Study on the Allocation of Emissions from International

Study on the Allocation of Emissions from International Aviation to the UK Inventory – CPEG7

Final Report to DEFRA Global Atmosphere Division

Allocation of International Aviation Emissions from Scheduled Air Traffic – Present day and Historical (Report 2 of 3)

December 2005

Lee D. S., Owen B., Graham, A., Fichter C., Lim L. L. & Dimitriu D.

Manchester Metropolitan University

Centre for Air Transport and the Environment (CATE)

Department of Environmental and Geographical Sciences

Faculty of Science and Engineering

2

Report prepared by Lee, D. S., Owen B., Graham A. and Fichter C.

Reviewed/checked by Professor Callum Thomas

Report no. CATE-2005-3(C)-2

Date of issue, Version no. 25-04-2005 v1.2 for initial review and customer comment

16-05-2005 v1.3 initial DEFRA and internal review comments

24-11-2005 v1.4 Final customer comments and new analyses

15-12-2005 v1.5 Minor amendments

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Table of contents

page

Executive Summary 9

1 Introduction 11

2 Allocation options and methodological approaches taken 14

2.1 Allocation Option 1 14








3 Modelling emissions allocations – the FAST model 17

3.1 Overview of model 17

3.2 Revisions – 2000 movements 18

3.3 Revisions – representative types 19

3.4 Revisions – analysis of cruise altitudes 23

3.5 Fuel flow modelling 30

3.6 Revisions – country database, resolution 33

4 Results 34

4.1 Global aviation emissions, 1990 and 2000 34

4.2 Allocation results 1990 and 2000 39

5 Discussion 43

5.1 Comparison of base case global data with other data 43

5.2 Implications of allocation methodologies for the UK inventory and EU25 44

5.2.1 Specific allocation Options 44

5.2.2 CO2 equivalents 46

5.2.3 Radiative forcing index for aviation 47

5.3 Uncertainties 47

5.3.1 Traffic 48

5.3.2 Airframe representative types 52

5.3.3 Engine representation 54

5.3.4 Other uncertainties 54

5.4 Choice of one allocation Option over another 56

6 Conclusions and recommendations 58

Acknowledgements 60

References 60

4

5

List of figures

Figure 1: Globally and annually averaged radiative forcing from aviation in 1992 and its sub-components (IPCC, 1999). The bars represent a best estimate of the forcing, whilst the lines represent the two thirds uncertainty range. Also presented are relative appraisals of the level of scientific understanding

Figure 2: Maximum flight level (hft, hecto feet) for A306 (Airbus 300-600) by mission distance (nm, nautical miles), all flights (upper panel); mean maximum flight level for selected distance ranges for PIANO modelling (lower panel)

Figure 3: Maximum flight level (hft, hecto feet) for A321 (Airbus 321) by mission distance (nm, nautical miles), all flights (upper panel); mean maximum flight level for selected distance ranges for PIANO modelling (lower panel)

Figure 4: Maximum flight level (hft, hecto feet) for B732 (Boeing 737-200) by mission distance (nm, nautical miles), all flights (upper panel); mean maximum flight level for selected distance ranges for PIANO modelling (lower panel)

Figure 5: Maximum flight level (hft, hecto feet) for CRJ1 (Canadair regional jet 100) by mission distance (nm, nautical miles), all flights (upper panel); mean maximum flight level for selected distance ranges for PIANO modelling (lower panel)

Figure 6: Spatial distributions of CO2 emissions (tonnes grid cell-1) 1990 (upper panel) and 2000 (lower panel)

Figure 7: Monthly variation of flight km (1000s) between the UK and non-EU15 states in 2000; scheduled (blue line), non-scheduled (pink line), and total (red line) – CAA data

Figure 8: Monthly variation of flight km (1000s) between the UK and EU15 states in 2000; scheduled (blue line), non-scheduled (pink line), and total (red line) – CAA data

Figure 9: Comparison of similar airframes with respect to fuel consumption by mission distance: A330-200 and A330-300 (upper panel) and; B7373-300, B737-400, B737-500 (lower panel)

Figure 10 Example (A320-200) of the effect of payload assumptions (60%, 70%, 80%) by mission distance

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7

List of Tables

Table 1 Representative aircraft types and equivalent actual aircraft used in FAST-2000

Table 2: Representative aircraft, PIANO airframe and engine selection and payload assumed

Table 3: Traffic (km yr-1), fuel usage and CO2 emissions from civil aviation for 1990 and 2000, by type and major international traffic regions (Tg yr-1)

Table 4: Fuel, CO2 and distance by aircraft class, 1990 and 2000

Table 5: Global fuel, distance and pollutant emission rates by aircraft type for 1990


Table 7: Overview of allocation of international aviation carbon dioxide emissions from different SBSTA Options (see text) for 1990, Gg CO2 yr-1

Table 8: Overview of allocation of international aviation carbon dioxide emissions from different SBSTA methodologies (see text) for 2000, Gg CO2 yr-1

Table 9: Matrix of emissions from international aviation between EU25 Member States, 2000 (Gg CO2 yr-1)

Table 10: Summary of global total fuel, CO2, NOx and km travelled for FAST-1990 and FAST-2000 inventories and other inventory sources

Table 11: Results of analysis of OAG and EUROCONTROL (EC) data, for January and July 2000 (km/month)

Table 12: Estimated bias in CO2 emissions for various traffic flows within and to/from EU15 for January and July, 2000 (Gg CO2/month)

Table 13: Comparison of number of international and intra-EU25 flights between the ANCAT/EC2 movement data and corresponding OAG data

Table 14: Comparison of different airframes (A332 with A333) with respect to trip distance (nautical miles – nm) and fuel consumption (kg)

Table 15: Comparison of different airframes (B733, B734 and B735) with respect to trip distance (nautical miles – nm) and fuel consumption (kg)

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9

Executive Summary

This report is the second of three, which examines options for the allocation of international aviation

emissions of carbon dioxide (CO2) from scheduled air traffic as part of a study conducted by Manchester

Metropolitan University for the UK Department of Environment, Food and Rural Affairs (DEFRA).

This report outlines the results of allocating international aviation emissions to the UK and the enlarged

European Union (EU25) by allocation methodologies proposed by the United Nations Framework

Convention on Climate Change’s (UNFCCC) Subsidiary Body for Scientific and Technological Advice

(SBSTA) in 1996. This report concentrates on Options 4 to 8 but also presents the results for Options 2

and 3, which are dealt with in more detail in a companion report. It was not possible to perform any

calculations for Option 7, which requires knowledge of the nationality of passengers/country of origin of

freight as no suitable data could be obtained. To date, progress on determining the implications of these

allocation methods has been hampered by the lack of an appropriate model by which some methods

need to be calculated.

In this report the results of a global modelling system, ‘Future Aviation Scenario Tool’ (FAST model) are

presented. FAST calculates aircraft emissions from global air traffic movement databases so that

allocation of emissions to Parties to the Kyoto Protocol can be estimated via the different SBSTA

Options. This analysis represents the first exhaustive global quantification of emission allocations via

different SBSTA Options with such a model.

The FAST model has been significantly updated and the detailed methodologies, parameterisations and

input data have been described here in detail. It is shown that the results from the model compare well

with other model estimations of global aviation emissions.

Calculations of global aviation emissions were made for 1990 and 2000 using the Official Airline Guide

(OAG) global scheduled movement data. Global emissions of carbon dioxide (CO2) from civil aviation

increased by a factor of 1.5 from 331 Tg yr-1 in 1990 to 480 Tg yr-1 in 2000; international CO2 emissions

represented 47% and 55% of these amounts respectively. Over the same period a 15% improvement in

global fuel efficiency, in terms of kg CO2/km, was calculated.

Of the calculated Options that are favoured by SBSTA (Options 3, 4, 5, 6), they were found to be in

close agreement for higher levels of aggregation, i.e. the EU25 and Annex I Parties, at less than 13%

variation between the totals. An analysis of the different options was made to determine the degree of

variability that they introduced. By far the largest variation resulted from inclusion of the UNFCCC data

(Option 3). The UNFCCC data are derived from national inventory authorities and, as such, large

uncertainties are introduced since different methodologies are used and different assumptions made in

determining the fraction of fuel that was used for international aviation. If the UNFCCC option 3 is

eliminated, the variability across the options is reduced, e.g. for the EU25, the variation is less than 10%

for the six Member States that account for 82% of the emissions from international aviation for the

EU25. Therefore, it is concluded that – as a generalisation for higher levels of aggregation – the choice

of one of these options over another does not appear to introduce any particular bias or distortion into

the system. This is in contrast to obviously different systems like Option 2 (proportioning to national

emissions) or Option 8 which penalises large land-mass countries with extensive over-flight.

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The main conclusion of this work is that of the favoured SBSTA options, they are equitable. Issues over

the ease of implementation and monitoring are not covered in this study and such considerations are

necessary before moving forwards with one of the allocation Options in the policy context.

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1 Introduction

At Kyoto in 1997, the Conference of the Parties (COP) to the United Nations Framework Convention on

Climate Change (UNFCCC) adopted the ‘Kyoto Protocol’. Since February 16th 2005, the Kyoto Protocol

has entered into force, which commits Annex I Parties to legally binding targets to limit or reduce their

greenhouse gas emissions by certain amounts1. The Protocol sets a reduction target for national

greenhouse gas (GHG) emissions for each of the Annex I countries. Six greenhouse gases are included

in the Kyoto Protocol; carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorcarbons

(HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF6). The reductions targets are for the

period 2008 –2012 relative to a base year of 1990 (the Kyoto reference year) and relate to national

emissions arising from economic activity taking place within national territories that are reported by

Parties to the UNFCCC in their national inventories. The Kyoto Protocol was ratified by the European

Union (EU) on 31st May 2002 and overall, the EU (i.e. the EU15, as opposed to the enlarged EU25) has

a target to reduce emissions by 8 per cent. Under the burden sharing agreement of the EU monitoring

mechanism, the UK has agreed to reduce emissions by 12.5% by the end of 2012.

Parties that ratified the Kyoto Protocol are required to submit annual greenhouse gas (GHG) inventories

to the UNFCCC to monitor progress towards their targets. The Intergovernmental Panel on Climate

Change (IPCC) has issued guidance on how to compile such inventories (IPCC, 1996). However, whilst

quantification of aviation emissions is possible, the method by which international emissions from

aviation and shipping are allocated is not yet resolved. Under Article 2, paragraph 2 of the Kyoto

Protocol, Annex I countries are required to reduce or limit GHG emissions from international aviation

working through the International Civil Aviation Organization (ICAO).

Domestic aviation emissions have no such problematic definition since they are simply those emissions

arising from a flight departing and arriving in the same country. The difficulty of defining how

international emissions are allocated was recognised early on in the UNFCCC process and the

UNFCCC Subsidiary Body for Scientific and Technological Advice (SBSTA) was presented with a

number of initial options for allocation of international aviation and marine bunker fuels (UNFCCC, 1996)

as follows:

• Option 1—No allocation.

• Option 2—Allocation of global bunker sales and associated emissions to parties in proportion

to their national emissions.

• Option 3—Allocation according to the country where the bunker fuel is sold.

• Option 4—Allocation according to the nationality of the transporting company, or to the country

where an aircraft or ship is registered, or to the country of the operator.

• Option 5—Allocation according to the country of departure or destination of an aircraft or

vessel; alternatively, emissions related to the journey of an aircraft or vessel shared by the

country of departure and the country of arrival.

1 See http://unfccc.int/essential_background/kyoto_protocol/items/3145.php

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• Option 6—Allocation according to the country of departure or destination of passengers or

cargo; alternatively, emissions related to the journey of passengers or cargo shared by the

country of departure and the country of arrival.

• Option 7—Allocation according to the country of origin of passengers or owner of cargo.

• Option 8—Allocation to a party of all emissions generated in its national space.

There are three issues at stake; those of adequate and consistent inventories, allocation of emissions,

and control options (UNFCCC, 1997). In reviewing the options listed above, SBSTA recommended that

Options 1, 3, 4, 5 and 6 should form the basis of further work (UNFCCC, 1997).

Allocation, ultimately, allows a transparent and simple mechanism by which emissions can be assigned

to Parties. It is important to draw a distinction between allocation of emissions to Parties and assignment

or distribution of emissions permits. Unfortunately, the two terms tend to be used differently by different

communities, e.g. the EU refers to distribution of emissions permits in ‘national allocation plans’ (Wit et

al., 2004). In this report, the term ‘allocation’ is used with reference to assignment of emissions to

Parties in the UNFCCC sense of being ‘responsible’ for them; not the assignment of emission permits in

a trading scheme.

In terms of the emissions from aviation, CO2 is the most important of the six GHG regulated under the

Kyoto Protocol. Of the other five gases, only small amounts – if any – of CH4 and N2O are emitted by

aircraft engines. This issue is discussed in detail in Section 5.2.2.

However, whilst aircraft emissions in the context of Kyoto are largely CO2, there are other emissions and

effects from aviation that are considered to impact upon climate change. This was the subject of an

IPCC Special Report, ‘Aviation and the Global Atmosphere’ (IPCC, 1999). The IPCC (1999) report

quantified these effects in terms of the climate metric ‘radiative forcing’ (in units of Watts per square

metre – W m-2). The radiative forcing (RF) concept has proven useful as there is an approximately linear

relationship between a change in the global mean radiative forcing (∆RF) multiplied by a constant, and

the global mean perturbed surface temperature (∆Ts). That is,

∆Ts ≈ λ ∆RF [1]

where λ is the climate sensitivity parameter (K (W m-2)-1). The climate sensitivity parameter has been

found to be relatively constant for the long-lived greenhouse gases for an individual global climate model

(GCM) but varies significantly across GCMs (Cess et al., 1990; 1996).

The IPCC (1999) found that: CO2 had a positive (warming) RF, emissions of nitrogen oxides (NOx, i.e.

NO + NO2) resulted in an enhancement of ozone (O3), a positive RF and a reduction of ambient CH4 – a

negative RF; a small increase in water vapour (H2O) – a positive RF; a small increase in sulphate

particles – a negative RF; a small increase in soot particles – a positive RF; a positive RF from

persistent contrails (condensation trails); and a potentially positive RF from enhancement of cirrus

clouds.

These effects, their confidence intervals and level of scientific understanding were summarised for

‘present-day conditions’ (1992, at the time of writing by the IPCC), reproduced below in Figure 1. The

values of the RFs have recently been updated by Sausen et al. (2005) for a base year of 2000.

How these RFs translate to equivalent emissions is a difficult issue since there is no straightforward

relationship between emissions and RF for some effects; indeed in the case of contrails, it is not clear

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that this even exists, since contrails are largely the result of the properties of the background

atmosphere, the aircraft emissions of water vapour and particles merely providing the ‘trigger’. This

issue, and how it relates to international aviation emissions and their allocation will be revisited in

Section 5.2.3.

Figure 1: Globally and annually averaged radiative forcing from aviation in 1992 and its sub-components (IPCC, 1999). The bars represent a best estimate of the forcing, whilst the lines represent the two thirds uncertainty range. Also presented are relative appraisals of the level of scientific understanding

In this report, model calculations are presented that allocate international aviation emissions from

scheduled aircraft movements for 1990 and 2000 to Parties. The detailed methodologies of the

allocation options that do not require any modelling of aviation emissions, namely Options 2 and 3, are

given in a companion report (Owen and Lee, 2005a) but the results are summarised here. Calculations

and presentation of potential future projections of allocation of international aviation emissions are given

in a further companion report (Owen and Lee, 2005b).

Other authors have considered facets of the ‘allocation issue’ in terms of tractability, feasibility, suitability

for trading in terms of monitoring, ‘leakage’, policy perspectives of e.g. ‘polluter pays’ principles etc. (e.g.

Wit et al., 2004; Nielsen, 2003; Cames and Deuber, 2004; Nordic Council, 2004). In this report the

analyses are restricted to numerical aspects, i.e. “what would the allocation of CO2 and CO2 equivalents

be to the UK and EU 25?” since such exhaustive calculations have not previously been performed.

Inevitably, the distinction becomes blurred in discussing the methodologies but in general, this and

companion reports do not consider policy questions or implications.

In Section 2, the allocation options and the methodological approaches taken here are outlined; in

Section 3, the modelling system and data inputs are described in some detail as the model used has

been specifically adapted and upgraded for these calculations; in Section 4 the basic results are

presented for the years 1990 and 2000; in Section 5 the results are discussed in terms of the

implications of the data for the UK inventory and an analysis of some of the uncertainties in both the

data and modelling; finally, in Section 6, conclusions are drawn and recommendations made.

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2 Allocation options and methodological approaches taken

Of the eight allocation options set out by SBSTA (UNFCCC, 1996), as described in the introduction,

Options 4, 5, 6, and 82 are considered in detail since their computation relies upon a sophisticated

model of global aviation emissions. Nonetheless, for completeness, the results of allocation Options 2

and 3 are also summarised here from a more detailed analysis (Owen and Lee, 2005a). This present

report provides the necessary background details to the SBSTA Options.

2.1 Allocation Option 1

Option 1 is ‘no allocation’, so is not considered further here.


The SBSTA define this allocation option as follows (UNFCCC, 1996):

“Option 2—Allocation of global bunker sales and associated emissions to parties in proportion to their

national emissions.”

The most obvious way to estimate the allocated emissions is via fuel sales in countries. However, this

assumes that bunker fuel sales are accurate overall and are able to discriminate between sales for

domestic and international purposes. It is reasonable that bunker fuel sales should be well known and

documented, although the domestic/international split is more problematic.

The phraseology of the SBSTA Option 2 could be construed as being ambiguous; “national emission”

could be interpreted as CO2 or all Kyoto emissions as CO2 equivalents3. As a result, this calculation has

been done in two ways: by allocating international aviation emissions of CO2 in proportion to national

CO2 emissions (hereafter, ‘Option 2a’); and, by allocating international aviation emission of CO2 in

proportion to national CO2 equivalent emissions (hereafter ‘Option 2b’). These Options are analysed in

more detail by Owen and Lee (2005a) but the results are also summarised here.


The SBSTA define this allocation option as follows (UNFCCC, 1996):

“Option 3—Allocation according to the country where the bunker fuel is sold.”

As is the case for Option 2, there is the difficulty of knowing how much aviation fuel was sold for

domestic traffic and how much for international traffic. As is the case for Option 2, emissions can be

estimated via the fuel sales directly, or by the amount implied from the FAST inventory modelling as fuel

used for international flights. This, in essence, has the same assumptions as Option 5 considered by

Lee et al. (2005). This is designated hereafter ‘Option 3a’.

2 It has not been possible to evaluate Option 7 since no data were available, see Section 2.7 3 The Global Warming Potential (GWP) is a commonly used metric to assess the effect of different greenhouse gases on climate. The GWP is defined as the ratio of the time-integrated (over some period) radiative forcing of a pulse emission of a gas to that of CO2 (a 100-year time horizon is used in the Kyoto Protocol). Thus to estimate the CO2 equivalent for CH4 and N2O (the only other Kyoto gases potentially emitted by aircraft), the mass of the trace gas is multiplied by the GWP of 23 and 296 respectively (IPCC, 2001).

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For international bunker fuels, UNFCCC reported data were taken as the de facto standard, hereafter

‘Option 3b’ and compare these data with the independent estimates of the International Energy Agency

(IEA, 2003). The derivation of data for Option 3 is given in more detail by Owen and Lee (2005a) but the

results are also given for completeness here. UNFCCC and IEA data were utilised since their estimation

are based upon bunker fuel statistics. However, in the derivation of international bunker fuels, there is

uncertainty within the UNFCCC data (see Lee and Owen, 2005a) and potential errors in reported data to

the IEA (Owen and Lee, 2005a).


Option 4 allocates emissions according to the nationality of the airline. Potentially, there are three

variants:

1. the nationality of the airline;

2. the country in which the aircraft is registered; and

3. the country from which the airline is operated.

Data from the OAG included the operator, so that the flight could be allocated to a country. Calculation

of emissions by individual operator was not possible since different operators often fly the same route.

Because of the constraint of the FAST model using a ‘route’ multiplied by a ‘frequency’, operators were

assigned a country of domicile in the pre-processing of the OAG data. This equivalence is given in

Appendix 1. Whilst for 2000 this was a relatively straightforward issue, ownership of airlines is becoming

progressively more complicated as they move away from their nationalised (in many cases) origins.

In this study, the first approach has been taken. Data were not available for sub-option 2; for sub-option

3, it was not clear that this approach could be rigorously taken with the OAG data, so it was excluded

from the analysis.


For Option 5, SBSTA states (UNFCCC, 1997):

“Allocation according to the country of departure or destination of an aircraft or vessel;

alternatively, emissions related to the journey of an aircraft or vessel shared by the country of

departure and the country of arrival.”

Thus, for this Option, the emissions of the out-bound flight are allocated to the country of departure and

the emissions of the return flight allocated to the destination country. The alternative proposed above by

SBSTA is considered to apply more to the shipping sector (since SBSTA consider allocation of

international aviation and maritime emissions together), where many stops may be made during one

trip. This is a rarer occurrence in the aviation sector (EUROCONTROL, private communication),

although not well documented when it does happen, as it is not clear whether this happens for refuelling

purposes or purposes of embarkation/disembarkation of people and/or goods. It should be noted that in

the strict policy context – as opposed to the numerical perspective – only Annex I Parties are allocated

emissions. Thus, if a flight departs from an Annex I Party to a non-Annex I Party, the outbound flight

emissions from the Annex I Party are allocated to that party but the return flight emissions are not, and

even although they can be numerically allocated to the non-Annex I Party, they are not in the policy

sense.

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This allocation option is potentially the most complex under consideration as, at its most comprehensive,

it would involve the tracking of passengers and goods beyond individual flights to include stopovers and

transfers to other flights. This is particularly a problem where interlining4 is significant.

Simplification of this option so that the destination and departure are considered on an individual flight

basis (i.e. not necessarily the ultimate departure and destination of the passenger/cargo) means that

allocation according this option would be possible if data on fuel used during a flight together with

passenger loadings were available. However, it should again be noted that this would not take into

consideration interlining and would mean that for Parties where interlining is significant, allocation would

be greater than if a more comprehensive approach to this option were taken.

The interpretation of this option made for allocation purposes in this study was to take departure and

destinations for individual flights (brief stopovers for embarkation/disembarkation of goods and/or

passengers were not included as departures or destinations). In this interpretation, account was

therefore not taken of passengers or freight transferring to other flights to reach their final destinations. A

Passenger Kilometre index has been developed for each international flight based on the seat bandings

of the aircraft and this has been applied to weight the fuel used according to country of departure or

destination.


This option requires data on the nationality of passengers and cargo owners using international flights.

However, there is a lack of reliable and complete information and Option 7 is perhaps the most

intractable of the proposed methods, a fact recognised by SBSTA (UNFCCC, 1997).

Data on passenger origin/destination and country of domicile are collected by a number of agencies in

Europe and the US for security purposes. These data are, however, not made publicly available. A

number of sources including EUROCONTROL (the European organisation responsible for the safety of

air navigation) and the International Air Transport Association (IATA) confirmed that suitable data were

not available. Other researchers such as van Velzen and Witt (2000) have used a surrogate of gross

national product (GNP) to indicate the volume of international aviation activities generated by its national

population. However, there are insufficient data to support a directly proportional relationship between

GNP and number of international aviation passengers on an individual country basis. Thus, in view of

the lack of suitable data, this option has not been considered further in this study.


Allocation of emissions according to this option also relies on actual flight data rather than fuel statistics.

The main disadvantage to this option is that is a large proportion of emissions from international aviation

would remain unallocated as they would occur above international waters. Currently, there is no

proposal as to how these ‘over sea’ emissions would be allocated. However, this method tends to

prejudice large land masses with significant amounts of over-flight. As stated earlier, this is not one of

the methods favoured by SBSTA (UNFCCC, 1997).

4 The ability to of an airline to share passenger space inventory with another airline

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In this study, allocation was made to country areas utilising the underlying country database. This

comprised a global grid at a resolution of 0.25º latitude by 0.25º longitude.

3 Modelling emission allocations – the FAST model

3.1 Overview of model

The FAST model (Future Aviation Scenario Tool) was originally developed for the UK Department of

Trade and Industry (DTI) and was subsequently used in the European Fifth Framework Project,

TRADEOFF5. FAST was used in TRADEOFF to calculate global civil aviation emissions for 1992 and

projections for 2000 (based on 1992 traffic) so that the data could be used to evaluation aviation impacts

of NOx emissions on O3 and CH4 (Gauss et al., 2005), contrails (Fichter et al., 2005) and cirrus cloud

enhancement by aviation (Stordal et al., 2005).

The basic FAST model was designed around the methodology employed for the ANCAT/EC1&26

inventories of aviation emissions (Gardner et al., 1997; 1998), the results of which were examined and

compared with other data in some detail by the IPCC (Henderson et al., 1999).

The FAST modelling system is based upon a dataset of aircraft movements for some year which

indicates the frequency of flights of a specific aircraft type between city pairs. From this database, the

aircraft types were grouped, with representative aircraft types assigned. A separate aircraft performance

model (the PIANO model described in Section 3.5), provided data on fuel flow for specific

aircraft/mission combinations using standard assumptions of load factor and fuel reserves etc. Fuel flow

data were then used as the basis of calculating NOx emissions, based upon an algorithm that relates

sea-level NOx emissions from Certification data to altitude (e.g. Deidewig et al., 1996). Fuel

consumption over a specific mission is calculated between a departure and arrival location, linked by the

great circle distance. The emissions are then allocated onto a 3D grid of variable resolution (in latitude,

longitude and height). The distances travelled over the grids are calculated accurately, via trigonometric

functions and not averaged in any way. These data provide the basis of input to other impact

assessment models such as GCMs and CTMs (Chemical Transport Models) for calculation of e.g.

tropospheric O3 enhancement, contrail coverage, and consequential radiative forcing.

FAST works in the Microsoft Access© environment using Visual Basic for Applications (VBA). Most data

are loaded as tables and computations are performed using a combination of Standard Query Language

(SQL) queries and VBA code. A simple query usually takes the order of seconds to a few minutes on a

modern (2004) desktop computer; gridding operations usually take of the order of 20 minutes,

depending on the number of movements and the grid resolution.

Whilst the primary driver for the FAST model development was to enable impact assessment, the

results of the emissions modelling are of direct interest in and of themselves and the FAST model

utilises an underlying country database in order to apportion emissions. Initial results of emission

5 http://www.iac.ethz.ch/tradeoff/

6 The ANCAT (ANCAT=Abatement of Nuisance Caused by Air Traffic, part of the European Civil Aviation Conference – ECAC – http://www.ecac-ceac.org/index.php) Emissions Inventory Database Group, in collaboration with the European Commission, was responsible for constructing two versions of a global emissions database of aviation emissions. See Gardner et al. (1998).

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allocation calculations were undertaken and provided to the UK Department for Transport (DfT), DTI and

DEFRA for Options 5 and 8 based upon 1992 movements (Lee, unpublished data).

The global results of FAST for fuel, NOx and km travelled in 1991/92 were given by Gauss et al. (2005)

and Fichter et al. (2005), which showed that very similar results to the original ANCAT/EC2 inventory

(Gardner et al., 1998) were obtained – see Section 5.1.

For this work, the original FAST model (v1_1) was significantly developed and upgraded to two model

versions ‘FAST-2000 (v1.0)’ and ‘FAST-1990 (v1.0)’, representing the different traffic years, and these

developments are described in some detail in the following subsections.

3.2 Revisions – 2000 movements

The original global aircraft movement database used in FAST was based upon the ANCAT/EC2

movement database for 1991/1992 which included data for the months/years of July 1991, October

1991, January 1992 and April 1992 (Gardner et al., 1997; 1998). These data were factored up for

calculation of annual emissions. The ANCAT/EC2 movement database was constructed largely from

scheduled flight traffic data, augmented with air traffic control data for specific regions, in particular for

Europe, using EUROCONTROL flight-plan data. Careful checks were made to obviate double-counting

of flights (Gardner et al., 1998).

The initial problem for the current study was to select and use a database for global aircraft emissions

and several possibilities, albeit limited, were open. Data for the Kyoto ‘reference’ year of 1990 and a

‘present day’ case (selected as 2000) were required for analysis. In the TRADEOFF project, the

ANCAT/EC2 movement data were projected forward using traffic growth rates to give a year 2000

estimation. However, whilst this was a satisfactory approach for the impact studies, which are not

sensitive to country-by-country estimates of emissions, it was deemed unsatisfactory for this study of

emissions allocations. Moreover, the ANCAT/EC2 movement database did not contain vital information

that facilitates calculation of some of the allocation options.

The primary requirement for the movement data was that they were global in coverage, have some

standing and recognition within the industry internationally, and are comprehensive and traceable.

EUROCONTROL movement data would have provided an excellent solution for all these requirements

with the critical exception of ‘global’ – as they only cover Europe and flights to/from Europe – and

therefore could not be used. ICAO, somewhat surprisingly, does not appear to maintain such a

database that can be distributed. Thus, the only option available at the outset of the work was the OAG

database.

The OAG is compiled by the Reed Business Information company, a member of the Reed Elsevier plc

Group7. The OAG database provides information on worldwide scheduled commercial and cargo flights.

The OAG database fulfilled all the requirements, with the caveat that it covers scheduled movements

only. Thus, charter and freighter traffic (non-scheduled), for example, are not included. The only other

alternative for ‘present day’ was the global movement database from the AERO2K project (Michot et al.,

2003; 2004). The AERO2K project8 was a European Fifth Framework Project to estimate global aviation

emissions, and the movement database was similar to that of ANCAT/EC2 in that it attempted to

7 http://www.oag.com 8 http://www.cate.mmu.ac.uk

19

capture non-scheduled as well as scheduled traffic for some regions of the world (principally Europe and

North America). However, a final version of the database was not available at the time of

commencement of work. Moreover, this was for a single year (2002) and no compatible historical

movement database was available. Other inventories have similarly used scheduled data, e.g. the

NASA-Boeing inventories (Baughcum et al., 1996; Sutkus et al., 2001) and the US Federal Aviation

Administration (FAA)-SAGE (System for Aviation Global Emissions) modelling system. Thus, whilst the

limitations of scheduled data are recognised, the advantage remains that they are from an accepted

source, are global and can be used for a self-consistent set of calculations for different years. The

limitations of using only scheduled data are examined in detail in Section 5.3

The following datasets were obtained from OAG for the following purposes:

• twelve individual months of data from OAG were obtained for 2000, the ‘present day’ analysis

year and;

• 3 months for 1990 (only 3 months were available electronically), the Kyoto ‘reference’ year;

• the months/years of July 1991, October 1991, January 1992, April 1992 for the purposes of

comparison with the original ANCAT/EC2 movement data (Gardner et al., 1998), which were a

compilation of OAG and EUROCONTROL data for Europe.

Data for 2000 were compiled at the level of detail necessary to address relevant Allocation Options, and

attention has thus initially been directed toward deriving CO2 emissions for this year.

The 2000 data included airport country, latitude and longitude; routes as identified by the airport of

departure and final destination together with any intermediate stops; aircraft type; flight duration; aircraft

carrier or carriers and the countries in which they are domiciled; and the number of flights per month,

over the twelve months of the year. The data allow some important issues to be addressed. Routes that

involve intermediate stops can either be broken down into their constituent legs or included in full, and

the effect on allocations of either mode of processing may thus be identified and the two effects

compared. Routes operated by more than one carrier may also be distinguished. Where carriers derive

from more than one country, route frequencies may be subdivided over carrier countries according to

the number of carriers from each of the countries.

The price paid for this level of detail was the inclusion of traffic movements in the database that are not

of interest in this project, and which must therefore be isolated and removed. In particular, code-sharing

flights had to be identified and duplicates discarded, and routes including a non-airborne leg had to be

broken up and these legs removed.

A suite of software routines to filter and pool the OAG data was developed and implemented in a

Microsoft Access environment.

3.3 Revisions – representative types

The original FAST model used 16 representative aircraft types developed in the ANCAT/EC2 inventory

(Gardner et al., 1998). For the calculations performed in this study, it was judged desirable to update

and expand this approach, since some of the aircraft types in the ANCAT/EC2 list were no longer so

prevalent (e.g. the B707 and B727), being older aircraft; also, newer aircraft such as the B777 and A340

were not extant at that time and a more detailed discretisation of representative types would improve

accuracy and representation of the global fleet. Other representative type lists have been developed by,

20

for example, NASA-Boeing (Baughcum et al., 1996; Sutkus et al., 2001), SAGE9 and AERO2K (Eyers et

al., 2004).

In theory, there is no reason why a full set of aircraft types should not be used. Such an approach

predicates that sufficient data are available on the fuel flow and aircraft performance, which is not

always the case (see Section 3.5). Moreover, the formulation of ‘representative types’ truncates the

modelling problem and has been found to be satisfactory in other studies, when compared with other

uncertainties in the inventory calculation.

Currently, the IPCC is revising its Greenhouse Gas Inventory Guidelines and a sub-group of the Energy

Sector, in collaboration with an ad hoc Expert Group from ICAO’s Committee on Aviation Environmental

Protection (CAEP) Working Group 3, has been formulating a list of representative types of aircraft on

which inventories may be based.

In order to make the FAST model as up to date as possible, it was decided to use this draft list from the

Expert Group. However, some modification was necessary since not all the types listed in the Expert

Group List were available in the PIANO aircraft performance model (see Section 3.5).

The list of representative types and their matching specific types is given in Table 1.

9 No published information was available on the SAGE model at the time of writing: however, some information is available at http://www.aee.faa.gov/emissions/global/sage.htm

21

Table 1: Representative aircraft types and equivalent actual aircraft used in FAST-2000

Type

Representative Aircraft Type Specific Aircraft Name and Type

Large Commercial Aircraft A300 Airbus 300 Freighter Airbus 300-600 Airbus 300-B2 Airbus 300-B4 A310 Airbus 310 A319 Airbus 318 Airbus 319 A320 Airbus 320 Airbus 320S A321 Airbus 321 A330-200 Airbus 330 Airbus 330-200 Airbus 330-300 A340-200 Airbus 340-200 Airbus 340 A340-300 Airbus 340-300 B707 Boeing 707 Boeing 707 Freighter Douglas DC-8 Freighter Ilyushin IL62

McDonnell-Douglas DC-8-10/20/30/40/50

B717 Boeing 717 B727-200 Boeing 727-100 Combi Boeing 727 all pax models Boeing 727-200S Boeing 727-200 Freighter B737-200 Boeing 737-200 Boeing 737-200 Combi Boeing 737-200 Freighter B737-500 Boeing 737 all series Boeing 737-300 Boeing 737-400 Boeing 737-500 B737-600 Boeing 737-600 B737-700 Boeing 737-700 B737-800 Boeing 737-800 Boeing 737-900 B747-100 Boeing 747 all Freighter models Boeing 747SP B747-200 Ilyushin IL76 Ilyushin IL86 Ilyushin IL96-300 Antonov 124 Boeing 747 Boeing 747 Combi Boeing 747-all pax models Boeing 747-300 D Boeing 747-300 E B747-400 Boeing 747-400 B757-200 Tupolev Tu204 Boeing 757 all pax models Boeing 757 Freighter Boeing 757-200 B757-300 Boeing 757-300 B767-200 Boeing 767 all Freighter models

22

Boeing 767 all pax models Boeing 767-200 B767-300 Boeing 767-300 B767-400 Boeing 767-400 B777-200 Boeing 777 all series Boeing 777-200 Boeing 777-300 DC10 McDonnell-Douglas DC-10 all series

McDonnell-Douglas DC-10-10/30/40 Freighter

DC9-14 Tupolev Tu134 DC9-34 McDonnell-Douglas DC-9-10/14/15/20 McDonnell-Douglas DC-9-30/40/50

McDonnell-Douglas DC-9-30/40/50 Freighter

L1011 Lockheed L-1011-1/-50/-100/-200 Freighter

Lockheed L-1011-1/-50/-100/-200 Tristar

Lockheed L-1011-500 Tristar MD11 McDonnell-Douglas MD-11 McDonnell-Douglas MD-11 Combi McDonnell-Douglas MD-11 Freighter MD80 McDonnell-Douglas MD-80 all series McDonnell-Douglas MD-87 MD90 McDonnell-Douglas MD-90 all series TU154M Tupolev Tu154 M / B Regional Jets BAC111 British Aerospace BAC 111 Yakovlev 40 CRJ100 Canadair Regional Jet 100/200 Canadair Regional Jet all series E145 Embraer RJ 135/140/145 all series Embraer RJ135 Embraer RJ145 Amazon F100 Fokker 100 Fokker F28 Fokker F70 J328 Fairchild Dornier 328 JET

RJ85 Aerospatiale-Sud Aviation Caravelle IIIA/B

Avro RJ-100 Avro RJ-70 Avro RJ-85 British Aerospace 146 all series British Aerospace 146-100 British Aerospace 146-200 British Aerospace 146-200 Freighter British Aerospace 146-300 YK42 Yakovlev 42 Low Thrust Jets C525 Cessna 525 Cessna Citation all series Dassault (Breguet Mystere) Falcon Gates Learjet Light corporate jet Israel Aircraft Industries 1124 Westwind Turboprops ATR 72 Aerospatiale/Alenia ATR 72 Aerospatiale/Alenia ATR all series Antonov 12 Antonov 24

23

Antonov 26 British Aerospace ATP British Aerospace Jetstream 41 Canadair CL-44 Convair CV-440/580/600/640 Freighter Convair CV-440/580/600/640 Pax Convair CV-580 Pax Curtis C-46 Commando De Havilland DHC-8 series 300 De Havilland DHC-8 series 400 Fairchild Dornier 328 Fokker F27 Fokker F50 Grumman G21 Goose

Gulfstream Aerospace G-159 Gulfstream

Hawker Siddley HS 748 Ilyushin IL114 Ilyushin IL18 Lockheed L-188 Electra Combi Lockheed L-188 Electra Freighter Lockheed L-382 (L-100) Hercules NAMC YS-11 Saab 2000 DHC8-100 Aerospatiale (Nord) 262 / Mohawk 298 Aerospatiale/Alenia ATR 42 Beechcraft 1900/1900C Beechcraft 1900D Brittish Aerospace Jetstream 31 CASA/IPTN CN-235 De Havilland Canada DHC-4 Caribou De Havilland Canada DHC-8 Dash 8 De Havilland DHC-7 Dash 7 De Havilland DHC-8 series 100 Douglas DC-3 Freighter Douglas DC-3 pax Douglas DC-6A/B/C Freighter Embraer EMB 120 Brasilia Grumman G73 Turbo Mallard Pilatus PC-12 Saab SF340A/B Shorts SD 330 Shorts SD 360

3.4 Revisions – analysis of cruise altitudes

In order to accurately represent fuel flow and therefore emissions of CO2, it is necessary to make some

assumptions regarding the altitudes at which aircraft fly. In the ANCAT/EC2 inventory, this was done

rather crudely, aircraft performance being simulated at optimal cruise altitudes for their mission distance

(Gardner et al., 1998). These optimised flights were then globally redistributed, with respect to altitude,

according to a limited dataset from one airline.

For the TRADEOFF 2000 inventory, a more refined technique was developed (Fichter et al., 2005), as

follows. Preliminary movement data from the AERO2K project (actually a combination of

EUROCONTROL and FAA data) were analysed for the limited list of ANCAT/EC2 representative types

to determine whether there was a relationship between aircraft type, mission distance and average

cruise altitude flown. It was found that this relationship provided a satisfactory and robust means of

24

specifying average maximum cruise altitudes by aircraft type and mission distance. The consequence of

not doing this and simply allowing an optimisation of the mission profile underestimated the amount of

fuel used and misrepresented the vertical distribution of traffic, and therefore emissions (Fichter et al.,

2005).

Thus, this parameterisation was adopted in this work but the analysis needed to be performed again for

the new representative types. The final AERO2K movement database was used as the basis for this.

This database of flight movements gives information on departure and arrival airports, aircraft type and

the flight level for each of the flight legs. For the altitude analysis by aircraft type and mission distance,

the following assumptions were made:

1. the month of December was representative of other months;

2. no regional patterns of cruise altitude by mission distance were analysed, rather a simple

global average ‘behaviour’ was assumed.

The first assumption was made to make the analysis tractable because of the large amount of data

processing involved in analysing one month alone; the second assumption was made again to make the

analysis tractable and in any case, the FAST modelling system could not readily allow for prescription of

a regional application of flight profiles.

A visual inspection of the distribution of maximum flight altitudes revealed a very small number of rather

high flight levels (FL), e.g. FL > 500 for a B752. After a quality check in the original flight legs data, it

transpired that the original data were not always reliable in these cases. To avoid including these

outliers in the analysis, flights with maximum flight levels higher than FL 470 were excluded.

The maximum flight altitudes for the representative aircraft types by mission distance were determined

by identifying the maximum flight level for each flight in the sample month. The average maximum flight

level was calculated for each of the representative aircraft types and groups of distance increments of

500 km (270 nautical miles, nm). The next ‘real’ FL (e.g. 290, 310, 330) to this average value was then

used in the aircraft performance model (PIANO).

Figures 2 to 5 show examples from the original movement data for several aircraft types and the

relationship of mission distances and maximum flight altitudes. How this relationship was applied within

the aircraft performance model is also shown. Appendix 2 gives detailed results of the analysis in terms

of implementation.

For some distances, PIANO was not able to perform the flight on the determined FL (marked with an

‘X’). There are a variety of possible reasons why this flight could not be modelled, e.g.:

- the particular flight(s) which were analysed to determine the flight altitude were close to the

next lower margin of distance increments, PIANO might not be able to perform the full 500 km

distance increment;

- the analysis of maximum flight altitudes might have included subtypes of a representative

aircraft type which could perform longer distances or fly on higher altitudes, (e.g. ER =

Extended Range, HGW = High Gross Weight, etc), whereas the more general aircraft type

used in PIANO could not perform these particular flight(s);

- all flights in PIANO were modelled with 70% of the maximum payload, which might not have

been true for all flights the analysis was based on (e.g. difference between freighter and

passenger aircraft, see Section 5.3.4).

25

For those cases where PIANO could not perform the flight on the requested / determined FL, the

following strategy was mapped out:

1. the flight was performed on the determined flight level with the maximum possible distance

(between the 500 km increments);

2. the flight was performed on the next lower level10;

3. the flight was performed on a combination of points 1 and 2;

4. if steps 1-3 not possible, fuel flow was not modelled for this distance; FAST would then

extrapolate the fuel flow from the nearest trip distance.

10 A quick analysis for a few examples showed that the difference in total fuel consumption is in the order of less than 1% up to nearly 5%, when performing a particular flight one or two flight levels lower. (In principle, for a lower flight altitude less fuel is needed for the climb period, but more fuel will be consumed during the cruise period.)

26

A306 - Maximum Flight Level by Distance Increments

0

50

100

150

200

250

300

350

400

450

500

000

0-02

70

027

0-05

40

054

0-08

09

080

9-10

79

107

9-13

49

134

9-16

19

161

9-18

89

188

9-21

58

215

8-24

28

242

8-26

98

269

8-29

68

Distance [nm]

Max

imum

Flig

ht L

evel

[hft]

Figure 2: Maximum flight level (hft, hecto feet) for A306 (Airbus 300-600) by mission distance (nm, nautical miles), all flights (upper panel); mean maximum flight level for selected distance ranges for PIANO modelling (lower panel)

27

A321 - Maximum Flight Level by Distance Increments

0

50

100

150

200

250

300

350

400

000

0-02

70

027

0-05

40

054

0-08

09

080

9-10

79

107

9-13

49

134

9-16

19

161

9-18

89

188

9-21

58

215

8-24

28

Distance [nm]

Max

imum

Flig

ht L

evel

[hft]

Figure 3: Maximum flight level (hft, hecto feet) for A321 (Airbus 321) by mission distance (nm, nautical miles), all flights (upper panel); mean maximum flight level for selected distance ranges for PIANO modelling (lower panel)

28

B732 - Maximum Flight Level by Distance Increments

0

50

100

150

200

250

300

350

000

0-02

70

027

0-05

40

054

0-08

09

080

9-10

79

107

9-13

49

134

9-16

19

188

9-21

58

Distance [nm]

Max

imum

Flig

ht L

evel

[hft]

Figure 4: Maximum flight level (hft, hecto feet) for B732 (Boeing 737-200) by mission distance (nm, nautical miles), all flights (upper panel); mean maximum flight level for selected distance ranges for PIANO modelling (lower panel)

29

CRJ1 - Maximum Flight Level by Distance Increments

0

50

100

150

200

250

300

350

0000-0270 0270-0540 0540-0809 0809-1079

Distance [nm]

Max

imum

Flig

ht L

evel

[hft]

Figure 5: Maximum flight level (hft, hecto feet) for CRJ1 (Canadair regional jet 100) by mission distance (nm, nautical miles), all flights (upper panel); mean maximum flight level for selected distance ranges for PIANO modelling (lower panel)

30

3.5 Fuel flow modelling

For determination of aircraft CO2 (and other) emissions, the means by which the fuel flow is determined

for aircraft types lies at the heart of the calculation methodology. Such data are not easily available,

since they are proprietary information to manufacturers and operators. However, there are a limited

number of means of estimating such data.

For input data to FAST, we have used the commercially available aircraft performance model, PIANO11

(Project Interactive ANalysis and Optimisation) which simulates aircraft performance and fuel-flow,

amongst many other things (Simos, 1993; 2004). This is a sophisticated aircraft performance model that

is widely used within the aviation industry. PIANO has also been extensively used in other inventory-

type work (e.g. Gardner et al., 1998; Fichter et al., 2005; Eyers et al., 2004).

The analysis of flight altitudes of representative aircraft types (see Section 3.3) by mission distance (see

Section 3.4) provided the basis of specifications for the PIANO fuel-flow modelling. About 480 separate

profiles for the fuel flow for 43 different types of representative aircraft and for a number of mission

distances were created. It was not always possible to use the Expert Group’s representative types, as

specified (see Section 3.3) as PIANO did not always contain entries for that particular type. These

exceptions were:

• B721 not available in PIANO → mapped to B722;

• B747-300 not available in PIANO → mapped to B747-200;

• BAE146 not available in PIANO → mapped to RJ85;

• Beech King Air (BE9L) (i.e. turboprops with shaft horsepower up to 1000 BHP per engine) not

available in PIANO → PIANO is unable to model such small aircraft, so it was ignored;

• DC8 not available in PIANO → mapped to B707;

• Tu-134 not available in PIANO → mapped to DC9(14).

After consultation with Lissys (the developers of PIANO), performance data were kindly provided by

them for the representative type Tu-156.

Table 2 (below) gives an overview of aircraft representative types modelled with PIANO, the (generic)

engines specified and assumptions for the payload used for the performance of the flight.

The fuel flow profile calculated by means of the aircraft performance model PIANO was used as the

basis for determining the CO2 emissions along a flight profile. The amount of CO2 emitted is calculated

from the fuel burn as a fixed ratio of 3.156 g CO2 to 1 g of aviation kerosene, assuming a mean

molecular formula of C12H23. From complete combustion of 1 g of such fuel, this also gives rise to 1.237

g H2O. Aircraft emissions also comprise some CO and unburned hydrocarbons (HCs) such that the C

content of these species should, in theory, be considered. However, in practice, this C content is less

than 1%: moreover, CO and HCs emitted by aircraft gas turbines can be quite variable (especially by

mode of flight, e.g. idle, taxi, take off etc.) and other uncertainties in the fuel usage are probably much

higher than 1% so that this fraction of the C content is ignored.

11 http://www.lissys.demon.co.uk

31

Table 2: Representative aircraft, PIANO airframe and engine selection and payload assumed

Representative Aircraft Type

Modelled in PIANO with

Engine / Generic Engine Engine description

Payload [kg] (70% of max

payload)

A300 A300-600R Hi-BPR Fan early90s

Typical early 1990s-technology turbofan, at approximately 52500 lbf fn*. Bypass-ratio 5.0

27,273 *

A310 A310-300 Hi-BPR Fan'90s DR

Large 1980s turbofan with nominal thrust of 62400lbf. Note this is a de-rated engine, showing little initial decay of takeoff thrust with Mac (FADEC control). By-pass ratio 5.1.

22,719 *

A319 A319 option Hi-BPR Fan mid80s

A 1980s-technology medium-to-large turbofan, nominal thrust of 40100 lbs. (178 kN). Bypass-ratio 4.1

11,657 *

A320 A320-200 basic Hi-BPR Fan mid80s

A 1980s-technology medium-to-large turbofan, nominal thrust of 40100 lbs. (178 kN). Bypass-ratio 4.1. Different fuel-flow and emission indices.

13,433 #

A321 A321-100 Hi-BPR Fan mid80s


16,028 #

A330-200 A330-200 Hi-BPR Fan'90s DR


32,830 *

A340-200 A340-200 Hi-BPR Fan early90s


30,800 *

A340-300 A340-300 Hi-BPR Fan early90s


35,630 *

ATR 72 ATR 72 TurboProp-norm1 Typical turboprop, 4440 lbf static thrust corresponding to approx 1800 shp (2.46 lbf/shp).

4,935 *

B707 / DC8 B707-320C PW JT3D PWJT3-D, based on JT3D matrix adjusted to match sfc = 0.829 at M.79, 35000 ft

16,148 #

B717 B717-200 BGW Medium-BPR Fan '90s

A medium-sized turbofan with a nominal thrust of 20500 lbf, 1990s technology. Bypass-ratio 4.0

8,310 #

B727-200 B727-200 A PW JT8D PWJT8 D-9, fn* = 14500 lbf. Bypass-ratio 1.05

13,431 #

B737-200 B737-200 PW JT8D PWJT8 D-9, fn* = 14500 lbf. Bypass-ratio 1.05

10814 #

B737-500 B737-500 option CFM56 CFM56-3-B1. This is a medium-sized turbofan with a sea level static thrust of 20000 lbf. Bypass-ratio 5.04

10,541 #

32

B737-600 B737-600 basic CFM56 CFM56-3-B1. This is a medium-sized turbofan with a sea level static thrust of 20000 lbf. Bypass-ratio 5.04

10,256 #


11,907 #


14,193 #

B747-100 B747-init 100 PW JT9D JT9D-7A, fn* 46100 lbf, bypass-ratio 5.1

48,578 #

B747-200 B747-200 B PW JT9D JT9D-7A, fn* 46100 lbf, bypass-ratio 5.1

46,515 #

B747-400 B747-400 mfrspec Hi-BPR Fan'90s DR


41,893 #

B757-200 B757-200 option 1 Hi-BPR Fan mid80s


18,400 *

B757-300 B757-300 Hi-BPR Fan mid80s


21,480 *

B767-200 B767-200 ER Hi-BPR Fan early90s


23,972 #

B767-300 B767-300 ER Hi-BPR Fan early90s


25,877 #

B767-400 B767-400 ER (X) Hi-BPR Fan early90s


29,307 #

B777-200 B777-200 B (590) Hi-BPR Fan'90s DR


39,372 #

BAC111 Rombac 1-11 Medium-BPR Fan late80s

A medium-sized turbofan with a nominal thrust of 15100 lbf (67.2 kN), 1980s technology. Bypass-ratio 3.1

8,698 #

C525 Cessna Citation V Small-bizjet Fan A very small biyjet turbofan with a nominal thrust of 8.45 kN (1900 lbf). Bypass-ratio 3.28

515 #

CRJ100 Canadair RJ100 CF34 guess1 CF34-3. This is a small turbofan with a nominal thrust of 8500 lbf. Bypass-ratio 6.0

3,842 #

33

DC10 DC 10-30 Hi-BPR Fan mid80s


31,753 #

DC9 DC 9-34 PW JT8D PWJT8 D-9, fn* = 14500 lbf. Bypass-ratio 1.05

8,351 #

DC9-14 / TU134

DC 9 (14) PW JT8D PWJT8 D-9, fn* = 14500 lbf. Bypass-ratio 1.05

6,605 #

DHC8-100 Dash 8 Series 100 Turboprop PW120 Approx. model of OW120 and RF-14 prop at TOSHP 2000 and assuming 2.7 lbf/shp giving equivalent 5400 lbs fn*, turboprop.

2,478 #

E145 Embraer EMB-145 CFE guess1 CFE 738. fn* 5725 lbf. Bypass-ratio 5.3

3,803 #

F100 Fokker F100 basic Medium-BPR Fan late80s

A medium-sized turbofan with a nominal thrust of 15100 lbf (67.2 kN), 1980s technology. Bypass-ratio 3.1

7,956 #

J328 Dornier 328 JET CF34 guess1 CF34-3. This is a small turbofan with a nominal thrust of 8500 lbf. Bypass-ratio 6.0

2,555 *

L1011 Lockheed L101-200

PW JT9D JT9D-7A, fn* 46100 lbf, bypass-ratio 5.1

28,391 #

MD11 MD 11 basic Hi-BPR Fan early90s


33,249 #

MD80 Douglas MD 81 PW JT8D PWJT8 D-9, fn* = 14500 lbf. Bypass-ratio 1.05

1,843 #

MD90 Douglas MD90-30 Medium-BPR Fan '90s

A medium-sized turbofan with a nominal thrust of 20500 lbf, 1990s technology. Bypass-ratio 4.0

13,336 #

RJ85 / BA46 Avro RJ 85 option CF34 guess1 CF34-3. This is a small turbofan with a nominal thrust of 8500 lbf. Bypass-ratio 6.0

7,779 #

TU154M TU 154M PW JT8D PWJT8 D-9, fn* = 14500 lbf. Bypass-ratio 1.05

13,090 #

YK42 Yakovlev Yak 42M Lotarev D36 guess1

D-36 data at fn* 14330 lbs, turbofan.

11,515 #

* Jane's All the world's aircraft 2004-2005 (Paul Jackson, ed.)

# internal information from PIANO 4.0 (Simos, 2004)

3.6 Revisions – country database, resolution

The original FAST country database was developed from a commercially available country boundary

database, intended for use in Geographical Information System (GIS) software. The gridding was

performed at a resolution of 0.5° latitude by 0.5° longitude. This included 264 named countries mapped

to 11 regions/major countries. In some cases, the named ‘countries’ were overseas territories of

countries, e.g. Greenland/Denmark, Falkland Islands/United Kingdom etc. Some of the geo-political

boundaries have changed since the design of the original country database for the period 1991/92.

34

Thus, two major modifications were deemed necessary: an improvement of the resolution of the

underlying database to 0.25° latitude by 0.25° longitude for a more accurate capture and allocation of

emissions; and, a revision of the geopolitical boundaries in line with the year 2000. As a result of more

accurate assignment of overseas territories and revisions arising from changed geo-political boundaries,

the country database now contains 246 entries assigned to 16 regions/major countries.

4 Results

4.1 Global aviation emissions, 1990 and 2000

The global fuel usage and emissions of CO2 from aviation in 1990 and 2000 are given in Table 3.

Distance travelled increased by a factor of approximately 1.7 over the period 1990 to 2000, fuel/CO2 by

a factor of approximately 1.5. This equates to an annual increase of approximately 5.5% for km travelled

and 3.8% for fuel usage. Globally, the average fuel efficiency improved by approximately 16% over the

decade, from 6.7 kg (fuel) km-1 to 5.7 kg (fuel) km-1.

In 1990, the estimated domestic CO2 emissions, globally, slightly exceeded international emissions –

53% cf 47%: in 2000, the situation was reversed with international emissions accounting for 55% of total

civil aviation CO2 emissions cf 45% from domestic air travel. Of these total emissions, Annex I countries

accounted for 31% and 35% in 1990 and 2000, respectively; also, EU25 States increased their share

from 13% to 17% of total civil aviation emissions. Note that these emissions are domestic and

international; i.e. not just intra-EU but rather domestic EU, plus EU to non-EU states (as per Option 5).

Table 3: Traffic (km yr-1), fuel usage and CO2 emissions from civil aviation for 1990 and 2000, by type and major international traffic regions (Tg yr-1)

1990 2000

Distance (km yr-1)

Fuel (Tg yr-1)

CO2

(Tg yr-1) CO2

(%) Distance (km yr-1)

Fuel (Tg yr-1)

CO2

(Tg yr-1) CO2

(%)

Global 1.57E+10 105 331 100 2.69E+10 152 480 100

Domestic 9.44E+09 55 175 53 1.43E+10 68 214 45

International 6.21E+09 50 156 47 1.27E+10 84 266 55

Annex I 32 102 31 54 170 35

EU25 14 44 13 25 79 17

In Table 4, the fuel usage and emissions are broken down by aircraft type, i.e. large commercial aircraft,

regional jets, low thrust jets and turboprops for 1990 and 2000. Between 1990 and 2000, the usage of

regional jets and turboprops has increased in terms of share of distance travelled.

35

Table 4: Fuel, CO2 and distance by aircraft class in 1990 and 2000

1990 Fuel (Tg)

CO2 (Tg)

CO2 (%)

Distance (km)

Distance (%)

Large commercial jet 99.68 314.59 95.4 1.38E+10 88.5 Low thrust jet 0.02 0.07 <0.1 1.39E+07 <0.1 Regional jet 2.16 6.83 1.8 4.94E+08 2.7 TurboProps 2.98 9.41 2.9 1.35E+09 8.6 Total 105 331 1.57E+10

2000 Fuel (Tg)

CO2 (Tg)

CO2 (%)

Distance (km)

Distance (%)

Large commercial jet 141.87 447.74 93.3 2.2985E+10 85.3 Low thrust jet <0.01 0.01 <0.1 2.5659E+06 <0.1 Regional jet 6.13 19.35 4.0 2.0015E+09 7.4 TurboProps 4.07 12.85 2.7 1.9577E+09 7.3 Total 152 480 2.6947E+10

The global usage of fuel and distance travelled and fuel efficiencies (kg/km) for representative aircraft

types are given in Tables 5 and 6 for 1990 and 2000, respectively. The tables are ordered by total fuel

usage. The change in usage of aircraft, from older to newer types is quite marked: for example, the ‘top

two’ aircraft in 1990 were the representative types B7471 and the B7272 accounting for some 35% of

global fuel usage: in 2000, they accounted for less than 9%. However, the pattern has also changed –

whilst the large 7474 representative type still dominated in 2000 (cf the 7471), there has been a slight

shift from the larger aircraft to medium-sized aircraft (e.g. B757). These data provide the basis for

average emissions factors used elsewhere in the report, and indeed can be used for Tier 2 equivalent

(see IPCC, 1996) calculations of aviation CO2 emissions.

Figure 6 shows the spatial distribution of the vertically-integrated CO2 emissions for 1990 and 2000.

Although the emissions are shown on a logarithmic scale, the increase in emissions can still be seen.

It is interesting to note that international aviation emissions of CO2 increased for all Annex I Parties to

the Kyoto Protocol (see Tables 7, 8 later). This is in contrast to the requirement of Article 2.2 of the

Protocol to reduce or limit emissions from international aviation, although the scope of ‘limit’ is open to

interpretation: there is no interpretation provided but this could be speculated to be ‘reduce or limit the

rate of growth of emissions from international aviation’ (our emphasis). However, this is not the wording

of the Protocol.

36

Figure 6: Spatial distributions of CO2 emissions (tonnes grid cell-1) 2000 (upper panel) and 1990 (lower panel)

37


Aircraft type12

Fuel (Tg)

CO2 (Tg) Distance (km)

Fuel/km (kg/km)

CO2/km (kg/km)

Distance

(%)

Fuel (%)

B7471 22.23 70.16 1,826,069,106 12.17 38.42 11.67 21.20 B7272 14.32 45.19 2,058,023,128 6.96 21.96 13.15 13.66 DC10 8.91 28.12 939,204,724 9.49 29.94 6.00 8.50 B7375 6.89 21.75 1,699,719,896 4.05 12.80 10.86 6.57 DC9 6.89 21.73 1,063,462,495 6.47 20.43 6.79 6.57 MD80 6.48 20.47 1,228,526,434 5.28 16.66 7.85 6.19 B7372 5.14 16.23 958,603,623 5.37 16.93 6.12 4.91 B7474 4.41 13.90 397,131,138 11.09 35.01 2.54 4.20 L1011 4.01 12.67 432,708,677 9.28 29.28 2.76 3.83 B7672 3.70 11.67 657,706,171 5.62 17.74 4.20 3.53 B707 3.49 11.03 447,861,087 7.80 24.62 2.86 3.33 A300 3.26 10.30 507,759,999 6.43 20.28 3.24 3.11 B7572 3.17 10.02 553,167,006 5.74 18.11 3.53 3.03 PROP6 1.99 6.28 882,683,616 2.25 7.12 5.64 1.90 A310 1.97 6.22 321,453,705 6.13 19.34 2.05 1.88 TU154M 1.84 5.81 265,238,274 6.94 21.90 1.69 1.76 F100 1.21 3.81 264,387,606 4.57 14.42 1.69 1.15 B7472 1.06 3.36 81,837,309 13.01 41.05 0.52 1.02 PROP7 0.99 3.13 464,193,041 2.13 6.74 2.97 0.95 B7673 0.96 3.02 152,402,168 6.27 19.79 0.97 0.91 RJ85 0.56 1.75 140,179,323 3.97 12.52 0.90 0.53 A320 0.47 1.48 114,974,288 4.09 12.89 0.73 0.45 TU134 0.37 1.16 78,929,247 4.64 14.66 0.50 0.35 BAC111 0.31 0.99 67,113,846 4.66 14.69 0.43 0.30 Concorde 0.10 0.31 14,147,765 7.00 22.10 0.09 0.09 YK42 0.09 0.28 22,520,861 3.87 12.22 0.14 0.08 C525 0.02 0.07 13,914,236 1.63 5.13 0.09 0.02 TOTALS 105 331 15,653,918,768

12 Note that these are representative types (i.e. some actual types had not entered into service in 1990)

38


Aircraft type

Fuel (Tg)

CO2 (Tg)

Distance (km)

Fuel/km (kg/km)

CO2/km (kg/km)

Distance (%)

Fuel (%)

B7474 27.61 87.15 2,498,801,996 11.05 34.88 9.27 18.16 B7375 15.04 47.45 3,821,902,734 3.93 12.42 14.18 9.89 MD80 10.18 32.13 2,006,851,537 5.07 16.01 7.45 6.69 B7572 9.85 31.10 1,832,576,254 5.38 16.97 6.80 6.48 B7471 6.77 21.36 561,314,746 12.06 38.05 2.08 4.45 B7772 6.68 21.07 867,691,169 7.69 24.28 3.22 4.39 B7673 6.20 19.56 1,092,350,406 5.67 17.91 4.05 4.08 B7272 5.97 18.84 873,624,465 6.83 21.57 3.24 3.93 A320 5.71 18.03 1,573,914,414 3.63 11.46 5.84 3.76 B7672 5.67 17.90 999,165,936 5.68 17.92 3.71 3.73 MD11 5.01 15.83 602,908,847 8.32 26.25 2.24 3.30 DC9 4.55 14.35 739,642,166 6.15 19.40 2.74 2.99 DC10 4.49 14.18 478,827,553 9.38 29.61 1.78 2.95 B7372 3.77 11.89 744,642,940 5.06 15.97 2.76 2.48 A300 2.91 9.18 453,332,806 6.41 20.24 1.68 1.91 A3302 2.74 8.64 425,221,291 6.44 20.32 1.58 1.80 A3403 2.69 8.49 343,889,497 7.82 24.69 1.28 1.77 PROP6 2.40 7.56 1,111,164,778 2.16 6.81 4.12 1.58 F100 2.33 7.34 574,439,384 4.05 12.78 2.13 1.53 A3402 2.20 6.95 304,471,685 7.23 22.83 1.13 1.45 A310 2.18 6.88 365,186,964 5.97 18.83 1.36 1.43 B707 1.82 5.75 232,244,031 7.85 24.77 0.86 1.20 PROP7 1.68 5.29 846,569,712 1.98 6.25 3.14 1.10 B7378 1.63 5.15 446,150,693 3.65 11.53 1.66 1.07 A319 1.59 5.02 475,347,799 3.35 10.57 1.76 1.05 B7377 1.44 4.56 418,244,995 3.45 10.89 1.55 0.95 RJ85 1.43 4.51 386,945,973 3.69 11.66 1.44 0.94 CRJ100 1.39 4.39 601,018,371 2.32 7.31 2.23 0.92 TU154M 1.37 4.33 207,703,950 6.60 20.84 0.77 0.90 B7472 0.86 2.72 67,580,507 12.75 40.23 0.25 0.57 L1011 0.80 2.52 85,982,712 9.29 29.33 0.32 0.53 A321 0.76 2.41 168,639,202 4.52 14.26 0.63 0.50 E145 0.72 2.26 362,200,881 1.98 6.23 1.34 0.47 MD90 0.69 2.17 142,768,261 4.82 15.22 0.53 0.45 TU134 0.31 0.97 75,223,322 4.09 12.90 0.28 0.20 B717 0.19 0.58 36,146,911 5.12 16.17 0.13 0.12 B7376 0.15 0.46 37,630,857 3.89 12.27 0.14 0.10 BAC111 0.13 0.41 27,452,550 4.71 14.87 0.10 0.09 YK42 0.10 0.32 27,050,459 3.71 11.71 0.10 0.07 J328 0.04 0.12 22,403,249 1.73 5.45 0.08 0.03 B7674 0.03 0.10 4,810,043 6.69 21.10 0.02 0.02 B7573 0.004 0.01 606,704 6.24 19.70 0.002 0.002 C525 0.003 0.01 2,565,864 1.01 3.20 0.01 0.002 TOTALS 152 480 26,947,208,614

39

4.2 Allocation results 1990 and 2000

The results for different Options for allocating international aviation CO2 emissions across the EU are

given in Tables 7 and 8 for 1990 and 2000 respectively; domestic emissions are also given in these

tables. Tables of domestic CO2 emissions for all countries are provided in Appendices 3 and 4, for 1990

and 2000, respectively. In addition, International CO2 emissions are provided in Appendices 5 and 6

(1990 and 2000) for Annex I countries, and in Appendices 7 and 8 (1990 and 2000) for all countries.

Whilst Options 2 and 3 are discussed in more detail by Owen and Lee (2005), results have been

provided here for completeness.

For the EU25 States, international CO2 emissions grew over the decade 1990 to 2000 by varying

amounts, between approximately 40 and 80%, depending upon the allocation methodology. Of the

Options favoured by SBSTA and that could be computed for both 1990 and 2000 (Options 3, 5 and 6):

EU25 international CO2 emissions grew by 179% (in all cases).

Emissions of international CO2 have also been computed for 2000 on a ‘matrix’ basis, presented in

Table 9. Here, international CO2 emissions from one EU Member State to another have been computed

via the Option 5 methodology, since this is the closest to the conventional definition of ‘international

aviation’ used by ICAO, IPCC and IEA. The total intra-EU25 international emissions for 2000 were 24.7

Tg yr-1, which amounts to approximately 5% of global aviation CO2 emissions. Note that the entries for

‘EU25’ in Tables 7 and 8 are for all international flights to from the EU25, i.e. intra-EU25 plus EU25 to

non-EU countries.

40

Table 7: Overview of allocation of international aviation carbon dioxide emissions from different SBSTA Options13 (see text) for 1990, Gg CO2 yr-1

Domestic %dom 2a 2a(%) 2b 2b(%) 3a 3a(%) 3b 3b(%) 4 4(%) 5 5(%) 6 6(%) 8 8(%) Austria 32 0.31 367 1.48 364 1.16 639 1.44 886 1.45 – – 640 1.44 1,389 2.51 751 3.40 Belgium 2 0.02 663 2.67 852 2.72 1,463 3.30 3,095 5.05 – – 1,430 3.22 2,152 3.89 631 2.85 Cyprus 1 0.01 – 0.00 – 0.00 310 0.70 – – – – 304 0.68 500 0.91 45 0.20 Czech Republic 33 0.32 902 3.64 1,251 3.99 229 0.52 617 1.01 – – 230 0.52 355 0.64 669 3.02 Denmark 208 2.01 325 1.31 401 1.28 1,399 3.15 1,762 2.88 – – 1,377 3.10 2,404 4.35 908 4.10 Estonia – – 204 0.82 293 0.94 – – – – – – 0 0.00 0 0.00 0 0.00 Finland 415 4.02 363 1.46 421 1.34 604 1.36 974 1.59 – – 606 1.36 858 1.55 259 1.17 France 1,635 15.82 2,669 10.77 2,843 9.07 6,538 14.73 8,618 14.07 – – 6,505 14.65 7,958 14.40 4,156 18.78 Germany 1,866 18.06 5,701 23.00 7,711 24.61 8,408 18.95 11,589 18.93 – – 8,416 18.96 9,763 17.66 3,463 15.65 Greece 282 2.73 493 1.99 598 1.91 917 2.07 2,840 4.64 – – 920 2.07 1,143 2.07 450 2.03 Hungary – – 477 1.93 626 2.00 260 0.59 475 0.78 – – 264 0.60 497 0.90 445 2.01 Ireland 61 0.59 250 1.01 232 0.74 609 1.37 1,059 1.73 – – 618 1.39 1,247 2.26 783 3.54 Italy 1,523 14.74 2,390 9.64 3,139 10.02 3,139 7.07 4,195 6.85 – – 3,137 7.07 4,234 7.66 1,409 6.37 Latvia – – 137 0.55 169 0.54 – – 104 0.17 – – 0 0.00 0 0.00 0 0.00 Lithuania – – 239 0.97 287 0.92 – – – – – – 0 0.00 0 0.00 0 0.00 Luxembourg – – 63 0.25 95 0.30 229 0.52 – – – – 221 0.50 339 0.61 69 0.31 Malta – – – 0.00 – 0.00 136 0.31 – – – – 137 0.31 182 0.33 0 0.00 the Netherlands 10 0.10 987 3.98 1,223 3.90 2,954 6.66 4,497 7.34 – – 2,970 6.69 3,580 6.48 764 3.45 Poland 26 0.25 2,652 10.70 3,618 11.55 382 0.86 – – – – 380 0.86 508 0.92 923 4.17 Portugal 105 1.02 289 1.16 307 0.98 946 2.13 883 1.44 – – 944 2.13 1,178 2.13 622 2.81 Spain 1,587 15.36 339 1.37 435 1.39 – – – – – – 0 0.00 0 0.00 0 0.00 Slovakia 33 0.32 94 0.38 113 0.36 – – 78 0.13 – – 0 0.00 0 0.00 0 0.00 Slovenia – – 1,351 5.45 1,602 5.11 2,824 6.36 3,432 5.61 – – 2,842 6.40 3,492 6.32 2,504 11.32 Sweden 1,102 10.66 342 1.38 403 1.29 915 2.06 1,335 2.18 – – 929 2.09 1,875 3.39 883 3.99 United Kingdom 1,409 13.64 3,496 14.10 4,351 13.89 11,476 25.86 14,791 24.16 – – 11,527 25.96 11,612 21.01 2,394 10.82 EU25 Total14 10,333 100 24,794 100 31,332 100 44,374 100 61,230 100 – – 44,399 100 55,266 100 22,126 100 Annex 1 Total 151,323 86,166 109,850 102,280 144,422 102,283 97,362 50,073 Global Total 174,675 156,377 156,377 156,377 156,377 156,377 80,564

13 Option 1=no allocation; Option 2a=proportional to national CO2 emissions; Option 2b=proportional to national GHG emissions; Option 3a=modelled departures; Option 3b=UNFCCC data; Option 4=nationality of airline but no data available for 1990; Option 5=international departures/arrivals; Option 6=passenger km index; Option 7= nationality of passengers/cargo no data available; Option 8 = allocation according to airspace 14 note that these are all international flights to/from the EU25 Members States, not intra-EU only

41

Table 8: Overview of allocation of international aviation carbon dioxide emissions from different SBSTA methodologies15 (see text) for 2000, Gg CO2 yr-1

Domestic %dom 2a 2a(%) 2b 2b(%) 3a 3a(%) 3b 3b(%) 4 4(%) 5 5(%) 6 6(%) 8 8(%) Austria 51 0.36 596 1.70 613 1.40 1,368 1.72 1,675 1.56 1,954 2.32 1,373 1.73 1,337 1.70 1,257 3.68 Belgium – – 1,093 3.12 1,350 3.09 2,847 3.58 3,907 3.64 94 0.11 2,787 3.51 2,740 3.48 1,186 3.47 Cyprus 8 0.05 0.00 0 0.00 415 0.52 – – 295 0.35 414 0.52 429 0.54 45 0.13 Czech Republic 5 0.03 1,074 3.07 1,463 3.35 387 0.49 439 0.41 488 0.58 387 0.49 341 0.43 798 2.34 Denmark 126 0.88 496 1.42 595 1.36 1,810 2.28 2,348 2.19 334 0.40 1,808 2.28 1,571 1.99 1,042 3.05 Estonia – – 144 0.41 194 0.44 41 0.05 – – 59 0.07 41 0.05 43 0.05 105 0.31 Finland 416 2.91 548 1.57 646 1.48 831 1.05 1,027 0.96 1,266 1.50 818 1.03 759 0.96 386 1.13 France 2,697 18.83 4,111 11.74 4,478 10.25 11,667 14.68 14,361 13.38 13,776 16.35 11,646 14.66 11,623 14.75 7,587 22.22 Germany 1,748 12.20 7,151 20.43 9,778 22.38 14,780 18.60 17,582 16.38 17,871 21.21 14,753 18.57 14,890 18.90 6,596 19.32 Greece 440 3.07 946 2.70 1,125 2.57 1,324 1.67 2,954 2.75 1,066 1.27 1,328 1.67 1,380 1.75 526 1.54 Hungary – – 606 1.73 655 1.50 440 0.55 634 0.59 555 0.66 440 0.55 392 0.50 756 2.21 Ireland 60 0.42 496 1.42 486 1.11 1,020 1.28 1,566 1.46 1,631 1.93 1,046 1.32 995 1.26 888 2.60 Italy 2,695 18.81 3,954 11.30 5,132 11.75 5,705 7.18 8,689 8.10 5,060 6.00 5,692 7.17 5,458 6.93 2,024 5.93 Latvia – – 72 0.20 77 0.18 52 0.07 51 0.05 54 0.06 52 0.07 39 0.05 161 0.47 Lithuania 5 0.01 164 0.47 164 0.38 57 0.07 – – 71 0.08 57 0.07 50 0.06 172 0.50 Luxembourg – – 43 0.12 55 0.13 581 0.73 1,051 0.98 1,442 1.71 566 0.71 493 0.63 88 0.26 Malta – – – – – – 187 0.24 – – 250 0.30 187 0.24 183 0.23 0 0.00 the Netherlands 15 0.10 1,577 4.50 2,006 4.59 6,877 8.65 10,067 9.38 7,915 9.39 6,863 8.64 6,727 8.54 1,006 2.95 Poland 44 0.31 2,808 8.02 3,553 8.13 468 0.59 336 0.31 576 0.68 468 0.59 440 0.56 1,595 4.67 Portugal 328 2.29 598 1.71 678 1.55 1,294 1.63 972 0.91 1,172 1.39 1,290 1.62 1,333 1.69 379 1.11 Slovakia 5 0.03 348 1.00 426 0.98 21 0.03 – – 21 0.02 21 0.03 17 0.02 272 0.80 Slovenia – – 143 0.41 174 0.40 58 0.07 – – 87 0.10 58 0.07 43 0.05 180 0.53 Spain 3,131 21.86 2,815 8.04 3,326 7.61 5,668 7.13 8,314 7.75 4,534 5.38 5,682 7.15 5,683 7.21 2,342 6.86 Sweden 837 5.84 501 1.43 572 1.31 1,426 1.79 1,926 1.79 3,235 3.84 1,402 1.77 1,256 1.59 1,185 3.47 United Kingdom 1,714 11.96 4,720 13.48 6,142 14.06 20,151 25.35 29,412 27.41 20,463 24.28 20,264 25.51 20,564 26.10 3,568 10.45

EU25 Total16 14,323 35,004 100 43,687 100 79,475 100 107,311 100 84,270 100 79,444 100 78,785 100 34,147 100

Annex I Total 175,489 125,468 159,029 169,722 200,799 174,620 169,553 169,306 78,954

Global total 213,885 265,616 265,616 265,616 265,616 265,616 265,616 132,519

15 Option 1=no allocation; Option 2a=proportional to national CO2 emissions; Option 2b=proportional to national GHG emissions; Option 3a=modelled departures; Option 3b=UNFCCC data; Option 4=nationality of airline but no data available for 1990; Option 5=international departures/arrivals; Option 6=passenger km index; Option 7= nationality of passengers/cargo no data available; Option 8 = allocation according to airspace 16 note that these are all international flights to/from the EU25 Members States, not intra-EU only

42

Table 9: Matrix of emissions from international aviation17 between EU25 Member States18, 2000 (Gg CO2 yr-1)

Domestic

AT BE CY CZ DK EE FI FR DE GR HU IE IT LV LT LU MT NL PL PT SK SL ES SE UK

AT 51 0.00 68.82 15.61 7.25 41.17 4.43 21.27 96.89 363.95 42.49 9.74 7.47 81.71 4.23 3.44 6.54 6.59 94.89 24.84 1.58 1.23 5.20 80.99 37.73 145.37

BE 0 68.82 0.00 4.91 23.52 102.22 0.00 57.00 187.61 220.27 70.87 29.65 53.06 354.33 0.00 0.00 4.70 12.19 25.98 28.21 97.20 0.00 8.89 316.02 110.80 367.22

CY 8 15.61 4.91 0.00 10.68 0.04 0.00 16.76 16.30 50.79 79.29 8.03 0.00 13.14 0.00 0.00 0.00 3.44 29.92 3.28 0.00 2.42 0.62 7.30 0.00 232.97

CZ 5 7.25 23.52 10.68 0.00 17.23 0.00 17.64 46.29 55.96 14.50 13.86 7.99 28.81 3.71 0.00 0.00 3.17 34.42 5.82 0.65 10.63 0.00 36.17 10.01 85.17

DK 126 41.17 102.22 0.04 17.23 0.00 18.23 103.79 147.45 221.81 30.10 20.69 41.95 105.48 29.40 23.44 10.75 1.42 96.28 42.25 32.39 1.31 1.31 108.71 272.58 342.80

EE 0 4.43 0.00 0.00 0.00 18.23 0.00 6.64 0.00 8.55 0.00 0.00 0.00 0.00 1.91 3.61 0.00 0.00 0.00 0.33 0.00 0.00 0.00 0.00 15.35 11.45

FI 416 21.27 57.00 16.76 17.64 103.79 6.64 0.00 81.41 214.10 3.63 31.99 0.24 20.93 11.96 7.27 0.00 0.00 64.53 18.97 11.06 0.00 0.00 95.91 223.95 146.29

FR 2,697 96.89 187.61 16.30 46.29 147.45 0.00 81.41 0.00 650.70 117.35 45.04 98.11 688.72 0.00 3.89 15.45 14.50 198.71 59.34 218.54 0.00 7.84 571.92 148.72 920.21

DE 1,748 363.95 220.27 50.79 55.96 221.81 8.55 214.10 650.70 0.00 713.79 116.71 73.74 701.41 9.97 24.47 21.23 40.24 249.20 130.70 379.71 3.44 21.05 2924.32 237.91 1214.39

GR 440 42.49 70.87 79.29 14.50 30.10 0.00 3.63 117.35 713.79 0.00 25.56 0.00 204.51 0.00 0.00 5.87 8.15 73.63 8.81 0.00 0.00 0.00 46.91 7.84 247.02

HU 0 9.74 29.65 8.03 13.86 20.69 0.00 31.99 45.04 116.71 25.56 0.00 7.17 21.42 1.52 0.00 0.00 1.37 43.06 12.52 0.00 0.00 0.00 14.36 11.67 79.84

IE 60 7.47 53.06 0.00 7.99 41.95 0.00 0.24 98.11 73.74 0.00 7.17 0.00 33.83 0.00 0.00 3.43 8.49 45.38 2.33 2.33 0.00 0.62 27.59 20.47 657.52

IT 2,695 81.71 354.33 13.14 28.81 105.48 0.00 20.93 688.72 701.41 204.51 21.42 33.83 0.00 0.00 0.00 17.08 35.87 287.01 29.81 99.47 0.00 0.04 516.24 60.61 971.23

LV 0 4.23 0.00 0.00 3.71 29.40 1.91 11.96 0.00 9.97 0.00 1.52 0.00 0.00 0.00 1.19 0.00 0.00 0.00 1.43 0.00 0.00 0.00 0.00 14.40 8.40

LT 5 3.44 0.00 0.00 0.00 23.44 3.61 7.27 3.89 24.47 0.00 0.00 0.00 0.00 1.19 0.00 0.00 0.00 8.57 3.42 0.00 0.00 0.00 0.00 6.56 14.08

LU 0 6.54 4.70 0.00 0.00 10.75 0.00 0.00 15.45 21.23 5.87 0.00 3.43 17.08 0.00 0.00 0.00 0.46 5.33 0.00 12.53 0.00 0.00 41.87 7.24 49.28

MT 0 6.59 12.19 3.44 3.17 1.42 0.00 0.00 14.50 40.24 8.15 1.37 8.49 35.87 0.00 0.00 0.46 0.00 13.67 1.48 1.46 0.00 0.00 0.72 7.61 99.93

NL 15 94.89 25.98 29.92 34.42 96.28 0.00 64.53 198.71 249.20 73.63 43.06 45.38 287.01 0.00 8.57 5.33 13.67 0.00 35.79 80.24 0.00 1.85 287.27 143.52 670.71

PL 44 24.84 28.21 3.28 5.82 42.25 0.33 18.97 59.34 130.70 8.81 12.52 2.33 29.81 1.43 3.42 0.00 1.48 35.79 0.00 0.00 0.00 0.00 16.67 16.29 100.74

PT 328 1.58 97.20 0.00 0.65 32.39 0.00 11.06 218.54 379.71 0.00 0.00 2.33 99.47 0.00 0.00 12.53 1.46 80.24 0.00 0.00 0.00 0.00 200.69 0.12 279.40

SK 5 1.23 0.00 2.42 10.63 1.31 0.00 0.00 0.00 3.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00

SL 0 5.20 8.89 0.62 0.00 1.31 0.00 0.00 7.84 21.05 0.00 0.00 0.62 0.04 0.00 0.00 0.00 0.00 1.85 0.00 0.00 0.00 0.00 0.03 1.70 13.05

ES 3,131 80.99 316.02 7.30 36.17 108.71 0.00 95.91 571.92 2924.32 46.91 14.36 27.59 516.24 0.00 0.00 41.87 0.72 287.27 16.67 200.69 0.03 0.03 0.00 83.26 1031.96

SE 837 37.73 110.80 0.00 10.01 272.58 15.35 223.95 148.72 237.91 7.84 11.67 20.47 60.61 14.40 6.56 7.24 7.61 143.52 16.29 0.12 0.00 1.70 83.26 0.00 381.82

UK 1,714 145.37 367.22 232.97 85.17 342.80 11.45 146.29 920.21 1214.39 247.02 79.84 657.52 971.23 8.40 14.08 49.28 99.93 670.71 100.74 279.40 0.00 13.05 1031.96 381.82 0.00

Totals 14,323 1,173 2,143 495 434 1,813 70 1,155 4,335 8,648 1,700 494 1,092 4,272 88 100 202 261 2,490 543 1,417 19 62 6,409 1,820 8,071

17 Essentially, this equates to Option 5 18 Standard international country notation used in table

43

5 Discussion

5.1 Comparison of global results with other data

The FAST-2000 and FAST-1990 data may be compared with other data, provided from other

estimations using a similar modelling approach on a global basis. These are given below in Table 10.

Note that only data for civil aviation are given.

Table 10: Summary of global total fuel, CO2, NOx and km travelled for FAST-1990 and FAST-2000 inventories and other inventory sources

Inventory Year Fuel (Tg/yr)

CO2 (Tg/yr)

NOx (Tg/yr)

Distance (km × 109)

Reference

FAST-1990(OAG) 1990 105 331 1.42 15.7 This work ANCAT/EC2 1991/92 114 360 1.60 - Gardner et al. (1998)

DLR-2 1991/92 112 354 1.60 - Schmitt and Brunner (1997)

TRADEOFF-91/92 1991/92 112 354 (1.61) 17.1 Fichter et al. (2005)

NASA 1992 114 359 1.44 - Baughcum et al. (1996)

FAST-91/92(T)19 1991/92 116 366 1.56 17.9 This work FAST-91/92(OAG)20 1991/92 109 343 1.48 16.6 This work NASA 1999 128 404 1.69 25.8 Sutkus et al. (2001)

TRADEOFF-2000 2000 152 476 1.95 25.1 Gauss et al. (2005)

FAST-2000(OAG) 2000 152 480 2.03 27.0 This work AERO2K 2002 156 492 2.06 33.2 Eyers et al. (2004)

A number of the inventories presented in Table 10 above are essentially very similar. The ANCAT/EC2

and DLR-2 inventories are based upon the same air traffic movement data but different engine models.

The TRADEOFF-91/92 inventory was calculated with the FAST model but uses the ANCAT/EC2 air

traffic movement database so that good agreement with ANCAT-ECT and DLR-2 is expected. FAST-

91/92(T) once again uses the ANCAT/EC2 air traffic movement database but includes all the new input

data files and new parameterisations/data described in this report. The small increase in emissions

(FAST-91/92(T) over TRADEOFF-91/92) is expected since the older TRADEOFF work did not include

turboprop aircraft which, from Table 4 was shown to amount to 6 Tg CO2 in 1990.

The differences between FAST-91/92(T) and FAST-91/92(OAG) essentially reflect the differences

between the movement databases. For the ANCAT/EC2 movement database, some air traffic control

data were included over and above the scheduled movements for some regions, principally Europe

(EUROCONTROL data) (Gardner et al., 1998). The same months as used in the ANCAT/EC2 inventory

were obtained from OAG in order to make this comparison, which is discussed in more detail in Section

5.3.1. The ANCAT-driven inventory was approximately 6% greater than the OAG-driven inventory in

terms of fuel/CO2.

19 FAST-91/92(OAG) uses the same four months as was used in FAST-91/92(T) but from the OAG, i.e. scheduled only 20 FAST-91/92(T) uses the same movements as in ‘TRADEOFF-91/92’ (i.e. the ANCAT/EC2 movement database) but the updated performance input data and assumptions as in this work for FAST-1990(OAG) and FAST-2000(OAG)

44

The TRADEOFF-2000 dataset was compiled using the ANCAT/EC2 movement database (1991/92) and

run in ‘forecast’ mode, using ICAO global RPK (revenue passenger kilometre) data to 1995 and then

extrapolating using the IS92e growth rate assumptions (see FESG, 1998).

The inventories calculated for this work – FAST-1990(OAG) and FAST-2000(OAG) – are broadly in

agreement with other total estimations of civil aviation fuel usage over time. The ‘outlier’ is the NASA

1999 data, which also uses OAG data – and should therefore be compatible with FAST-2000(OAG), and

is the smallest estimation for recent years and does not fit the overall trend so well. It is difficult to

understand why the NASA estimation should be so much smaller (for 1999) than others. However, the

internal consistency of the FAST/TRADEOFF modelling shows that the overall trend is as expected and

the introduction of new input data/parameterisations has not altered the estimations beyond that

expected.

The most serious issue for the usage of OAG-based inventories is the underestimation of traffic and

emissions in Europe, a point returned to in Section 5.3.1 in some detail.

5.2 Implications of allocation methodologies for the UK inventory and EU25

5.2.1 Specific allocation Options

Option 1

This Option was favoured by SBSTA for further consideration. Whilst this Option (no allocation) may

seem to require no further consideration, there is a different question at stake, i.e. “is allocation

needed?” (Wit et al., 2004). Non-allocation would require an international body, such as ICAO, to

establish and maintain a system by which emissions were administered. Thus, emissions permits would

be distributed to legal entities such as airlines. In such a case, only the domestic emissions of the UK

would need to be considered.

Currently, domestic (and international) emissions are calculated for the UK by AEA Technology’s

National Atmospheric Emission Inventory (NAEI) using a relatively simple methodology (IPCC Tier 1). A

number of possibilities arise in order that the UK’s domestic emissions are calculated with better

accuracy: a higher tier methodology could be used by the NAEI; a methodology similar to that presented

here could be used21; or data from airlines could be used directly. Currently, only British Airways report

their fuel usage. However, under the UK aeronautical industry’s ‘Civil Aviation Sustainability Strategy’

(CASS) initiative, a commitment to report fuel usage by UK airlines has been made. This last option

would be the most accurate since airlines have accurate records of fuel used.

Option 2

This Option was not favoured by SBSTA for further consideration. The details of the data and their

calculation and compilation are given by Owen and Lee (2005a) but the results are provided here for

1990 and 2000 and given in Tables 7 and 8. For ‘Option 2a’, international aviation emissions of CO2 are

apportioned in proportion to a country’s national CO2 emissions; for ‘Option 2b’, they are apportioned in

21 The methodologies employed in the report where the FAST model has been utilized could be classified as ‘advanced’: the current IPCC (1996) Guidelines do not include a higher tier category but the IPCC Guidelines currently under preparation include a Tier 3b category (most advanced), into which the modelling presented here would fall

45

proportion to the country’s total CO2 equivalent emissions (i.e. accounting for other Kyoto GHGs via

GWPs).

Calculations for Option 2b were only possible for Annex I parties, where the reporting of emissions was

relatively complete. The data for both 2a and 2b for non-Annex I parties is less complete, since they are

not obliged to report data or follow any particular guidelines. In many cases, the data are estimated by

third parties (Owen and Lee, 2005a). Option 2 could be considered to introduce distortions; whilst there

is for some countries a relationship – or at least a correspondence – between national CO2 emissions

and international aviation emissions, for some other countries it is not the case. An example is Poland,

which has relatively large national emissions of CO2 but a relatively small share of international aviation

activity. Many other examples of this phenomenon also exist, when looked at from a wider scope than

the EU25.

Options 3, 4, 5, and 6

These Options were favoured for further work by SBSTA (UNFCCC, 1997). These were all calculated

for 1990 and 2000 with the exception of Option 4, for which the relevant parameters were not available

in the 1990 OAG data.

Option 3a and Option 3b are calculated quite differently: Option 3a is a FAST model calculation of

international departures, whereas Option 3b is from UNFCCC data. In principle, there is no difference

between these Options in that they are based on fuel usage for international aviation. In Option 3a, this

is calculated consistently with an IPCC Tier 3b type methodology22 the UNFCCC data are a compilation

of Tier 1 and Tier 2 methods calculated by individual Parties. It is difficult to determine the cause of

differences between such results since the inputs to the UNFCCC by Parties are so variable. This is

discussed in more detail by Owen and Lee (2005a). The UNFCCC total (Option 3b) for the EU25 is

greater than the FAST-modelled data (Option 3a) by 38% in 1990 and 35% in 2000.

Option 6 is approximately 24% greater than Option 5 in 1990 for the EU 25. Unsurprisingly, Option 5 is

very similar to Option 3a, since they are very similar calculations.

For 2000, the results of international allocation for the EU25 are similar for Option 3a, 4, 5 and 6. The

difference between Options 5 and 6 calculated for 1990 were not shown in 2000 for the EU25.

For the UK specifically, a similarity between Options 3a, 4, 5 and 6 was shown in 1990 (11.4 – 11.6 Tg

CO2) and in 2000 (20.1 – 20.6 Tg CO2).

Option 7

This Option was not favoured by SBSTA for further work. As mentioned in the methodology

descriptions, it was not possible to model Option 7, so this is not discussed further.

Option 8

This Option was not favoured by SBSTA. The results of this Option show strong differences with those

of Options 3 to 6. Large territories such as Denmark (Denmark has declared Greenland as a domestic

territory), Poland and France are prejudiced by this Option; conversely, smaller territories such as the

UK, Portugal and the Netherlands that have extensive international aviation activities that are allocated

22 Tier 3a and 3b definitions for aviation are currently under development by the IPCC

46

only small amounts. As widely recognised, this Option does not allow – without further formulation –

allocation of emissions over international waters.

5.2.2 CO2 equivalents

Aviation’s principle emissions and effects were summarised in Section 1. However, the IPCC (1996)

Greenhouse Gas Guidelines provide emissions factors for N2O and CH4.

A ‘CO2 equivalent’ is the mass of CO2 multiplied by the 100 Global Warming Potential (GWP) of the

trace gas considered. Thus, using 100 year GWPs for CH4 and N2O from the IPCC Third Assessment

Report (IPCC, 2001), CO2 mass is multiplied by 23 and 296, respectively. The GWP is a commonly

used metric to assess the capacity of different greenhouse gases on climate and is the ratio of the

integrated RF arising from a pulse emission of a gas over an arbitrary period of time relative to that of

CO2 (a 100-year time horizon is used in the Kyoto Protocol).

Emission factors for CH4 and N2O for aviation are commonly referred to from the Revised IPCC

Guidelines for National Greenhouse Gas Inventories (IPCC, 1996). However, the underlying source of

these data is not so transparent. A footnote to Table 1-50 (of IPCC, 1996) refers to CH4 being 10% of

total VOC, citing Olivier (1991). Olivier (1991) is a review, and derives a figure of approximately 10%

from Shareef et al. (1988). Shareef et al. (1988) appears to be a review/compilation of generalised data,

so that the provenance of the IPCC emission factor for CH4 is questionable. The footnote to Table 1-50

(of IPCC, 1996) for N2O simply refers to default values provided for Tier 1 assessment – these are not

referenced.

There are very few measurements of minor gas-phase species from aircraft engine exhaust, as this

tends to be a specialised measurement activity, entirely in the research domain. Measurements of CH4

and N2O are not required for the ICAO engine emissions certification process, so manufacturers do not

routinely report such data.

A few comprehensive measurements of aircraft exhaust composition have been made by Spicer et al.

(1992; 1994) and Wiesen et al. (1994), and a review was provided by Brasseur et al. (1998). The

measurements by Spicer et al. (1992, 1994) were made for older engines, some of which were military

turbo-jets with afterburners, not representative of modern high bypass engines. Wiesen et al. (1994)

provide the most recent and authoritative measurements: they concluded that for the engine types P&W

305 and an R-R RB211, CH4 was a very small component of the exhaust stream at idle, and that at

higher power settings (representing climb-out and cruise), the CH4 present was more likely to be from

ambient air entering the engine core or bypass air. Thus, they could provide no best estimate of an

emissions factor for CH4. For N2O, their best estimate was an emission factor of 0.15 g N2O/kg fuel

(Wiesen et al., 1994).

This report does not provide an estimate of the CH4 emissions because of the lack of a reliable

emissions factor – that is nonetheless evidently small. For N2O, the emissions factor of 0.15g/kg fuel of

Wiesen et al is adopted. (1994). For a global fuel usage in 1990 of 105 Tg, this equates to

approximately 15.8 Gg N2O. In 2000, the emission of N2O globally would have been 22.8 Gg. The IPCC

Third Assessment Report (IPCC, 2001) gives an estimate of global N2O emissions of 16.4 Tg N yr-1 or

(51.5 Tg N2O yr-1) for 2000 such that the global equivalent CO2 emissions of N2O from aviation, at 4.7

and 6.7 Tg, globally (approximately 1.4% of the total aviation CO2 emissions). Such a small amount of

N2O is well within the uncertainties of other aspects of the calculation of CO2 from aviation.

47

5.2.3 Radiative forcing index for aviation

In the IPCC (1999) Special Report on aviation, a new simple metric was defined, the ‘Radiative Forcing

Index’ (RFI), which is the sum of the individual aviation forcings divided by that from CO2 alone. This has

proven to be a useful metric to express the total RF of a sector – in this case aviation – in relation to its

CO2 forcing.

The IPCC (1999) estimated the RFI for aviation in 1992 to be 2.7 with a range of 1.9 to 4.0. However,

the figure of 2.7 must not be interpreted with spurious accuracy: “about 3” would be faithful to the

uncertainties of the underlying science (although this does not constitute a recommendation to use 3 in

an arithmetic sense): a similar conclusion was reached by the Royal Commission on Environmental

Pollution (RCEP, 2002). This is emphasised as it has been attempted by some to ‘correct’ the aviation

RFI to a total ground-level source RFI of 1.523 (which was given in Section 6.2.3 of IPCC, 1999, page

200). Such corrections are considered to attribute spurious accuracy to the RFI for aviation.

The TRADEOFF project has recently re-assessed aviation RFs (Sausen et al., 2005) and the RFI for

2000 was calculated to be approximately 2 (1.9). This is because of changed assessment of non-CO2

effects from new parameterisations, better assumptions or improved modelling.

The emissions data presented here have not been multiplied by the RFI as this is not strictly correct or

in line with the basis of RF, which underlies the RFI. Whilst RFI gives the total RF of aviation in relation

to the RF of CO2 from aviation, a straightforward relationship with CO2 emissions is difficult. This is

because CO2 emission rates must be converted to atmospheric concentrations of CO2, usually via a C-

Cycle model. Then, the CO2 RF is calculated which must account for the saturation effect of CO2 in the

atmosphere: a natural logarithm function fits the data quite well (Myhre et al., 1998).

The relationship between CO2 emissions and aviation RFI is dealt with in more detail in Appendix 11.

5.3 Uncertainties

In this section, some of the uncertainties introduced into the allocation calculations for 3a, 4, 5, 6 and 8

are considered in terms of those introduced by the global aviation emissions model, FAST. Such

inventory models are now relatively well developed and have been under development for almost 15

years. Nonetheless, they are also complex and must introduce some simplifying assumptions for the

sake of tractability. In the following sub-sections, some of the most relevant and quantifiable

assumptions are examined to determine the largest factors of uncertainty introduced into the allocation

calculations.

23 In fact, such a ‘correction’ is a selective and incorrect use of the data: IPCC says “For comparison, in the IS92a scenario the RFI for all human activities is about 1; for greenhouse gases alone, it is about 1.5, and it is even higher for sectors that emit CH4 and N2O without significant fossil fuel use.” The origin of “about 1” is Table 6-2 of IPCC and is in fact 0.9 (and 1.7 for the greenhouse gases alone). The usage of 1.5 is selective (i.e. greenhouse gases alone) and ignores the negative forcing of aerosols listed in the same table. A scientifically compatible correction to the aviation RFI would be to include the aerosol forcing for all human activities, which would increase the effective RFI of aviation to 3.0. However, such ‘corrections’ are not recommended as they imply a level of accuracy to RFI values which cannot be justified.

48

5.3.1 Traffic

One of the significant uncertainties in this study was the magnitude of the bias introduced by the usage

of OAG data. The OAG data included scheduled passenger and freighter movements, often prepared in

advance of the traffic performed. The OAG data were used as they were global in scope and at the

commencement of the study, no other data were (or are now) available on a global basis for the years

required.

Previously, the assessment of total traffic has been addressed in major inventory initiatives that have

been much larger in scope than this present study. The ANCAT/EC2 global aviation inventory for

1991/92 (Gardner et al., 1998) used air traffic control data for Europe, parts of the Far East and

Australasia. The AERO2K inventory also used a similar approach, substituting OAG data in Europe and

the United States with EUROCONTROL and FAA air traffic data (Michot et al., 2001). The Boeing-NASA

inventories used OAG data along with an estimate of charter traffic (Baughcum et al., 1996; Sutkus et

al., 2001). The current SAGE study uses data that are similar to those compiled for AERO2K (Michot et

al., 2004).

In order to address this particular uncertainty in this work, two comparisons were made. Firstly, OAG

data for the concomitant months of the ANCAT/EC2 inventory were obtained (July 1991, October 1991,

January 1992 and April 1992), so that a comparison of European data could be made, since the

European movements of ANCAT/EC2 were EUROCONTROL data. Secondly, in order that a more up to

date comparison could be made, data were kindly provided by EUROCONTROL for Europe for January

and July of 2000.

The EUROCONTROL data were significantly different to the OAG data both in content and level of

complexity and would have taken much processing to incorporate them into the FAST model which was

beyond the scope of the work. Therefore, a simplified approach was taken that quantified the

EUROCONTROL data in terms of traffic and provided an estimate of emissions, based upon average

factors derived from Tables 5 and 6. The EUROCONTROL data incorporate all air traffic that fly under

Instrumented Flight Rules (IFR) and therefore include many kinds of movement not covered by OAG.

These include: charter flights, military flights, pleasure flights, general aviation and positioning flights.

Thus, a simple comparison of movements, or even total flight km would be misleading, since there are

many movements that result in small rates of fuel burn. Thus, the EUROCONTROL data were filtered to

reflect the representative (and their represented) aircraft types in FAST-2000. The EUROCONTROL

data comprised flight plan data, i.e. flight legs (or, composite portions of flights, hereafter designated

‘actual’) as opposed to the great circle (GC) distance assumed – out of necessity – in the global FAST-

1990 and -2000 models. Thus, a comparison of distances flown would introduce another bias. In order

to make the data as comparable as possible, great circle distances were calculated for the

EUROCONTROL routes. A small number of flights and routes could not be matched, e.g. where the

origin and destination was the same airport – such flights were excluded from the analysis.

The analysis was performed for: intra EU25 flights; UK to EU25 flights; EU25 to non-EU25 flights; and

UK to non-EU25 flights. The results are presented in Table 11 below.

49

Table 11: Results of analysis of OAG and EUROCONTROL (EC) data, for January and July 2000 (km/month)

OAG EUROCONTROL EC

(actual)

% difference OAG–

EC(actual)

EUROCONTROL EC(GC)

% difference OAG–EC(GC)

% difference EC(actual)–

EC(GC) Intra EU25 January 225,467,358 276,311,659 -22.6 243,464,276 -8.0 -11.9 July 264,568,037 382,446,090 -44.6 339,263,630 -28.2 -12.7

Mean 245,017,698 329,378,874 -34.4 291,363,953 -18.9 -13.0

UK to EU25 January 55,542,262 74,711,055 -34.5 66,259,950 -19.3 -11.3 July 67,182,644 118,931,969 -77.0 106,834,421 -59.0 -11.3

Mean 61,362,453 96,821,512 -57.8 86,547,185 -41.0 -11.9

EU to non-EU25 January 372,437,572 370,096,600 0.6 350,515,759 5.9 -5.6 July 427,994,291 469,170,747 -9.6 443,745,913 -3.7 -5.7

Mean 400,215,932 419,633,674 -4.9 397,130,836 0.8 -5.7

UK to non-EU25 January 99,955,750 98,275,069 1.7 93,579,886 6.4 -5.0 July 114,916,591 119,719,058 -4.2 114,050,632 0.8 -5.0

Mean 107,436,171 108,997,064 -1.5 103,815,259 3.4 -5.0

The EUROCONTROL (actual) data show substantially more km performed for intra-EU and UK to EU25

flights; by contrast, the differences between OAG and EUROCONTROL (actual) data for both EU25 and

UK to non-EU25 destinations are much smaller, as might be expected since intra-continental charter

traffic is greater than inter-continental (which dominates the UK to non-EU25 traffic). This indicates that

a large fraction of air traffic in Europe is not captured by the OAG data. However, this comparison is not

necessarily the best one, as actual flown distances were recorded in the EUROCONTROL data.

Once the great circle (GC) distances were substituted into the EUROCONTROL data in order to provide

better comparability with the OAG data, the differences become smaller. So, for example, on this basis,

OAG data underestimates intra-EU25 traffic in terms of km flown by -8% in January and approximately

-28% in July. The variability will be much higher for individual EU25 States: for the UK, this

underestimation is approximately -19% in January and -59% in July.

The difference between the EUROCONTROL ‘actual’ and EUROCONTROL GC data conveniently

provides an estimate for the bias introduced by the assumption of utilising GC distances. This is higher

in Europe, being approximately 13% as an annual average. For the EU25 to non-EU destinations,

however, this comparison is not as straightforward since the EUROCONTROL data only provide actual

distances within the EUROCONTROL air traffic domain: for flight legs outside of the domain, GC is

calculated.

Using average global emission factors by representative type from Table 6, the bias introduced into CO2

emissions can be estimated, which is given by regional flow, as above, for January and July 2000 in

Table 12.

The estimated bias in CO2 emissions is likely to be larger than implied simply from the km flown

because the use of scheduled data introduces an underestimation of CO2 emissions on intra EU flights

of approximately -10% in January and -35% in July.

50

Table 12: Estimated bias in CO2 emissions for various traffic flows within and to/from EU15 for January and July, 2000 (Gg CO2/month)

OAG (Gg CO2/month)

Eurocontrol (Gg CO2/month)

difference (%)

Intra EU January 2,744 3,024 -10.2 July 3,194 4,316 -35.1

Mean 2,969 3,670 -23.6 UK to EU January 682 858 -25.8 July 815 1,428 -75.3

Mean 749 1,143 -52.7 EU to non-EU January 8,974 7,706 14.1 July 10,221 9,536 6.7

Mean 9,598 8,621 10.2 UK to non-EU January 2,663 2,351 11.7 July 3,032 2,810 7.3

Mean 2,848 2,581 9.4

In order to understand the seasonality better (in the absence of other months of EUROCONTROL data),

air traffic data were obtained from the UK Civil Aviation Authority (CAA)24 for 2000. These data include

statistics of monthly km flown for scheduled and non-scheduled air traffic to/from the UK for passenger

and cargo flights for different traffic flows (UK domestic, UK to EU15 States and UK to non-EU15

countries).

The UK to non-EU15 traffic flows (1000 km) and UK to EU15 flows are shown for total, scheduled and

non scheduled traffic (cargo and passenger flights are combined in all cases) in Figs 7 and 8.

Figure 7: Monthly variation of flight km (1000s) between the UK and non-EU15 states in 2000; scheduled (blue line), non-scheduled (pink line), and total (red line) – CAA data

24 http://www.caa.com.uk see ‘Air Traffic Statistics’

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

fligh

t km

(100

0s)

Sc&NSc tot non EUNSc tot non EUSc tot non EU

51

For the flows in terms of UK to non-EU15 States, as in the OAG/EUROCONTROL data comparison, the

CAA data show that scheduled traffic (in terms of km flown) represents approximately 80% of the traffic

as an annual average whereas the OAG/EUROCONTROL comparison indicates 95–100%.

For the UK to EU States, the comparison is not easily made as the CAA data are for EU15 States,

whereas the comparison made in this work is for EU25 States. Nevertheless, the CAA data imply that as

an annual average, UK to EU15 scheduled traffic is approximately 50% of the total, whereas the

comparison made with the EUROCONTROL data implies that scheduled traffic for the same flow is

approximately 70%. The other factor that may confound the comparison is the potential inclusion of

military transport and freighter aircraft in the analysis of EUROCONTROL data; however, based upon

previous experience, this is unlikely to explain the discrepancies between the analysis of OAG,

EUROCONTROL and CAA data.

Explaining differences between air traffic data is notoriously difficult. The approach taken here was to

use the factors obtained from the comparison of the January and July EUROCONTROL data and the

OAG to suggest correction factors. This could be refined with a more complete analysis in the future,

which is strongly recommended.

Figure 8: Monthly variation of flight km (1000s) between the UK and EU15 states in 2000; scheduled (blue line), non-scheduled (pink line), and total (red line) – CAA data

The second analysis made was a comparison of the ANCAT/EC2 data with the same months utilised

from OAG. The ANCAT/EC2 data were essentially EUROCONTROL data but the blending with OAG

data for the global database and removal of duplicate flights was done elsewhere, so that the

provenance of the ANCAT/EC2 data is less transparent.

This analysis examined aircraft movements for the four months July 1991, October 1991, January 1992

and April 1992. An analysis of international and intra-EU flights was undertaken, which is presented

below in Table 13.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

fligh

t km

(100

0s)

Sc tot EUNSc tot EUSc&NSc tot EU

52

Table 13: Comparison of number of international and intra-EU25 flights between the ANCAT/EC2 movement data and corresponding OAG data

International Flights Intra-EU flights ANCAT/EC2 OAG ANCAT/EC2 OAG

Jul-91 1,920,541 1,813,495 156,119 98,217 Oct-91 1,962,961 1,780,255 155,978 100,382 Jan-92 1,936,796 1,782,868 125,523 99,739 Apr-92 1,922,993 1,748,630 147,228 103,490

This analysis is not compatible with the previous analyses, since it is on number of flights, rather than

flight km, However, it shows the same overall trend that internationally, the OAG underestimates by a

approximately -9%. For intra-EU25 flights, by approximately -46%.

Underestimation of traffic also occurs in other work; the Boeing-NASA inventories have used OAG, or

scheduled data (Baughcum et al., 1996; Sutkus et al., 2001). Sutkus et al. (2001) compared their

calculations of fuel usage for domestic flights in the US with the FAA ‘Form 41’ system, whereby airlines

report their fuel usage and found that the underestimation was of the order 5%. It is clear that the

systematic bias introduced in using scheduled only traffic data is larger in Europe, being estimated here

to be approximately -23% on CO2 emissions for intra-EU traffic and up to -50% on UK to EU traffic.

Without further significant work, it is not possible to calculate a robust correction factor for all

international aviation emissions for the UK. Provisionally, it is recommended to adopt the EU-wide factor

of -23%, although it is likely that this is a conservative estimate, and that the actual bias might be

greater.

Nonetheless, given that the allocation of international aviation emissions of CO2 have produced

remarkably similar results for Options 2 to 6, this bias is unlikely to affect any policy considerations of

the merits of the different allocation Options for the UK.

5.3.2 Airframe representative types

An analysis was performed to estimate the effect of grouping several aircraft types to one representative

aircraft type. As an example, the differences between fuel flows were investigated for A330-200 and

A330-300, and B737-300, B737-400 and B737-500, which are combined to one representative aircraft

type respectively.

Figure 9 shows that differences in fuel consumption for different airframes increase with increasing

mission distance performed and that the extent of the differences varied for the airframes which are

compared. The difference in fuel consumption between the A332 and the A333, reaches approximately

5% for a distance of more than 4,800 nm (Table 14). The comparison of the B734 and the B735 reveals

a difference in fuel consumption of more than 10% for a distance of only 1,600 nm (Table 15).

It might therefore be necessary in future work to undertake a more detailed analysis and take also the

frequency of flights with those aircraft types into account. However, for some types it might be more

sensible to split them up into separate groups.

53

Figure 9: Comparison of similar airframes with respect to fuel consumption by mission distance: A330-200 and A330-300 (upper panel) and; B7373-300, B737-400, B737-500 (lower panel)

Airframe Uncertainty - Comparison of B737-300, B737-400 and B737-500

0

2000

4000

6000

8000

10000

12000

270 540 809 1079 1349 1619 1889 2158

Trip Distance [nm]

Fuel

burn

[kg]

B737-300B737-400B737-500

Airframe Uncertainty - Comparison of A330-200 and A330-300

0

10000

20000

30000

40000

50000

60000

70000

270 540 809 1079 1349 1619 1889 2158 2428 2689 2968 3238 3507 3777 4047 4317 4587 4856 5126

Trip Distance [nm]

Fuel

burn

[kg]

A330-200A330-300

54

Table 14: Comparison of different airframes (A332 with A333) with respect to trip distance (nautical miles – nm) and fuel consumption (kg)

Trip distance

(nm)

A332, fuel consumption

(kg)

A333, fuel consumption

(kg)

increase in fuel consumption from A330-

200 to A330-300 (%)

270 4,519 4,595 1.7 540 6,904 7,061 2.3 809 9,481 9,756 2.9

1,079 12,349 12,717 3.0 1,349 15,074 15,544 3.1 1,619 17,966 18,560 3.3 1,889 20,904 21,628 3.5 2,158 23,898 24,881 4.1 2,428 26,954 27,923 3.6 2,689 29,953 31,049 3.7 2,968 33,217 34,458 3.7 3,238 36,434 37,821 3.8 3,507 39,704 41,246 3.9 3,777 43,046 44,763 4.0 4,047 46,461 48,348 4.0 4,317 49,958 52,059 4.2 4,587 53,515 55,894 4.4 4,856 57,193 59,872 4.7

Table 15: Comparison of different airframes (B733, B734 and B735) with respect to trip distance (nautical miles – nm) and fuel consumption (kg)

Trip distance

(nm)

B733, fuel consumption

(kg)


(kg)


(kg)

increase in fuel consumption from B735 to

B733 (%)

increase in fuel consumption from B735 to

B734 (%) 270 1,833 1,893 1,773 3.4 6.8 540 3,119 3,246 2,991 4.3 8.5 809 4,469 4,671 4,274 4.6 9.3

1,079 5,913 6,197 5,656 4.5 9.6 1,349 7,391 7,756 7,065 4.6 9.8 1,619 8,907 9,362 8,497 4.8 10.2 1,889 10,465 11,014 9,948 5.2 10.7 2,158 11,994 11,285 6.3

5.3.3 Engine representation

The aircraft performance model PIANO comes for most aircraft types with a generic engine (see Table

2). PIANO does not have engine data available which were suitable for more than one aircraft type,

therefore it is not possible to undertake an uncertainty analysis with respect to engine representation.

However, different types of engines that are suitable for one particular aircraft type tend to be similar

with respect to fuel burn for competitive reasons; therefore the fuel flow profiles calculated by means of

PIANO are unlikely to change considerably.

5.3.4 Other uncertainties

One of the fundamental difficulties of estimating aviation fuel usage is making reasonable assumptions

over the way in which aircraft fly. As is clear from the methodology section, the inventory is ‘statistical’ in

nature, as detailed data on aircraft type, route, altitude, loading are not available. Estimating the load

55

factor of an aircraft is a typical example of such an assumption, as load factor will vary by airline, route,

time of day/week/year, and a plethora of external factors.

The choice of an average load factor is a necessary assumption as no detailed data are available for

reasons of commercial confidentiality and competitiveness. The fuel consumption of an aircraft is

directly dependent on its mass: for the calculation of fuel flow profiles by means of the PIANO aircraft

performance model, an average payload (passengers and/or freight) of 70% of the maximum payload

for each of the representative aircraft types and each mission was assumed. This is the same payload

factor which was used for the compilation of e.g. the ANCAT/EC2 (Gardner et al., 1998) and the

TRADEOFF inventories (Fichter et al., 2005). This payload factor is somewhat higher than the

assumption used for the AERO2K inventory of 60.9% for a 2002 base year (Eyers et al., 2004), an

assumption that was referenced to ICAO data: however, 70% is more representative of longer term

averages25 and allows a better comparison with the earlier inventories.

The payload and fuel masses vary depending on factors such as the category of operation (freight or

passenger). Such flight-by-flight variation cannot be taken into account in inventory compilation because

of the statistical assumptions that are necessary. However, a payload variation of 10% around a base

case of 70% is estimated to change the fuel consumption up to approximately 2.5%. An example is

shown for the A320-200 in Figure 10.

Figure 10 Example (A320-200) of the effect of payload assumptions (60%, 70%, 80%) by mission distance

One of the deviations from an average overall load factor is ‘tankering’. Tankering is the practice of

loading more fuel than is necessary for the mission performed. The significance of tankering is difficult if

not impossible to quantify on the scale necessary for this study. The IPCC (1999) report suggested that,

based upon information from one airline, additional fuel burn as a result of tankering may be of the order

25 e.g., see http://www.iata.org/pressroom/pr/2004-07-28-01.htm

Effect of different payload assumptions on fuel consumption for A320-200

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

270 540 809 1079 1349 1619 1889 2158 2428

Distance [nm]

Fuel

con

sum

ptio

n [k

g]

60% 70% 80%

56

of 0.5% of total aircraft fuel consumption. Note that PIANO simulates flight profiles with between 27 and

90% extra fuel for diversions (higher percentage for shorter flights).

A major European airline provided some confidential data on this issue. Based on a 3 month sample,

they estimated that 13.6% of their short-haul flights and 5.3% of their long-haul flights tanker fuel. As a

rule, the longer the outbound trip, the less capacity there is to tanker. Other factors also do come into

play such as landing weight restrictions and payloads. Where tankering occurs on short-haul flights, the

tankered amount can be up to 100% of the fuel necessary to travel from origin to destination. For long-

haul flights, the tankered amount is on average about 50% of the fuel necessary to travel from origin to

destination; but for longer routes it can be significantly lower.

The airline noted that the tankered fuel is dependent on particular airline practices and internal costs,

route network, aircraft types being operated, absolute market prices, regional market dislocations and

local distribution costs. Thus, it is not possible, even on a regional basis to estimate the effect of

tankering.

5.4 Choice of one allocation Option over another

SBSTA has already made clear that the preferred allocation Options for further work and analyses are

Options 1, 3, 4, 5, and 6 (UNFCCC, 1997). Option 1 requires no allocation and as such is not

considered in this study since it is primarily concerned with a numerical analysis, rather than a policy

analysis. Option 3 (country of sale of bunker fuels) is discussed in detail by Lee and Owen (2005a),

although its is reiterated here that bunker fuel sales were effectively accessed via UNFCCC and IEA

data on international aviation CO2 emissions, since both UNFCCC submissions and IEA calculations

use national bunker fuel data for calculating emissions. The major issue for both sources is the means

by which international and domestic emissions are derived and this represents a major uncertainty.

Moreover, some bunker fuel statistics from countries may not be reliable (Lee and Owen, 2005a).

Of the remaining Options, these have been calculated with the FAST model.

The question arises “how similar or dissimilar are the allocated emissions?” This analysis is primarily

concerned with the EU25 and the year 2000. The year 1990 has only incomplete data (by month) and

not all the allocation options could be calculated. Moreover, the data for 2000 are known to be of higher

quality.

In order to attempt to answer the above question on similarity/dissimilarity, a simple method of

examining the differences between results of allocation options has been devised that involves the

calculation of the statistical variability. For this, a coefficient of variation26 (COV) has been used. COVs

have been calculated for EU25 countries and for different combinations of Options derived from the data

presented in Table 8. Only the Options 3, 4, 5 and 6 were considered and the resulting COV statistics

are presented in Table 16.

26 The standard deviation divided by the mean, expressed as a percentage

57

Table 16: Coefficient of variation statistics for different combinations of allocation options, ordered by largest international emissions (see text) for EU25 Member States and EU25, Annex I totals

Country

Option 5

(Gg CO2 yr-1)

Option 5(%) cum%

COV 3a,3b,4,5,6

‘A’

COV 3a,4,5,6

‘B’

COV 3b,4,5,6

‘C’

COV 3a,3b,5,6

‘D’

COV 3a,5,6

‘E’

United Kingdom 20,264 25.5 26 16.34 0.80 17.16 17.42 0.86 Germany 14,753 18.6 44 8.97 8.52 8.95 7.76 0.40 France 11,646 14.7 59 9.52 7.58 9.61 9.54 0.15 The Netherlands 6,863 8.6 67 16.42 6.72 16.94 18.42 0.99 Italy 5,692 7.2 75 21.32 4.76 23.14 20.88 2.02 Spain 5,682 7.1 82 20.92 9.19 22.91 18.02 0.12 Belgium 2,787 3.5 85 51.19 55.20 58.82 15.78 1.57 Denmark 1,808 2.3 87 42.59 44.33 48.69 15.12 6.49 Sweden 1,402 1.8 89 39.43 44.48 39.90 16.84 5.52 Austria 1,373 1.7 91 15.58 17.10 15.80 9.55 1.17 Greece 1,328 1.7 93 42.27 9.60 44.23 39.94 1.90 Portugal 1,290 1.6 94 10.86 4.74 11.74 11.90 1.49 Ireland 1,046 1.3 96 22.73 22.60 22.18 20.49 2.04 Finland 818 1.0 97 19.81 22.04 20.57 11.74 3.90 Luxembourg 566 0.7 97 44.24 50.50 43.37 32.84 7.03 Poland 468 0.6 98 16.74 10.67 18.78 12.69 2.88 Hungary 440 0.5 98 18.07 13.14 18.81 19.52 5.34 Cyprus 414 0.5 99 13.95 13.95 15.80 1.63 1.63 Czech Republic 387 0.5 99 12.36 13.41 13.33 8.93 5.83 Malta 187 0.2 100 13.83 13.83 14.85 1.02 1.02 Slovenia 58 0.1 100 25.93 25.93 29.14 13.34 13.34 Lithuania 57 0.1 100 12.98 12.98 14.71 6.04 6.04 Latvia 52 0.1 100 10.86 12.13 11.99 11.34 12.86 Estonia 41 0.1 100 16.41 16.41 16.90 2.26 2.26 Slovakia 21 <0.1 100 8.66 8.66 9.59 9.59 9.59 EU25 Total 79,444 12.70 2.73 13.33 14.10 0.40 Annex I Total 169,553 6.88 1.29 7.28 7.64 0.10

The data have been ordered by the largest emissions of CO2 (Gg yr-1) as calculated by Option 5, since

this is the closest to the definition of international aviation as used by IPCC, IEA, ICAO, etc. The

percentage of each EU25 Member States’ emissions using Option 5 and the cumulative percentage

(see column ‘cum %’) indicate that the total is dominated by a few countries. The various combinations

of data used in the COV calculations are given in the heading columns of Table 16 but have also been

labelled ‘A’ to ‘E’ for brevity of description.

Any description and interpretation of these data is inevitably only semi-quantitative but it is striking that

82% of the EU25’s emissions of CO2 from international aviation arise from only the 6 largest emitting

Member States. The analyses of COVs for different combinations attempts to identify which options

introduce larger variation, so that some comment can be given to these results. Unsurprisingly,

combination ‘A’ (the most diverse) provides the most variability across the results: however, it should still

be noted that of the ‘top six’ countries (82% of international CO2 emissions) that the COVs are generally

not more than approximately 20% and that across the EU25 totals, approximately 13%. Analyses ‘B’

and ‘C’ show that of the two variants of Option 3, it is Option 3b (i.e. usage of UNFCCC data to estimate

bunker fuels) that introduces more variability. Elimination of Option 3b reduces the variability

58

dramatically, the ‘top six’ having variability of less that 10% (analysis ‘B’). Analysis ‘E’ was included as it

further eliminates Option 4 (nationality/flag of carrier): this was performed as for the future scenarios,

this allocation option could not be analysed because it would be highly speculative and as such,

unjustifiable (Lee and Owen, 2005b). Analysis ‘E’ further reduces the variability for the ‘top six’ to

approximately 2% or less.

Some of the COVs calculated are, much larger, e.g. a maximum of approximately 60%. However, often

these large variations are associated with only a small fraction of the EU25’s international aviation

emissions (as defined above). Thus, at a higher level of aggregation, this analysis shows that between

the allocation options analysed, much of the EU25’s international aviation emissions of CO2 are

explained by only a few countries and the variability of allocation results is relatively small, i.e. ~20% or

less. If the Option 3b (UNFCCC data) is removed from this analysis, the variability is less than 10% for

82% of the EU25’s emissions. Option 3b, the UNFCCC data introduces these larger uncertainties

because of the variety of inherent calculation methodologies used by national inventory producers and

the variable assumptions that they have to make.

6 Conclusions and recommendations

• A global model of aircraft movements and emissions of CO2 has been constructed. This is

based upon the Future Aviation Scenario Tool (FAST model) which uses the methodology of

Gardner et al. (1997, 1998) as reviewed by Henderson et al. (1999). The new/enhanced model

incorporates improved parameterisations and additional input data and is based upon

scheduled air traffic movements from the commercial OAG database. Models were

constructed for the years 1990 and 2000 (FAST-1990, FAST-2000).

• An analysis of SBSTA’s eight methodological Options for allocation of international aviation

emissions (UNFCCC, 1996) was performed and is reported here. This report concentrates on

Options 4, 5, 6 and 8 but also presents the results for Options 2 and 3, which are dealt with in

detail in a companion report (Owen and Lee, 2005a). It was not possible to perform any

calculations for Option 7, which requires knowledge of the nationality of passengers/country of

origin of freight because of a lack of data.

• Global aircraft emissions were calculated for 1990 and 2000 discriminating on a domestic and

international basis. SBSTA allocations Options 2 to 8 (with the exception of Option 7) were

calculated. These calculations represent the first exhaustive effort to quantify emissions

according to allocation options.

• The global emissions of CO2 from scheduled civil aviation were 331 Tg yr-1 in 1990 and 480 Tg

yr-1 in 2000. International emissions were 47% and 55% of the global totals in 1990 and 2000,

respectively: 156 and 266 Tg CO2 were unallocated to parties.

• Over the decade from 1990, aviation CO2 emissions increased by a factor of 1.5, even with a

15% improvement in global fuel efficiency (in terms of kg CO2/km). International aviation

emissions increased for all Annex I Parties to the Kyoto Protocol. This is in contrast to the

requirement of Article 2.2 of the Protocol to reduce or limit emissions from international

aviation, although the scope of ‘limit’ is open to interpretation.

59

• Of the Options favoured by SBSTA presented here (3a, 3b, 4, 5, 6), they were in close

agreement for Annex I Party and EU25 totals, and also for the UK, especially in 2000. Variation

was found at a level of individual countries. Within the EU25 Member States, the variation

between these different allocation options was approximately 20%, or less, for six countries

that explained 82% of international aviation emissions. Elimination of Option 3b from this

analysis (the UNFCCC data), on the basis that this was them most inconsistent dataset,

reduced the variability to less than 10% for the same six countries. Therefore, of the favoured

options of SBSTA, the choice of one of these Options over another does not introduce any

particular bias or distortion into the system at higher levels of aggregation, although at an

individual country level, the variation could be quite large. Perhaps the main conclusion of this

work is therefore, to some extent, of the favoured SBSTA allocation Options, they are

equitable. Therefore, the only significant discussion over the choice between these Options

should be the ease of implementation and monitoring.

• It is therefore recommended that DEFRA give further consideration to the relative merits of the

SBSTA allocation Options in terms of implementation and monitoring. This study has only

considered ‘allocation’: however, the backdrop for the necessity of allocation is emissions

trading and an accurate, robust and easily monitored system would expedite the incorporation

of international aviation into an emissions trading scheme.

• The uncertainties in this analysis of global and regional emissions were addressed in terms of

the modelling methodology and the input data. Various simplifying assumptions have to be

made in constructing even advanced models of emissions, such as used in this work. Where

possible, the uncertainties have been identified and quantified. It is concluded that the most

significant uncertainty in this analysis is the civil air traffic data, which only included scheduled

data.

• Globally, the systematic bias of including only scheduled civil data was estimated to be

approximately 9%, based on equivalent calculations for 1991/1992. This bias appears to be

strongly regional. Independent studies by Boeing (Sutkus et al., 2000) have shown that in the

US, their OAG-based inventory methodology underestimates emissions by 5%. Two analyses

were performed for this study: a comparison of the ANCAT/EC2 movement data for 1991/92 in

Europe – which was essentially EUROCONTROL data (Gardner et al., 1998) – with OAG data

for the same four months utilised and; a comparison of traffic to/from/within Europe using OAG

and EUROCONTROL data for 2000.

• Usage of OAG data for estimation of international (and domestic) CO2 emissions across

Europe within a sophisticated emissions inventory model such as FAST introduces a

systematic bias. A comparison of OAG data with flight schedule data from EUROCONTROL

shows that this bias for intra-EU flights is approximately -19% on flight km and -24% for CO2

emissions (as estimated by a simplified methodology) in 2000. The bias between EU Member

States will show more variability: for the UK to other EU States, the bias was shown to be -41%

for flight km and -53% on CO2 emissions. This introduces a large bias that should be rectified in

future calculations.

• It is not considered that the biases (within European air traffic) introduced by the use of

scheduled only data affect the main conclusions of the comparison of allocation Options.

60

• Therefore, it is recommended that due consideration be given to the use of air traffic control

flight data for the specific estimation of aircraft emissions across Europe for the purposes of

future development of policy options.

• It is also recommended that further investigation and comparative studies between the

modelling undertaken here and that undertaken and under development at other agencies

should be initiated, in order to better quantify uncertainties introduced by scheduled traffic data

and demonstrate how the best possible data might be made available to European Member

States for inventory activities.

Acknowledgements

We gratefully acknowledge the funding of this work by DEFRA, Global Atmosphere Division under contract CPEG7, under the guidance of Dr Steve Cornelius, Dr Jim Penman, Dr Trudie Mansfield, Ms Nicola Lettington and Dr Sarah Baggott. Guidance was also provided by Mr Peter Newton (DTI) & Ms Jill Adam (DfT). EUROCONTROL very kindly provided air traffic data for the uncertainty analysis in this study (Mr Patrick Tasker and Mr Andrew Watt). We are also grateful for fruitful discussions with Mr Ron Wit, CE-Delft (Netherlands) over allocation methodologies. Mr Dan Allyn of Boeing and Mr Theo Rindlisbacher of the Swiss Federal Office for Civil Aviation are thanked for their assistance with interpreting the draft IPCC aircraft type list. Mr Ted Elliff of EUROCONTROL is thanked for useful discussions regarding European air traffic data. Dr Dimitri Simos of Lissys is thanked for assistance with using the PIANO model and preparing specific non-standard aircraft performance data for usage in PIANO. The DTI are particularly thanked for granting permission to use the original FASTv1_1 model, from which the model presented here was developed. The original FAST model was constructed by Dr Roger Kingdon, now of DSTL, to whom the authors are indebted.

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Documents

Study on the Allocation of Emissions from International