Carbon offsetting and reduction scheme with sustainable aviation fuel options: Fleet-level carbon emissions impacts for U.S. airlines

Transportation Research Part D 75 (2019) 42–56

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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

Carbon offsetting and reduction scheme with sustainable aviation fuel options: Fleet-level carbon emissions impacts for U.S. airlines

T

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Hsun Chaoa, Datu Buyung Agusdinatab, , Daniel DeLaurentisa, Ellen B. Stechelc a b c

School of Aeronautics and Astronautics, Purdue University, 701 W. Stadium Ave., West Lafayette 47907, IN, United States School of Sustainability, Arizona State University, 800 Cady Mall, Tempe, AZ 85287, United States School of Molecular Science, Arizona State University, 551 E University Dr, Tempe, AZ 85281, United States

A R T IC LE I N F O

ABS TRA CT

Keywords: Aviation emissions policy U.S. airlines operations Sustainable aviation fuels Greenhouse gas emissions

To reduce aviation carbon emissions, the International Civil Aviation Organization initiated the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA), which will take effect in 2021. In response, airlines have taken measures through various means, including the use of sustainable fuels. This article investigates the potential effects of a CORSIA-type policy when implemented in the United States. The study uses a combined model of airlines operations and multi-feedstock sustainable aviation fuels (SAFs) to represent decisions of several actors, such as farmers, bio-refineries, airlines, and policymakers. The research employed a life-cycle assessment and Monte-Carlo simulation to evaluate two policy scenarios on the amount of SAF consumption and the resulting emissions. Implementing a CORSIA-type policy could stimulate the demand and production of SAFs, while also reducing air travel growth by increasing airfare. As a result of this combined effect and improved aircraft technology, there is a 3.5% chance that the U.S. airlines industry can reduce greenhouse gas (GHG) emissions by 37.5–50% by the year 2050, compared to the 2005 emission levels. Despite a projected increase in air travel in 2050 by a factor of 2.75 (the median value), the emissions in 2050 are expected to rise to only 120% (the median value) of the 2005 level. The price of petroleum-based aviation fuels followed by the growth rate of the carbon price are the two most important factors to determine whether the CORSIA-type policy would achieve the emission reduction target.

1. Introduction The aviation industry has been at the forefront of efforts to reduce carbon emissions. The International Civil Aviation Organization (ICAO) will initiate the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) after 2021 to prevent carbon emissions from international aviation from exceeding the 2020 emissions level. The International Air Transport Association (IATA), a trade association of the world's airlines that represents 82% of total air traffic, has set the ambitious target of reducing net aviation carbon emissions to 50% of the 2005 emission level by 2050 (IATA, 2019). The U.S. aviation industry is the largest in the world in terms of traffic volumes and aircraft movements (ICAO, 2016a,b). Hence, it is critical to whether the global aviation industry can achieve its greenhouse gas (GHG) reduction target. Domestically, U.S. aviation represented 9% of the total 2017 U.S. transportation GHG emissions, equal to approximately 2.6% of the total national GHG

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Corresponding author. E-mail addresses: [email protected] (H. Chao), [email protected] (D.B. Agusdinata), [email protected] (D. DeLaurentis), [email protected] (E.B. Stechel). https://doi.org/10.1016/j.trd.2019.08.015

Available online 26 August 2019 1361-9209/ © 2019 Elsevier Ltd. All rights reserved.

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emissions (U.S. EPA, 2019a). However, no domestic emission policy schemes have been planned or implemented for U.S. airlines. This study investigates how domestic emissions policies similar to CORSIA would likely perform in a U.S. context. Previous studies have examined aviation environmental impacts with different models and approaches. For example, the Federal Aviation Administration (FAA) commissioned the System Tool for Assessing Aviation’s Global Emission (SAGE) to predict aircraft fuel burn and emissions for all commercial (civil) flights globally (Kim et al., 2007; Lee et al., 2007). The literature offers documentation of the impacts of emissions policies. For instance, one study on 22 airlines from 2008 to 2012 has found that the European Union Emission Trading Scheme has increased the operational and business efficiency of airlines. European airlines have higher efficiencies on average compared to non-European counterparts (Li et al., 2016). Implementing air traffic emissions taxes could reduce emissions through higher aircraft operating costs, higher airfares, and lower passenger demand. However, these gains could be offset by increased emissions once people divert to using automobiles for transport (Hofer et al., 2010). Few studies have accounted for decision dynamics and the penetration of sustainable aviation fuels (SAFs). Besides market-based measures, the adoption of SAFs is a necessary option for reaching the carbon emissions reduction target (ICAO, 2016a,b; IATA, 2019). The use of SAFs in the US could potentially reduce GHG emissions to a level ranging from 55 to 92% (median value of 74%) compared to the 2005 baseline level. Nevertheless, the adoption rate is sensitive to the price of oil (Moolchandani et al., 2011). Several airlines have conducted trials on SAFs. For instance, United Airlines began to use alternative aviation fuels in its outbound flights from Los Angeles in 2016 (United Airlines, 2016). Other U.S. airlines, including Southwest and Alaska Airlines, have established commercial agreements with SAF producers (Csonka, 2016). Recent studies on reducing commercial aviation net carbon emissions have included the analysis of production chains for sustainable fuels, fleet penetrations of leading aircraft technology, and interactions between alternative fuels and commercial aviation industries. For example, Winchester et al. (2013) have studied the economic and environmental impacts of SAFs from hydro-processed esters and fatty acids (HEFA) on U.S. commercial aviation. However, the research considered only one type of alternative fuel and did not address the impacts of higher levels of aircraft technology that are available to the airlines. Although the National Aeronautics and Space Administration (NASA) Subsonic Fixed Wing project (Haller, 2012) has focused on defining and exploring environmental improvement opportunities for future aircraft technologies, such opportunities do not ensure the mitigation of environmental impacts in the U.S. air transportation system. Finally, the Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis from the U.S. Environmental Protection Agency (EPA) (Sissine, 2010) investigated properties of SAFs in several regions from various production paths. Currently, the RFS2 program is primarily focused on alcohol, but also provides approved fuel pathways for SAFs (US EPA, 2019b). However, the analysis did not account for the penetration of SAFs usage in other U.S. transportation systems. Several commercial-scale facilities have used alternative pathways that qualify for use in the aviation industry under the ASTM International Specification D7566. However, not all of these commercial facilities produce aviation fuels. Several other technology developers are working toward production facilities that provide confidence in the capability of scaling-up production and, if needed, fuel volumes and data for proceeding through the ASTM process for pathway approval. The first year of the commercial-scale production of alternatives to petroleum-based aviation fuel in the US was 2016. In this year, the U.S. aviation sector consumed over a million gallons that were sourced predominantly from the AltAir facility, which delivered the tallow-derived HEFA fuel to Los Angeles Airport (LAX). As of June 2018, about 19 facilities in commercial operation, planned commercial operation, or demonstration within the US have an expected capacity of approximately 1 billion gallons per year (CAAFI, 2018). To support a commercially viable SAF industry in the US, the U.S. Department of Agriculture, the aviation trade organization Airlines for America, and the aircraft manufacturer Boeing have established the Farm-to-Fly initiative. Later, the initiative added the Transportation Department's Federal Aviation Administration, the Department of Energy and the Department of Defense, and major private partners, such as the Commercial Aviation Alternative Fuels Initiative (CAAFI). The new initiative, entitled Farm-to-Fly 2.0 (F2F2), aimed to increase the nation's supply of renewable aviation fuel to about 1 billion gallons of drop-in aviation biofuels a year by 2018. Through the F2F2 public-private partnership efforts, CAAFI has continued to foster supply-chain development activities in several states across the US. Hence, the industry has demonstrated progress in commercial operation and is poised to contribute to the reduction of GHG emissions among the aviation sector. This paper first describes the two models that were used to assess the impacts of SAF production, aircraft technology, and airlines operations on fleet-level carbon emissions. Second, it specifies areas of uncertainty that are associated with some key variables. Third, it presents the design of emissions policy options for U.S. airlines and domestic and international routes that mimic a simplified ICAO CORSIA scheme. Subsequently, the report provides results from multiple cases that were run to simulate emissions trajectories up to 2050. Based on those results, the study identifies the conditions that can lead to achieving the net carbon reduction target. In addition, further results are presented from a sensitivity analysis to assess the influence and magnitude of major uncertainties. 2. The simulation models This section presents the two models that were employed for the study, namely the Fleet-level Environmental Evaluation Tool (FLEET) and the Sustainable Aviation Fuel Life Cycle Assessment (SAF LCA) model. The latter assesses the economic and environmental developments of SAFs and extends the capabilities of the former in evaluating fleet-level environmental impacts. 2.1. Fleet-level Environmental Evaluation Tool The Fleet-level Environmental Evaluation Tool assesses the environmental footprints of airline operations in U.S. commercial aviation (Chao et al., 2016; Moolchandani et al., 2017; Ogunsina et al., 2017). The tool methodologically employs a resource 43

20–50 51–99 100–149 150–199 200–299 300+

Class Class Class Class Class Class

1 2 3 4 5 6

Seats

Seat Class

Canadair RJ200/RJ440 (0.1935) Canadair RJ700 (0.1930) Boeing 737–300 (0.2717) Boeing 757–200 (0.1387) Boeing 767–300 (0.1317) Boeing 747–400 (0.1451)

Representative in Class (lb/pax-nmi)

Table 1 Aircraft classes showing average fuel efficiency.

Embraer ERJ 145 (0.2098) Embraer 170 (0.1700) Boeing 737–700 (0.1199) Boeing 737–800 (0.1311) Airbus A330-200 (0.1134) Boeing 777-200ER (0.1302)

Best in Class (lb/pax-nmi)

Small Regional Jet (0.1134) CS100 (0.1146) Boeing 737–700 Re-engined (0.1251) Boeing 737–800 Re-engined (0.0974) Boeing 787 (0.1100) Large Twin Aisle (0.0657)

New in Class (lb/pax-nmi)

Improved CRJ 200 (0.1064) Improved CRJ 700 (0.1062) Small Purdue ASAT (0.1017) D-8 Double- Bubble (0.0249) Improved Boeing 767 (0.0527) Improved Boeing 777 (0.0912)

Future in Class (lb/pax-nmi)

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allocation method to distribute the aircraft operations on the airlines' route networks to represent airline behaviors. It models the evolution of airfares, airlines' fleet compositions and sizes, and route demands across airlines' networks through a system dynamics approach. Moolchandani et al. have detailed the fleet acquisition and retirement model, ticket fare model, aircraft operation time allocation problem, and detailed definitions of aircraft technology in their previous works (Chao et al., 2016; Moolchandani et al., 2017, 2013, 2011; Ogunsina et al., 2017). The Fleet-level Environmental Evaluation Tool constructs an abstract airline network according to a subset of the Worldwide Logistics Management Institute Network Queuing Model (W WLMINET) 257 airports (“List of WWLMINET 257 Airports,” 2009) and the airline operations from Bureau of Transportation Statistics (BTS) T-100 Segment database (RITA/BTS Office of Airline Information, 2016a). This database covers regular flight take-offs and landings at U.S. airports. The abstract network is comprised of 103 U.S. airports, 66 international airports, and 2,134 routes and encompasses 65% of all passenger air traffic as well as 80% of international passengers who traveled to or from a U.S. airport in 2005. Table 1 depicts the representative aircraft model of each seat capacity and technology generation class. The 24 aircraft models in FLEET are classified into six classes by seat capacity. Each seat capacity class is further categorized into four technology generations: representative in class (most used in 2005), best in class (more fuel-efficient than the previous generation), new in class (released in 2015), and future in class (still in the conceptual design stage). The newer-generation aircraft has either a higher seat capacity, longer flying range, or greater fuel efficiency, resulting in lower fuel consumption per passenger nautical mile compared to the previous generation aircraft. While aircraft fuel efficiency is a function of route ranges, Table 1 shows the average fuel efficiency in order to quantitatively indicate technological improvements. Overall, the fuel efficiency of the best in class aircraft is 10% higher than the representative in class. As well, when compared to the representative in class, new in class and future in class aircraft are 29% and 50% more fuel efficient, respectively. The Fleet-level Environmental Evaluation Tool models the route-wise passenger market demand and records the route-wise satisfied passenger demand. For passenger market demand, FLEET first calculates the travel demand in each airport. Then, it applies a market demand growth rate to each airport, which is a function of population growth and Gross Domestic Product (GDP) growth rate. Finally, the market demand is allocated to each route based on route-wise market demand in the previous year. 2.2. Sustainable aviation fuel life cycle assessment model The SAF LCA Model assesses the emissions impacts of multiple SAF production pathways (Agusdinata et al., 2012, 2011). The model applies an agent-based approach to simulate the interdependencies among airlines, policymakers, bio-refineries, and farmers. Airlines aim to satisfy passenger demand and their shareholders by generating profits. Their decision to adopt SAFs depends on the costs relative to petroleum-based fuels and policy incentives. Policymakers can stimulate SAF demand by enacting policies such as emission trades and carbon taxes and setting an emissions target. Bio-refineries will build new plants to meet SAF demand if there is a reasonable prospect of profitability. Their investment decision relies on metrics, such as the internal rate of return (IRR) and net present value (NPV) of the capital and operating expenses to produce certain types of SAFs. Finally, the investment in bio-refining capacity will create a demand for SAF feedstock, in turn leading farmers to cultivate alternative feedstock based on its expected profit margin. The SAF LCA model considers five feedstocks: camelina, algae, corn stover, switch-grass, and short-rotation woody crop (SRWC). The model assumes that farmers use only marginal lands to cultivate camelina, switch-grass, and SRWC to avoid competition with food production. Data on marginal land derive from the U.S. Department of Energy dataset (Energy, n.d.). The feedstock model utilizes work from RFS2 (Sissine, 2010) to build technology development scenarios. Camelina is not the most common oilseed that is grown in the US, but it is included in the model since commercial airlines have used it experimentally (Hileman et al., 2009). For algae, the model includes only an open-pond approach, as current technology suggests that open ponds require lower capital investments and operation costs compared to photo-bio-reactors (Jorquera et al., 2010; Stephenson et al., 2010). For corn stover, the model extrapolates historical data for corn grain yield from 1995 to 2009 to determine the future grain yield. The model assumes that the collection rate for the corn stover is 50% of corn grain yield with no soil erosion. However, it is necessary to replenish the soil nutrient with fertilizers after corn stover collection. The model further assumes that farmers can harvest switch-grasses once per year, and cultivated land can continue producing feedstock for the next 10 years after an initial build-up stage. The switch-grass yield can increase based on field management, the timing of fertilizer applications, and the improvement of species. Finally, the model assumes that the SRWC yield on marginal land equals that occurring in a natural forest; such simplifying assumption is necessary because of a lack of published data on SRWC growth on marginal land (Agusdinata et al., 2012; Chao, 2016). The model considers two types of fuel-refining technologies: (1) hydro-treating/hydro- cracking technology, which uses camelina and algae feedstock; and (2) Fisher-Tropsch (FT) conversion following gasification of corn stover, switch-grass, or SRWC. A biorefinery plant for hydro-treating/hydro-cracking has an assumed 350,000 m3/year production capacity. Meanwhile, a facility for FT has an assumed production capacity of 2000 tonnes of feedstock input per day and an output of 33.2 million gallons of jet fuels per year (Agusdinata et al., 2012; Chao et al., 2017). 3. Integration of U.S. airlines fleet-level operations and SAF LCA Fig. 1 contains a flowchart for integrating the two models, namely the SAF LCA model and FLEET. First, through a Bayesian inference approach (Ng et al., 2011), bio-refineries predict total aviation fuel demand for the following year based on aviation fuel consumption and GDP growth rate in the current year. Then, the bio-refineries optimize their estimated profits by determining SAF 45

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Fig. 1. Flowchart for integrating the SAF LCA model into the FLEET airline operations model.

prices, net carbon emissions, and production quantities in each U.S. state. The decision process takes into account the availability of feedstock, cross-state feedstock transportation costs, GHG emissions, certification requirements for the aviation fuel, and potential profits of the airline under emission policy schemes. The bio-refineries subsequently evaluate NPVs and IRRs for building new plants to produce the SAFs. When the demand for SAFs materializes, farmers can choose which feedstock to cultivate to satisfy the feedstock demand based on the profitability of each feedstock. The harvests of feedstock from farmers determine the quantities and qualities of SAFs in each state. Policymakers can then update fuel specification regulations, free emissions quotas, and carbon credit prices for the subsequent simulation year. The FLEET airline model extracts information from the SAF LCA module to simulate the airline operations for the following simulation year. 3.1. Profit optimization of bio-refinery plants The SAF LCA model simulates the decision process of bio-refineries by using an optimization algorithm. It assumes that the consumption of the SAF supply will remain within the producing state. Transporting feedstock across states produces extra carbon emissions and requires additional transportation costs. Initially, bio-refineries estimate the total aviation fuel demand based on past trends, the total fuel consumption in the previous year, and the GDP growth rate. Next, bio-refineries optimize their expected profits by determining SAF prices, quantities, and raw material flows for each state based on the predicted fuel demand, feedstock availability, and bio-refinery operations. The optimization problem includes the following constraints to ensure that the solution is feasible. 1. Fuel specification requirement: the current standard requires that the blending ratio of SAF with petroleum-derived fuels should be lower than 50% (by volume) to ensure the required fuel characteristics according to ASTM D-7566 standard. 2. Feedstock resources constraint: feedstock availability limits a bio-refinery's ability to produce SAFs. The raw material supplies are different in each state because of their varying climate and soil conditions. Hence, the composition of SAFs will differ in each state depending on the availability of feedstock. 3. Airline profit margin constraint: the fuel-related direct operating costs of airlines under carbon emission policy schemes will include petrol fuel costs, biofuel costs, and carbon-offset expenses. Because the bio-refinery industry is still in its early development stage, actual SAF prices can exceed those of petroleum-based fuels even after accounting for cost reductions from the 46

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carbon offset. The airlines that are represented by FLEET are profit-seeking enterprises; this assumption precludes the possibility that airlines will strategically adopt SAF at a loss, which may occur with other airlines, such as United Airlines (United Airlines, 2016). Hence, the airline profit margin constraint requires that the fuel adoption strategy from a simulation year has a fixed percentage of reductions or increments in fuel-related operation costs compared to the fuel usage strategy from the previous year. In this study, the fuel-related operation cost margin can range from a savings of 10% to an increase of 10% in fuel-related direct operating costs. 4. Bio-refinery plant operation constraint: to account for downtime, the model assumes that each bio-refinery plant operates at 80% of its production capacity all year. In addition, the variations in SAF prices across states are within just 10% of the average price. The 10% constraint ensures that a company could not increase the SAF prices appreciably in high-demand states and lower the price in low-demand states. The results of the expected profit optimization problem are the state-wise feedstock demand, state-wise SAF prices, and state-wise SAF production quantities. Subsequently, farmers can use the state-wise feedstock demand as input to decide whether to cultivate feedstock that satisfies the SAF demand.

3.2. Profit optimization of farmers and SAF production Farmers satisfy all of the feedstock demand of bio-refineries. For algae and corn stover, farmers can satisfy demand up to the maximum feedstock production and collection rate, respectively. Farmers must then select the cultivation areas for camelina, switchgrass, and SRWC, which all compete for land. Farmers maximize their profitability by allotting cultivation areas for each of the three feedstocks based on its properties and the suitability of the land in each state. Lastly, bio-refineries produce SAFs based on the availability of feedstock from farmers. Airlines must use the same SAFs for both international and domestic flights, although they can freely determine the blending ratio of the SAFs. Previous work by the authors (Chao, 2016) has provided a more detailed description of the farmers' profit optimization problem.

3.3. Emissions reduction policy scheme: ICAO CORSIA The emission policy for the U.S. airlines international and domestic route network is designed after the ICAO CORSIA policy schemes. As an offset scheme, CORSIA incentives airlines to purchase carbon credits or invest in projects that reduce emissions in other sectors. Airlines need to pay for the carbon offsets, which are equal to the individual reported emissions multiplied by international commercial aviation carbon growth rates based on the average emissions levels for 2019 and 2020 (“IATA – CORSIA,” n.d.). The CORSIA involves three phases: the pilot phase, the first phase, and the second phase. While ICAO encourages member states to voluntarily take part in the first two phases, all member states, apart from those with exemptions, must join CORSIA after 2027. The ICAO will grant exemptions to countries and airlines according to the gross national income (GNI) of the country and the revenue tonnes kilometer (RTK) of the airline's routes. Table 2 illustrates a simplified ICAO CORSIA policy scheme. The pilot phase and first phase will take effect from 2021 to 2026. The policy boundary through the first phase includes international routes between nations that volunteer to participate. In the second phase, all member nations without exemptions must participate in the ICAO CORSIA. Hence, the policy boundary in the second phase covers all international routes between member nations with no exemptions. For all phases, the baseline emissions are the average of the 2019 and 2020 emissions from the routes within the policy boundary of the respective phase. Airlines that do not have an exemption will have to pay for carbon offsets for emissions beyond the baseline emissions.

3.4. Policy scenarios for U.S. airlines emissions This paper considers two domestic emission policy scenarios in FLEET. The first scenario is no domestic emission policy, which reflects the current situation. The second adopts a simplified version of the ICAO CORSIA (see Table 3). The model for the domestic CORSIA-type policy covers the whole domestic network with a different free emission quota profile from the international ICAO CORSIA. The domestic CORSIA-type model maintains free emissions quotas at the emissions level one year before the start of the domestic scheme. The model for the international CORSIA-type policy scheme includes all international routes that are served by U.S. airlines. The free emission quota depends on a constant emissions level that is averaged from the 2019 and 2020 levels. Table 2 The ICAO CORSIA policy scheme. Pilot Phase Period Policy Boundary Baseline Emission

First Phase

2021–2023 2024–2026 Voluntarily Participating Nations The average emissions in 2019 and 2020 of routes, which are in the policy boundary

47

Second Phase After 2027 All Member Nations

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Table 3 Emissions policy models in FLEET.

Policy Boundary Free Emission Quota

Domestic CORSIA-type Scheme

International CORSIA-type Scheme

U.S. domestic network in FLEET Constant domestic emissions level before the policy starting year

U.S.-based International network in FLEET Constant averaged from 2019 and 2020 international emissions levels

3.5. Aircraft allocation problem for free quotas and SAFs Airlines will need to pay for emissions beyond the free emissions quotas and have the choice of whether to use SAFs in the CORSIA emissions policy scheme. It is assumed that the free emissions quotas for international and domestic networks are not shareable, and airlines cannot make a profit by selling either of the free emissions quotas. In FLEET, drop-in SAFs presumably have the same properties as petroleum-based fuel; hence, airlines can determine the mixing ratios of SAFs and petroleum-based fuels in the allocation problem. Finally, the fuel mixing ratios cannot exceed 50% to meet the requirements for aviation fuel specification. 3.6. Simulation setup The present work involved Monte-Carlo simulations with stochastic variables to represent the uncertainty in the economic environment and the domestic emissions scheme design parameters. The setting of the feedstock and bio-refinery process derives from previous work by Agusdinata et al. (2011). Table 4 lists the stochastic variables that relate to FLEET and the SAF LCA model. The model runs via a MATLAB and GAME setup, whereby the MATLAB program uses CPLEX to solve the fleet allocation problem through the GAME interface. The forecasted growth rate in air transportation demand in the U.S. is 1.9% per year (Corning et al., 2018). Air passenger growth highly correlates with economic growth, as demonstrated by the GDP growth rate. The minimum and maximum GDP growth rates derive from the historical GDP growth compiled by the World Bank (“The World Bank GDP at market prices (current US$),” 2016). The U.S. Energy Information Administration (EIA) has published three aviation fuel price scenarios from 2016 to 2050 (see Fig. 2), which describe low, reference, and high scenarios, respectively (“EIA Petroleum & Other Liquids Price,” 2016). The GDP growth rate and fuel price scenarios originate from the previous work from Project 10 from the FAA Center of Excellence for Alternative Jet Fuels & Environment (FAA ASCENT) (Mavris et al., 2018). In Project 10, the research groups conducted surveys and concluded several likely aviation economic environment scenarios. Table 5 displays the GDP growth rate estimation of each continent for each scenario setting, while Fig. 2 visualizes the petroleum-based aviation-fuel price trajectory for each scenario setting. Due to the large impact of free allocations to certain sectors, the EU CO2 unit prices have fluctuated widely. From 2015 to 2018, carbon prices ranged from €4.15–24.85 ($5.35–31.95) per tonne (Sandbag, 2019). However, because prediction of carbon price is beyond the scope of our study, the model assumes the emission allowance carbon price ($/tonne CO2 equivalent) will increase linearly from 2012 to 2050. The model also includes an initial carbon price in 2012, with a constant carbon price growth rate. The historical carbon price from the European Union Aviation Allowance (EUAA) sets the estimation of the average carbon price in 2012 at $7.53 per tonne of carbon emissions (EUAA, 2016). An equilibrium carbon price has been estimated at approximately €40-50 euros ($51.4–64.3) per tonne (IFRI, 2019). Thus, a range of $0–1.32 carbon price growth rate per year is predicted, based on the fluctuations of the European Union Emission Trading Scheme (ETS) and the previous work of Schaefer et al. (2010). It is important to note that some of the uncertain variables of the model involve complex dynamics. For example, Zhuang et al. (2014) have identified cross-correlation between oil and carbon prices. For simplicity, our model did not take this dynamic into account, instead considering them as independent scenarios. The SAF LCA model also simulates the risk attitude of bio-refineries toward future demand for aviation fuel. The model assumes that future aviation fuel demand will follow a Gaussian distribution with GDP growth rate corrections. The SAF module then uses a Table 4 Stochastic Input Variable for FLEET and SAF Model. Input Variable

Value Range

Prob. Distribution

GDP scenario Fuel price scenario Carbon price growth rate Refinery risk attitude κ Airlines fuel cost risk attitude λ Bio-refinery IRR threshold value Framer profit margin threshold Starting year of domestic scheme

(Nominal, High) (Nominal, High) [0, 1.32] ($/(ton CO2e)/year) [0.1, 1.0] [-0.1, 0.1] [0.05, 0.15, 0.25] [0.05, 0.15] [2020, 2030]

Uniform Uniform Uniform Uniform Uniform Triangular [22] Uniform [22] Uniform

Note: The probability distribution shape is based on a subjective judgement of the variable uncertainties and is guided by availability of data in the literature. A uniform distribution is used only when minimum and maximum variable values are known. A triangular distribution is employed to estimate a minimum—a “best guess”—and the maximum value of a variable. 48

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Fig. 2. The petroleum-based aviation fuel nominal and high price scenario. Table 5 Growth Rate of GDP for Each Continent Segregated by GDP Scenarios (Mavris et al., 2018).

Nominal High

North America

South America

Europe

Africa

Asia

Oceania

2.8%/year 4.0%/year

4.2%/year 5.3%/year

2.4%/year 4.2%/year

2.8%/year 4.0%/year

4.3%/year 5.9%/year

2.8%/year 4.0%/year

Bayesian inference model to simulate the total fuel consumption prediction from bio-refineries (Chao, 2016). The refinery risk attitude κ represents the relative weighting of prediction based on bio-refineries results from the previous year. The model uses the BTS P-12a database (RITA/BTS Office of Airline Information, 2016b), which includes historical aviation fuel supplies in airports in the U.S., to estimate ranges for the risk attitude κ. Utilizing the risk attitude κ values should ensure historical data are always within one standard deviation of the prediction from the Bayesian inference model. A higher value of refinery risk attitude κ suggests a higher weighting of the fuel consumption prediction from the previous year, resulting in a lower fuel consumption prediction. For profitability criteria for commercial actors, bio-refineries would only consider building refining capacity if the investment will lead to a positive IRR of 5–25% (Agusdinata et al., 2011). Similarly, farmers will base their feedstock cultivation decisions on an expected profit margin of 5–15% of the cultivation costs (Agusdinata et al., 2011). Airlines' fuel cost risk attitude λ signifies the airlines’ expectations for the potential operating cost reduction from using SAFs. Airlines will adopt SAFs when such adoption does not affect their fuel costs or they can profit from it. However, since the SAF industry is in its infancy stage and lacks historical data or the possibility of subsidy, this model assumes that airlines can accept a slight increase in fuel costs for a λ value range of −0.1 to 0.1. The air fare pricing model of FLEET assumes half of the additional fuel costs are passed on to passengers. According to economic theory, the resulting increase in airfare will lead to a fall in demand, with the magnitude depending on the slope of the demand curve. For the U.S. market, the elasticity of demand for air travel with respect to price has been estimated within the range of −0.56 to −1.82, depending on travel distance (Bhadra, 2012). This effect is most prominent in the short-haul market, where an alternative ground transportation mode is available (Hofer et al., 2010). Furthermore, given that SAFs are still in early development, the SAF LCA module considers both the experience and the learning curve effects. As SAF production accumulates over time, production costs will decline as a result of learning (i.e., experience curve). For the baseline case, a progress ratio of 81% was assumed for refinery cost reductions; this figure derives from experiences within the bioethanol industry (Bake et al., 2009). This experience curve level implies that doubling the accumulated SAF production will reduce the refinery costs by 19%. Factoring in farmer or feedstock producer and bio-refinery profitability requirements and risk attitudes, land availability, and suitability, as well as time delay and technological learning factors, can yield a more realistic estimate of the level of SAF supply and emissions reduction.

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Fig. 3. The evolution of satisfied passenger demand (no. of enplaned passengers) under no domestic emission policy scenario. The demand figures are given as a ratio to the 2005 level.

4. Simulation results and analysis The uncertainty space that is specified in Table 4 is the basis of the Monte-Carlo simulation. Through a Latin-Hypercube sampling of the uncertainty space, 1700 simulations were conducted for both no domestic policy and CORSIA type scenarios. The following sections compare and analyze the results of the two scenario settings. 4.1. Evolution of passenger demand Figs. 3 and 4 illustrate the satisfied passenger demand, measured by the number of enplaned passengers (i.e. passenger boarded on an aircraft), with and without the emission policy scheme. The box plot summarizes the distribution of values in terms of minimum, first quartile (Q1), median (Q2), third quartile (Q3), maximum, and outliers (i.e. 0.7% of the data). Under no domestic emission policy scenario, both the market demand and its uncertainty increase over time (Fig. 3), while the

Fig. 4. The evolution of satisfied passenger demand (no. of enplaned passengers) under a CORSIA-type emission policy scenario. The demand figures are given as a ratio to the 2005 level. 50

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Fig. 6. Fraction of SAFs to total fuel consumption under no domestic emission policy scenario.

stochastic variables in this study (Table 4) drive up the market demand uncertainty. In 2050, the median value of the demand level is predicted to be 2.78 times the value in 2005, with minimum and maximum values of 2.34 and 3.28, respectively. In this scenario, the penetration of SAFs is limited (see Fig. 6). By comparison, under the CORSIA-type domestic emission policy, the stochastic variable impacts differ slightly because of the penetration of SAFs. The median value of the demand level in 2050 in this scenario is 2.72 times that in 2005, with the minimum and maximum values of 2.08 and 3.31, respectively. These demand figures are in line with FAA’s prediction that system traffic in revenue passenger miles (RPMs) is projected to increase by 2.2% a year between 2019 and 2039, implying demand will double every 32 years (FAA, 2019). With reduced fuel and carbon offset costs, airfares would decrease, thus market demand would subsequently increase.

4.2. Evolution of SAF market share Figs. 6 and 7 demonstrate the evolution of the market share of SAFs for the two scenarios. The total fuel consumption level in 2005 is used to normalize the SAF consumption level. Under no domestic emission policy, no SAFs become commercially viable until around 2035 (Fig. 6). However, the manifested market share occurs at the margin of the distribution since both the Q1 and Q2 values

Fig. 7. Fraction of SAFs to total fuel consumption under CORSIA-type domestic emission policy scenario. 51

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Fig. 8. Normalized carbon emission trajectory under no domestic emission policy scenario.

are zero for all years except 2050. The uncertainties, which are apparent from the inter-quartile range (Q3–Q1), also increase. Under the CORSIA-type domestic emission policy (Fig. 7), a more stable SAF demand emerges. The SAFs become commercially viable around 2030 and reach about 10% of the total fuel consumption level in 2050. Although the maximum values of SAF demand are similar between the two scenarios, the value from the CORSIA-type policy has lower uncertainty and a higher median value compared to the value from no domestic policy. Since SAFs have lower carbon emissions compared to aviation fossil fuels, they can decrease the operating costs of airlines if the SAF prices are acceptable and the carbon price is sufficiently high. The emission policy scheme can further increase the SAF demand and stabilize it against economic uncertainties. The SAF demand, rather than the SAF supply, limits the maximum SAF production. Since the scenario with domestic CORSIA is supposed to create more SAF demand, the similarity of the maximum SAF consumption between the two scenarios reflects that SAF raw material supplies limit the SAF supply. This result is consistent with previous work by the authors (Chao, 2016). Importantly, the production of SAF for the U.S. domestic market may have the unintended consequence of carbon leakage, whereby competitive effects lead to significant offsetting emissions increases in countries without controls (Gerlagh and Kuik, 2007). A lower global energy price (i.e., reduced demand in the constrained economies exerting a downward pressure on energy prices) encourages substitution toward energy in countries without a carbon constraint. In the case of aviation fuels, global fossil jet fuel prices may decrease due to a reduction in U.S. domestic demand, leading to increased use in non-policy-affected regions. Thus, carbon leakage warrants consideration in future studies. 4.3. Evolution of aviation life cycle carbon emissions Figs. 8 and 9 present box plots of normalized carbon emissions for the two emissions policy scheme scenarios over the simulation years. The resulting emissions derive from the combined consumption of both petroleum-based fuels and SAFs in each year. The red horizontal lines represent the IATA emission reduction target, which is 50% of the 2005 carbon emissions level. The total carbon emissions in 2005 is used to normalize the carbon emission levels in each year. With no domestic emission policy, the median value (Q2) highlights a growing emissions trend (Fig. 8). In 2050, the median of emissions is about 1.33 times the 2005 level and has maximum and minimum values of 1.79 and 0.71, respectively. No simulation result predicts achievement of the reduction target. The inter-quartile range of carbon emissions (Q3–Q1) increases as time progresses. In contrast, Fig. 9 indicates that the median emission in 2050 under a CORSIA-type domestic emission policy is around 1.20 times the 2005 level and has maximum and minimum values of 1.84 and 0.48, respectively. From 2030 onward, the uncertainty range of carbon emissions (Q3–Q1) decreases. For a CORSIA-type domestic ETS scenario, 60 out of 1700 instances represent a 3.5% chance that the emission level can be reduced by 37.5–50% compared to the 2005 level. 4.4. Sensitivity analysis 4.4.1. Relative influence of uncertainty variables A Spearman rank correlation method was employed to evaluate the relative importance of variables in each domestic emission 52

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Fig. 9. Normalized carbon emission trajectory under CORSIA-type emission policy scenario.

policy scenario to the 2050 carbon emissions level. The method is a non-parametric alternative to linear regression and correlation (Kendall and Gibbons, 1990), measuring the strength and direction of association between two ranked variables. As well, the Spearman rank correlation method is based on a monotonic relationship, which is less restrictive than the assumption of linearity. The correlations are statistically significant (i.e. the p-value is less than 0.05). For both scenarios, the results illustrate that the 2050 carbon emissions levels negatively correlate with the fuel price and positively correlate with the GDP growth rate (Figs. 10 and 11). Both factors relate to the passenger demand of airlines. Ticket fares would be higher with higher fuel prices, which would reduce passenger demand. In addition, a higher GDP growth rate would lead to more travel demand. A change in the fuel price would have a stronger effect on the emissions level compared to the GDP growth rate. Under the CORSIA-type policy scenario, the price of petroleum-based fuels is the most significant factor that affects emission levels followed by the carbon price growth rate (Fig. 11). Higher carbon prices would incentivize airlines to adopt SAFs by making it more expensive to emit emissions. As a result, SAF options would become more competitive compared to fossil fuels. The fuel cost risk attitude of airlines is also influential, but its role differs in each scenario. In the CORSIA-type policy scenario, the higher λ value implies that airlines have higher expectations for the capability of SAFs to reduce the operating costs. In the no policy scenario, the lower λ translates to higher carbon emissions due to less SAF usage. In this scenario, the smaller SAF demand prompts bio-refineries to pursue a more aggressive strategy for SAF prices to ensure their profits, which results in fewer incentives for airlines to adopt SAFs. The bio-refinery profitability criterion of IRR is relevant in the no domestic emission policy scenario in view of the risks that are associated with SAF production. A higher IRR set by bio- refineries indicates a smaller quantity of SAFs that are produced – and,

Fuel Price GDP Growth Rate

Fig. 10. Spearman rank correlation result for no domestic emission policy scenario. 53

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Fuel Price

GDP Growth Rate

Fig. 11. Spearman rank correlation result for CORSIA-type domestic emission policy scenario.

hence, a higher emissions level. Lastly, in the CORSIA-type policy scenario, the timing of the policy implementation is significant, as earlier implementation of the policy corresponds to lower overall emissions.

4.4.2. Analysis of conditions leading to region close to emission target The CORSIA-type policy scenario reveals 60 simulation instances that accomplish an emission reduction that is close to the target amount (a 37.5–50% reduction of the 2005 level). Table 6 summarizes the significant input variable settings in terms of mean and standard deviation (ST.D.) to indicate the necessary conditions for nearly achieving the emission reduction target. Both the fuel price scenario and the GDP growth rate scenario have only two settings. The results evidence that the fuel price scenario should be on the “high” setting, while the GDP growth rate scenario is most likely in the “low” setting. The carbon price growth rate should be as close to the highest setting as possible to increase the chance of reaching the 2050 emission target. In addition, the results demonstrate that airlines should expect SAFs to reduce fuel- and emission-related operating costs by over 4% (the Q1 value). Finally, the start and end years of the domestic emission scheme can be 2023 and 2028, respectively, with the mean value of 2026.

5. Concluding remarks The aviation industry can contribute to the reduction of carbon emissions. In this regard, this study has compared two scenarios for the emission trajectory of U.S. airlines: business as usual, wherein there is no emission policy implemented, versus a domestic emission policy that resembles ICAO CORSIA. The comparison has considered various uncertainties that relate to the macro-economic environment and decision criteria of relevant stakeholders. The results of the study suggest that implementing a CORSIA-type policy could stimulate demand for SAFs and reduce emissions while also maximizing the profitability of airlines. Therefore, such policy could contribute to carbon emissions reduction. The sensitivity analysis has revealed the magnitude and direction of the influence of key factors that affect net carbon emissions. This study on the impact of carbon emission schemes is limited to the aviation sector. First, it is important to note that the offsets purchased in the CORSIA-type scheme are not included in the accounting of CO2 reductions, as the study only considers flight emissions. Consequently, the reductions presented in this study may be lower than the potential reductions across multiple industry sectors. Second, the final emission offset unit price will be largely determined by overall supply and demand of different sectors. Lastly, as previously mentioned, carbon leakage may occur at the global level, and compensate for emission reduction gains in one country or region. To obtain a more complete picture of overall emission reduction impacts, future research should consider interdependencies and interactions among multiple industry sectors and across countries/regions. Table 6 Summary of parameter settings with a 37.5–50% emission reduction. Stochastic Variable

Mean

ST.D.

Fuel price scenario Carbon price growth rate Airlines fuel cost risk attitude λ GDP growth rate scenario Dom. ETS starting year

High 1.10 0.05 Low 2026

– 0.16 0.03 – 3.1218

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