MIT Joint Program on the
Science and Policy of Global Change
The Impact of Climate Policy on U.S. Aviation
Niven Winchester, Christoph Wollersheim, Regina Clewlow, Nicolas C. Jost,
Sergey Paltsev, John Reilly and Ian A. Waitz
Report No. 198
May 2011
The MIT Joint Program on the Science and Policy of Global Change is an organization for research, independent policy
analysis, and public education in global environmental change. It seeks to provide leadership in understanding scientific,
economic, and ecological aspects of this difficult issue, and combining them into policy assessments that serve the needs
of ongoing national and international discussions. To this end, the Program brings together an interdisciplinary group
from two established research centers at MIT: the Center for Global Change Science (CGCS) and the Center for Energy
and Environmental Policy Research (CEEPR). These two centers bridge many key areas of the needed intellectual work,
and additional essential areas are covered by other MIT departments, by collaboration with the Ecosystems Center of the
Marine Biology Laboratory (MBL) at Woods Hole, and by short- and long-term visitors to the Program. The Program
involves sponsorship and active participation by industry, government, and non-profit organizations.
To inform processes of policy development and implementation, climate change research needs to focus on improving
the prediction of those variables that are most relevant to economic, social, and environmental effects. In turn, the
greenhouse gas and atmospheric aerosol assumptions underlying climate analysis need to be related to the economic,
technological, and political forces that drive emissions, and to the results of international agreements and mitigation.
Further, assessments of possible societal and ecosystem impacts, and analysis of mitigation strategies, need to be based
on realistic evaluation of the uncertainties of climate science.
This report is one of a series intended to communicate research results and improve public understanding of climate
issues, thereby contributing to informed debate about the climate issue, the uncertainties, and the economic and social
implications of policy alternatives. Titles in the Report Series to date are listed on the inside back cover.
Ronald G. Prinn and John M. Reilly
Program Co-Directors
For more information, please contact the Joint Program Office
Postal Address: Joint Program on the Science and Policy of Global Change
77 Massachusetts Avenue
MIT E19-411
Cambridge MA 02139-4307 (USA)
Location: 400 Main Street, Cambridge
Building E19, Room 411
Massachusetts Institute of Technology
Access: Phone: +1(617) 253-7492
Fax: +1(617) 253-9845
E-mail: g lob a lch a n g e @mit.e d u
Web site: h ttp ://g lob a lch a n g e .mit.e d u /
Printed on recycled paper
The Impact of Climate Policy on U.S. Aviation
Niven Winchester*,†, Christoph Wollersheim‡, Regina Clewlow§, Nicolas C. Jost‡,
Sergey Paltsev*, John Reilly* and Ian A. Waitz‡,**
Abstract
We evaluate the impact of an economy-wide cap-and-trade policy on U.S. aviation taking the American
Clean Energy and Security Act of 2009 (H.R.2454) as a representative example. We use an economywide model to estimate the impact of H.R. 2454 on fuel prices and economic activity, and a partial
equilibrium model of the aviation industry to estimate changes in aviation carbon dioxide (CO2)
emissions and operations. Between 2012 and 2050, with reference demand growth benchmarked to
ICAO/GIACC (2009) forecasts, we find that aviation emissions increase by 130%. In our climate policy
scenarios, emissions increase by between 97% and 122%. A key finding is that, under the core set of
assumptions in our analysis, H.R. 2454 reduces average fleet efficiency, as increased air fares reduce
demand and slow the introduction of new aircraft. Assumptions relating to the sensitivity of aviation
demand to price changes, and the degree to which higher fuel prices stimulate advances in the fuel
efficiency of new aircraft play an important role in this result.
Contents
1. INTRODUCTION...................................................................................................................... 1
2. MODELING FRAMEWORK.................................................................................................... 2
3. SCENARIOS.............................................................................................................................. 4
4. RESULTS .................................................................................................................................. 6
5. SENSITIVITY ANALYSIS ..................................................................................................... 10
5.1 Aircraft Retirement Decisions .......................................................................................... 11
5.2 New Aircraft Fuel Efficiency ........................................................................................... 11
5.3 Income and Price Elasticities ............................................................................................ 12
6. CONCLUSIONS ...................................................................................................................... 12
7. REFERENCES ......................................................................................................................... 13
APPENDIX: LITERATURE REVIEW ....................................................................................... 16
1. INTRODUCTION
Worldwide aviation is expected to grow by just under 5% per year for the next two decades
(Airbus, 2009; Boeing, 2010). This growth will have environmental consequences via noise, air
quality and climate impacts (Mahashabde et al., 2010). Potential mitigation methods include
regulations and standards, technological improvements involving aircraft and engine
performance and/or the development of alternative fuels, operational improvements, and marketbased policies. In regard to market-based policies, the House of Representatives recently passed
the American Clean Energy and Security Act of 2009 (H.R. 2454, also known as Waxman*
Joint Program on the Science and Policy of Global Change, MIT, Cambridge, Massachusetts, U.S.A.
Department of Economics, University of Otago, Dunedin, New Zealand.
‡
Department of Aeronautics and Astronautics, MIT, Cambridge, Massachusetts, U.S.A.
§
Engineering Systems Division, MIT, Cambridge, Massachusetts, U.S.A.
**
Corresponding author: Ian Waitz (Email: iaw@mit.edu)
†
1
Markey Bill). H.R. 2454 proposes an upstream cap-and-trade policy to curtail greenhouse gas
(GHG) emissions. Such a policy would affect all U.S. industries, as refineries would be required
to purchase allowances for each potential ton of carbon dioxide (CO2) emissions. In the long run,
refiners will pass on these increased costs to consumers, such as airlines. Based on the
specifications of H.R. 2454, this study analyzes the impact of climate policy on aviation
operations, financial outcomes and emissions.
The impact of cap-and-trade programs on aviation has been examined by several studies,
which are summarized in Appendix 1. Most analyses focus on the emissions and economic
impacts of the EU Emissions Trading Scheme (ETS).1 Other studies, also with an EU-ETS focus,
examine legal issues associated with including aviation in cap-and-trade programs (Oberthuer,
2003; Haites, 2009; Peterson, 2008), or the impact of climate policy on airline competition
(Albers et al., 2009; Scheelhaase et al., 2010; Forsyth, 2008). To our knowledge, Hofer et al.
(2010) is the only study to focus on the impact of a U.S. cap-and-trade policy. Hofer et al. (2010)
uses a partial equilibrium model to simulate the environmental impacts of an assumed airfare
increase. We contribute to this literature by analyzing the impact of a cap-and-trade policy on
U.S. aviation using an economy-wide model and a detailed partial equilibrium model of the
aviation industry. We use our economy-wide model to estimate the impact of a cap-and-trade
policy (taking H.R. 2454 as a representative example) on the fuel price, the price of emissions
allowances and the overall level of economic activity. Given these predictions, a partial
equilibrium model is employed to estimate changes in aviation emissions and operations. To our
knowledge, we are the first to analyze the impact of climate policy on aviation by combining a
climate policy model and a detailed aviation model.
This paper has five further sections. Our modeling framework is detailed in Section 2. Section
3 outlines the scenarios we consider. Modeling results are presented in Section 4. A sensitivity
analysis is detailed in Section 5. The final section provides a short summary and conclusions.
2. MODELING FRAMEWORK
Our analysis employs version 4 of the Emissions Prediction and Policy Analysis (EPPA)
model (Paltsev et al., 2005) and version 4.1.3 of the Aviation Environmental Portfolio
Management Tool for Economics (APMT-E, MVA Consultancy, 2009). EPPA is used to
determine the economy-wide impacts of climate policy and, given predicted changes in key
variables simulated by EPPA, APMT-E calculates the impact of climate policy on aviation
emissions and operations.
EPPA is a recursive dynamic, computable general equilibrium (CGE) model of the global
economy that links GHG emissions to economic activity. The model is maintained by the Joint
Program on the Science and Policy of Global Change at the Massachusetts Institute of
1
See, for example, Anger (2010), Boon et al. (2007), Ernst & Young (2007), Hofer et al. (2010), Mayor and Tol
(2010), Mendes (2008), Morrell (2007), Scheelhaase and Grimme (2007), Vespermann and Wald (2010) and Wit
et al. (2005).
2
Technology, and has been widely used to evaluate climate policies (see, for example, Paltsev et
al., 2007, 2009). EPPA models the world economy and identifies the U.S., 15 other regions and
nine sectors, including electricity and refined oil. Reflecting EPPA’s focus on energy systems,
electricity can be produced using conventional technologies (for example, electricity from coal
and gas) and advanced technologies (for example, large scale wind generation and electricity
from biomass). Advanced technologies enter endogenously when they become economically
competitive with existing technologies. Refined oil includes refining from crude oil, shale oil,
and liquids from biomass, which compete on an economics basis and can be used for
transportation. EPPA is calibrated using economic data from the Global Trade Analysis Project
(GTAP) database (Dimaranan, 2006), energy data from the International Energy Agency (IEA,
2004), and non- CO2 GHG and air pollutant from the Emission Database for Global Atmospheric
Research (EDGAR) 3.2 database (Olivier and Berdowski, 2001) and Bond et al. (2004). The
model is solved through time, in five-year increments, by imposing exogenous growth rates for
population and labor productivity.
APMT-E is one of a suite of models developed by the Office of Environment and Energy at
the Federal Aviation Administration (FAA) for assessment of aviation-related environmental
effects. The model is designed to examine aviation industry responses to policy measures.
APMT-E is a global model that determines operations for country pair-stage length
combinations, known as schedules. Most country pairs have one stage length, but some country
pairs involving large countries have more than one stage length. For example, the U.S.-UK
country pair has five stage lengths, reflecting geographic disparity in U.S. points of departure or
disembarkation. AMPT-E also identifies six world regions (North America, Latin America and
the Caribbean, Europe, Asia and the Pacific, The Middle East and Africa), 23 route groups (for
example, North Atlantic, Domestic U.S., North America-South America and Europe-Africa),
nine distance bands (for example, in kilometers, 0-926, 927-1853, and 6483-8334), 10 aircraft
seat classes defined by the number of available seats (for example, 0-19, 20-50 and 211-300) and
two carrier types (passenger and freight).
Aviation operations are determined in each year by retiring old aircraft based on retirement
functions. The surviving fleet is then compared to forecast capacity requirements to determine
capacity deficits by carrier region, distance band and carrier type. Potential new aircraft, which
are combinations of available airframes, engines and seat configurations, are evaluated by
calculating operational costs (including fuel, depreciation, finance and maintenance costs, and
route and landing charges) per seat hour over the life of the aircraft. Once new aircraft are
purchased to meet fleet deficits and added to the surviving aircraft, the fleet is deployed across
schedules and operating costs are calculated. Assuming a constant cost markup, cost calculations
are used to derive airfares, which are combined with route group price elasticities to compute
demand. The constant markup assumption implies that all costs associated with a cap-and-trade
policy are passed on to consumers. Although airlines may absorb some short-run cost increases,
it is difficult to imagine an industry repeatedly absorbing cost increases to continue to operate, so
a full pass-through assumption is commonly used in long-run assessments of the impact of cap3
and-trade policies on aviation (see, for example, Anger and Köhler, 2010). APMT-E price
elasticities, sourced from Kincaid and Tretheway (2007), represent the average response of
business and leisure travelers and vary by route group. Price elasticities range from -0.36 for the
Transpacific route group to -0.84 for the Intra-European route group. After a solution is found,
differences between actual and forecast demands are used to update demand projections for the
next period.
Our APMT-E reference scenario mainly follows ICAO/GIACC (2009). Traffic and fleet
forecasts are from the Forecast and Economic Analysis Support Group (FESG) at the
International Civil Aviation Organization (ICAO), adjusted by the short-term FAA Terminal
Area Forecast (TAF). Aircraft fuel efficiency rises by 1% per year. Airspace management
improvements driven by Next Generation (NextGen) High Density Analysis are first
implemented in the U.S. and then in other regions with a five-year lag. NextGen improvements
are assumed to result in detour reductions relative to great circle distances in the U.S. of 3% in
2015 and 10% in 2025. Corresponding decreases for non-U.S. regions occur in 2020 and 2030.
Load factors, average stage lengths and aircraft retirement schedules are consistent with FESG
forecasts used for the eighth meeting of the Committee on Aviation Environmental Protection
(CAEP/8).
The main influence of H.R. 2454 on aviation is likely to occur via the bill’s impact on the fuel
price and the overall level of economic activity. Accordingly, in our policy scenarios, using
output from EPPA, we change fuel prices and demand forecasts used by APMT-E. Guided by
Gillen et al. (2002), we assume an income elasticity of demand of 1.4 to convert GDP changes
estimated by EPPA into changes in aviation demand. As EPPA has a five-year time step and
APMT-E is solved for each year, we use linear interpolation to generate annual series for EPPA
predictions.
3. SCENARIOS
We model climate policy in the U.S. using H.R. 2454 as representative cap-and-trade policy.
The first major action by the U.S. Congress to address climate change, H.R. 2454 sets an
economy-wide target for GHG emissions, measured in CO2 equivalent (CO2-e) units, released
between 2012 and 2050.2 The chief emissions reduction instrument in H.R. 2454 is a cap-andtrade system that would cover between 85% and 90% of all U.S. emissions. Other provisions,
such as efficiency programs, target emissions from uncovered sectors. Following Paltsev et al.
(2009a, Appendix C), we simplify analysis of the policy by assuming that the cap-and-trade
program applies to all sectors. The cap is gradually tightened through 2050. It is 80% of 2005
emissions (5.6 gigatons, Gt, of CO2-e) in 2020, 58% (4.2 Gt) in 2030, and 17% (1.2 Gt) in 2050.
Under H.R. 2454, U.S. emissions may exceed the above targets if carbon offsets are used.
Offsets allow businesses to support eligible offset projects, as determined by the Environmental
2
CO2-e units measure concentrations of other GHGs, such as methane and nitrous oxide, relative to the climate
impacts of one unit of CO2 over a specified time period, usually 100 years.
4
Protection Agency (EPA), in lieu of turning in allowances. Offsets are restricted to 2 Gt – 1 Gt
from domestic sources and 1Gt from international sources – per year. We consider two offset
scenarios to capture uncertainties concerning the evolution of the market for offsets and the
impact of competition from foreign cap-and-trade programs. In a “full offsets” scenario, 2 Gt of
offsets are available each year at a specified cost. In a “medium offsets” case, we assume that the
quantity of offsets available increases linearly from zero in 2012 to 2 Gt in 2050. Another
provision in H.R. 2454, allowance banking, allows firms to over-comply in early years and bank
the excess of allowances for use in later years. As the stringency of the emissions constraint
increases over time in H.R. 2454, banking reduces compliance costs. When firms can bank
allowances, optimal behavior will equate the discounted price of CO2 allowances across years.
We assume a discount rate of 4%.
We model climate policies in other regions following a recent Energy Modeling Forum
scenario (EMF, Clarke et al., 2009). In the EMF scenario, developed nations (excluding the
U.S.) gradually reduce emissions to 50% below 1990 levels by 2050, and China, India, the
Former Soviet Union, and South America begin curtailing emissions in 2030. A difference
between H.R. 2454 and climate policy in the EU is that H.R. 2454 requires energy producers,
such as oil refineries, to turn in allowances while end users, such as airlines, are required to
submit allowances in the EU-ETS. However, as we assume that costs are passed through to
consumers, “upstream” and “downstream” policies produce identical results. Additionally,
although allocation rules have distributional effects, in our analysis the value of emissions
allowances represent windfall gains or losses, so allocation rules do not influence operating
decisions.
A contentious issue in the application of climate policy to aviation is how a nation’s climate
policy will influence foreign carriers flying to and from that nation, as is evident from court
action by the Air Transport Association (ATA) challenging the legality of requiring flights by
U.S. airlines to and from the EU to purchase allowances under the EU-ETS. In our analysis,
airlines pay carbon charges embedded in fuel prices, so airlines are influenced by foreign capand-trade programs when they uplift fuel abroad. For example, a U.S. airline flying to the EU
pays the U.S. CO2-e price when uplifting fuel in the U.S. and the EU CO2-e price when refueling
in the EU.
In addition to CO2 emissions, aviation operations may influence climate via emissions of nonCO2 gases and soot and sulfate particles which alter the atmospheric concentration of GHGs.
Although some additional effects contribute to warming and others to cooling, non- CO2 effects
are believed to contribute to warming in aggregate (Penner et al., 1999; Lee et al., 2009).
Aviation climate impacts from non- CO2 sources are sometimes characterized by a metric –
known as a multiplier – that divides the total impact of aviation on climate by the CO2 impact.
Although proposals requiring airlines to purchase allowances for non- CO2 effects have been
discussed in policy circles, such as the European Parliament, it remains uncertain whether such
measures will be applied. To capture these uncertainties, we consider multiplier coefficients of
one and two. When the multiplier is one, non- CO2 effects are not considered, while a multiplier
5
of two is used to capture non- CO2 effects in our analysis. We refer to simulations employing a
multiplier of one as “no aviation multiplier” scenarios and simulations with a multiplier of two as
“aviation multiplier” scenarios. To reflect the increased scarcity of allowances in multiplier
scenarios, we reduce economy-wide emissions caps based on estimates of the contribution of
aviation to aggregate GHG emissions from Lee et al. (2009).
To consider alternative offset and multiplier combinations, we implement four scenarios: full
offsets with no aviation multiplier (F1), full offsets with an aviation multiplier (F2), medium
offsets with no aviation multiplier (M1), and medium offsets with an aviation multiplier (M2).
These scenarios capture a wide range of possible outcomes. The cap on cumulative emissions
between 2012 and 2050 is 17% lower in full offset scenarios than in medium offset scenarios,
and policy-induced aviation fuel price increases in multiplier scenarios are more than twice as
large as fuel price increases in no multiplier scenarios.
4. RESULTS
Table 1 reports per ton CO2-e prices ($/t CO2-e), measured in 2005 dollars, and proportional
changes in GDP due to climate policy in three years: 2015, 2030 and 2050. In the F1 scenario,
the emissions price is $7 in 2015 and rises by 4% (our assumed discount rate) per year so that the
price reaches $13 by 2030 and $29 by 2050. In the medium offsets scenario without an aviation
multiplier, M1, CO2-e prices are around three times larger than in the F1 scenario. In the two
multiplier scenarios, F2 and M2, increased demand for allowances increases CO2-e prices
relative to the corresponding no multiplier scenarios but only slightly (for example, in the
medium offsets case, including an aviation multiplier increases the 2015 CO2-e price from
$21.31 to $22.25). The treatment of the aviation multiplier also has a small impact on GDP
changes, which range from -0.11% to -0.33% in 2015 and from -0.95% to -1.95% in 2050.
As noted above, H.R. 2454 will result in income-induced aviation demand changes and raise
fuel prices. Income-induced demand changes are estimated by multiplying GDP changes by 1.4,
to reflect an underlying income elasticity of demand. The largest income-induced demand
change, which occurs in scenario M2 in 2050, is 2.73%. Therefore, this effect is modest
compared to the impacts on demand of changes in fuel prices. Proportional change in aviation
fuel prices reflect the price of CO2-e emissions and the number of allowances that must be
purchased per ton of emissions. In scenario F1, the price of aviation fuel increases by 4.7% in
2050 relative to the reference scenario, while the corresponding price increase in the M1 scenario
is 26.7%. Fuel price changes in multiplier scenarios are more than twice as large as in
corresponding non-multiplier scenarios, as the multiplier is applied to a higher CO2-e price than
in no-multiplier scenarios.
6
Table 1. U.S. CO2-e prices (in 2005 dollars) and aviation fuel price and demand changes
relative to the reference scenario.
CO2-e price
($/t CO2-e)
Scenario
2015
GDP-induced demand
change (%)
2030
2050
2015
2030
2050
Fuel price change
(%)
2015
2030
2050
F1
$7.27
$13.09
$28.69
-0.11%
-0.43%
-0.95%
5.83%
4.60%
4.74%
F2
$7.79
$14.03
$30.74
-0.12%
-0.48%
-1.15%
12.50%
9.20%
18.10%
M1
$21.31
$38.39
$84.07
-0.31%
-1.01%
-1.83%
17.50%
17.82%
26.72%
M2
$22.25
$40.07
$87.08
-0.33%
-1.09%
-1.95%
36.67%
41.38%
65.09%
How do changes in fuel prices influence aviation emissions and operations? Estimated annual
aviation CO2 emissions for the period 2012-2050 under our reference and four policy scenarios
are presented in Figure 1. In the reference scenario, CO2 emissions increase from 250 million
metric tons (Mt) in 2012 to 580 Mt in 2050, a 130% increase. In policy scenarios, emissions
trajectories are lower than in the reference case but absolute emissions continue to increase
substantially over the policy period, by between 97% in the most stringent scenario (M2) and
122% in the least stringent scenario (F1). Thus, airlines respond to H.R. 2454 primarily by
paying higher fuel prices rather than changing operations. This finding is consistent with other
studies of the impact of climate policy on aviation emissions (see Anger, 2010; Mendes and
Santos, 2008; Vespermann and Wald, 2010).
Three explanations emerge for the small impact of H.R. 2454 on aviation emissions. First, the
results indicate that aviation emissions mitigation options are more expensive than mitigation
operations elsewhere. This is because, as fuel costs are a significant share, around 26% (IATA,
2010), of total aviation costs, airlines already operate closer to the fuel efficiency frontier than
other industries. There is also limited scope for airlines to switch to alternative, low-emitting fuel
sources compared to industries such as electricity, where coal can be replaced by several
relatively inexpensive, low-carbon energy sources. In our EPPA simulations, the largest
proportional reduction in sectoral emissions is observed for electricity in all scenarios. In the M1
scenario, electricity emissions fall by 60% relative to reference 2050 emissions. Second, despite
large fuel price increases, demand changes induced by H.R. 2454 are small, as aviation demand
is inelastic and the policy results in modest GDP changes. Third, as detailed below, the fleet
becomes less fuel efficient in our policy scenarios than in the reference case.
7
650
600
550
[billion kg]
500
450
400
350
300
250
200
2
% Change vs. Reference [%]
0
-2
-4
-6
-8
-10
-12
-14
-16
-18
Reference
F1
F2
M1
M2
Figure 1. Aviation CO2 emissions in the reference and policy scenarios, 2006-2050.
8
Our finding that H.R. 2454 reduces fleet efficiency warrants greater discussion. Fleet
efficiency is gauged by fuel use per available ton kilometer (ATK). As there is a one-to-one
mapping between fuel burn and CO2 emissions, proportional changes in fuel use equal
proportional changes in CO2 emissions. Fuel use per revenue ton kilometer (RTK), which
depends on load factors, is also informative, as this metric indicates fuel use per passenger
kilometer. Changes in these variables induced by H.R. 2454 relative to the reference are
presented in Table 2. In scenario M1, fuel per ATK increases by 0.2% in 2015, 0.5% in 2030
and 0.6% in 2050. Similarly, in other scenarios, H.R. 2454 increases fuel per ATK relative to the
reference through time. Decreases in load factors, relative to the reference, are larger in 2015
than later years, as it takes some time for airlines to adjust aircraft stocks in response to demand
changes. Load factor decreases are larger in more stringent scenarios than less stringent
scenarios. Fuel use per RTK is influenced by fleet efficiency and load factors. The largest
changes in fuel per RTK are observed for the M2 scenario, where fuel per RTK increase by 4.7%
in 2015, 1.6% in 2030 and 2.3% in 2050, relative to the reference.
Table 2. Changes in fuel use per ATK and per RTK, and load factors relative to the
reference.
Scenario
ATK
2015
2030
RTK
2050
2015
2030
Load factor
2050
2015
2030
2050
F1
0.1%
0.2%
0.3%
1.0%
0.5%
0.5%
-0.9%
-0.3%
-0.2%
F2
0.2%
0.5%
0.7%
1.8%
1.2%
1.3%
-1.6%
-0.6%
-0.6%
M1
0.2%
0.5%
0.6%
2.4%
0.7%
1.2%
-2.1%
-0.3%
-0.5%
M2
0.5%
1.0%
1.4%
4.7%
1.6%
2.3%
-4.1%
-0.6%
-1.0%
We examine the drivers of our fleet efficiency results by displaying proportional changes in
fleet size, average aircraft age and average aircraft size relative to the reference in Table 3. In all
scenarios, fewer new aircraft are purchased relative to the reference, so the number of aircraft in
the fleet decreases and aircraft age increases on average. As new aircraft tend to be larger than
existing aircraft, average aircraft size also decreases. Thus, the decrease in fleet efficiency is not
only driven by airlines flying older, less efficient aircraft, but also, when selecting from the same
set of technologies as in the reference scenario, airlines purchasing smaller aircraft, which burn
more fuel per ATK. In APMT-E, fleet efficiency changes are driven by two opposing forces: (i)
rising fuel prices, which accelerate the introduction of new, more fuel efficient aircraft, and (ii)
reduced demand, which slows the introduction of new aircraft. In our simulations, the demand
effect dominates the fuel-price effect.
9
Table 3. Changes in the number of aircraft in the fleet, average aircraft age and average
aircraft size relative to the reference.
Scenario
Number of aircraft
Average age
Average size
2015
2030
2050
2015
2030
2050
2015
2030
2050
F1
-0.5%
-1.9%
-4.2%
0.5%
0.4%
0.8%
0.0%
-0.2%
-0.7%
F2
-1.2%
-3.8%
-8.3%
1.0%
0.8%
1.5%
0.0%
-0.3%
-1.1%
M1
-1.9%
-4.7%
-8.7%
1.5%
1.0%
1.2%
0.0%
-0.1%
-0.3%
M2
-3.7%
-8.7%
-16.0%
3.1%
1.6%
2.2%
0.0%
-0.1%
-0.3%
A major concern for the aviation industry is the impact of H.R. 2454 on profitability. In this
connection, the U.S. Air Transport Association (ATA) estimates that the bill would increase U.S.
airline industry costs by $5 billion in 2012 and $10 billion in 2020 (Khun, 2009). Our results are
in broad agreement with these numbers: estimated fuel cost increases range from $1.1 (F1)
billion to $6.8 billion (M2) in 2012, and from $2.7 billion (F1) to $21.4 billion (M2) in 2020.
However, cost increases in APMT-E are passed on to consumers via increased fares, so cost
increases only influence profits via their impact on demand.
To examine changes in financial viability of the aviation sector, we report changes in financial
indicators relative to the reference in Table 4. Unit (per seat mile) operating costs are affected by
rising fuel prices, decreased average efficiency, and reduced load factors. Consequently, unit
operating costs increase by 1.9% in 2015, 2.9% in 2030 and 5.0% in 2050 in scenario F1, and
larger cost increases are observed for scenarios with higher CO2-e prices. As expected, profit
changes are negatively correlated with fuel price changes. The largest proportional profit
decreases, for scenario M2, are 6.2% in 2015, 9.4% in 2030 and 17.1% in 2050, or (in 2006
dollars) $0.4 billion in 2015, $0.9 billion in 2030 and $2.9 billion in 2050.
Table 4. Changes in total operating costs, unit costs and operating profits relative to the
reference.
Scenario
Operating Costs
2015
2030
Unit Costs
Operating Profits
2050
2015
2030
2050
2015
2030
2050
-0.2% -1.3%
1.9%
2.9%
5.0%
-1.1%
-2.5%
-5.4%
F1
0.5%
F2
1.1%
1.2%
2.1%
4.0%
7.6%
15.6%
-2.2%
-5.0% -10.3%
M1
1.3%
0.4% -0.3%
5.5%
6.4%
11.5%
-3.2%
-5.0%
M2
2.9%
3.0%
11.5%
15.3%
29.8%
-6.2%
-9.4% -17.1%
4.8%
-9.5%
5. SENSITIVITY ANALYSIS
An important finding in our analysis is that H.R. 2454 reduces fleet efficiency. In this section,
we investigate the robustness of this result to key components of AMPT-E. Specifically, we
examine the sensitivity of results to aircraft retirement decisions, aircraft fuel-efficiency
improvements, and income and price elasticities. As above, proportional changes in fuel use per
ATK relative to the reference scenario are used to measure efficiency changes. Table 5 reports
10
efficiency changes in 2015, 2030 and 2050 for our F1 scenario, for the base case and for each
sensitivity analysis. Sensitivity results for other scenarios follow a similar pattern.
Table 5. Changes in fuel use per ATK relative to the reference for the F1 scenario.
Sensitivity scenario
2015
2030
2050
Base
0.10%
0.24%
0.27%
Lower old aircraft survival probability
0.10%
0.21%
0.22%
Higher new aircraft efficiency (reference & policy)
0.11%
0.27%
0.29%
-0.44%
-4.01%
-10.63%
0.06%
0.19%
0.24%
-0.40%
-1.59%
-2.69%
Higher new aircraft efficiency (policy only)
No income-induced demand change
Zero price elasticity demand
5.1 Aircraft retirement decisions
As noted in Section 2, aircraft retirements in APMT-E are based on FESG-CAEP8 retirement
functions. Retirement curves are assumed to follow a Weibull distribution. Specifically, the
probability that an aircraft of a certain age will survive in a given period under policy, ρpolicy, is:
ρ 1
1 1⁄ρ 1 (1)
e∆ where ρbase is the survival probability in the FSEG forecast, ∆C is the cost of flying the existing
aircraft minus the cost of flying new aircraft, and λ is the sensitivity of the survival probability to
cost differences.
In a sensitivity analysis, we increase λ from 0.03 to 0.04, which decreases the survival
probability of existing aircraft under climate policy, relative to our base F1 scenario. As
illustrated in Table 5, earlier retirement of old aircraft decreases fuel per ATK relative to our
base F1 scenario. However, changes in fleet fuel burn per ATK are small (fuel burn relative to
the reference case increases by 0.21% in 2030 and 0.22% in 2050, compared to 0.24% in 2030
and 0.27% in 2050 in our base F1 scenario).
5.2 New aircraft fuel efficiency
In another sensitivity analysis, we increase the scope for airlines to respond to rising fuel
prices by purchasing new technologies. New aircraft fuel efficiency rises by 1% per year in
APMT-E. In a sensitivity analysis, we impose a 1.5% annual fuel efficiency improvement, which
we apply to both the reference and policy scenarios. With higher new aircraft efficiency, fuel use
per ATK increases relative to the reference, as in our base F1 scenario.
We also consider a sensitivity analysis where higher fuel prices induced by H.R. 2454 spur
development of new technologies. In this analysis, new aircraft efficiency improves by 1.5%
annually in the policy case and 1% annually in the reference case. In this case, unlike in our base
scenario, fleet fuel efficiency increases relative to the reference – by 0.44% in 2015, 4.01% in
11
2030, and 10.63% in 2050. These efficiency changes are large, but not unexpected given that the
growth rate for new aircraft efficiency is 50% larger in the policy scenario than in the reference.
This simulation indicates the importance of technology responses to price changes.
5.3 Income and price elasticities
H.R. 2454 slows the introduction of new aircraft by reducing the demand for aviation.
Accordingly, in separate sensitivity analyses, we examine the impact of the income elasticity of
demand and price elasticities of demand. In one analysis, we set the income elasticity of demand
for aviation equal to zero, so GDP changes induced by climate policy do not influence aviation
demand. Under such a scenario, fuel per ATK decreases relative to the base F1 scenario, but still
increases relative to the reference scenario, by 0.24% in 2050. In another analysis, we set the
price elasticity of demand for aviation equal to zero. Under this extreme price-response
assumption, fuel use per ATK, relative to the reference, decreases by 0.40% in 2015, 1.59% in
2030 and 2.69% in 2050.
6. CONCLUSIONS
We examined the impact of climate policy on aviation emissions and economic outcomes in
the U.S.. Our analysis, benchmarked to ICAO/GIACC (2009) forecasts, estimated that aviation
emissions between 2012 and 2050 will increase by between 97% and 122% under H.R.2454,
compared to 130% without climate policy. These results indicate that aviation emissions
abatement options are costly relative to mitigation options in other sectors. Key determinants of
marginal abatement costs in APMT-E include the specification of new aircraft capital costs, fuel
efficiency improvements, and retirement rates. Currently, there are limited opportunities for
airlines to replace more CO2-intensive energy sources with less CO2-intensive energy sources,
and to substitute between energy and other inputs.
Another noteworthy finding is that, under the set of assumptions in our framework, GDP and
fuel price changes induced by H.R. 2454 reduce fleet efficiency, as the demand effect
outweighed the fuel-price effect. That is, the impact of reduced demand for aviation on new
aircraft purchase decisions dominated incentives to purchase more efficient aircraft in the face of
rising fuel prices. We examined the sensitivity of our results to several key modeling
assumptions. In general, our finding that H.R. 2454 reduces average fleet efficiency is robust to
plausible alternative modeling assumptions examined in our sensitivity analyses. However, our
fleet efficiency results were overturned when we assumed that aviation demand was perfectly
inelastic, and when we assumed that fuel price increases induced by H.R 2454 increased the fuel
efficiency of new aircraft by 50%, relative to the reference scenario.
Several caveats to our analysis should be noted. First, as we focused on long-run trends,
adjustments associated with business cycles were not considered. In economic downturns,
airlines may park old aircraft, which are replaced by new aircraft in high-growth periods rather
than brought back into service. Second, our modeling framework did not capture some
adjustments available to airlines using the existing fleet. For example, we did not allow airlines
to retrofit seat configurations or use slower flight speeds in response to fuel price changes. Third,
12
we did not consider how changes in air traffic management, induced by climate policy, might
improve operations and reduce emissions. When these adjustments are considered, climate
policy may have a larger positive impact on fleet efficiency than in our study. Future research
will focus on this.
Acknowledgments
The authors would like to thank Thomas Cuddy, Maryaclie Locke, Richard Hancox, Caroline
Sinclair, Carl Tipton and Daniel Rankin for valuable input. Any errors are the sole responsibility
of the authors. This work is funded by the U.S. Federal Aviation Administration Office of
Environment and Energy under FAA Award Number: 06-C-NE-MIT, Amendment Nos. 018 and
028. The project is managed by Thomas Cuddy of FAA. The Joint Program on the Science and
Policy of Global Change is funded by the U.S. Department of Energy and a consortium of
government and industrial sponsors (for a complete list, see
http://globalchange.mit.edu/sponsors/current.html). Additional funding for Wollersheim was
provided by the Erich-Becker Foundation. Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the authors and do not necessarily
reflect the views of the FAA, NASA or Transport Canada. This report is also released as
Partnership for AiR Transportation Noise and Emissions Reduction (PARTNER) Report No.
PARTNER-COE-2011-001.
7. REFERENCES
Airbus, 2009: Flying smart, thinking big, Global Market Forecast 2009-2028, Blagnac Cedex.
Albers, S., J.A. Bühne, and H. Peters, 2009: Will the EU-ETS instigate airline network
reconfigurations? Journal of Air Transport Management, 15(1): 1-6.
Anger, A., and J. Köhler, 2010: Including aviation emissions in the EU ETS: Much ado about
nothing? A review. Transport Policy, 17(2): 38-46.
Boeing, 2010: Current Market Outlook 2009-2028.
(http://active.boeing.com/commercial/forecast_data/index.cfm).
Bond, T.C., D.G. Streets, K.F. Yarber, S.M. Nelson and J. Woo, 2004: A technology-based
global inventory of black and organic carbon emissions from combustion. Journal of
Geophysical Research, 109: D14203.
Boon, B., M. Davidson, J. Faber and A. van Velzen, 2007: Allocation of allowances for aviation
in the EU ETS – The impact on the profitability of the aviation sector under high levels of
auctioning. A report for WWF UK, Delft, CE Delft, March 2007.
Clarke, L., J. Edmonds, V. Krey, R. Richels, M. Tavoni and S. Rose 2009: International climate
policy architectures: Overview of the EMF 22 international scenarios. Energy Economics,
31(S2): S64-S81.
Dimaranan, B.V. (ed.) 2006: Global Trade, Assistance, and Production: The GTAP 6 Data Base.
Center for Global Trade Analysis, Purdue University.
Ernst and Young, 2007: Analysis of the EC proposal to include aviation activities in the
Emissions Trading Scheme, A report by Ernst and Young and York Aviation, York.
Forsyth, P., 2008: The impact of climate change policy on competition in the air transport
industry. Department of Economics, Monash University, Discussion paper No. 2008-18.
13
Gillen, D.W., W.G. Morrison and C. Stewart, 2002: Air travel demand elasticities: Concepts,
issues and measurement, Final Report, Department of Finance, Canada.
Haites, E., 2009: Linking emissions trading schemes for international aviation and shipping
emissions. Climate Policy, 9(4): 415-430.
Hofer, C., M.E. Dresner, and R.J. Windle, 2010: The environmental effects of airline carbon
emissions taxation in the U.S.. Transportation Research Part D (Transport and
Environment), 15: 37-45.
IATA (International Air Transport Association) 2010: Fuel impact on operating costs
(https://www.iata.org/pressroom/facts_figures/fact_sheets/Pages/fuel.aspx).
ICAO/GIACC (International Civil Aviation Organization / Group on International Aviation and
Climate Change), 2009: U.S. fuel trends analysis and comparison to GIACC/4-IP/1, Fourth
Meeting of the Group on International Aviation and Climate Change, May 25 to 28, 2009,
Montreal.
IEA (International Energy Agency), 2004: World Energy Outlook: 2004. OECD/IEA: Paris.
Khun, M., 2009: U.S. cap and trade scheme could cost billions: ATA, FlightGlobe, Sutton, UK.
(http://www.flightglobal.com/articles/2009/05/28/327041/us-cap-and-trade-scheme-couldcost-billions-ata.html).
Kincaid, I. and M. Tretheway, 2007: Estimating air travel demand elasticities, InterVISTAS.
Lee D.S., D. Pitari, V. Grewe, K. Gierens, J.E. Penner, A. Petzold, M.J. Prather, U. Schumann,
A. Bais, T. Bernsten, D. lachetti, L.L. Lim, R. Sausen, 2009: Transport impacts on
atmosphere and climate: Aviation, Atmospheric Environment, In Press.
(doi:10.1016/j.atmosenv.2009.06.005).
Mahashabde, A., P. Wolfe, A. Ashok, C. Dorbian, Q. He, A. Fan, S. Lukachko, A.
Mozdzanowska, C. Wollersheim, S. Barrett, M. Locke, and I. A. Waitz, 2010: Assessing the
environmental impacts of aircraft noise and emissions. Progress in Aerospace Sciences, in
Press.
Mayor, K. and R.S.J. Tol, 2010: The impact of European climate change regulations on
international tourist markets. Transportation Research Part D (Transport and Environment),
15: 26-36.
Mendes, L.M.Z. and Santos, 2008: Using economic instruments to address emissions from air
transport in the European Union. Environment and Planning A, 40(1): 189-209.
Morrell, P., 2007: An evaluation of possible EU air transport emissions trading scheme
allocation methods. Energy Policy, 35: 5562-5570.
MVA Consultancy, 2009: Aviation Environmental Portfolio Management Tool (APMT):
APMT-Economics. Algorithm Design Document (ADD), MVA Consultancy, London and
Manchester.
Oberthür, S., 2003: Institutional interaction to address greenhouse gas emissions from
international transport: ICAO, IMO and the Kyoto Protocol. Climate Policy, 3: 191-205.
Olivier, J.G.J. and J.J.M. Berdowski, 2001: Global emissions sources and sinks. In: The Climate
System, J. Berdowski, R. Guicherit and B.J. Heij (eds.), A.A. Balkema Publishers/Swets &
Zeitlinger Publishers, Lisse, The Netherlands, pp. 33-78.
Paltsev, S., J. Reilly, H.D. Jacoby, R.S. Eckaus, J. McFarland, M. Sarofim, M. Asadooria and M.
Babiker, 2005: The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version
14
4. MIT JPSPGC Report 125, August, 72 p.
(http://globalchange.mit.edu/files/document/MITJPSPGC_Rpt125.pdf).
Paltsev S., J. Reilly, H. Jacoby, A. Gurgel, G. Metcalf, A. Sokolov and J. Holak, 2007:
Assessment of U.S. cap-and-trade proposals. Climate Policy, 8(4): 395-420; MIT Joint
Program Reprint 2008-16;
http://globalchange.mit.edu/files/document/MITJPSPGC_Reprint08-16.pdf.
Paltsev S., J. Reilly, H. Jacoby, and J. Morris, 2009: The cost of climate policy in the United
States. Energy Economics, 31(S2): S235-S243; MIT Joint Program Reprint 2009-16;
http://globalchange.mit.edu/pubs/abstract.php?publication_id=2020.
Penner J.E., D.H. Lister, D.J. Griggs, D.J. Dokken and M. McFarland, 1999: Aviation and the
Global Atmosphere: A Special Report of IPCC Working Groups I and III, Intergovernmental
Panel on Climate Change, Cambridge University Press, Cambridge/New York.
Petersen, M., 2008: The legality of the EU’s stand-alone approach to the climate impact of
aviation: the express role given to the ICAO by the Kyoto protocol. Review of European
Community and International Environmental Law, 17(2): 196-204.
Scheelhaase, J.D., and W.G. Grimme, 2007: Emissions trading for international aviation – an
estimation of the economic impact on selected European airlines. Journal of Air Transport
Management, 13(5): 253-263.
Scheelhaase, J.D., M. Schaefer, W.G. Grimme and S. Maertens, 2010: The inclusion of aviation
into the EU emission trading scheme – Impacts on competition between European and nonEuropean network airlines. Transportation Research Part D (Transport and Environment),
15(1): 14-25.
Vespermann, J. and A. Wald, 2010: Much ado about nothing? – An analysis of economic
impacts and ecologic effects of the EU-emissions trading scheme in the aviation industry.
Transportation Research Part A, in press.
Wit, R.C.N., B.H. Boon, A. van Velzen, M. Cames, O. Deuber, and D.S. Lee, 2005: Giving
wings to emission trading – Inclusion of aviation under the European emission trading
system (ETS): Design and impacts, CE Solutions for Environment, Economic and
Technology, Report for the European Commission, Delft.
15
APPENDIX: LITERATURE REVIEW
Several studies examine the impact of climate policy on aviation. To date, most papers focus
on the EU-ETS. Some studies analyze the general implications of cap-and-trade schemes or
carbon taxes. To our knowledge, only one study, Hofer et al. (2010), focuses on U.S. Table A1
summarizes the literature to date. Studies can be grouped into three categories: (1) papers
focusing on aviation financial indicators and environmental benefits from reduced aviation
emissions, (2) analyses of the competitive effects of carbon policies, and (3) studies that analyze
legal and political aspects. Studies focusing on legal aspects include Haites (2009), Oberthuer
(2003), and Peterson (2008). Studies examining the impact of climate policy on airline
competition tend to analyze case studies (Albers et al., 2009; Forsyth, 2008; Scheelhaase et al.,
2010). Papers that assess the financial and environmental implications of including aviation in
climate policy either utilize existing models (Anger, 2010; Boon et al., 2007; Wit et al, 2005) or
develop new models (Ernst and Young, 2007, Hofer et al., 2010; Mendes, 2008, Morrell, 2007,
Scheelhaase and Grimme, 2007; Vespermann, and Wald, 2010). All studies in this group use an
assumed allowance price, except Anger (2010), who uses a dynamic macroeconomic model.
16
Table A1. Studies evaluating the impact of climate policy on aviation.
Methodology
Scope
Key Assumptions
Key Findings
Anger (2010)
Dynamic macro.
model
EU-ETS
Allowance prices €5-€40 per ton of
CO2; full cost pass-through;
efficiency improvements of 1% p.a.
Air transport CO2 emissions decrease by
up to 7.4%, relative to baseline in
2020.
Boon et al. (2007)
AERO-MS model
EU-ETS
Allowance prices €15 to €45; 47.3%
to 100% cost pass-through in 2020.
Evidence of perfect competition.
Ernst & Young (2007)
Simulation model
EU-ETS
Allowance prices €6- €60; 29% to
35% cost pass-through; fuel
efficiency improvements of 1% p.a.
Airline profits decrease by €40 billion
over the period 2011 to 2022.
Hofer et al. (2010)
Simulation model
Carbon tax on
U.S. Aviation
Air fare increase of 2%; price
elasticity of -1.15; cross-price
elasticity (air/car) of 0.041%.
Five billion ton reduction in aviation
emissions. Increase in vehicle
emissions of 1.65 billion lbs (2,540
million additional passenger miles).
Mayor and Tol (2010)
Hamburg
Tourism Model
EU-ETS;
Carbon taxes
in some
countries
EU-ETS allowance price €23, full cost
pass-through; carbon taxes of €11€45 in the Netherlands and €14- €54
in the UK.
The three policies reduce global welfare
by €12.6 billion. Tourists shift away
from the taxed areas.
Demand reductions due to EU-ETS less
than 2%. Small supply-side emissions
reductions due to high abatement
costs.
17
Study
Mendes and Santos
(2008)
Simulation model
EU-ETS
Allowance prices €7, €15, and €30,
full cost pass-through.
Morrell (2007)
Three UK case
studies
EU-ETS
Allowance price $40; full cost passthrough.
Climate policy has a larger impact on
low cost carriers than full service
airlines.
EU-ETS
Allowance prices €15-€30; full cost
pass-through; fuel efficiency
improvements of 1% to 1.47% p.a.
Small costs increases due to the EUETS compared to recent fuel price rises;
the EU-ETS creates competitive
distortions.
Risk analysis
software
EU-ETS
Allowance prices are normally
distributed with an expected value of
€25.
The EU-ETS reduces the annual growth
of CO2 emissions by about 1% per
year; Small financial impacts on
aviation; Small competition distortions.
AERO-MS Model
EU-ETS
Allowance prices €10-€30; full/no
cost pass-through; fuel efficiency
improvements of 1% p.a.
Industry-wide CO2 emissions reductions
of 20-26 Mt (13%-22%) in 2012.
Scheelhaase and
Grimme (2007)
Vespermann and Wald
(2010)
Wit et al. (2005)
Simulation model
REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change
1. Uncertainty in Climate Change Policy Analysis
Jacoby & Prinn December 1994
2. Description and Validation of the MIT Version of the
GISS 2D Model Sokolov & Stone June 1995
3. Responses of Primary Production and Carbon Storage
to Changes in Climate and Atmospheric CO2
Concentration Xiao et al. October 1995
4. Application of the Probabilistic Collocation Method
for an Uncertainty Analysis Webster et al. January 1996
5. World Energy Consumption and CO2 Emissions:
1950-2050 Schmalensee et al. April 1996
6. The MIT Emission Prediction and Policy Analysis
(EPPA) Model Yang et al. May 1996 (superseded by No. 125)
7. Integrated Global System Model for Climate Policy
Analysis Prinn et al. June 1996 (superseded by No. 124)
8. Relative Roles of Changes in CO2 and Climate to
Equilibrium Responses of Net Primary Production and
Carbon Storage Xiao et al. June 1996
9. CO2 Emissions Limits: Economic Adjustments and the
Distribution of Burdens Jacoby et al. July 1997
10. Modeling the Emissions of N2O and CH4 from the
Terrestrial Biosphere to the Atmosphere Liu Aug. 1996
11. Global Warming Projections: Sensitivity to Deep Ocean
Mixing Sokolov & Stone September 1996
12. Net Primary Production of Ecosystems in China and
its Equilibrium Responses to Climate Changes
Xiao et al. November 1996
13. Greenhouse Policy Architectures and Institutions
Schmalensee November 1996
14. What Does Stabilizing Greenhouse Gas
Concentrations Mean? Jacoby et al. November 1996
15. Economic Assessment of CO2 Capture and Disposal
Eckaus et al. December 1996
16. What Drives Deforestation in the Brazilian Amazon?
Pfaff December 1996
17. A Flexible Climate Model For Use In Integrated
Assessments Sokolov & Stone March 1997
18. Transient Climate Change and Potential Croplands of
the World in the 21st Century Xiao et al. May 1997
19. Joint Implementation: Lessons from Title IV’s Voluntary
Compliance Programs Atkeson June 1997
20. Parameterization of Urban Subgrid Scale Processes
in Global Atm. Chemistry Models Calbo et al. July 1997
21. Needed: A Realistic Strategy for Global Warming
Jacoby, Prinn & Schmalensee August 1997
22. Same Science, Differing Policies; The Saga of Global
Climate Change Skolnikoff August 1997
23. Uncertainty in the Oceanic Heat and Carbon Uptake
and their Impact on Climate Projections
Sokolov et al. September 1997
24. A Global Interactive Chemistry and Climate Model
Wang, Prinn & Sokolov September 1997
25. Interactions Among Emissions, Atmospheric
Chemistry & Climate Change Wang & Prinn Sept. 1997
26. Necessary Conditions for Stabilization Agreements
Yang & Jacoby October 1997
27. Annex I Differentiation Proposals: Implications for
Welfare, Equity and Policy Reiner & Jacoby Oct. 1997
28. Transient Climate Change and Net Ecosystem
Production of the Terrestrial Biosphere
Xiao et al. November 1997
29. Analysis of CO2 Emissions from Fossil Fuel in Korea:
1961–1994 Choi November 1997
30. Uncertainty in Future Carbon Emissions: A Preliminary
Exploration Webster November 1997
31. Beyond Emissions Paths: Rethinking the Climate Impacts
of Emissions Protocols Webster & Reiner November 1997
32. Kyoto’s Unfinished Business Jacoby et al. June 1998
33. Economic Development and the Structure of the
Demand for Commercial Energy Judson et al. April 1998
34. Combined Effects of Anthropogenic Emissions and
Resultant Climatic Changes on Atmospheric OH
Wang & Prinn April 1998
35. Impact of Emissions, Chemistry, and Climate on
Atmospheric Carbon Monoxide Wang & Prinn April 1998
36. Integrated Global System Model for Climate Policy
Assessment: Feedbacks and Sensitivity Studies
Prinn et al. June 1998
37. Quantifying the Uncertainty in Climate Predictions
Webster & Sokolov July 1998
38. Sequential Climate Decisions Under Uncertainty: An
Integrated Framework Valverde et al. September 1998
39. Uncertainty in Atmospheric CO2 (Ocean Carbon Cycle
Model Analysis) Holian Oct. 1998 (superseded by No. 80)
40. Analysis of Post-Kyoto CO2 Emissions Trading Using
Marginal Abatement Curves Ellerman & Decaux Oct.
1998
41. The Effects on Developing Countries of the Kyoto
Protocol and CO2 Emissions Trading
Ellerman et al. November 1998
42. Obstacles to Global CO2 Trading: A Familiar Problem
Ellerman November 1998
43. The Uses and Misuses of Technology Development as
a Component of Climate Policy Jacoby November
1998
44. Primary Aluminum Production: Climate Policy,
Emissions and Costs Harnisch et al. December 1998
45. Multi-Gas Assessment of the Kyoto Protocol
Reilly et al. January 1999
46. From Science to Policy: The Science-Related Politics of
Climate Change Policy in the U.S. Skolnikoff January 1999
47. Constraining Uncertainties in Climate Models Using
Climate Change Detection Techniques
Forest et al. April 1999
48. Adjusting to Policy Expectations in Climate Change
Modeling Shackley et al. May 1999
49. Toward a Useful Architecture for Climate Change
Negotiations Jacoby et al. May 1999
50. A Study of the Effects of Natural Fertility, Weather
and Productive Inputs in Chinese Agriculture
Eckaus & Tso July 1999
51. Japanese Nuclear Power and the Kyoto Agreement
Babiker, Reilly & Ellerman August 1999
52. Interactive Chemistry and Climate Models in Global
Change Studies Wang & Prinn September 1999
Contact the Joint Program Office to request a copy. The Report Series is distributed at no charge.
REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change
53. Developing Country Effects of Kyoto-Type Emissions
Restrictions Babiker & Jacoby October 1999
54. Model Estimates of the Mass Balance of the
Greenland and Antarctic Ice Sheets Bugnion Oct 1999
55. Changes in Sea-Level Associated with Modifications
of Ice Sheets over 21st Century Bugnion October 1999
56. The Kyoto Protocol and Developing Countries
Babiker et al. October 1999
57. Can EPA Regulate Greenhouse Gases Before the
Senate Ratifies the Kyoto Protocol?
Bugnion & Reiner November 1999
58. Multiple Gas Control Under the Kyoto Agreement
Reilly, Mayer & Harnisch March 2000
59. Supplementarity: An Invitation for Monopsony?
Ellerman & Sue Wing April 2000
60. A Coupled Atmosphere-Ocean Model of Intermediate
Complexity Kamenkovich et al. May 2000
61. Effects of Differentiating Climate Policy by Sector:
A U.S. Example Babiker et al. May 2000
62. Constraining Climate Model Properties Using
Optimal Fingerprint Detection Methods Forest et al.
May 2000
63. Linking Local Air Pollution to Global Chemistry and
Climate Mayer et al. June 2000
64. The Effects of Changing Consumption Patterns on the
Costs of Emission Restrictions Lahiri et al. Aug 2000
65. Rethinking the Kyoto Emissions Targets
Babiker & Eckaus August 2000
66. Fair Trade and Harmonization of Climate Change
Policies in Europe Viguier September 2000
67. The Curious Role of “Learning” in Climate Policy:
Should We Wait for More Data? Webster October 2000
68. How to Think About Human Influence on Climate
Forest, Stone & Jacoby October 2000
69. Tradable Permits for Greenhouse Gas Emissions:
A primer with reference to Europe Ellerman Nov 2000
70. Carbon Emissions and The Kyoto Commitment in the
European Union Viguier et al. February 2001
71. The MIT Emissions Prediction and Policy Analysis
Model: Revisions, Sensitivities and Results
Babiker et al. February 2001 (superseded by No. 125)
72. Cap and Trade Policies in the Presence of Monopoly
and Distortionary Taxation Fullerton & Metcalf March ‘01
73. Uncertainty Analysis of Global Climate Change
Projections Webster et al. Mar. ‘01 (superseded by No. 95)
74. The Welfare Costs of Hybrid Carbon Policies in the
European Union Babiker et al. June 2001
75. Feedbacks Affecting the Response of the
Thermohaline Circulation to Increasing CO2
Kamenkovich et al. July 2001
76. CO2 Abatement by Multi-fueled Electric Utilities:
An Analysis Based on Japanese Data
Ellerman & Tsukada July 2001
77. Comparing Greenhouse Gases Reilly et al. July 2001
78. Quantifying Uncertainties in Climate System
Properties using Recent Climate Observations
Forest et al. July 2001
79. Uncertainty in Emissions Projections for Climate
Models Webster et al. August 2001
80. Uncertainty in Atmospheric CO2 Predictions from a
Global Ocean Carbon Cycle Model
Holian et al. September 2001
81. A Comparison of the Behavior of AO GCMs in
Transient Climate Change Experiments
Sokolov et al. December 2001
82. The Evolution of a Climate Regime: Kyoto to
Marrakech Babiker, Jacoby & Reiner February 2002
83. The “Safety Valve” and Climate Policy
Jacoby & Ellerman February 2002
84. A Modeling Study on the Climate Impacts of Black
Carbon Aerosols Wang March 2002
85. Tax Distortions and Global Climate Policy
Babiker et al. May 2002
86. Incentive-based Approaches for Mitigating
Greenhouse Gas Emissions: Issues and Prospects for
India Gupta June 2002
87. Deep-Ocean Heat Uptake in an Ocean GCM with
Idealized Geometry Huang, Stone & Hill
September 2002
88. The Deep-Ocean Heat Uptake in Transient Climate
Change Huang et al. September 2002
89. Representing Energy Technologies in Top-down
Economic Models using Bottom-up Information
McFarland et al. October 2002
90. Ozone Effects on Net Primary Production and Carbon
Sequestration in the U.S. Using a Biogeochemistry
Model Felzer et al. November 2002
91. Exclusionary Manipulation of Carbon Permit
Markets: A Laboratory Test Carlén November 2002
92. An Issue of Permanence: Assessing the Effectiveness of
Temporary Carbon Storage Herzog et al. December
2002
93. Is International Emissions Trading Always Beneficial?
Babiker et al. December 2002
94. Modeling Non-CO2 Greenhouse Gas Abatement
Hyman et al. December 2002
95. Uncertainty Analysis of Climate Change and Policy
Response Webster et al. December 2002
96. Market Power in International Carbon Emissions
Trading: A Laboratory Test Carlén January 2003
97. Emissions Trading to Reduce Greenhouse Gas
Emissions in the United States: The McCain-Lieberman
Proposal Paltsev et al. June 2003
98. Russia’s Role in the Kyoto Protocol Bernard et al. Jun ‘03
99. Thermohaline Circulation Stability: A Box Model Study
Lucarini & Stone June 2003
100. Absolute vs. Intensity-Based Emissions Caps
Ellerman & Sue Wing July 2003
101. Technology Detail in a Multi-Sector CGE Model:
Transport Under Climate Policy Schafer & Jacoby July 2003
102. Induced Technical Change and the Cost of Climate
Policy Sue Wing September 2003
103. Past and Future Effects of Ozone on Net Primary
Production and Carbon Sequestration Using a Global
Biogeochemical Model Felzer et al. (revised) January 2004
Contact the Joint Program Office to request a copy. The Report Series is distributed at no charge.
REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change
104. A Modeling Analysis of Methane Exchanges
Between Alaskan Ecosystems and the Atmosphere
Zhuang et al. November 2003
105. Analysis of Strategies of Companies under Carbon
Constraint Hashimoto January 2004
106. Climate Prediction: The Limits of Ocean Models
Stone February 2004
107. Informing Climate Policy Given Incommensurable
Benefits Estimates Jacoby February 2004
108. Methane Fluxes Between Terrestrial Ecosystems
and the Atmosphere at High Latitudes During the
Past Century Zhuang et al. March 2004
109. Sensitivity of Climate to Diapycnal Diffusivity in the
Ocean Dalan et al. May 2004
110. Stabilization and Global Climate Policy
Sarofim et al. July 2004
111. Technology and Technical Change in the MIT EPPA
Model Jacoby et al. July 2004
112. The Cost of Kyoto Protocol Targets: The Case of
Japan Paltsev et al. July 2004
113. Economic Benefits of Air Pollution Regulation in the
USA: An Integrated Approach Yang et al. (revised) Jan. 2005
114. The Role of Non-CO2 Greenhouse Gases in Climate
Policy: Analysis Using the MIT IGSM Reilly et al. Aug. ‘04
115. Future U.S. Energy Security Concerns Deutch Sep. ‘04
116. Explaining Long-Run Changes in the Energy
Intensity of the U.S. Economy Sue Wing Sept. 2004
117. Modeling the Transport Sector: The Role of Existing
Fuel Taxes in Climate Policy Paltsev et al. November
2004
118. Effects of Air Pollution Control on Climate
Prinn et al. January 2005
119. Does Model Sensitivity to Changes in CO2 Provide a
Measure of Sensitivity to the Forcing of Different
Nature? Sokolov March 2005
120. What Should the Government Do To Encourage
Technical Change in the Energy Sector? Deutch May ‘05
121. Climate Change Taxes and Energy Efficiency in
Japan Kasahara et al. May 2005
122. A 3D Ocean-Seaice-Carbon Cycle Model and its
Coupling to a 2D Atmospheric Model: Uses in Climate
Change Studies Dutkiewicz et al. (revised) November 2005
123. Simulating the Spatial Distribution of Population
and Emissions to 2100 Asadoorian May 2005
124. MIT Integrated Global System Model (IGSM)
Version 2: Model Description and Baseline Evaluation
Sokolov et al. July 2005
125. The MIT Emissions Prediction and Policy Analysis
(EPPA) Model: Version 4 Paltsev et al. August 2005
126. Estimated PDFs of Climate System Properties
Including Natural and Anthropogenic Forcings
Forest et al. September 2005
127. An Analysis of the European Emission Trading
Scheme Reilly & Paltsev October 2005
128. Evaluating the Use of Ocean Models of Different
Complexity in Climate Change Studies
Sokolov et al. November 2005
129. Future Carbon Regulations and Current Investments
in Alternative Coal-Fired Power Plant Designs
Sekar et al. December 2005
130. Absolute vs. Intensity Limits for CO2 Emission
Control: Performance Under Uncertainty
Sue Wing et al. January 2006
131. The Economic Impacts of Climate Change: Evidence
from Agricultural Profits and Random Fluctuations in
Weather Deschenes & Greenstone January 2006
132. The Value of Emissions Trading Webster et al. Feb. 2006
133. Estimating Probability Distributions from Complex
Models with Bifurcations: The Case of Ocean
Circulation Collapse Webster et al. March 2006
134. Directed Technical Change and Climate Policy
Otto et al. April 2006
135. Modeling Climate Feedbacks to Energy Demand:
The Case of China Asadoorian et al. June 2006
136. Bringing Transportation into a Cap-and-Trade
Regime Ellerman, Jacoby & Zimmerman June 2006
137. Unemployment Effects of Climate Policy Babiker &
Eckaus July 2006
138. Energy Conservation in the United States:
Understanding its Role in Climate Policy Metcalf Aug. ‘06
139. Directed Technical Change and the Adoption of CO2
Abatement Technology: The Case of CO2 Capture and
Storage Otto & Reilly August 2006
140. The Allocation of European Union Allowances:
Lessons, Unifying Themes and General Principles
Buchner et al. October 2006
141. Over-Allocation or Abatement? A preliminary
analysis of the EU ETS based on the 2006 emissions
data
Ellerman & Buchner December 2006
142. Federal Tax Policy Towards Energy Metcalf Jan. 2007
143. Technical Change, Investment and Energy Intensity
Kratena March 2007
144. Heavier Crude, Changing Demand for Petroleum
Fuels, Regional Climate Policy, and the Location of
Upgrading Capacity Reilly et al. April 2007
145. Biomass Energy and Competition for Land
Reilly & Paltsev April 2007
146. Assessment of U.S. Cap-and-Trade Proposals
Paltsev et al. April 2007
147. A Global Land System Framework for Integrated
Climate-Change Assessments Schlosser et al. May 2007
148. Relative Roles of Climate Sensitivity and Forcing in
Defining the Ocean Circulation Response to Climate
Change Scott et al. May 2007
149. Global Economic Effects of Changes in Crops,
Pasture, and Forests due to Changing Climate, CO2
and Ozone Reilly et al. May 2007
150. U.S. GHG Cap-and-Trade Proposals: Application of a
Forward-Looking Computable General Equilibrium Model
Gurgel et al. June 2007
151. Consequences of Considering Carbon/Nitrogen
Interactions on the Feedbacks between Climate and
the Terrestrial Carbon Cycle Sokolov et al. June 2007
Contact the Joint Program Office to request a copy. The Report Series is distributed at no charge.
REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change
152. Energy Scenarios for East Asia: 2005-2025 Paltsev &
Reilly July 2007
153. Climate Change, Mortality, and Adaptation:
Evidence from Annual Fluctuations in Weather in the U.S.
Deschênes & Greenstone August 2007
154. Modeling the Prospects for Hydrogen Powered
Transportation Through 2100 Sandoval et al.
February 2008
155. Potential Land Use Implications of a Global Biofuels
Industry Gurgel et al. March 2008
156. Estimating the Economic Cost of Sea-Level Rise
Sugiyama et al. April 2008
157. Constraining Climate Model Parameters from
Observed 20th Century Changes Forest et al. April 2008
158. Analysis of the Coal Sector under Carbon
Constraints McFarland et al. April 2008
159. Impact of Sulfur and Carbonaceous Emissions from
International Shipping on Aerosol Distributions and
Direct Radiative Forcing Wang & Kim April 2008
160. Analysis of U.S. Greenhouse Gas Tax Proposals
Metcalf et al. April 2008
161. A Forward Looking Version of the MIT Emissions
Prediction and Policy Analysis (EPPA) Model
Babiker et al. May 2008
162. The European Carbon Market in Action: Lessons
from the first trading period Interim Report
Convery, Ellerman, & de Perthuis June 2008
163. The Influence on Climate Change of Differing
Scenarios for Future Development Analyzed Using
the MIT Integrated Global System Model Prinn et al.
September 2008
164. Marginal Abatement Costs and Marginal Welfare
Costs for Greenhouse Gas Emissions Reductions:
Results from the EPPA Model Holak et al. November
2008
165. Uncertainty in Greenhouse Emissions and Costs of
Atmospheric Stabilization Webster et al. November
2008
166. Sensitivity of Climate Change Projections to
Uncertainties in the Estimates of Observed Changes
in Deep-Ocean Heat Content Sokolov et al. November
2008
167. Sharing the Burden of GHG Reductions Jacoby et al.
November 2008
168. Unintended Environmental Consequences of a
Global Biofuels Program Melillo et al. January 2009
169. Probabilistic Forecast for 21st Century Climate
Based on Uncertainties in Emissions (without Policy)
and Climate Parameters Sokolov et al. January 2009
170. The EU’s Emissions Trading Scheme: A Proto-type
Global System? Ellerman February 2009
171. Designing a U.S. Market for CO2 Parsons et al.
February 2009
172. Prospects for Plug-in Hybrid Electric Vehicles in the
United States & Japan: A General Equilibrium Analysis
Karplus et al. April 2009
173. The Cost of Climate Policy in the United States
Paltsev et al. April 2009
174. A Semi-Empirical Representation of the Temporal
Variation of Total Greenhouse Gas Levels Expressed
as Equivalent Levels of Carbon Dioxide Huang et al.
June 2009
175. Potential Climatic Impacts and Reliability of Very
Large Scale Wind Farms Wang & Prinn June 2009
176. Biofuels, Climate Policy and the European Vehicle
Fleet Gitiaux et al. August 2009
177. Global Health and Economic Impacts of Future
Ozone Pollution Selin et al. August 2009
178. Measuring Welfare Loss Caused by Air Pollution in
Europe: A CGE Analysis Nam et al. August 2009
179. Assessing Evapotranspiration Estimates from the
Global Soil Wetness Project Phase 2 (GSWP-2)
Simulations Schlosser and Gao September 2009
180. Analysis of Climate Policy Targets under
Uncertainty Webster et al. September 2009
181. Development of a Fast and Detailed Model of
Urban-Scale Chemical and Physical Processing Cohen
& Prinn October 2009
182. Distributional Impacts of a U.S. Greenhouse Gas
Policy: A General Equilibrium Analysis of Carbon Pricing
Rausch et al. November 2009
183. Canada’s Bitumen Industry Under CO2 Constraints
Chan et al. January 2010
184. Will Border Carbon Adjustments Work? Winchester et
al. February 2010
185. Distributional Implications of Alternative U.S.
Greenhouse Gas Control Measures Rausch et al. June
2010
186. The Future of U.S. Natural Gas Production, Use, and
Trade Paltsev et al. June 2010
187. Combining a Renewable Portfolio Standard with a
Cap-and-Trade Policy: A General Equilibrium Analysis
Morris et al. July 2010
188. On the Correlation between Forcing and Climate
Sensitivity Sokolov August 2010
189. Modeling the Global Water Resource System in an
Integrated Assessment Modeling Framework: IGSMWRS Strzepek et al. September 2010
190. Climatology and Trends in the Forcing of the
Stratospheric Zonal-Mean Flow Monier and Weare
January 2011
191. Climatology and Trends in the Forcing of the
Stratospheric Ozone Transport Monier and Weare
January 2011
192. The Impact of Border Carbon Adjustments under
Alternative Producer Responses Winchester February
2011
193. What to Expect from Sectoral Trading: A U.S.-China
Example Gavard et al. February 2011
194. General Equilibrium, Electricity Generation
Technologies and the Cost of Carbon Abatement
Lanz and Rausch February 2011
195. A Method for Calculating Reference
Evapotranspiration on Daily Time Scales Farmer et al.
February 2011
Contact the Joint Program Office to request a copy. The Report Series is distributed at no charge.
REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change
196. Health Damages from Air Pollution in China Matus et
al. March 2011
197. The Prospects for Coal-to-Liquid Conversion: A
General Equilibrium Analysis Chen et al. May 2011
198. The Impact of Climate Policy on U.S. Aviation
Winchester et al. May 2011
Contact the Joint Program Office to request a copy. The Report Series is distributed at no charge.