ISSN 1880-8468
Technical Report ofInternational Development Engineering࿖㓙㐿⊒Ꮏቇႎ๔TRIDE-2010- 05

March 23, 2010

Shinya HANAOKA and
Puspita DIRGAHAYANIReview on the Applications of Bottom-up
Approach for Aviation Emission
Inventory and Carbon Calculator

Department of International Development Engineering,
Graduate School of Science and Engineering,Tokyo Institute of Technology
http://www.ide.titech.ac.jp/TR

Review on the Applications of Bottom-up Approach for Aviation Emission
Inventory and Carbon Calculator
Shinya HANAOKA* and Puspita DIRGAHAYANI***Associate Professor
Department of International Development Engineering, Tokyo Institute of Technology
2-12-1-I4-12, O-okayama, Meguro-ku, Tokyo 152-8550, Japan
Tel/Fax +81-3-5734-3468, E-mail: hanaoka@ide.titech.ac.jp** Researcher
Department of International Development Engineering, Tokyo Institute of Technology

1. Introduction
Civil aviation is one of the world’s fastest growing transport means (Kahn-Ribeiro et al., 2007).
International Civil Aviation Organization (ICAO)’s1 analysis shows that aviation scheduled traffic(revenue passenger-km, RPK) has grown at an average annual rate of 3.8% between 2001 and 2005
despite the downturn from the terrorist attacks and SARS (Severe Acute Respiratory Syndrome)during this period, and is currently growing at 5.9% per year. Thus, it is increasingly important toprovide accurate estimate the impact of aviation on climate change.
This report reviews the currently available methodologies for estimating aviation emissions. It
begins with the effects of aviation on climate change in Section 2, followed by the factors influencing
aircraft fuel consumption and emissions in Section 3. Section 4 provides detail descriptions about
tiered methodologies developed by IPCC. This report further categorizes those tiered methodologies
into top-down and bottom-up approach. The applications of bottom-up approaches for aviation
emission inventory at country or regional level are discussed in Section 5. Section 6 summarizes theapplications of bottom up approach for carbon calculator. Based on the previous sections, Section 7
reveals the uncertainty sources of all methodologies and improvements made by related studies. A
brief summary and recommendation of methods is offered in the final section.
2. Effects of Aviation on Climate Change
In 1999, Intergovernmental Panel on Climate Change (IPCC)2 released its report, Aviation and the
Global Atmosphere, conducted at the request of the ICAO. The report was the first comprehensive
assessment of aviation’s impacts on climate change using the climate metric Radiative Forcing (RF) 3.
IPCC estimates that aviation emissions currently account for about 2 percent of global humangenerated carbon-dioxide emissions. The 2 percent estimate includes emissions from all global
aviation, including both commercial and military. Global commercial aviation, including cargo,accounted for over 80 percent of this estimate (Forster et al., 2007).
Emissions from aviation come from combustion of jet fuel (jet kerosene and jet gasoline) andaviation gasoline4 (IPCC, 2006). Aircraft engine emissions are roughly composed of about 70% CO2,
a little less than 30% H2O and less than 1% each of NOx, CO, SOx, NMVOC, particulates and othertrace components including hazardous air pollutants. Little or no N2O emissions occur from modern
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
1

ICAO is a UN organization that aims to promote the establishment of international civilian aviation standards
and recommended practices and procedures.
2
To develop a better understanding of the effects of human-induced climate change and identify options foradaptation and mitigation, two United Nations organizations established IPCC in 1988 to assess scientific,
technical and socio-economic information on the effects of climate change. IPCC releases and periodically
updates estimates of future greenhouse gas emissions from human activities under different economic
development scenarios.
3
RF is used to gauge the effects of aviation on climate change. It expresses the perturbation or change to theenergy balance of the earth atmosphere system in watts per square meter (Wm-2). Positive values of RF imply a
net warming, while negative values imply cooling.
4
A fuel used only in a small piston engine aircraft, and which generally represents less than 1 percent of fuelused in aviation.

1	
  
	
  

gas turbine (IPCC, 1999). Methane (CH4) may be emitted by gas turbines during idle and by older
technology engines, but recent data suggest that little or no CH4 is emitted by modern engines.
Besides these emissions, aviation also yields formation of persistent linear contrails and aviationinduced cloudiness (AIC).
These emissions and cloud effects modify the chemical and particle microphysical properties of
the upper atmosphere, resulting in the changes of RF of earth’s climate change impacts, which canpotentially lead to climate change impacts and ultimately result in damage and welfare/ecosystem loss
(Lee et al, 2009a).
Many climate experiments found an approximately linear relationship between a change in global
mean RF and a change in global mean surface temperature (ΔTs), when the system reached a new
equilibrium, with some proportionally constant, i.e.
Equation 1 – Radiative Forcings

ΔTs = λRFwhere:λ	
   is the climate change parameter (K (W m-2)-1, the value of which has been found to be model specific but stable
across forcings.

€

The IPCC (1999) report concluded that aviation represents 3.5% of the total anthropogenic RF in
1992 (excluding AIC), which was projected to increase to 5% for a mid-range emissions scenario by
2050.
The RF effects of aviation were re-evaluated quantitatively by Sausen et al. (2005) for the year
2000, which resulted in a total RF og 47.8 mW m-2 (excluding AIC), which was not dissimilar to thatgiven by IPCC (1999) for 1992 traffic (48.5 mW m-2, excluding AIC), despite the increase in traffic
over the period 1992-2000.
3. Factors Influencing Aircraft Fuel Consumptions and Emissions
3.1 Energy Intensity Model
Lee et al. (2001) introduced the term Energy Intensity (EI) as a measure for the technological
performance of individual aircraft on aircraft fleet. EI expresses the energy consumption per available
seat-mile and depends on the physical determinants of aircraft operation and on consumer demand for
air travel. One basic model useful in describing the mechanics of a commercial aircraft in flight is the
Breguet range (R) equation.
Equation 2 – The Breguet Range Equation
L
) ⎡
⎤
W fuelDR=ln⎢1 +
⎥
g.SFC ⎢⎣ W payload + W structure + W reserve ⎥⎦V(	  In this equation, propulsion, aerodynamic and structural characteristics are represented by threeparameters: specific fuel consumption (SFC), lift-to-drag ratio (L/D) and structural weight (Wstructure).
€
Given the technologicalcharacteristics as well as other operability parameters, including the amountof payload (Wpayload) and fuel on board (Wfuel), the Breguet range equation can be used to determine
maximum range for a level, constant speed flight. Because SFC, L/D and speed (V) are assumed to beconstant during the flight, the take-off, climb and descent portions of flights are not well represented.
However, application of this equation is a useful predictor of fleet operation.
The Breguet range equation can be reorganized to obtain an equation for aircraft energy usage(EU) in terms of fuel burn or energy per available seat-km (ASK). In this formulation, the influence of
aircraft capacity is explicitly included. With further modification, EI can be expressed in terms of fuelburn or energy per RPK through inclusion of the load factor (fraction of seats filled), a measure ofcapacity utilization. The EI can be further modified to include the effects of other inefficiencies inutilization, such as ground and flight delays. When all of these effects are included, EI can be directly
2	
  
	
  

translated into aircraft emissions characteristics and can be used as a rough surrogate for technology
maturity and operational efficiency.
The ratio of aircraft operating empty weight (OEW) to maximum take-off weight (MTOW) is
used as a measure of structural efficiency. This is a measure of the weight of aircraft structure relative
to the weight it can carry (the structure itself + payload + fuel). The error in specification of Wstructureis estimated to be ±5% based on assessments made by Lee et al. (2001). Although aircraft structuralweights vary for the same type of aircraft depending on configuration modifications, a comparison
weight reported by some studies points to a small error in the specification of structural weight.
Reported Wfuel and Wpayload are assumed to be accurate within ±5%. Lee et al. (2001) assumed that a
40-min fuel reserve is required. Using this value, the error in fuel reserve, Wreserve is assumed
conservatively to ±10 min or 25% of the fuel reserve. In the performance of a flight, reserve fuel is an
extra weight, as it is typically not used during a flight.
Further variability in the specification of technology parameters derives from changes during aflight. For example, SFC and L/D are not constant during a flight and may deviate from cruise by asmuch as 50% at take-off. Furthermore, reported parameters are for new aircraft, and usage leads todegradation in engine and aircraft performance. Variations in weight due to fuel burn are accounted
for in the Breguet range equation.
Infrastructure characteristics also affect efficiency. In particular, delays on the ground and in the
air can increase EI. Extra fuel is burned on the ground during various non-flying operations, and hours
spent in the air (airborne hours) do not account for more than 0.75-0.9 of total operational hours of theaircraft (block hours). The ratio of airborne to block hours can be treated as a ground-time efficiency(ηg). Similarly, non-cruise portions of the flight, poor routing, and delays in the air constituteinefficiencies related to spending fuel during the flight beyond that which would be required for agreat circle trip at a constant cruise speed. This inefficiency can be measured by the ratio of minimum
flight hours to airborne hours (ηa). The minimum flight hours represents the shortest time required to
fly a certain stage length and reveals any extra flight time due to the non-ideal flight conditions. The
multiplication of ηg	
  and	  ηa	
  gives the flight time efficiency (ηft).
Equation 3 – The Breguet Range Equation as Predictor of Fleet Operation
EI =

EI =

€

Q ×Wf
# Seats × α × SL × η ft

≡

1
	
  
ηI

Q × W f g × SFC
1
1
, 	
  
×
×
, 	
  	
  	
   EU ≡ αE I ≡
⎛
⎞
W p€
L
η
Wf
ft
V( )
⎟⎟ × η ftln⎜⎜ 1 +D
Wi⎝ W p + Ws + Wr ⎠
EU =

Q ×Wf
Seats

×

€ 1
g × SFC	  ×⎛ L ⎞⎛⎞Wf
V ⎜ ⎟ ln⎜ 1 +⎜ W + W + W ⎟⎟ × η ft
⎝ D ⎠
p
s
r ⎠
⎝

Where EI is energy intensity (in kg-fuel/RPK or megajoules/RPK), EU is energy usage (in kg-fuel/ASK or
megajoules/ASK), ηI is€fuel efficiency (in RPK/kg-fuel or RPK/megajoule), ηU is fuel efficiency (in ASK/kg-fuelor ASK/megajoule), α is passenger load factor (RPK/ASK), Q is lower heating value of jet fuel, SL is stage
length as calculated using the range equation, Wf is fuel weight, Wi is weight of a passenger plus baggage (90.7kg), Wp is payload weight, Wr is reserve fuel weight, and Ws is structural weight.

	  The operational factors inherent in EI include aircraft usage and size characteristics. These arereflected in RPK and ASK data, respectively, as well as in operating hours, which is proportional tostage length, as all large commercial aircraft fly at approximately the same altitude and same Mach5
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
5

Speed of an object moving through air, or any fluid substance, divided by the speed of sound

3	
  
	
  

number. Influence coefficients reflecting the impact of technological and operational parameters on EI
are also derived.
By rearranging the Breguet range equation, Lee et al. (2001) modeled aircraft EI as in the
Equation 3. EU (megajoules/ASK) captures the efficiency of mechanical performance of aircraft
systems as measured by potential utility. EU is practically independent of load factor. This is becauseof the weak dependence of range on α due to changes in payload and structural weight. Over the rangeof load factors typical for current aircraft (0.75–0.9), EU is constant to within 3% and can thus beconsidered a reference value.
The 95% confidence interval (2σ) in EI due to uncertainties in the technology and operability
parameters is ±22.3%, based on the mean value of all the propagated errors for the 31 aircraft types intheir study. SFC and L/D have the largest impacts on the propagated error. Because the 2σ error
interval for the calculated EI is approximately ±30%, based on a curve fit to the actual EI calculated,
the propagated error of the technology and operability parameters account for about 74% of the total
variance in the calculated fuel efficiency values.
Utilizing a Taylor series expansion of the EI equation, technological and operational influences onaircraft fuel burn can be quantified. Overall, a 2.7% reduction in EI can be achieved by simultaneous
improvements in engine, aerodynamic, and structural efficiencies of 1%. Structural efficiency does
not have as strong influence as SFC or L/D. Improvement in EI due to 1% reduction in structuralweight varies between 0.7% for larger aircraft and 0.75% for smaller aircraft. Based on these
influence coefficients and the historical constancy of ft and OEW/MTOW, Lee et al. (2001) estimatedthat reductions in aircraft EI since 1959 can be attributed to improvements in SFC (57%) and L/D(22%), as well as to increased load factor (17%) and other changes, including seating capacity (4%).
Again, these characteristics are interdependent, and it is important to note that improvements in somecategories are achieved at the expense of improvements in others. If less fuel is carried as a reserve, EI
can also be reduced.
All aircraft body-engine combinations have almost the same fuel efficiency improvement
potential with respect to technology improvements. This is largely because aircraft of the types thisstudy considered have similar geometric configurations. Thus, for all types of commercial jet aircraft,
whether short-range or long-range, the emissions reduction potential due to technology advancement
is about the same.
3.2 Energy Usage Model: Efficiency of Aircraft Technological Parameters
Fuel burn for airplanes is a matter of constant detailed design, considering both the aerodynamics
of shape and lift and the engine efficiency (Swan and Adler, 2006). According to Babikian et al.
(2002)’s model, aircraft technology characteristics were described by three aircraft performance
metrics, which relate directly to the energy usage of aircraft in cruise flight according to the specificair range (SAR) equation. Engine efficiencies were quantified in terms of thrust specific fuelconsumption (TSFC), the thrust produced by the engine divided by the rate of fuel flow. Aerodynamic
efficiencies were assessed in terms of maximum lift over drag ratio (L/DMAX). Finally, structural
efficiency was evaluated using OEW divided by maximum take-off MTOW, a measure of structuralweight necessary to carry the structure itself, fuel and payload.
The technological parameters can be used to estimate the cruise values of EU (EU,CR). EU,CR can becalculated using SAR equation which is the basic model for describing the physics of aircraft insteady cruise flight, and it quantifies the distance flown per unit of energy consumed.
Aircraft operations-airport served, stage lengths flown, and flight altitudes-have a particularly
significant impact on the EU of regional aircraft. They fly shorter stage lengths than large aircraft, andas a result, spend more time at airports taxiing, idling, and maneuvering into gates, and in generalspend a greater fraction of their block time in non-optimum, non-cruise stages of flight. A useful
efficiency metric for evaluating the amounts of time aircraft spend on the ground compared to in the
air is the ratio of airborne hours to block hours (ηg). Aircraft that fly short stage lengths have lower ηgbecause of the need to taxi and maneuver more often for every unit of time spent in the air. They
therefore incur a fuel consumption penalty relative to longer-flying aircraft. Aircraft flying stage
lengths below 1000 km have EU values between 1.5 to 3 times higher than aircraft flying stage lengths
above 1000 km.
4	
  
	
  

Equation 4 –Energy Usage at Cruise Phase for Regional Jets
EU,CR =

EU,CR =€1
SAR JET × Capacity

TSFC × W⎛ L ⎞
Velocity × ⎜ ⎟ × Capacity⎝ D ⎠

where:€W = WFUEL+ WPAYLOAD+WSTRUCTURE+WRESERVE

Equation 5 – Energy Usage at Cruise Phase for Turboprop
EU,CR =

1
SARTURBOPROP × Capacity

EU,CR =€PSFC × W⎛ L ⎞η PR × ⎜ ⎟ × Capacity⎝ D ⎠

where:
W = WFUEL+ WPAYLOAD+WSTRUCTURE+WRESERVEη€PR = propeller efficiency

Regional jet aircraft of the US aviation system is found to spend a significant part of theirairborne hours climbing to or descending from cruise altitudes. During these stages of flight, theenergy usage of an aircraft is different than during the energy-intensive climb stage. The larger thefraction of airborne time an aircraft spends climbing, the longer it spends at high rates of fuelconsumption. This characteristic of short stage length flight contributes to the higher EU of regional
aircraft. It is worth noting that the airborne efficiency metric also captures the influence of other inflight inefficiencies, such as indirect routings, flight plan changes due to airway congestion and timespent performing holding patterns above congested airports. Each of these inefficiencies, in addition
to take-off and climb effects, increases the average energy usage above that incurred during cruise.
Babikian et al. (2002) concluded that regional aircraft have values of energy usage on the order of
1.5-2 times greater than larger aircraft. The difference in EU is not caused by significant differences intechnological sophistication, but rather by operational differences associated with the airborne
efficiency.
3.3 Impact of Aircraft Size on Fuel Efficiency
Across aircraft sizes, fuel consumption is almost linear in weight, which in turn is almost
proportional to seating capacity. Aircraft (measured in seats per service) can be divided into twogroups: the single-aisle and the twin-aisle aircraft, also known as narrow-body and wide-body aircraft,respectively (Givoni and Rietveld, 2009a). Other than larger seat capacity, the wide-body aircraft can
typically also fly further, probably the result of technological constraints but also market demand. The
narrow-body aircraft typically have maximum range of about 6000 km (e.g. B737-700; 6230 km,
A320:5700) while wide-body aircraft typically can fly more than 10,000 km (e.g. B777-300: 11,029
km, A330-200: 12,500). Another group of aircraft size, a subgroup of narrow-body aircraft, is theregional jets, with seat capacity of fewer than 100 seats.
The choice of aircraft size depends on a variety of factors related to market condition, including
competitive conditions related to regulation of markets, airport policies and cost parameters. Givoni
and Rietveld (2009a) assumed that the scope of changing aircraft size is limited on long-haul routes,
as at these distances only large aircraft can be used. On low-demand routes, between regional airports
or from regional to hub airport, there is also almost no scope for changing aircraft size and only small
capacity aircraft are usually considered. Thus, the choice of aircraft size is more relevant at the shorter
distances and high-demand routes, typically the hub-to-hub markets.
5	
  
	
  

Equation 6 – Specification of Givoni and Rietveld’s Model (2009a) in Estimating Choice ofAircraft Sizeln	
  Seat/Flight	
  =	
  β0	
  +	
  	
   β1	  ln	  Market	
  size	
  +	
  β2	
  ln	
  Dist	
  +	
  β3	
  ln	
  HH	
  +	
  β4	
  LCC	  +	  β5	  Europe	
  +	
  β6	
  North	  America	
  
+	
  β7	
  RWy1	
  +	
  β8	
  ln	
  Rwy2	
  +	
  β9	
  Hub1	
  +	
  β10	
  Hub2	
  +	
  β11	
  Slot1	
  +	
  β12	
  Slot2	
  	
  	
  	
  	
  	
  	
  	
  
where:
Seat/Flight: average number of seats per flightMarket size: route density, no of weakly seat (two-way)
Dist: great circle distance (km)HH: Herfindahl-Hirschman index (the sum of square airlines’ market share on the route)
LCC: dummy, at least one low cost carrier operates on the route
Europe: dummy, route is within Europe
N. America: dummy, route is within N. America
Rwy1: no. of runways (larger airport on the route)Rwy2: no. of runways (smaller airport on the route)
Hub1: dummy, one of the route airports is a hub (transfer passengers > 15%)
Hub 2: dummy, two of the route airports are hubs (transfer passengers > 15%)
Slot1: dummy, one of the route airports is slot coordinated (level 3)
Slot2: dummy, two of the route airports are slot coordinated (level 3)

Usually two seating configurations are considered. For short-haul flights these are 1-class (highdensity) and 2-class (standard) configurations, while for long-haul flights these are 2-class (highdensity) and 3-class (standard) configurations. On hub-to-hub routes, low-cost carriers do not usuallyoperate and standard 2-class configuration is most common (Givoni and Rietveld, 2009b).
It is found that regression analysis of over 500 routes in the US, Europe and Asia provides
empirical evidence that the choice of the aircraft size depends on market size with an elasticity of 0.35
resulted from the model in equation 6, indicating that in the airline industry carriers give priority toincreases in frequency. Another result is that aircraft size increases with distance, a natural result of
the trade-off between cost of loading/unloading, and cost of flying. Further, the presence of low cost
carriers leads to somewhat larger aircraft (Giovani and Rietveld, 2009a).
Morrell (2009) also attempted to examine the relationship between fuel/emission efficiency andaircraft size or capacity, including both passenger and cargo. It is evaluated for both short/mediumhaul and long-haul operations. This study assumed that single-aisle aircraft represents short/mediumhaul routes while twin-aisle for long-haul operations.
Fuel efficiency was computed by dividing fuel burn by the typical number of two-class seats.
These are reported in Flight International’s Annual Aircraft Directories, from information provided bymanufacturers in standard configurations. On short/medium-haul routes the number of business classseats is generally varied by moving a curtain or divider along the cabin.
For short/medium-haul flights, fuel burn per seat was calculated for seven of the most commonlyused short/medium-haul jet aircraft for a 90 min sector. It was found that fuel efficiency is positively
related to aircraft seating capacity, and for every 1% increase in seat capacity a 0.83% reduction infuel might be obtained.
In the case of long-haul flights, a 6-h sector was taken and a typical three-class seating was used6
together with the available space for cargo in the lower deck compartment. Numbers of seats weretaken from the standard configurations provided by the manufacturers in the Flight Directory, andthese may differ appreciably from those used by particular airlines.
There are two approaches that could be used to incorporate lower deck cargo. First, by relating
fuel burn to passenger and cargo payload carried (tone-kms); and, second, by estimating the fuelrequired to carry the lower deck cargo, subtracting it from fuel burn and diving by passenger andbaggage payload. The first is used by Morrell (2009), since it is simpler and the concern is not the fair
allocation of emissions between passenger and cargo.
Seats were converted to tones using 80 kg per seat (passengers and carry-on bags bur excluding
checked baggage), while a typical cargo density of 167 kg per cubic meter was applied to the hold
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
6

Seat pitches of around 60 inches for first class, 38-40 inches for business class and 31-31 inches for economy.

6	
  
	
  

volume to give a realistic payload. Lower deck cargo is the difference between this payload and 24 kg
multiplied by the full passenger complement (based on realistic network carrier checked baggage
weights).
Yet, for these aircraft, there was no discernable relationship between fuel efficiency andpassenger and cargo capacity. However, if the double-deckers (B747s and A380) were removed fromthe sample, there was a reasonably good correlation of fuel efficiency against payload, but with asmaller coefficient (0.4) than for single-aisle aircraft. Furthermore, leaving out the very large aircraft
resulted in a 0.65% improvement in fuel efficiency for every 1% increase in maximum payload forboth single- and twin-aisle aircraft.
It should be noted that Morrell (2009)’s analysis has not taken into account the extra range thatmany of the long-haul types can operate over. By taking a 6-h sector, none of them has reached thepoint where extra range is traded against payload and cargo capacity is reduced. These are hit by adouble penalty: the loss of cargo payload and for flights of over around 4000 km an increase in fuelare required to carry the extra fuel and the structures needed to accommodate the fuel.
4. Tiered Methodology for Estimating Emissions from Aviation
IPCC (2006) provides a three-tiered methodology in the “Greenhouse Gas Inventory Reference
Manual” as a framework for estimating and reporting the emissions from aviation particularly CO2,CH4 and N2O. The first-tier “Tier 1” is the simplest methodology, based only on an aggregate number
for fuel consumption to be multiplied with average emission factors.
Equation 7 – Tier 1 Algorithm

E p = AR fc × EFp
where:Ep = annual emission of pollutant for each of the LTO and cruise phases of domestic and international flights
ARfc = activity rate by fuel consumption for each of the flight phases and trip types
EFp = emission factor of pollutant€for the respective flight phase and trip type	
  Equation 8 – Tier 2 Algorithm

E p = ∑ AR fc,at × EFp,at
atwhere:Ep =annual emission of pollutant for each of the LTO and cruise phases of domestic and international flights
ARfc,at = activity rate by fuel consumption for each of the flight phases and trip types, for each aircraft typeEFp,at = emission factor of pollutant€ for the respective flight phase and trip type, for each aircraft type	  The second “Tier 2” methodology estimates emissions in two-flying phases: the landing and takeoff (LTO)7 and cruise phases8. Fuel burn is higher in the LTO phase than cruise phase as the aircraft
engines are working harder. As the aircraft reaches fuel cruise altitude the engines can work less hard
and also less fuel is burnt at higher altitudes due to the thinner atmosphere.

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
7

All activities near the airport that take place under the altitude of 914 meters (3000 feet) including taxi-in andout, climbing and descending (IPCC, 1997).
8
Defined as all activities that take place at altitudes above 914 meters (3000 feet), no upper limit is given.

7	
  
	
  

Figure 1 – Standard Flying Cycles
Source: EEA, 2009

In Tier 1 and 2, the fuel sold is assumed to be equal to the fuel used. Moreover, the fuel used in
the cruise phase is estimated as a residual: total fuel use (sold) minus fuel used in the LTO phase of
the flight. Fuel use is estimated for domestic and international aviation separately. Tier 2 method ispreferable instead of Tier 1 if the LTO data are available for individual aircraft. It should be notedthat the accuracy of these two methodologies rely heavily on the quality of fuel statistics or fuelconsumption data. It is important to account for all fuel used for aviation in the country. The methods
are based on total fuel use, and should completely cover CO2 emissions. However, the allocation
between LTO and cruise will not be complete for Tier 2 method if the LTO statistics are not complete.
Also, Tier 2 method focuses on passenger and freight carrying scheduled and charter flight, and thus
not all aviation. On the other hand, if the fuel statistics is not reliable, bottom-up approach or Tier 3
will provide much higher accuracy.
The higher tier accommodates the fact that emissions depend on the number and type of aircraft
operations, the types and efficiency of the aircraft engines the fuel used; the length of flight; thepower setting; the time spent at each stage of flight; and to a lesser degree, the altitude at which theexhaust gases are emitted. Accordingly, “Tier 3” goes further in details by using movement data for
individual flights including, at a minimum, information on the origin and destination, aircraft type,and data of individual flights. Hence, these methodologies are bottom-up, flight-based, rather than
top-down calculation-based on the fuel consumed as in Tier 1 and 2. EEA (2009) distinguishes Tier 3
method into two categories: Tier 3A which takes into account cruise emissions for different flight
distances and Tier 3B in which the calculation of fuel burnt and emissions throughout the fulltrajectory of each flight segment using aircraft- and engine-specific aerodynamic performance
information.
Regardless the method, the completeness and accuracy of activity data collected on domestic
aviation separately from international aviation affected the level of uncertainty of the estimates.
Generally, all flights departing and arriving in the same country are defined as domestic flights, while
for journeys departing from one country and arriving in another country are international flights.
However, energy statistics used in Tier 1 often do not accurately distinguish between domestic andinternational fuel use or between individual source categories. Based on past experiences compilingaviation emission inventories, IPCC (2006) suggests that difficulties have been identified regarding
the international/domestic split, particularly in obtaining the information on passenger and freight
drop-off and pick-up stops in the same country. If national energy statistics do not already provide
data consistent with the definition made by IPCC (2006), countries are urged to estimate the split
between domestic and international fuel consumption, using the approaches set below:
1. Top-down data can be obtained from taxation authorities in cases where fuel sold for domestic use
is subject to taxation, but that for international use is not taxed.
8	
  
	
  

2. Bottom-up data can be obtained from surveys of airlines companies for fuel used on domestic andinternational flights, or estimates from aircraft movement data and standard table of fuelconsumed or both. For examples: statistical offices or transport ministries as part of national
statistics, airport records, air traffic control records, air carrier schedules, etc.

	  Figure 2 – Tier 2A and Tier 2B Methodology (Modified from EEA, 2009)
The choice of methodology depends on the type of fuel, the data available and the relative
importance of aircraft emissions. IPCC (2006) suggested that it is a good practice to first identify key
categories in a national inventory in order to choose the proper methodology to use. A key category is
one that is prioritized within the national inventory system because its estimate has a significant
influence on a country’s total inventory of greenhouse gases in terms of the absolute level, the trend
or the uncertainty in emissions and removals. Given the current limited knowledge of CH4 and N2Oemission factors, more detailed methods will not significantly reduce uncertainties.
However, if an emission is considered as a key category and the use of a higher tier would
improve the estimate or the split between international/domestic flights, a country is encouraged todevelop method for collecting data for Tier 2 or Tier 3 method. Methodological approaches for
identifying this key category are provided in details by IPCC (2006).
Lee et al. (2005) pointed out that the reasons why higher tier methodologies should be undertaken
are that they give the possibility to obtain time series reflecting changes in technology, verify theestimates, report emissions from cruise and LTO separately, and provide more accurate NOx estimates.
Tier 2 is used in spite of tier 3 if LTO and aircraft type data are available but no information on cruise
distance.

9	
  
	
  

5. Application of Bottom-up Approach for Aviation Emission Inventory
As indicated above, Tier 3 methodology requires more detailed data and adopts bottom-up
disaggregated approach to estimate emissions accurately reflecting actual air transport activity.
Activity data is collected to identify the actual flight movement data including trajectory of each flightand time-in-mode for LTO phases.
5.1 Tier 3A
In Tier 3A, cruise phase, fuel use and emissions are estimated using great circle distance-theshortest distance ignoring the extra distance due to airspace and navigational constraints- between two
airports. The aircraft and flight details can be obtained from Civil Aviation records, airport records,
ATC such EUROCONTROL9, or the Official Airline Guide (OAG) timetable. This will identify theaircraft that were used in the inventory period, the number of LTOs for each and the mission distanceflown.
The emission factors for Tier 3A methodology are listed in spreadsheets available from the
EMEP/EEA Guidebook website (EEA, 2009) containing fuel consumption, emission (kg) andemission indices (g/kg fuel) of NOx, HC and CO for the different phases of flights (LTO, taxi out,
take-off, climb-out, climb/cruise/descent, approach landing, and taxi in) of different distances in nm
ranging from 125 nm to 2,000 nm (1 nm equals to 1.852 km). The data is available for a set ofrepresentative aircraft types, particularly for IFR-flights. As for non-IFR flights, a range of emission
factors is shown in MEET for piston-engined aircraft, helicopters and military flights (1997) and also
ANCAT, British Aerospace/Airbus (for military aircraft). Nonetheless, at present it is not possible torecommend the default emission factors due to limited information available (IPCC, 2006).
By referring to the spreadsheets, an estimate of emissions and fuel used during LTO phase can beobtained based on the associated representative aircraft and the distance that is actually being flown.
The total quality of fuel used for the mission is the sum of fuel used for LTO plus the fuel used in all
operations above 3,000 ft (914 m) or cruise phase. By referring again to the table of pollutants (NOx,
CO and HC) emitted versus mission distance, an estimate of cruise phase emissions will be acquired.
The earliest inventories applying “bottom-up approach” in which an aircraft movement database
was compiled, as reviewed in Henderson et al. (1999), include the NASA, ANCAT and DLR 3-D.
Furthermore, in these inventories, aircraft/engine combinations in operation were identified (todiffering levels of details) and calculations of fuel burned and emissions along great-circle paths
between cities were made. Flight operation data were calculated as the number of departures for eachcity pair by aircraft and engine type-which, combined with performance and emissions data, gave fuelburned and emissions by altitude along each route. Different approaches were taken for constructing
underlying movement databases especially for NASA inventory, while the DLR 3-D inventory used
ANCAT/EC2 civil movement database. The summarized differences are provided in detail by
Henderson et al. (1999). The rest of this section focuses on ANCAT approach since it is continuously
developing.
5.1.1 ANCAT/EC2
The essential components of ANCAT/EC2 inventory include: an aircraft movement database; arepresentation of the global fleet in terms of aircraft and engines; a fuel-flow model; calculation ofemissions at altitude from fuel flow; and LTO data (Lee et al., 2002).
The global aircraft movement database was compiled from a mixture of air traffic control (ATC)and scheduled data (where ATC data were unavailable). ATC data were obtained from 38 nonEuropean countries and Europe (from EUROCONTROL which covers 15 member states). A notable
exception was the United States, for which only scheduled data were available. These data werefactored up by 10% to compensate for this problem.
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
9

On behalf of participating states, EUROCONTROL receives and stores detailed traffic information of all
flights operated entirely or in part in the European Civil Aviation Conference (ECAC) area in its Pan-European
Repository of Information Supporting Management of EATM (PRISME) data warehouse. The database consistsof: (i) information from the (last) filed flight plan; (ii) aircraft type; (iii) airport of departure/airport ofdestination (city pair); (iv) the ICAO designator for aircraft operating agency followed by the flight
identification.

10	
  
	
  

Sixteen civil jet aircraft types were selected based upon size and performance/technology level torepresent the world’s fleet of narrow and wide-bodied aircraft for short, medium and long-hauloperations. Even with these representative aircraft, this would imply almost 100 different
aircraft/engine combinations. Since such combinations could not be accurately assigned to particularroutes or airlines, a further simplifying assumption was made. A list of engines fitted to these aircraft
was obtained from current databases and generic fuel flow characteristics were applied, weighted forengine populations.
The use of energy and, therefore, emissions, depends on the aircraft operations and the time spentat each stage. ICAO databank specified engine power settings and times-in-mode for the LTO-cycle
based on manufacturers’ submitted Certification data. In this model, a correction of the ‘standard’assumed ICAO taxi time was being reduced from 26 to 14 minutes since the former ICAO assumption
reflected the worst case, while the current taxi times were generally much shorter.
Since fuel flow data are often proprietary, they were modeled using PIANO (Project Interactive
Analysis and Optimization)10 (Lee et al., 2002). PIANO was used for the representative aircraft andgeneric engines to generate fuel profiles covering the flight cycle (excepting the LTO), including steps
in cruise.
5.1.2 FAST Model
Another application is the FAST Model (Future Aviation Scenario Tool) which was originallydeveloped for the UK Department of Trade and Industry (DTI) and was subsequently used in theEuropean Fifth Framework Project TRADEOFF (Lee et al., 2005; Sausen et al., 2005; Gauss et al.,2006). The basic FAST model was designed around the methodology employed for the ANCAT/EC
1&2 inventories based upon a dataset of aircraft movements for some years which indicates thefrequency of flights of specific aircraft between city pairs. From this database, the aircraft types weregrouped, with representative aircraft types assigned. PIANO aircraft performance model was also
used. To up- date the representative aircraft data, it was decided to use this draft list from the Expert
Group. However, some modifications were made since not all types listed in the Expert Group List
were available in the PIANO aircraft performance model (Lee et al., 2005).
In ANCAT/EC2 inventory, aircraft performance was being simulated at optimal cruise altitudes
for their mission distance. These optimized flights were then globally redistributed, with respect toaltitude, according to a limited dataset from one airline. For the TRADEOFF 2000 inventory, a morerefined technique was developed in FAST model, as follows. Preliminary movement data from
AERO2k project, using approximately 53,000 flights (actually a combination of EUROCONTROL
and FAA data, explained further in section 4.2), were analyzed for the limited list of ANCAT/EC2
representative types to determine whether there was a relationship between aircraft type, mission
distance and average altitude flown (Fichter et al., 2005; Gauss et al., 2006). It was found that thisrelationship provided a satisfactory and robust means of specifying average maximum cruise altitudes
by aircraft type and mission distance. The consequence of not doing this and simply allowing an
optimization of the mission profile underestimated the amount of fuel used and misrepresented thevertical distribution of traffic, and therefore emissions.
Thus, this parameterization was adopted for the FAST model with some further analysis for the
new representative aircrafts. The maximum flight altitudes for the representative aircraft types by
mission distance were determined by identifying the maximum flight level for each flight in thesample month (in this case, December). The average maximum flight level was calculated for each of
the representative aircraft types and groups of distance increments of 500 km. The next real flightlevels (e.g. 290, 310, 330) to this average value was then used in the PIANO model.
5.1.3 ANCAT 3
EUROCONTROL further developed a more complex model, the ANCAT3, which calculates fuelconsumption and emissions in LTO and cruise phase based on 19 generic aircraft types representing
the world’s passenger jet fleet (Graichen, 2007). This method is identical to the “detailed”
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
10
	
  A sophisticated aircraft performance model widely used in the aviation industry,
http://www.lissys.demon.co.uk and has been extensively used in other inventory-type work including
FAST/TRADEOFF and AERO2k.

11	
  
	
  

methodology of CORINAIR/ EMEP (EEA, 2006). Calculations are performed for individual flights.
A mapping table indicates the generic aircraft type which should be used for aircrafts not included in
the model. Calculations of fuel burn and emissions are performed separately for LTO and cruise to
take different operating conditions between the two phases better into account. The cruise length isdetermined using great circle distances between airports and adding a factor to adjust the difference
between real route and the optional theoretical route.
There are several reasons why theoretical fuel burn calculated using ANCAT3 might differ fromreal fuel burn (Graichen, 2007):• Load factors: the take-off weight of an aircraft has a high influence on total fuel burn for a trip.
The take-off weight depends on the actual number of passengers, their luggage and any cargo thatmight be transported as well. Fuel burn values in ANCAT3 use typical load factors which might
not fully reflect reality.
• Route flown: the implementation of ANCAT3 at EUROCONTROL uses typical flight routes
based on the great circle distance between two airports. Due to atmospheric conditions, available
air space, and airport congestion, the actual routes might be longer or shorter than the typicaldistances used in the model.
• Aircraft types: to reduce the complexity of the data requirements, only a limited number ofdifferent generic aircrafts are included in the model. In reality, fuel burn does not only depend on
the aircraft type but also on the engine which might be different between two aircrafts of the sametype.
• Aircraft performance: the age of an aircraft, maintenance standards, and the actual operation all
have an influence on fuel consumption. Again, these factors are only included as approximate
values through average fuel consumption rates.
5.1.4 UK SERAS
Through The South East and East of England Regional Air Services Study (SERAS) (DfT, 2003),
UK also applied Tier 3A and produced estimates based on the assumptions that aircraft use “great
circle” route. In addition, the SERAS estimates were based on the assumption that UK’s share ofinternational flights is one-half of the total traffic. The model used fuel burn data for representative
aircraft ‘types’ for domestic, short-haul and long-haul services.
In SERAS, passenger and freight aircraft movement forecasts were split by six seat band classes(less than 70 seats, 71-150 seats, 151-250 seats, 251-350 seats, 351-500 seats, and more than 500
seats) Representative aircraft are chosen for each seat band class and aggregated them into 15
destination regions (10 international and 5 domestic).
SERAS carried out a cautious approach in recalculating the fuel burn data to reflect differences inaverage aircraft age and engine technology by including only known aircraft types and performancedata. More disaggregated fuel burn data was multiplied by ATM data by destination and aircraft size
to give forecasts of total aviation fuel usage. Aircraft fuel usage in tones is multiplied by 3.1511 to giveCO2 emissions. Surface access related CO2 emissions were calculated by multiplying total vehicle km
by an average emission rate of 147 grams per km.
5.1.5 UK NETCEN
Still for UK’s GHG inventory, the other example of Tier 3A application is the improved model of
National Environment Technology Center (NETCEN) (Watterson et al., 2004). The previous
NETCEN model was similar to IPCC Tier 2 (CORINAIR ‘Simple’) in that it uses fleet-averaged
emission factors based on fuel uplifted at all UK airports for the non-LTO flight stages but moredetailed information for the LTO cycle. The improved NETCEN method includes emissions per LTO
cycle based on detailed airport studies and engine-specific emission factors from the ICAO database.
In cases where different engines might be used on a single type of aircraft, a weighted average of theemissions from the different engines was used.

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
11

More precisely 3.157 which is a constant representing the number of tones of CO2 produced by burning a toneof aviation fuel (ICAO, 2009)

12	
  
	
  

Equation 9 – NETCEN Method on Treatment of Time-in-mode during LTO phase

E LTOa,m, p,s = N s × Ta,m,s × Fa,s ( t a,m,s ) × Ia, p,s ( t a,m,s )where:
is the emissions in modeof pollutant

for a specific aircraft typeat airport type(kg)

is the airport type€
is the modeis the pollutant
is the specific aircraft type
is the number of engines on aircraft type
is the time in mode

for a specific aircraft typeat airport type

is the weighted average fuel flow for an engine on aircraft type(s)

at airport type

for thrust

(kgs-

1

)
is the weighted average emission factor of pollutant p for an engine on aircraft type
for thrust

at airport type(kg / kg fuel)is the engine thrust setting during mode

for aircraft typeat airport type(%)

For the cruise phase, fuel use and emissions are estimated using distances (based on great circles)
traveled from each airport for a set of representative aircraft. Since aircraft do not fly the shortest
distances, NETCEN study increased the distance travelled between airports by 9.5% for all years, yet
this requires further review.
Equation 10 – NETCEN Method for Calculating Emission during Cruise Phase applying Linear
Regression

E Cruised,g, p = mg, p × d + Cg, pwhere:
is the emissions in cruise of pollutant

for generic aircraft typeand flight distance

(kg)

€

is the flight distanceis the generic aircraft type
is the pollutant (or fuel consumption)is the slope of regression for generic aircraft type
is the intercept of regression for generic aircraft type

and pollutant
and pollutant(kg / km)(kg)

Emissions from additional sources (such as aircraft auxiliary power units) are also included.
Furthermore, in order to avoid double counting of cruise emissions, the entire cruise emissions havebeen associated with the departure airport.
SERAS’ estimates (for international aviation in 2000 were about 25% lower than (26.1 Mt of
CO2) the estimates resulted from the improved NETCEN methodology (32.2 Mt of CO2) (Pejovic etal., 2008). The likeliest reason for the smaller estimate is that the modeling assumed that all aircraft
fly great circle distances.
5.2 Tier 3B
Tier 3B relies on detailed database consists of 4-D flight trajectories (latitude, longitude, altitude,
and time) enabling calculation of more accurate fuel consumption and emissions along the fulltrajectory. To use Tier 3B, sophisticated computer models are involved to address all equipment,
performance and trajectory variables and calculations for all flights in a given year. Thus, models used
13	
  
	
  

for Tier 3B can generally specify output in terms of aircraft, engine, airport, region, global totals, aswell as by latitude, longitude, altitude and time, for fuel burn and emissions of CO, HC, CO2, H2O,
NOx, and SOx.
5.2.1 AERO2k and SAGE: Comparison
To date, there are two well-recognized model applying Tier 3B methodology (IPCC, 2006), theAERO2k (Eyers et al., 2004) developed by the European Commission (EC) and System for Assessing
Aviation’s Global Emissions (SAGE) by United States Federal Aviation Administration (FAA) (Kimet al., 2007).
For the activity data, AERO2k has taken the best available civil and military flight information forthe year 2002. For civil aviation, this included radar tracked flight data from North America andEurope showing actual latitude, longitude, and altitude along the flight path. Routing information wasused to place timetabled flight from the rest of the world onto global grid. The collection of data for2002 was spread over six representative periods of one week, the six weeks chosen to take account ofdiurnal, weekly and seasonal variation in air traffic. Data for the rest of the world were extracted fromthe BACK Aviation commercial database and added to the chosen representative weeks.
The collection of data required the collaboration of aviation authorities in order to gather as manymeasured flight trajectory data as possible. Schedule data from the Back Aviation database does notinclude any flight trajectory information and it was necessary to complete this data with flight routeand aircraft performance information when available. For the days of the year for which no data werecollected, flight data were derived from inventories realized for the data collection periods while
trends were obtained from Back Aviation scheduled flights database.
Instead of using a portion (e.g. a day, week, month, etc.) of the world flight schedules and flightplans as used in AERO2k or other past studies, SAGE aimed to include all commercial flights
worldwide using non-proprietary databases mainly the Enhanced Traffic Management System(ETMS) which provides radar data and reported flight plans and the OAG which only provides
planned schedules of flights by commercial airlines. Approximately 45% of ETMS flights (or about
22.5% of all global flights modeled in SAGE) include radar data covering North America, parts ofwestern Europe and South America. Besides the two, SAGE also includes various supporting data.
The current worldwide coverage in SAGE includes approximately 30 million commercial flights per
year which allow SAGE to be used to model flights for all years from 2000 to 2006.
Due to the use of OAG flight schedules for areas outside of ETMS coverage, unscheduled andcancelled flights cannot be directly modeled. Instead, their effects are indirectly accounted for throughthe use of scaling factors that generally increase the number of flights. The factors are a function of
the scheduled OAG flights (operations) at an airport, and were developed based on a comparison
analysis of ETMS and OAG flights.
As part of the movements, delays are modeled in SAGE through a sub-model called
WWLMINET. It is a worldwide version of LMINET, a queuing model developed by NASA thatpredicts hourly airport ground and approach airborne delays. WWLMINET starts with a flightdemand that is propagated through a network of queues. The delays associated with serving thatdemand level are determined. The WWLMINET network currently contains 102 US airports, 122
European airports, and 33 other airports outside US and Europe. Together, these 275 airports
represent approximately 75% of global commercial air traffic as defined by OAG schedules. Airports
not included in this network are assumed to have no delays.
AERO2k uses 40 representative aircraft types which were selected representing 300 different
types or variants within the world’s fleet. AERO2k applies three main steps in the selection process:(i) grouping aircraft by seat capacity, engine technology, maximum take-off weight (MTOW) andconfiguration; (ii) determination of the numbers of aircraft within a particular seatcapacity/technology category; and (iii) availability of suitable performance data.
Generic emissions characteristics were developed by weighting the emission index for NOx forthe number of engines in the fleet’s population, for each of the four thrust settings of the LTO cycle.
In the next step, the generic emission characteristics were compared with real engine data and theclosest fit determined by a polynomial least-square regression. The final choice of engine was notdetermined solely using this method; one additional criterion was used in the selection process. Once
all the representative aircraft had been assigned an engine using the above method, the selected
14	
  
	
  

engines were compared for similarity of emissions characteristics. Where close similarities were
found, for instance within families of engines from one manufacturer, the most representative engine
was chosen. In this way the number of engines was consolidated.
Instead of using generic aircraft types and engines as the starting point, SAGE does not carry out
this step but intends to preserve as much of the specificity of each flight as possible and, accordingly,
resulting in inclusion of over 200 different aircraft types. In SAGE, the aircraft type for each flight isidentified through the use of aircraft codes specified in ETMS and OAG flight plans and schedules,
respectively. These codes are mapped to the aircraft listings within the SAGE performance databases.
It is estimated that about 90% of flown distances modeled in SAGE reflect good to perfect aircraftmapping, while the remaining are substitutions, such as Ilyushin IL86 with an Airbus A343 (A343
and A341 are some examples of a good map).
The engine type is assigned based on one of three methods. The first and preferred method (14%)
is through an exact assignment by identifying the tail number of the aircraft from Bureau ofTransportation Statistics (BTS) Airline On-time Performance Data through matching of flight ID
numbers and aircraft type. Once this is accomplished, the tail number is matched to the one in BACK
Aviation’s world fleet database and the exact engine is assigned. The BACK world fleet database
contains a listing of worldwide commercial flight built since 1940 and provides various aircraftspecific information including tail numbers, engine types, weight, size, seating and airline. Since the
BTS data cover only the top 10 US airlines, the second method is to assign engines based on
popularity within the world fleet (77%). The BACK world fleet database is used to develop
distributions of engine counts based on airline and aircraft categories as provided in the BACK
database. The third method involves the use of default engines for each specific aircraft type (9%).
In AERO2k, fuel used for each flight was calculated using performance data from PIANO aircraft
performance tool. By employing the latest publicly available information on emission factors,
emissions were calculated based on aircraft height, weight, speed, throughout the flight. New
information on non-volatile particulate emissions has been added to provide a first gridded estimate ofparticulate emissions from civil aviation. Extensive validations have also been performed to ensure
the integrity of the data processing and output.
In case of SAGE, aircraft performance is modeled dynamically using a combination of the data
and methodologies found in the FAA’s Integrated Noise Model (INM) and BADA version 3.6. The
reason is largely because they are publicly available, and this are in accordance with FAA’s intent tokeep all SAGE methods and data sources non-proprietary. BADA provides aircraft performance datafor cruise (speed schedule, fuel flow), while INM provides performance for landing and takeoff(LTO) modes. Atmospheric information pertinent to flight performance, such as temperature andpressure, are based on the International Standard Atmosphere (ISA), where temperature and pressure
at sea-level are defined as 288.15 K (59 degree F) and 101325 Pa (1 atm), respectively. Relative
humidity and the specific heat ratio are assumed to be constant at 60% and 1.4, respectively.
These two models leave many rooms for improvements. Specifically for AERO2k, to improve theestimate, the model still require to increase the number of week data, the number of aircraft models
and engine combinations, improve detail of LTO assumptions such as reduced throttle settings or idle
settings, and incorporate additional tail number and fleet data to provide granularity of fleet make up(Eyers et al., 2004). Uncertainties also occur in SAGE model such as not correcting for winds aloft,
uncertain aerodynamic and engine performance, and simplified assumption about aircraft take-off
weight and flight speed (Kim et al., 2007). Additional uncertainties associated with the use ofcurrently available trajectory inventories and aircraft performance data remain important to becontinuously improved.
5.2.2 Combination of RAMS Plus and AEMIII Model
More recently, Pejovic et al. (2008) attempted to improve SERAS and NETCEN’s Tier 3A
method by using the RAMS simulation which allows actual flight paths to be modeled. RAMS Plus
simulates the four dimensional profile of each flight as a series of events. A detailed flight profile isobtained describing the operation of the aircraft at each of these events. This allows the fuel burn foreach element of the journey to be calculated.
RAMS Plus simulations were conducted using traffic, route network, and sector data provided by
National Air Traffic Services in UK (NATS). A 24-h air traffic sample for Friday, 3rd September 2004
15	
  
	
  

was used with 7,074 flights, each with specified departure and destination airports and simulation
entry times. To permit calculation of the total fuel burn for the traffic, fuel burn rates from theperformance tables of the EUROCONTROL Experimental Centre (EEC) BADA Revision 3.6 wereincorporated into RAMS Plus. Flight speed and rate of climb/descent were also defined according to
the BADA performance tables. This dataset has detailed coverage, describing 91 aircraft types
directly and a further 204 by equivalence to a directly specified type. Each aircraft in the trafficsample was allocated to one of these 91 performance groups. In the few instances where the aircraft
type specified in the traffic sample could not directly related to one of the 204 types supported by
BADA, the aircraft was substituted with an aircraft type with similar operating characteristics (size,
range, optimum cruise speed, and altitude).
Table 1 – Uncertainties Sources of RAMS Plus and AEMIII Model
Uncertainty Sources

BADA aircraft performance data
provides climb rates and fuel burnconsumption for three aircraft mass
scenarios (low, nominal, and high) for
each aircraft type without take off mass
data in the traffic sample.

Details••••
The calculation of flight trajectories
outside UK airspace due to detailedroutes are defined only within UK
airspace•••
Extrapolation of a 1-day sample togenerate an annual estimate•

Assumed that all aircraft are at nominal mass which is likely tounderestimate CO2 emissions where route length is close to the
maximum range for the aircraft type.
The simulator does not calculate reductions in aircraft mass due
to fuel burn while in flight.
BADA aircraft types are each defined for a selection of given
airframe-engine combinations; data on the exact type of eachaircraft in the traffic sample is not available
Assumed that international (EU and non-EU) departures follow
a great circle route from the edge of UK airspace to thedestinations leading to underestimate cruise distance.
The daily CO2 estimates are adjusted to correct underestimationof the fuel burn by assuming an additional 4.9% for EU
departures and 6% for international departures. This could berectified using additional data, however, this type of data is notreadily available.
The date of the traffic sample is not severely affected by severe
weather or other operational difficulties, which would
significantly increase the number of diverted landings.
Comparison with the CAA statistics leads to a conclusion thatthe 1-day traffic sample may overestimate air transport
movements by 10% for calculation of the monthly (September)mean emission values, with this overestimation largest for nonEU international routes and in part as a consequence of reduced
traffic on weekends.
The assumption that the distribution of routes and aircraft types
does not vary throughout the year affects the calculated value.
Instead, seasonal variation in route length and disaggregating
the CO2 emissions into domestic, EU and other internationaltraffic are used to improve the estimate further.

Source: Pejovic et al., 2008

The configuration of RAMS Plus with the UK air traffic sample has two limitations. Firstly, as
detailed data on airport configurations and ground movements was not available, it cannot be used to
calculate emissions for this phase of the flight. Secondly, the available air traffic sample only allowedemissions within UK airspace to be calculated. Since one of the objectives of the study was toestimate UK CO2 emissions which can be allocated to the UK CO2 budget for all domestic andinternational traffic departing from UK airports, it was necessary to perform additional calculations
using the Advanced Emission Model (AEM) tool.
16	
  
	
  

Pejovic et al. (2008) utilized AEMIII which is a stand-alone system used to estimate aviation
emissions (CO2, H2O, SOx, NOx, HC, CO, Benzene, VOC, TOG) and fuel burn. It is able to analyze
flight profile data, on a flight-by-flight base, for air traffic scenarios of almost any scope (from local
studies around airports to global emissions from air traffic) (Jelinek et al., 2004). The model uses
flight profile information (in this technique refers to RAMS Plus) to calculate information about fuelconsumption and emission produced. It uses the ICAO Engine Exhaust Emissions Data Bank
(05/2003), the EUROCONTROL Base of Aircraft Data (BADA v3.5) and an improved version (EECBM2) of the Boeing Method2 (BM2) in order to produce the emission estimations for all phases offlight. It is used here for its capability to simulate segments of the flight trajectory outside of the air
traffic simulation zone.

Table 2 – Summary of Application Bottom-up Approach for Aviation Emission Global
Inventory
Atmospheric/Operational Conditions

Emission Data

ANCAT/
EC2• ATC data of 38 nonEuropean countries;• EUROCONTROL (15member states);• US scheduled flight data(factored up 10%)• Sixteen civil jet
aircraft types ofalmost 100 different
aircraft/engine
combinations• Fuel flow
(excepting LTO)
modeled using
PIANO

Simulated at optimalcruise altitude, according
to a limited dataset fromone airline• ICAO databankspecified engine
power settings
and times-inmode for the
LTO-cycle• ‘Standard’assumed ICAO
taxi time wasbeing reduced
from 26 to 14
minutes

FAST(FutureAviation
Scenario
Tool)

Designed around themethodology of ANCAT/EC
1&2 inventories based upon adataset of aircraft movements
for some year which indicatesthe frequency of flights ofspecific aircraft between city
pairs

• ANCAT/EC2
aircraft database• PIANO aircraft
performance model,
updated through theExpert Group List

Maximum flight altitudes
for the representative
aircraft types by mission
distance based on
preliminary movement
data from AERO2k project

ANCAT/EC 2

ANCAT 3

• EUROCONTROL data (allflights entering air space of
29 member countries 2003 –2006• Flights operate partiallyoutside EUROCONTROL
only flight plans are available

19 generic aircraft
types representing theworld’s passenger jet
fleet

n/a

• ICAO (LTO
cycle)• CORINAIR/EMEP• Representative
aircraft ‘types’ fordomestic, shorthaul and long-haul
services.
• Split by six seatband classes• Recalculating the fuelburn data based on
average aircraft age andengine technology byincluding only known
aircraft types andperformance data.
• Average load, flight
distance and flight
altitude assumptions• Aircraft fuelusage in tones ismultiplied by
3.15 to give CO2
emissions.
• Surface access
CO2 emissions:multiplying total
vehicle km
multiplied by
EFs of 147
g/km.

Model

Trajectory

Aircraft
Performance

Tier 3A: Generic Aircraft Type & Origin and Destination

UK SERAS

15 destination regions (10international and 5 domestic).

17	
  
	
  

ModelUK
Improved
NETCEN

Trajectory• Detailed activity data by
CAA (airport, aircraft type,intl/domestic, sector length of
GCD)• To avoid double counting of
cruise emissions, allassociated with departure
airport.

Aircraft
Performance

CAA specifies
aircraft type but notengine types. Thus, aweighted average ofemissions fromdifferent engines wasused.

Atmospheric/Operational Conditions

Emission Datan/a

• ICAO databank(LTO)• Emissions fromaircraft auxiliary
power units(APU) are also
included• Cruise phase:
EMEP/CORINA
IR (GCD
factored up by
9.5%)

Tier 3B: Actual Route Flown & Detailed Aircraft Information• Radar-tracked flight data
• Atmospheric conditions
showing actual longitude,• Generated in
not consideredlatitude and altitude along
Emission• No data on wind
the flight path
• 40 representative
Parameterizati• Take-off weight is• Scheduled flight data fromaircraftson Module
calculated from the
BACK Aviation database
representing 300
varying with
GCD plus allowances
AERO2k• All airports are at sea level
different types ofMach number,1
for diversion and delay
and not affecting time tothrottle setting
fleets• Payload mass 60.9%
climb to cruise altitude andand altitude• PIANO(ICAO standard)
overall aircraft performance;
performance tool
• ICAO• Flight speed which gives
the horizontal distanceemission
99% of the max specifictravelled during LTO cycle
databank
air range (SAR)
is not accounted for.
• More than 200• ISA temperature,
types of aircraftpressure and density No
Boeing Fuel Flow
• Radar-based flight trajectory
includedhead or tail wind
Method 2
and speed data for ETMS
• BADA• SAE AIR2 1845 take-off (BFFM2) applied
flights
SAGE
aerodynamic
weight based on stage
to ICAO EI• Artificial flight trajectory
performance data
lengthdatabase for NOx,and constant cruise speed for• BADA fuel flow
• SAE AIR 1845 take-off
HC, and CO
OAG flights
model
and landing procedure
emissions• FAA INM
• Full power take-off• Traffic, route network, and• 91 aircraft types
sector data (UK NATS)
directly and a
RAMS Plus• ICAO (LTO)• A 24-h air traffic sample
further 204 byand• BADA Revisionused with 7,074 flights
equivalence to a
EUROCON
3.5• The AEM completes the
directly specified
TROL AEM• For above 3,000
whole flight profile by
type.
n/a
III3 (inft: an improved
adding LTO legs from
• BADA (altitude
Pejovic etversion (EECdeparture and arrival airports
and attitudeal., 2008;
BM2) of theand linking to the first anddependent
Jelinek, etBoeing Method2
last known position of theperformance andal., 2004)(BM2)
aircraft according to thefuel burn data offlight file from RAMS
>150 aircraft types)
Note:
1
Reserve allowances for long-haul flights includes an additional 5% of the fuel required to complete the mission, plus
fuel for a 200nm diversion and 30 minute low altitude hold; for short-haul 5% allowance to complete the mission plus
fuel for 100nm diversion and a 45 minute low altitude hold (Eyers et.al, 2004)
2
Society of Automotive Engineers Aerospace Information Report 1845 provides engine and aerodynamic
performance equations and coefficients for standard LTO procedures cover over 9,000 unique aircraft/engine
combinations including all engine types in the ICAO data bank (Kim et al, 2007)
3
Since the configuration of RAMS Plus with UK air traffic sample has some limitations, it was necessary to perform
additional calculations using AEM tool (Pejovic et al., 2008)

18	
  
	
  

Normally, emissions estimations should begin at the flight’s pushback time, which is the moment
when an aircraft departs from its gate. This time was not available for their scenario. For some flights,
the start-of-take-off time was provided and for some the profile began at some altitude above theground. However, using the AEM tool it was possible to adjust flights’ entry times and to complete
the flight profile by adding the missing legs. AEMIII includes a database for taxi times for about
3,000 airports. The AEM completes the whole flight profile by adding LTO legs from departure andarrival airports and linking to the first and last known position of the aircraft according to the flight
file from RAMS. Timings for the taxi-in and taxi-out phases of the flight are airport-dependent andare predefined in the AEM model.
The air traffic sample available had detailed information only on flights within UK airspace. Forinternational departures, the AEM completes the flight profile assuming that the aircraft uses theshortest (great circle) distance between the point of departure from UK airspace and the destination
airport. On average, journeys are about 10% longer than this great circle route because of airspaceconstraints and meteorological factors (IPCC, 1999).
One of the benefits of this estimation technique is that it allows analysts to further disaggregate
the sample to identify CO2 contributions from those flights and aircraft that are the largest emitters.
One additional analysis by aircraft group (based on size) revealed some interesting observations.
Aircraft group categories are based on RAMS Plus default allocations for aircraft size and consist of
five categories: Heavy (e.g. B747/777 and A330/340), Light Medium (e.g. A319/320, B727/737 andFokker70/100), Light (e.g. Beech, Cessna or Embraer aircraft and LJ45), Small (e.g. Dash8,
Embraer145, and Fokker50), and Ultra Medium (e.g. B757 and DC8). CO2 emitted per flight-km ofeach aircraft group was estimated as a function of distance for short-haul flights (total distance lessthan 2,000 km), medium-haul flights (total distance between 2,000 and 5,000 km) and long-haul
flights (greater than 5,000 km).
The result shows that figures from the AERO2k model for the year 2002 are well within the rangeof this estimate. The SAGE model produced slightly larger estimates for 2004 for international flights,
which can likely be explained by the more detailed flight profiles used for airspace outside the UK.
The underestimation of the distance in the cruise phase of the flight (and hence fuel burn and CO2) forinternational flight, however, is still less than 4% compared with the SAGE estimate and less than 2%
when total CO2 estimates are compared.
6. Application of Bottom-up Approach for Configuration- and Distance-based Carbon
Calculator
Carbon calculators are used by Governments for international emissions reporting, for businesses’
declarations of corporate social responsibility, and also by individuals wishing to reduce their own
environmental impact (Miyoshi and Mason, 2009; Jardine, 2009).
6.1 Tier 2A: DEFRA Method
UK DEFRA developed their own emissions calculator methodology to promote consistency byusing data and factors across Government departments. In terms of aviation, DEFRA studied average
emission level of air transport and presented emission factors for domestic and international flights on
several routes using default average factors for CO2 emissions and average flight distances and loadfactors.
A number of criticisms in the assumptions used in the calculation of the emission factors releasedin July 2007 have been raised by the aviation industry. Following the feedbacks, the factors wererevised (DEFRA, 2009). The new average factors have been calculated in the same basic
methodology, using the aircraft specific fuel consumption/emission factors from EMEP/CORINAIR
Atmospheric Emissions Inventory Guidebook (2006). The principal changes to the calculation
methodology, data and assumptions include:a. A significantly wider variety of representative aircraft have been used to calculate emission
factors for domestic, short- and long-haul flights.
b. Average seating capacities, load factors, and proportions of passenger km by the different aircraft
types have all been calculated from CAA (Civil Aviation Authority) statistics for UK registeredairlines for the year 2006 (the latest available complete dataset).
19	
  
	
  

c. Short-haul flights average load factor changed from 65% to 81%. The revised figure is theaverage for all European international flights calculated from CAA statistics for the selectedaircraft. This figure may be compared to an average of 79.7% for all international flights fromDfT Transport Statistics for 2006.
d. Long-haul flights average and load factor has been changed from 79.7% to 78%. The revised
figure is the average for all non-European international flights calculated from CAA statistics forthe selected aircraft.
e. Freight transported on passenger services has also been taken into account. Accounting for freightmakes a significant difference to long-haul emission factors.
f. An uplift of 10% to correct underestimation of emissions by the CORINAIR methodology
compared to real-world fuel consumption.
g. Since the CAA data show that almost all freight carried by passenger aircraft is done on scheduled
long-haul flights, freight load is treated in one of 2 ways under DEFRA methodology. First,
emissions are allocated in the proportions of the respective weights of passengers and freight,
giving a freight load of 28.8% for long haul and less than 1% for short-haul. A second variant
takes into account the additional weight necessary for passenger services (seats, galley, etc) andallocates a lower percentage to freight (11.9% for long haul). The final average CO2 emission
factors for all freight for 2009 GHG conversion factors for domestic, short- and long-haul flights
are 1.92 kgCO2/tkm, 1.40 kgCO2/tkm, and 0.59 kgCO2/tkm, respectively.

Table 3 – DEFRA Seating Class-based Emission Factors for Passenger Flights for 2009
Flight Type

Load
Factor(%)

gCO2/ pkm

Number of
Economy Seats% of avg
gCO2/ pkm

% of Total
Seats

Domestic flights (Average)
65.2
171.0
1.00
100
100
Short-haul flights (Average)
80.9
98.3
1.05
100
100
Economy class
80.9
93.6
1.00
95
90
First/business class
80.9
140.5
1.50
143
10
Long-haul flights (Average)
77.8
112.2
1.37
100
100
Economy class
77.8
81.9
1.00
73
80
Economy+ class
77.8
131.1
1.60
117
5
Business class
77.8
237.5
2.90
212
10
First class
77.8
327.6
4.00
292
5
Note:
*
Include the uplift of 10% to correct underestimation of emissions by the CORINAIR methodology and do notinclude the 9% uplift for Great Circle Distance, which needs to be applied separately.
**
Besides average factors, the emission factors are also derived based on the seating class. A review was carriedout on the seating configuration from a selection of 16 major airlines and average seating configuration
information from Boeing and Airbus websites. 24 different aircraft variants including those from the Boeing 737,
747, 757, 767 and 777 families, and the Airbus A319/A320, A330, and A340 families were considered.
Source: DEFRA, 2009

6.2 Tier 2B: Miyoshi and Mason Method
Miyoshi and Mason (2008) attempted to adopt a disaggregated (bottom-up) approach in order todevelop more accurate calculated reflecting actual air transport activity. Their study aims todemonstrate current emission levels by route, stage length, aircraft type used, number of seats
supplied on each aircraft and the distance flown on each route. It focuses on three air transport
market: the UK domestic routes, the intra-EU routes serving UK and the North Atlantic routes.
Fundamentally, their approach follows the acknowledged methodologies based on revised 1996
IPCC Guidelines.
1. Aircraft type, cruise altitude and sector distance are used to calculate the fuel consumption andtherefore emissions during the LTO cycle and cruising stage on each route. Emissions during
LTO cycle, by aircraft type, are obtained from IPCC (1997) guidelines and the Emission
Inventory Guidebook 2006. Subsequently, fuel consumptions during cruise stage are calculatedusing performance tables from BADA Revision 3.4 and 3.6 (EEA data were used for the aircraft
mission from the BADA dataset).
20	
  
	
  

2. These calculations are based on the most frequently used cruise altitude for each route. On shorter
sectors lower cruise altitudes are generally used and here the fuel efficiency of the aircraft is lessthan if a higher altitude is used.
3. To allow for the climb from leaving exiting the Take-off phase (at 3,000 ft) to the operated cruise
altitude (perhaps 30,000 ft), the model adds 10-15 minutes to the cruise time depending on thelength of the sector and ultimate cruise altitude.
4. Traffic data for 2006 (1626 routes and 59 aircraft types) on UK domestic routes and the intra-EU
routes serving UK airports was obtained by the UK Civil Aviation Authority. The GCD is used
for sector distances following NASA’s 1994 methodology.
5. The estimate is made based on the assumptions of typical seat configuration (the number of seats
supplied) and 75% load factor across a range of sector length for different aircraft.
6. The emission levels per sector can be segmented into groups based on the type of airline
operations: network carriers from outside EU and European Environment Agency (EEA)
countries, charter airlines, network carriers in EU, low cost carriers and regional airlines. It is
assumed that low cost carriers tend to operate new fuel-efficient aircraft, have high seating
density and report exceedingly high load factors. Together this means passengers on low cost
carriers tend to have relatively low carbon emissions on a gram per passenger kilometer basis
compared with passengers on network or regional carriers.
6.3 Tier 3A: ICAO Method
ICAO methodology (2009) employs a distance-based approach to estimate an individual’s
aviation emission using data currently available on a range of aircraft types. Table 4 shows the steps
carried out and data used in this methodology.
6.4 Tier 3B: Sabre Holdings Method
Jardine (2009) introduces a new carbon calculator methodology developed by Sabre Holdings.
Sabre® is a computer reservations systems (GDS) used by airlines, railways, hotels, travel agents andother travel companies. The Sabre database contains information about all flights including the data oftravel, airline, departure point and destination, as well as technical details about the plane used for theflight (model and seating configuration). Further, the model used SAGE model to give modeled fuelburn for a large number of aircraft types (more than 200, see section 5.2 for details). Thus, it is notnecessary to assume a ‘typical’ plane for the flight. Instead, the characteristics of the actual plane canbe modeled. Another data source is the Passenger Name Record (PNR) containing information about
the individual flights and is utilized for booking flights for passengers. The PNR contains information
about the point of origin, destination, airline, plane type used and can access seating configuration.
The latter two parameters, used in conjunction with the SAGE model provide accurate CO2 emissions
calculations on a flight-by-flight basis.
As in ICAO carbon calculator, based on the departure point and destination provided by the PNR,
Sabre model calculates the distance between them by a simple GCD calculation from known attitude
and longitude coordinates. However, no factor is applied since the extra fuel burn for stacking anddeviation from GCD is accounted for in SAGE. Using the PNR details on the plane type used for theflight, the Sabre Holdings model developed fuel burn formulas as a function of distance for each plane
type.
Afterwards, the fuel burn per seat is calculated. First, emissions related to cargo are removed,
based on data from US Form 41 traffic data. Second, fuel burn is allocated per seat. The model
contains seating configuration data disaggregated by airline and plane model, based on data held in
the Sabre Holdings reservation system. This allows more accurate representation of the efficiency of aparticular airline.
Finally, CO2 emissions per seat can be calculated by multiplying by an emissions factor of 3.157
kg CO2/kg Fuel, the same as ICAO’s. It should be noted that this is for CO2 only and does not include
a multiplier for the additional climate impacts of emissions at altitude.

21	
  
	
  

Table 4 – ICAO Carbon Calculator Methodology

Steps

Data

Descriptions

User input

Origin and Destination Airports• Only includes individual routings for single flightnumbers with multiple stops• For connecting flights with different flight numbers,
user can choose to calculate each journey legs
separately and adding them up

Trip Distance

ICAO Location Indicators
contains longitude and latitude forthe airports

Calculates the Great Circle Distance based on thosecoordinates and corrected by a factor:• < 550 km  + 50 km• Between 550 and 5500 km  + 100 km• > 5500 km  + 125 km

Traffic Data

Assigned passenger load factor

Based on 17 international route groups plus 5 domestic
areas

Aircraft Mapping

Aircraft fuel consumption
database EMEP/CORINAIR

If not in the database, the aircraft is mapped into one of
the 50 equivalent aircraft types available in the database•

Fuel Burn Data

Economy Class(Y) Seat Capacity

Extrapolated from Emission
Inventory Guidebook of
EMEP/CORINAIR• Tier 3A method•

Cabin floor plans from, the “Manual on Airplane Characteristics for Airport Planning”•

CO2 per Economy
Passenger

CO2 per pax = 3.157 * (total fuel* pax-to-freightfactor)/(number of
y-seats * pax loadfactor)•••Cabin Class

The factors considered include: load factor, flight
distance, the proportion of overall payload bypassenger traffic, cabin class flown, type ofequivalent aircraft.
The amount of fuel used on a route is the weighted
average of total fuel burnt based on the frequenciesof the scheduled aircraft types flown.

Depending on user selection••

Pax-to-freight factor is the ratio calculated fromICAO statistical database based on no. of
passengers and the tonnage of mail and freight,
transported in a given routeAn average passenger mass with baggage isassumed as 100 kg, plus a 50 kg add-on to accountof the onboard equipment (e.g. seats, toilets,
galleys and crew)
3.157 is constant representing the no. of tones of
CO2 produced by burning a tone of aviation fuel.
A multiplicative cabin class factor is applied toadjust the CO2 per Y-passenger, on those routes
where multiple class passenger services areavailable
The cabin class correction factor is used only onequivalent aircraft types that support suchdifferentiation, and on flights > 3,000 km
Employs simplified approach by using two cabinclass factors (“economy” and “premium”) withratio 1:2

Source: ICAO, 2009

22	
  
	
  

Table 5 –Passenger- and Distance-based Aviation Carbon Emission Calculator
Tier 2A

Tier 2B

Tier 3A

DEFRA

Miyoshi and Mason

ICAO

Traffic Data

UK CAA

UK domestic and theintra-EU routes serving
UK airports (UK CAA)

17 internationalroute groups plus 5
domestic areas

The Passenger
Name Record(PNR)

Distance Flown

10%

Around 10%

Up to 11% (basedon distance flown
range)

Accounted for in
FAA/SAGE

Aircraft Mapping

Indicative short,medium, long haulcalculated fromrange of typicalaircraft

59 aircraft types

Based onscheduled aircraft
mapped onto 50
equivalent aircraft
types

Scheduledaircraft mapped
onto >200
equivalentaircraft types(SAGE
database)

Fuel Burn Data

EMEP/CORINAIR• ICAO, BADA
• Cruise: mostfrequently used
altitude per route• Adds 10-15 min to thecruise time for theclimb

EMEP/CORINAIR

FAA/SAGE

Freight Factor<1% domestic andshort-haul; 28.8%
long-hauln/a47-88% dependingon route andwide/narrow body;
34 classes

20% wide body;
10% narrow
body; 1%
regional jets

Per seat/passenger

Passenger

Passenger

Passenger

Seat

Load Factor

65.2% domestic;
80.9 short-haul;
77.8% long-haul

75%; real load factor for
long-haul international
flights (AEA, 2004)

Assigned passenger
load factor

n/aSeating Configuration

Representativefrom CAA data
(2006)

Typical seatconfiguration

Economy-class(Cabin floor plans)Specific toairline andaircraft model

Cabin class adjustment(economy:premium)Range of ratios fordifferent seatclasses indomestic, shortand long-hauln/a

1:2 based on space
allocation

Multiplier for non-CO2
pollutants

No

No

No

Parameter

Tier 3B
Sabre
Holdings

1:1.1 narrow
body; 1:1.5 wide
body; based
upon relative
weightNo, but may be
applied to ax
term

23	
  
	
  

7. Uncertainty Sources and Improvements Made by Current Methodologies
This report focused on bottom-up approach or Tier 3 application for two purposes: global
emission inventory and carbon calculator. In the following subsection, the uncertainty sources of thisapproach along with its evolutions are discussed.
7.1 Aircraft Mapping
As mentioned previously, Tier 3 approach considers the type of aircraft used in an individual
flight. Due to information limitation, these individual flights are often represented by several set ofgeneric aircraft types based on engine technology, seating capacity, configuration, MTOW, etc. The
classification is further consulted with the availability of suitable performance data. It should be noted
though that the actual aircraft performance: the age of an aircraft, maintenance standards, and theactual operation all have an influence on fuel consumption. Again, these factors are only included as
approximate values through average fuel consumption rates. Even in Tier 3A, the potential evolutionof engine mix for a given aircraft type is ignored.
Tier 3B’s AERO2k model attempted to improve the representativeness of generic emission
characteristics by weighting the emission index for NOx for the number of engines in the fleet’s
population for each of the four thrust settings of the LTO cycle. Further, the generic emission
characteristics were compared with real engine data and closest fit determined by a polynomial leastsquare regression. In the improved NETCEN which adopted Tier 3A method, where different engines
might be used in a single type of aircraft, a weighted average of the emissions from different engine
was used. It also considered the airport type in specifying aircraft performance.
On the contrary, Tier 3B’s SAGE model skipped the step and included 200 different aircraft type
instead. It used BACK world fleet database which recorded all aircraft type produced since 1940. The
database provides tail numbers, engine types, weight, size, seating and airlines. As can be seen, withthe evolving data and methodologies through Tier 3B models, the activity data’s accuracy andcompleteness are increasingly improved.
7.2 Emission Factors
EEA (2009) suggests that it is a key priority to update the fuel consumption and emission factors
in order to better reflect the emission performance of today’s aircraft in use both for LTO and cruise.
7.2.1

LTO
It is also recognized that ICAO’s thrust settings and time-in-mode for the LTO cycle, may notreflect the actual operational time-in-mode vary from airport to airport depending on traffic,
environment consideration, aircraft types and topographical conditions. ICAO estimates that theuncertainties of the different LTO factors are approximately 5-10%, while for cruise stage, it isassumed to be 15-40%.
7.2.2

CruiseEmission factors for cruise operations are characterized by a high degree of uncertainty(Romano et al., 1997; IPCC, 2006; EEA, 2009). First, current methods usually use great circle
distance and add it with an adjustment factor to reflect the actual route flown (about 10%). Somestudies pointed out that it may be the reason of smaller estimates. Additionally, cruise altitude isusually assumed to be constant (the optimal altitude), although recent study applied frequent cruise
altitude used for each route differ by distance.
According to Babikian, et al.’s (2002), aircraft that fly short stage lengths have lower ratio of
airborne hours to block hours because of the need to taxi and maneuver more often for every unit oftime spent in the air. The time for taxiing and maneuvering will also vary for different airport
condition. They therefore incur a fuel consumption penalty relative to longer-flying aircraft. Aircraft
flying stage lengths below 1000 km have EU values between 1.5 to 3 times higher than aircraft flying
stage lengths above 1000 km. Thus, taking into account the actual altitude and distance flown will
likely improve the accuracy of estimate since lower cruise altitude equals to higher fuel consumption
rate and hence also the emissions and also the rate of production of NOx.

24	
  
	
  

Table 6 – Input Data, Assumptions, Prerequisites and Applications of Current Methodologies
INPUT DATAMETHOD

Activity

Technology
Stratification

ASSUMPTIONS

Average EFs: the default
for CO2, CH4, N2O andNOx (Emission Factor
Database, IPCC)•

PREREQUISITESAPPLICATIONSTOP-DOWN APPROACH
Tier 1:
Pure fuelconsumption
based

•
•

Aggregate quantity of fuelconsumption data for aviation
Split into domestic andinternational flights•

10% of the fuel is used for
the LTO phase of the flight
Methane EFs are averaged
over all flying phases••

Aviation gasoline aircrafts
Jet-fueled aviation whenaircraft operational use data
are not available

EFs are suggested for an
old and an average fleet byrepresentative aircraft
CH4 are assumed to be zerounless new information
becomes available•

For jet fuel use in jet aircraft
engines
Recommended to distinguishinternational flights into short(<1000 nm)1 and long(>1000 nm)

BOTTOM-UP APPROACH
Tier 2A:
Generic
Aircraft
LTO•

Tier 2B:
Aircraftspecific
LTO•

Tier 3A:
Generic
AircraftType &
Origin and
Destination

•

•

•
•

•

•

Fuel sales sub-divided into
domestic and international use,
as for Tier 1
Total LTO numbers fordomestic and international

Use average fleet mix(i.e. Generic aircraftEFs) and average factors
for LTO and cruise

Fuel sales sub-divided into
domestic and international use,
as for Tier 1
LTO numbers for domestic andinternational per aircraft typeUse aircraft specific
LTO EFs and average
EFs for cruise2

Should include all aircraft types
frequently used for domestic
and international aviation

IFR Flights: Emissions
per flight distance
EMEP/ CORINAIR(EEA, 2009)3•

Includes IFR and non-IFR
flights
Details on origin (departure)and destination (arrival)
airports and aircraft typeFor military/pleasure aircraft:
no. of hours flight or fuel used
can be activity indicator

Non-IFR Flights:
At present no default
EFs is being
recommended, but rangeof EFs are shown in
MEET (1997)

•

•

•

Takes into account cruise
emissions for different
flight distances
Average fuel consumption
and emission data (LTO
and cruise) for an array ofrepresentative aircrafts
The use of energy referring
to engine power settings
and times-in-mode for eachstage of the LTO cycle
specified by ICAO.

•

If LTOs per aircraft typeavailable but no information on
cruise distances•••

If OD of flights and air
movements data available
The total estimated fuel use
for domestic aviation must becompared to sales statistics
or direct reports from theairline companies.
For pollutants not given in
the spreadsheet, EEA (2009)
recommends using Tier 2
approach based on theestimate fuel use calculatedusing the Tier 3A approach.

CO2 Calculator:
DEFRA

CO2 Calculator:
Mayoshi and Mason

Global Inventory:• ANCAT/EC2• FAST• ANCAT3• UK SERAS• UK Improved
NETCEN
CO2 Calculator:
ICAO Carbon
Calculator

25	
  
	
  

INPUT DATAMETHOD

Activity

Technology
Stratification•
Tier 3B:
Full Flight
Trajectory& Detailed
Aircraft Info

Radar tracked trajectory data
showing longitude, latitude, altitude
and time throughout the whole
phase of flight, in addition to basic
data similar to Tier 3A•

Aircraft and engine
specific
aerodynamic
performance
information
EFs contained
within the models
necessary to employ
the methodology

ASSUMPTIONSPREREQUISITES•

Calculation of fuel burnt andemissions throughout the fulltrajectory of each flightsegment•

Sophisticated computermodels are required toaddress all the equipment,
performance and trajectories
variables and calculations for
all flights in a given year
Must based on input data thattake into account air-trafficchanges, aircraft equipmentchanges or any input-variable
scenario

APPLICATIONS

Global Inventory:• AERO2k• SAGE• RAMS Plus
• AEM III
CO2 Calculator:
Sabre Holdings Model

Note:
1

Where nm = nautical miles, 1 nm = 1,852 km.
Emission factors are based on the specific national aircraft fleet and typical airport TIM (International Civil Aviation Organization Engine Exhaust Emissions Databank).
Another possibility is US EPA (1985): Compilation of air pollutant emission factors, Vol. II: Mobil sources, 4th Edition, or US Office of Environment and Energy (1991) FAA
Aircraft Emission Database User’s Manual.
3
Emission factors for the Tier 3 methodology are available from the EMEP/EEA Guidebook website (www.eea.europa.eu/emep-eea-guidebook)
Source: IPCC (2006); EEA (2009)
2

26	
  
	
  

The inclusion of 4D radar-tracked flight activity data into the estimation procedure of Tier 3B as
in AERO2k and SAGE is one significant way to improve the accuracy. SAGE also adjusts take-off
weight to account for fuel tinkering (the practice of purchasing fuel in regions with lower prices), bysystematically increased flight weights by two stage lengths. Such uncertain factors are not modeledin AERO2k. However, AERO2k model also takes into consideration aircraft in-flight weight changes
which in turn result in variations in fuel burn and emissions (Eyers et al., 2004; Kim et al., 2007).
7.3 Operational Efficiencies for Carbon Calculator Methodology
In the case of developing a carbon calculator that accurately reflects the actual air transport
activity, a more cautious approach should be carried out in detailing the operational arrangements.
One factor is the aircraft size used which does not depend only on the aircraft model but also its
configuration (number and class of seats, distribution between seating and cargo capacity). Thisconfiguration gives significant influence on fuel burner per passenger-km, differs among airlines andis usually based on market consideration.
Givoni and Rietveld’s (2009a) findings show that the choice of the aircraft size depends onmarket size, indicating that in the airline industry, carriers give priority to increases in frequency.
Another result is that aircraft size increases with distance, a natural result of the trade-off between costof loading/unloading, and cost of flying. Furthermore, the presence of low cost carriers leads tosomewhat larger aircraft with higher seating density and relatively newer and fuel-efficient aircraft(Mayoshi and Mason, 2009; Morrell, 2009). Morrell (2009) suggests that there is a strong linear
relationship between fuel efficiency and size for modern commercial aircraft, with a higher coefficientfor single-aisle than twin-aisle aircraft. Cautious approach on such trend will improve the assumed
average load factor accuracy which influences an aircraft take-off weight and hence fuel consumption.
Accordingly, recent carbon calculators such as Tier 3A’s ICAO and Tier 3B’s Sabre Holdings
model use more rigorous approach in assigning load factor, seat configuration, cabin classadjustments, and cargo factor to each individual flight.
8. Summary and Recommendation
IPCC developed tiered methodology to estimate aviation emissions aiming for harmonious reports
for worldwide countries. Using tier 1, emissions are estimated purely based on fuel consumption
which is assumed to be equivalent with the amount of fuel sold. On the other hand, through thehigher tier methodology -Tier 2 and Tier 3 - more specificity is taken into account to some extent.
Whilst Tier 2 is based on the number of LTOs and fuel use, Tier 3 use more extensive aircraft typeand more detailed movement data which, at minimum, consists of information on the origin anddestination, aircraft type, and date of individual flights.
This report categorized Tier 2 and Tier 3 methodology into bottom-up approach since they take
into account at least the aircraft type to capture the individual flight conditions. It reviews theapplications of bottom-up approach in estimating aviation emission for two objectives. First is theapplication for composing emission inventory at country or regional level. Second is for developing
carbon calculator which considers seat configuration, distance and other operational factors.
Global databases of aircraft performance during LTO and cruise stage for representative aircraftmapping and obtaining emission factors for each type of pollutants have been available and can beaccessed widely. Nevertheless, every application for different regions/countries should verify the
representativeness of those average aircraft type to the types of aircraft operating. When aircraft types
used are relatively homogeneous, top-down approach may be sufficient.
In applying top-down approach, it is important to take into account all fuel used for aviation andto distinguish between domestic and international flights. Both requirements remain encounteringsignificant challenges, especially for non-scheduled flights, military flights and general aviation. For
countries that have a good quality of fuel statistics, top-down approach may provide sufficiently
accurate results. In cases where air traffic data required for bottom-up approach are not available andtherefore leave top-down approach as the only alternative, each country is urged to estimate the split
obtained from fuel taxation data or surveys of airline companies.
Another common allocation problem is measuring fuel consumption by airline for international
flights. Most airlines operate internationally and, thus, allocating them based on country will be
27	
  
	
  

problematic. The agreed approach used by most estimates is by basing on the departing airport andeach flight’s emissions are allocated to the country from which they departed.
Furthermore, energy usage of an aircraft throughout all phases of a full “gate-to-gate” flight varies
greatly for different types of aircraft according to the level of advancement, size, distance flown,
propulsion system type, and various operational efficiencies including altitude of operation and traffic
volume. In order to incorporate those factors into the estimation, some attempts have been made:(i)Worldwide flight schedules have been available which includes types of aircraft use for eachflight;(ii) Actual air traffic data availability have been considerably progressing including radar-tracked
flight data flight showing longitude, latitude and altitude along the flight path. It can further
improve the accuracy of cruise fuel consumption by applying actual distance flown instead of
relying solely on factored-up GCD and by applying actual altitude compared with the optimum
altitude;(iii) Cautious approaches in assigning load factor, seat configuration, cabin class adjustments andcargo factor to each individual approach have also been carried out especially to calculate
carbon emission per passengers;(iv) Advanced models with sophisticated computer assistance have been able to estimate annual
emissions by using a year-full actual air traffic data and complete the “gate-to-gate” flight path.
Although most estimates extrapolate the annual traffic from 1-day, 1-week or 1-month traffic
data by applying some assumptions to address differences between weekend and weekday or toaddress seasonal variation. Extrapolations are found to result in overestimate.
Needless to say, detailed data are not available for all countries. Thus, in case such detailed
information can be obtained, bottom-up approach is recommended.
Conclusively, the choice of which method to be used depends heavily to the availability of data
where locally and publicly available data should be used as much as possible.

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APPENDIX
List of Abbreviation
AIC
Aviation-induced Cloudiness
AEM
Advanced Emission ModelATCAir Traffic Control
ANCAT Abatement of Nuisance Caused by Air Transport
ASK
Available Seat Kilometer
BTS
Bureau of Transportation Statistics
BADA
Base of Aircraft Data
BM2
Boeing Method 2
DEFRA United Kingdom Department for Environment, Food and Rural Affairs
DfT
United Kingdom Department for Transport
DLR
Deutsche Forschungsanstalt fur Luft- and Raumfahrt, German Aerospace Center
DTI
United Kingdom Department of Trade and Industry
CAA
United Kingdom Civil Aviation Authority
EEA
European Environment Agency
EC
European Commission
EF
Emission Factor
EMEP
European Monitoring and Evaluation Program
ETMS
Enhanced Traffic Management System
FAA
United States Federal Aviation Authority
FAST
Future Aviation Scenario Tool
GCD
Great Circle Distance
GDS
Global Distribution Systems
GHG
Greenhouse Gas Emission
ICAO
International Civil Aviation Organization
INM
Integrated Noise Model
IPCC
Intergovernmental Panel on Climate Change
ISA
International Standard Atmosphere
LTO
Landing and Take-off
MTOW Maximum Take-off WeightNASA
National Aeronautics and Space Administration
NATS
United Kingdom National Air Traffic Services
NETCEN United Kingdom National Environment Technology Center
OAG
Official Airline Guide
OEW
Operating Empty Weight
OD
Origin-Destination
PIANO Project Interactive Analysis and Optimization
PNR
Passenger Name Record
RF
Radiative Forcing
RPK
Revenue Passenger Kilometer
SAGE
System for Assessing Aviation’s Global Emission
SAR
Specific Air Range
SARS
Severe Acute Respiratory Syndrome
SERAS South East and East England Regional Air Service Study
SFC
Specific Fuel Consumption
TSFC
Thrust Specific Fuel Consumption

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ACKNOWLEDGMENT
This work was supported by the Global Environment Research Fund (S-6-5) of the Ministry of theEnvironment, Japan.
	
  

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