant seeds and tree fruits are turned into liquid hydrocarbons suitable for use as jet fuels45. In a second step, performance metrics are selected as criteria for comparison of the different feedstock options. These are related to the technical, economic, and environmental aspects of the production of alternative fuels. These metrics may be quantitative or qualitative. Qualitative criteria values are modeled using a discrete scale from 1 to 9. Quantitative criteria are defined by minimum and maximum values obtained from a broad scope of journal paper 46 and relevant publications from aviation industry stakeholders, biofuel research entities47, and various universities48. The set of evaluation criteria considered in this study is summarized in Table 4. Table 4: Qualitative and Quantitative Performance Metrics

Availability of feedstock and processing technologies

Maximize / Minimize Maximize

Land use (ha/ Million L of biofuel)

Maximize / Minimize Minimize

Reliability of farming and processing practices

Maximize

Water consumption (L/L biofuel)

Minimize

Sustainability of farming and processing practices

Maximize

Fertilizer and pesticides usage (kg/ha)

Minimize

Qualitative Criteria

Quantitative Criteria

5

Renewability of feedstock

Maximize

Lifecycle GHG emissions (gCO2eq/MJ)

Minimize

Land disturbance due to biomass growth

Minimize

CO2 absorption (gCO2eq/MJ)

Maximize

Competition of biomass growth with food production

Minimize

Production yield (t/ha)

Maximize

Soil enrichment from biomass growth

Maximize

Biofuel yield (L/ha)

Maximize

Ability of biomass to grow on marginal land

Maximize

Energy consumption (MJ/kg feedstock)

Minimize

Production costs ($U.S./ha)

Minimize

Feedstock price ($U.S./kg)

Minimize

Refining costs ($U.S./L)

Minimize

B. Probability Distributions and Monte Carlo Simulations In a third step, a literature review is performed to determine the nature and the parameterization of the uncertain quantitative performance metrics indentified in the previous step. These uncertainties are modeled using probability distributions31,49 as exemplified for miscanthus in Table 5. Table 5: Example Probabilistic Distribution Parameters Miscanthus Distribution Type

Parameters

Fertilizer usage

Uniform

Min: 219 kg/ha/yr Max: 365 kg/ha/yr

Lifecycle GHG emissions

Triangular

Production yield

Uniform

Energy use

Triangular

Min: 376 gCO2eq/MJ Max: 627 gCO2eq/MJ Apex: 502 gCO2eq/MJ Min: 22.6 t/ha Max: 45.5 t/ha Min: 4 MJ/t feedstock Max: 10 MJ/t feedstock Apex: 7 MJ/t feedstock

In a fourth step, Monte Carlo simulations are performed to sample the space of quantitative selection criteria and to generate many possible evaluation matrices. In an evaluation matrix, the various biomass feedstock options are 5

Lifecycle GHG emissions encompass all GHG emissions (not only CO 2 but also CH4, H2O, NOx, and in some instances, XFCs and SF6) from the growth of the biomass feedstock to the actual combustion of the alternative jet fuel in the aircraft engine, as depicted in Figure 2 (b) in the case of CO2. 8 American Institute of Aeronautics and Astronautics

represented by a set of fixed qualitative values and quantitative values obtained from a Monte Carlo simulation for the performance metrics identified in Table 4. C. Preference Scenarios In a fifth step, several preference scenarios are defined to represent different decision-maker preferences. Six scenarios of interest have been modeled. These reference scenarios are called “Low Conventional Fuel Price”, “Stringent Land and Water Use Regulations”, “Stringent Land and Water Pollution Regulations”, “Stringent GHG Emission Regulations”, “Stringent Energy Regulations”, and “Unlimited CAPEX and High Energy Prices.” Each scenario is characterized by a vector of weights assigned to each evaluation metric and can take on any discrete value from 1 (not important) to 100 (extremely important). A description of the six weighting scenarios considered in this study is given in Table 6. Table 6: Combined Weighted Preference Matrix for Six Scenarios of Interest and Their Descriptions SCENARIOS / CRITERIA

Low Conventional Fuel Price (Scenario 1)

Feed. and tech availability Farm. and process. reliability Farm. and process. sustainability Feed. renewability Land Disturbance Competition with Food Prod. Soil Enrichment Land Use Water Consumption Fertilizer and Pesticides Use Lifecycle GHG Emissions CO2 Uptake Production Yield Biofuel Yield Energy Input Production Costs Feedstock Price Refining Costs

100 100 100 100 100 100 100 1 1 1 1 1 1 1 1 100 100 100


Stringent Land and Water Use Regulations (Scenario 2) 100 100 100 100 100 100 100 100 100 1 1 1 1 1 1 1 1 1

Stringent Land and Water Pollution Regulations (Scenario 3) 100 100 100 100 100 100 100 1 1 100 1 1 1 1 1 1 1 1

Stringent GHG Emission Regulations (Scenario 4)

Stringent Energy Regulations (Scenario 5)

100 100 100 100 100 100 100 1 1 1 100 100 1 1 1 1 1 1

100 100 100 100 100 100 100 1 1 1 1 1 100 100 100 1 1 1

SCENARIOS DESCRIPTION * Conventional jet fuel is cheap to acquire * Bio-jet fuels can only compete if the associated costs of biomass acquisition, bio-jet fuel production and refining are small

Unlimited CAPEX and High Energy Prices (Scenario 6) 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 1 1 1

DECISION-MAKER GOAL Determine biomass feedstocks that result in the lowest possible costs of production of alternative jet fuels

Stringent Land and Water Use Regulations (Scenario 2)

* Utilization of uncultivated and unpopulated land, and of irrigation water is highly regulated for the production of alternative jet fuels

Obtain biomass feedstock options that are able to resist extreme environmental conditions such as droughts, and that can grow on very small parcels of marginal or degraded lands

Stringent Land and Water Pollution Regulations (Scenario 3)

* Some stringent measures have been taken to minimize land and water pollution

Minimize the utilization of pesticides, herbicides, and fertilizers


* Some stringent GHG emission regulations have been implemented to minimize climate change

Determine biomass feedstocks that yield the lowest lifecycle GHG emissions

Stringent Energy Consumption Regulations (Scenario 5)

* Consumption of energy (electricity, fuel,…) has been regulated for the production of alternative jet fuels

Maximize biomass production yield and biofuel yield, while minimizing energy consumed along the bio-jet fuel production pathway (maximize energy ratio)

Unlimited CAPEX (Scenario 6)

* Decision-maker has endless funding and thus does not really care about any costs involved in the production of alternative jet fuels, from the growth, harvesting and processing of the biomass feedstocks, to the refining of the bio-jet fuels

Find biomass feedstock options that score well across the environmental, social, and technical aspects regardless of their associated costs


D. TOPSIS Ranking Finally, for each scenario represented by a vector of preferences, a large number of evaluation matrices are generated from Monte Carlo simulations using the probabilistic definition of the performance criteria exemplified in Table 5. The main steps of the original TOPSIS method are then applied to each sample evaluation matrix weighted according to each scenario of interest. A bar chart showing the output of this step for a specific case study is depicted in Figure 4 for scenario #4. Switchgrass Algae Miscanthus Jatropha Camelina Sweet Sorghum Rapeseed Corn Stover SRWC Wood Residues Waste Fat Palm Soy

0.34 0.39 0.45 0.45 0.46 0.49 0.53 0.55 0.61 0.62 0.64 0.66 0.68

Scenario #4: Stringent GHG Emission Regulations

Figure 4: Bar Chart Showing TOPSIS Scores (Distance to Positive Ideal Solution) for Scenario #4 For instance, considering a sample evaluation matrix and the vector of preferences corresponding to scenario #4 (GHG emission regulations) provided in Table 6, switchgrass, algae, miscanthus, jatropha and camelina rank as the top five feedstock options for producing alternative jet fuels. The ranking of biomass feedstock options for all evaluation matrices obtained from Monte Carlo simulations and for all preference scenarios of interest may be performed to provide some insight into the most promising solutions.

V. Results and Conclusion The motivation for this work is to explore the development of cost-effective, lower-carbon alternative fuels for aviation to meet the 2050 net aggregated GHG emissions reduction goal of 50% compared to 2005 levels5. The goal of the study is to provide the aviation industry with a methodology for selecting optimal feedstock options for the production of alternative jet fuels while accounting for social, economic, environmental, and climatic uncertainties. A probabilistic TOPSIS method is developed and a series of visualization displays are combined into an interactive dashboard. This provides a means for timely and efficient exploration of the outputs. A. Scatterplot Matrices The first component of this dashboard concerns the representation of the design space of performance metrics identified in Table 4 after a series of Monte Carlo simulations has been run. A sample scatterplot matrix depicting the results of the probabilistic analysis is displayed in Figure 5. For instance, Figure 5 shows that using switchgrass or sweet sorghum tends to minimize lifecycle GHG emissions, while maximizing production yield. Although it results in similar production and biofuel yields as switchgrass, miscanthus seems more promising to absorb CO2 and thus to reduce the net GHG emissions. Finally, despite large production and refining costs and non-negligible fertilizers consumption, the use of algae has the potential to absorb noticeable amounts of CO2 and to significantly decrease water consumption, land usage, and lifecycle GHG emissions. The results displayed in Figure 5 may be better visualized through a set of 3-D scatterplots. An example is provided in Figure 6.


Corn Stover

Camelina

Wood Residues

Switchgrass

Sweet Sorghum

Jatropha

SRWC

Algae

Canola

Waste Fat

Miscanthus

Figure 5: Scatterplot Matrix Depicting Probabilistic Analysis Results Figure 6 shows that the use of sweet sorghum, switchgrass, camelina, and jatropha tends to minimize lifecycle GHG emissions, while minimizing water consumption. Although it results in similar production yield and water consumption as switchgrass, miscanthus is associated with slightly larger lifecycle GHG emissions. The careful analysis of Figure 5 and of various 3-D scatterplots similar to the one depicted in Figure 6 enables the decisionmaker to get a first insight into the most promising biomass options across dimensions of interest. However, the TOPSIS results yield more detailed information about which biomass feedstock is better suited for a specific set of requirements.


Corn Stover

Sweet Sorghum Canola

Camelina Jatropha Waste Fat

Wood Residues SRWC Miscanthus

Switchgrass Algae

Figure 6: Example 3-D Scatterplot B. TOPSIS Results The TOPSIS method has been applied to a large number of sample evaluation matrices weighted according to all six preference scenarios of interest summarized in Table 6. For instance, Figure 7 shows the resulting biomass feedstock rankings for scenarios #2 and #4 across all generated evaluation matrices. In Figure 7, the stacked histogram shows the cumulative conditional rank order statistics for the first six biomass feedstock options. The blue bars show the probability that the corresponding biomass option ranks first, the red bars depict the probability that the corresponding biomass feedstock ranks second given that it did not rank first, and the green bars represent the probability that the corresponding biomass option ranks third given that it did not rank first and second. Blue: Red: Green:

Probability of biomass ending in first position Probability of biomass ending in second position, given that it is not in first position Probability of biomas ending in third position, given that it is neither in first nor in second positions

Blue: Red:

Probability of biomass ending in first position Probability of biomass ending in second position, given that it is not in first position Green: Probability of biomas ending in third position, given that it is neither in first nor in second positions

3rd Place

1st Place

2nd Place 91%

1st Place

2nd Place 87%

3rd Place 100%

100% 73% 30%

95% 81%

64% 36% Algae

47%

66%

64% 28%

Switchgrass Jatropha

25% Camelina Miscanthus

Sweet Sorghum

(a) Stringent Land and Water Use Regulations

Switchgrass

28%

11%

Algae

Miscanthus

26% Sweet Sorghum

Jatropha

Camelina

(b) Stringent GHG Emission Regulations

Figure 7: Example Biomass Options Ranking for Scenario #2 (a) and Scenario #4 (b) Figure 7 shows that algae are very efficient when very little marginal land and very little irrigation water are available for biomass growth and processing. Algae arrive in first position 64% of the time, in front of switchgrass 12 American Institute of Aeronautics and Astronautics

(second 95% of the time), jatropha (third 64% of the time), camelina, and miscanthus. Furthermore, switchgrass ranks first 66% of the time when it comes to minimizing GHG emissions while algae arrive in second place 81% of the time, and miscanthus ranks third 47% of the time in front of sweet sorghum, jatropha, and camelina. Table 7 summarizes the rankings of biomass feedstocks for all six preference scenarios. Table 7: TOPSIS Results for All Preference Scenarios and Sample Evaluation Matrices

Biomass Feedstocks Ranking


Stringent Land and Water Use Regulations (Scenario 2)

Switchgrass

1

2

Sweet Sorghum

5

Stringent Land and Water Pollution Regulations (Scenario 3) 1


Stringent Energy Regulations (Scenario 5)

Unlimited CAPEX (Scenario 6)

1

3

2

4

1

1

2

2

5 1

Algae Jatropha

2

3

3

Camelina

3

4

2

5

4 5

5

In the next study, all scenarios are combined together to perform a robustness analysis and to check whether some biomass options are globally optimal. The results are depicted in Figure 8 and show that switchgrass is undeniably the biomass feedstock that has the most potential. Sorghum arrives in second position, before algae, miscanthus, camelina, and jatropha. Blue: Red:

Green:

Probability of biomass ending in first position Probability of biomass ending in second position, given that it is not in first position Probability of biomas ending in third position, given that it is neither in first nor in second positions 3rd Place 2nd Place

97%

1st Place

82%

10% 26%

53% Switchgrass

Sweet Sorghum

11% 14% 20% Algae

28%

46%

24% 20%

Miscanthus Camelina

18% Jatropha

Figure 8: Overall Biomass Feedstock Options Ranking for Probabilistic Analysis Finally, a sensitivity analysis was performed on the preference scenarios. A full factorial design of experiment was performed on the design space of preference weights for each performance metric assuming they can only take their two extreme values, namely 1 (not important) or 100 (extremely important). The results of this sensitivity analysis are provided in Figure 9.


Blue: Red:

Green:

Probability of biomass ending in first position Probability of biomass ending in second position, given that it is not in first position Probability of biomas ending in third position, given that it is neither in first nor in second positions

3rd Place

96%

2nd Place 1st Place

86% 27%

21% 54% Switchgrass

43%

30%

36%

16%

Sweet Sorghum

Miscanthus

23%

20% Algae

Jatropha

11% Camelina

Figure 9: Overall Biomass Feedstock Options Ranking for Sensitivity Analysis on Preference Scenarios Figure 9 shows that switchgrass is, one more time, undeniably the biomass feedstock that has the most potential across the design space of preference scenarios, or in other words, the design space of potential decision-maker preferences. Sorghum arrives at the second position, in front of miscanthus, algae, jatropha, and camelina. Finally, the extensive literature search performed as part of this study gives the reduction in GHG emissions associated with the production and combustion of alternative jet fuels relative to those associated with the production and combustion of conventional jet fuel. These relative GHG emission reductions are summarized in Table 8. Table 8: Relative GHG Emission Reductions for Biomass Sources Compare to Fossil Sources Biomass Feedstock Corn Stover Sweet Sorghum Canola Camelina Jatropha Waste Fat Wood Residues SRWC Miscanthus Switchgrass Algae

GHG Emission Reduction Compared to Conventional Jet Fuel 55% 133% 44% 86% 42% 87% 148% 145% 72% 63% 124%

Table 8 shows that switchgrass has the potential to reduce GHG emissions by 63% compared to conventional fossil fuel sources. Also, the production of alternative jet fuels from sorghum yields 133% GHG emission savings, compared to 124% for algae, 72% for miscanthus, 86% for camelina, and 42% for jatropha. These results are visually represented in Figure 10. Figure 10 depicts GHG emission savings resulting from the utilization of 100% alternative jet fuels produced from the most promising biomass feedstock options depicted in Figure 9, and of a mixture of 50% alternative jet fuel and 50% conventional jet fuel.


140%

124%

133%

120%

100% (%)

86%

80% 60%

40%

72%

63% 42%

21%

31%

62%

36%

66%

43%

20% 0%

GHG Savings - 100% alternative GHG Savings - 50% alternative / 50% conventional

Figure 10: GHG Emission Savings From the use of Biomass Feedstocks Compared to Fossil fuel Sources In summary, sweet sorghum, switchgrass, miscanthus, algae, jatropha, and camelina end up being the most promising biomass feedstock options to not only reduce GHG emissions associated with the production of bio-jet fuels, but also to minimize production and refining costs, water, land, fertilizer, and energy usage, while maximizing production and biofuel yields. C. Conclusions The goal of the paper was to explore ways to reduce greenhouse gas emissions. It has been showed that the development of more efficient technologies for aircraft and engines may yield GHG emission reductions up to about 18%. However, even in the unlikely event that a compatible portfolio of more fuel efficient technologies were to be infused into a single aircraft design, the end-result would still fall short of the goal of reducing aviation CO2 emissions by 50% by 2050. Relying exclusively on technology advancement is therefore not sufficient and a more radical approach to cut emissions is warranted to supplement the improvements yielded by new technology adoptions. Another way to achieve this goal is to progressively replace fossil carbon-based energy sources by renewable and sustainable solutions. A methodology has been proposed to account for the inherent uncertainties associated with the selection of biomass feedstock options and corresponding refining processes to produce bio-jet fuels suitable for use in existing engine designs. A probabilistic analysis encompassing most of the likely scenarios has been carried out and various visualization tools have been explored to substantiate and facilitate decision making by aviation industry stakeholders. In a first step, several biomass feedstocks of interest have been identified. In a second step, various performance criteria have been researched in the literature to compare the biomass alternatives. In a third and fourth steps, quantitative metrics have been assigned a probability distribution and Monte Carlo simulations have been performed to define a large number of sample evaluation matrices. In a fifth step, scenarios of interest have been defined to model potential decision-maker preferences. Finally, the TOPSIS method has been applied to highlight a set of most promising biomass feedstock options. Results have shown that switchgrass, sweet sorghum, miscanthus, algae, jatropha, and camelina have the potential to significantly enhance the environmental impact of aviation. Not only they use little land, water and fertilizers to grow, but they are also associated with high production and biofuel yields and very small GHG emissions. For instance, the exclusive use of alternative jet fuels produced from switchgrass would yield about 63% GHG emission savings, while the use of a 50%-50% mix would lower GHG emission by about 31%. In both cases, this is a significant step forward in meeting the ambitious goal of aviation stakeholders of adopting a carbon-neutral growth from 2020, and reducing the world’s aviation carbon footprint by 50% by the year 2050.


References Periodicals 10 SESAR Joint Undertaking, “Airspace users at the heart of SESAR,” SESAR Magazine, Today’s partners for Tomorrow’s aviation, Issue No. 6, 2011 19 Rye, L. Blakey, S., and Wilson, C. W., “Sustainability of supply or the planet: a review of potential drop-in alternative aviation fuels,” Energy and Environmental Sciences, Vol. 3, pp. 17-27, 2010 23 Bailis, R., and Baka, J., “Greenhouse Gas Emissions and Land Use Change from Jatropha Curcas-Based Jet Fuel in Brazil,” Environmental Science & Technology, Vol. 44, pp. 8684-8691, 2010 24 Huo, H., Wang, M., Bloyd, C., and Putsche, V., “Life-Cycle Assessment of Energy Use and Greenhouse Gas Emissions of Soybean-Derived Biodiesel and Renewable Fuels,” Environmental Science & Technology, Vol. 43, pp. 750-756, 2009 29 Stratton, R. W., Wong, H. M., and Hileman, J.I., “Quantifying variability in life cycle greenhouse gas inventories of alternative middle distillate transportation fuels,” Environmental Science and Technology, Vol. 45, No. 10, pp. 4637–4644, 2011 30 Perimenis, A., Walimwipi, H., Zinoviev, S., Müller-Langer, F., and Miertus, S., “Development of a decision support tool for the assessment of biofuels,” Energy Policy, Vol. 39, pp. 1782-1793, 2011 31 Agusdinata, D. B., Zhao, F., Ileleji,K., and DeLaurentis, D., “Life Cycle Assessment of Potential Biojet Fuel Production in the United States,” Environmental Science and Technology, Vol. 45, pp. 9133-9143, 2011 32 Diakoulaki, D., Karangelis, F., “Multi-criteria decision analysis and cost–benefit analysis of alternative scenarios for the power generation sector in Greece,” Renewable and Sustainable Energy Reviews, Vol. 11, pp. 716-727, 2007 34 Wang, J.,-J., Jing, Y.-Y., Zhang, C.-F., Zhao, J.-H., “Review on multi-criteria decision analysis aid in sustainable energy decision-making,” Renewable and Sustainable Energy Reviews, Vol. 13, pp. 2263-2278, 2009 35 Thomas, A. R. C., Bond, A. J., and Hiscock, K. M., “A multi-criteria based review of models that predict environmental impacts of land use-change for perennial energy crops on water, carbon and nitrogen cycling,” GCB Bioenergy, Vol. 5, pp. 227242, 2013 36 Farine, D. M., O’Connel, D. A., Raison, R. J., May, B. M., O’Connor, M. H., Crawford, D. F., Herr, A., Taylor, J. A., Jovanovic, T., Campbell, P. K., Dunlop, M. A., Rodriguez, L. C., Poole, M. L., Braid, A. L., and Kriticos, D., “An assessment of biomass for bioelectricity and biofuel, and for greenhouse gas emission reduction in Australia,” GCB Bioenergy, Vol. 4., pp. 148–175, 2012 37 Pearlson, M., Wollersheim, C., and Hileman,J., “A techno-economic review of hydroprocessed renewable esters and fatty acids for jet fuel production”, Biofuels, Bioproducst, and Biorefining, Vol. 7, pp. 89–96, 2013 39 Yoon, K., “A reconciliation among discrete compromise situations,” Journal of Operational Research Society, Vol. 38, pp. 277–286, 1987 40 Hwang, C. L., Lai, Y. J., Liu, T. Y., “A new approach for multiple objective decision making,” Computers and Operational Research, Vol. 20, pp. 889–899, 1993 41 Fischer, F., Tropsch, H., Brennstoff-Chemie, Vol. 7, pp. 97, 1926 42 Fischer, F., Tropsch, H., Brennstoff-Chemie, Vol. 11, pp. 489, 1930 43 Schulz, H., “Short history and present trends of Fischer–Tropsch synthesis,” Applied Catalysis A, General 186, pp. 3-12, 1999 44 Elliott, D. C., Hart, T. R., Neuenschwander, G. G., Rotness, L. J., and Zacher, A. H., “Catalytic Hydroprocessing of Biomass Fast Pyrolysis Bio-oil to Produce Hydrocarbon Products,” American Institute of Chemical Engineers, Environmental Progress & Sustainable Energy, Vol. 28, No. 3, pp. 441-449, 2009 49 Cai, H., Dunn, J. B., Wang, Z., Han, J., and Wang, M. Q., “Life-cycle energy use and greenhouse gas emissions of production of bioethanol from sorghum in the United States,” Biotechnology for Biofuels, Vol. 6, pp. 141-155, 2013 Books 4 Metz, B., Davidson, O. R., Bosch, P. R., Dave, R., Meyer, L. A. (eds), “ Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,” Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2007 38 Hwang, C. L., Yoon, K., “Multiple attribute decision-making methods and application: a state-of-the-art survey,” New York, USA: Springer-Verlag, 1981 Proceedings 11 Karagozian, A., Dahm, W., Glasgow, E., Howe, R., Kroo, I., Murray, R., and Shyu, H., “Technology Options for Improved Air Vehicle Fuel Efficiency: Executive Summary and Annotated Brief,” United States Air Force Scientific Advisory Board, 2006 13 Liebeck, R. H., “Design of the Blended Wing Body Subsonic Transport,” Journal of Aircraft, Vol. 41, No. 1, pp. 10-25, 2004 33 Endres, C., Falter, C., Roth, A., Riegel, F., and Sizmann, A., “Renewable Aviation Fuels – Assessment of Three Selected Fuel Production Pathways,” Deutscher Luft- und Raumfahrtkongress, 2012 Reports, Theses, and Individual Papers 1 McCollum, D., Gould, G., and Greene, D., “Greenhouse Gas Emissions from Aviation and Marine Transportation: Mitigation Potential and Policies”, PEW Center on Global Climate Change, 2009 2 Office of Transportation and Air Quality, “Greenhouse Gas Emissions from the U.S. Transportation Sector 1990–2003”, Environmental Protection Agency (EPA 420 R 06 003), 2006


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Oﬃce of Environment and Energy, “Aviation & Emissions: A Primer,” Federal Aviation Administration, Washington, DC, 2005 5 IATA, “The IATA technology roadmap report,” 3rd Edition, Montreal, QC, Canada, 2013 6 ICAO: 37th Assembly Working Papers, A37-WP/402, Report of the Executive Committee on Agenda Item 17, Section on Climate Change, Montreal, QC, Canada, 2010 7 ICAO: 37th Assembly Working Papers, A37-WP/25, Consolidated Statement of Continuing Policies and Practices Related to Environmental Protection – Climate Change, Montreal, QC, Canada, 2010 8 United Nations Framework Convention on Climate Change, “Compilation and synthesis of supplemental information incorporated in fifth national communications submitted in accordance with Article 7, paragraph 2, of the Kyoto protocol,” FCCC/SBI/2011/INF.2, Subsidiary Body for Implementation, 34th session, Bonn, 2011 9 U.S. Government Accountability Office, “Aviation and the Environment: NextGen and Research and Development Are Keys to Reducing Emissions and Their Impact on Health and Climate”, Statement of Gerald L. Dillingham, 2008 12 Greener by Design Science and Technology Sub-Group, “Mitigating the Environmental Impact of Aviation: Opportunities and Priorities,” Royal Aeronautical Society, 2005 14 Berdy, P., “Biofuels: The Next Gold Rush, An Alternative to Costly Traditional Fuel,” Ascend, Special Edition, pp. 55- 63 16 Justin C., Briceno S. and Mavris D., “A competitive and real-options framework for the economic analysis of large aerospace programs,” 28th International Congress of the Aeronautical Sciences, ICAS, Brisbane, 2012 18 International Air Transport Association, “IATA 2012 Report on Alternative Fuels,” 7th Edition, Montreal, QC, Canada, 2012 20 ASTM, “Standard Specification for Aviation Turbine Fuels,” Designation: D1655-13, 2013 21 ASTM, “Standard Specification for Aviation Turbine Fuel Containing Synthesized Hydrocarbons,” Designation: D7566-13, 2013 26 American Society for Testing and Materials (ASTM), “Standard Speciﬁcation for Aviation Turbine Fuel Containing Synthesized Hydrocarbons,” Designation: D7566–13, Book of Standards Volume: 05.04, 2013 44 Byogy Renewables, “Alcohol- To Jet (“ATJ”), Emerging Through ASTM,” ICAO Aviation and Sustainable Alternative Fuels Workshop, Montreal, QC, Canada, 18-20 October 2011 22 Eisentraut, A., “Sustainable Production of Second-Generation biofuels: Potential and perspectives in major economies and developing countries,” International Energy Agency Information Paper, February 2010 23 Sims, M., Taylor, M., Saddler, J., and Mabee, W., “From 1st- to 2nd- generation biofuel technologies: An Overview of current industry and RD&D activities,” International Energy Agency, Global Bioenergy Partnership, IEA Bioenergy, November 2008 25 Stratton, R. W., Wong, H. M., and Hileman, J. I., “Life Cycle Greenhouse Gas Emissions from Alternative Jet Fuels,” PARTNER Project 28 Report, June 2010 27 International Air Transport Association, “IATA 2013 Report on Alternative Fuels,” 8th Edition, Montreal, QC, Canada, December 2013 Electronic Publications 15 Lane, J., “Aviation biofuels: which airlines are doing what, with whom?,” BiofuelsDigest, June 2012 17 ETH Zürich, “Synthesized 'solar' jet fuel: Renewable kerosene from sunlight, water and carbon dioxide,” Science Daily, May 2014 Other 46 AIChE Journal, Journals of Applied Energy, of Bioenergy Research, of Biofuel Byproducts and Biorefining, of Biomass and Bioenergy, of Bioresource Technology, of Bioscience and Bioengineering, of Biotechnology for Biofuels, of Energy and Environmental Science, of Energy Conversion and Management, of Energy for Sustainable Development, of Energy Policy, of Environmental Science and Technology, of Fuel, Environmental Progress & Sustainable Energy, of Industrial Crops and Products, of Natural Resources Research, of Resources, Conservation and Recycling, of Sustainability, of Water Policy, of Water Resources Research, the International Journal of Life Cycle Assessment, the International Journal of Molecular science, the American-Eurasian Journal of Agriculture & Environmental Science, the European Journal of Scientific Research. 47 ACRP, CAAFI, EIA, EPA, FAA, IATA, IEA, ICAO, NASA, U.S. D.O.T., U.S. D.O.E., U.S.D.A., the Agro-Food and Bioscience Institute, the Air Transport Action Group, Alternative Fuels online, Alternative Fuel Suppliers, Biofuels Digest online, Biofuels Reviews, the British Columbia Innovation Council, the Center for Sustainable Aquaculture Research, the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Eco-invent Centre Publications, Environmental Research Letters, the European Union Directorate General for Energy, the European Biomass Industry Association Publications, the European Bioenergy Networks, (Bio)Fuel Refineries, Proceedings of the National Academy of Sciences, Renewable and Sustainable Energy Reviews, the World Academy of Science 48 University of Kentucky, University of Tennessee, University of Florida, University of Nevada, Iowa State, Oregon State, Kansas State, Michigan State, Texas A&M, North Carolina State, Berkeley, and Massachusetts Institute of Technology
