EUROCONTROL Medium-Term Forecast · economic growth forecast for 2012 from 1.7% to 0%. This drop in...

EUROCONTROL

EUROCONTROL Medium-Term Forecast

February 2012Flight Movements

2012 - 2018

EUROCONTROL Medium-Term Forecast: IFR Flight Movements 2012-2018

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EXECUTIVE SUMMARY

This report presents the February 2012 update of the EUROCONTROL Medium-Term flight forecast. The forecast considers the development of air traffic in Europe over the next 7 years.

In recent months, Europe has seen a 5 percentage point drop in flight growth and a cut in the economic growth forecast for 2012 from 1.7% to 0%. This drop in flight growth is sooner and deeper than the economic indicator would suggest. These two factors have strongly influenced the short-term outlook for flights leading to a downward revision on the forecast issued in October 2011.

Over the full 7 years of the forecast Europe1 is expected to grow to 11.3 million IFR flights in 2018, 16% more than in 2011. However, traffic in 2012 will decline (below -1% on 2011) and an anaemic recovery in traffic growth (+1.5%) is expected for 2013. The average growth rate over the 7 years remains weak at around 2% per year.

This forecast predicts that European traffic will reach the 10 million-per-year mark first passed in 2007 by 2014, two years later than last year’s prediction of 2012.

Figure 1. Summary of the forecast for the ESRA082.

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

H . . . . . . 9,759 9,999 10,414 10,805 11,229 11,693 12,057 3.0%

B 9,561 10,043 10,083 9,413 9,493 9,784 9,658 9,803 10,078 10,372 10,686 11,002 11,305 2.1% IFR Movements

(thousands)

L . . . . . . 9,565 9,626 9,778 9,968 10,182 10,365 10,560 1.1%

H . . . . . . -0.3% 2.5% 4.1% 3.8% 3.9% 4.1% 3.1% 3.0%

B 3.7% 5.0% 0.4% -6.6% 0.8% 3.1% -1.3% 1.5% 2.8% 2.9% 3.0% 3.0% 2.8% 2.1% Annual Growth (compared to previous year)

L . . . . . . -2.2% 0.6% 1.6% 1.9% 2.2% 1.8% 1.9% 1.1%

Short-Term (2012-2013) European flights in 2012 are expected to decline by 1.3% (+/-1%). This decline is widespread amongst the busiest states in Western Europe and spreads out to Mediterranean countries (except Malta). The traffic forecast is largely influenced by the poor economic outlook and the declines in traffic growth since the beginning of the Winter timetable.

On top of these factors, the 2012 European flight forecast also reflects

a slow recovery of traffic following the end of the Arab Spring,

the flat to negative prospects for Summer flight growth (based on the schedules).

To the East, traffic growth remains positive in nearly all emerging markets though at slower rates than the past two years.

1 ‘Europe’ is represented by the EUROCONTROL Statistical Reference Area (ESRA), see Annex B. 2 EUROCONTROL Statistical Reference Area in the extended 2008 definition (see Annex B).


Current growth trends are strongly influenced at the regional and local levels by a number of factors and events. These imply specific risks to the forecast. In several cases where recovery could be faster than anticipated, these are upside risks and traffic growth might be stronger. Risk areas include: recovery of flights to Egypt, which affects much of South-East Europe but particularly Cyprus and Turkey; reactions to the failures of Spanair and Malev (which cut 1% from total flights); airlines’ continuing revisions to their plans for the Summer; the restoration of Libyan overflights; growth to and from Russia and the recent climb in fuel prices.

At European level, 2013 sees a weak flight growth: +1.5% (+/-1%) with same level of traffic as in 2011 expected overall. Economic forecasts for 2013 have also seen recent downward revisions. Figure 2 shows disparities observed at State level in average growth rates over the first 2 years of the forecast. Some States (eg. UK, Spain, Italy) will struggle to come back to positive growth (compared to 2011) while others, on the east, will show strong growth levels of around 5%.

Figure 2. Average annual growth 2011-2013 for each State.

Medium-Term (to 2018) For the 2014-2018 period, traffic growth is expected to remain stable at around 3%.This is a slightly faster rate of flight growth than previously expected, but still below the pre-2009 long-term trend. This means a 6-7 year hiatus in flight growth; longer than the 4-year gaps following the escalation of oil prices in 1980 or the over expansion of the late 1990s (see Figure 18 in the report).

Again, some differences are observed at State level: growth is clearly stronger in the East (Figure 3).

Over the whole period, a number of effects will reduce growth:

Lack of airport capacity means a reduction of around 134,000 IFR departures (1.2% off total growth over 7 years);

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Continuing improvements to the high-speed train network reduce growth by around 47,000 IFR departures (0.4% off total growth to 2018) in Europe;

Aviation has now joined the EU Emissions Trading Scheme (ETS), reducing growth by a further 0.2% in Europe over the 7 years.

Figure 3. Average annual growth 2011-2018 for each State.

Any user of the forecast is strongly advised to use the forecast range (low-growth to high-growth) as an indicator of risk as part of the risk management process, notably during planning activities. This forecast includes upside and downside risks discussed in the report (section 4.1) and, by 2018, the high-growth scenario has around 750,000 more flights compared to the base-scenario (+6.6%); the low-growth scenario has around the same number of flights fewer (-6.6%).

The EUROCONTROL 7-year flight forecast will next be refreshed in September 2012. The first two years will also be updated in May 2012.

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EUROPEAN ORGANISATION FOR THE SAFETY OF AIR NAVIGATION

EUROCONTROL

EUROPEAN AIR TRAFFIC MANAGEMENT PROGRAMME

EUROCONTROL Medium-Term Forecast: IFR Flight

Movements 2012-2018

Edition Number : v1.0 Edition Date : 28/2/2012 Status : Released Issue Intended for : General Public


DOCUMENT CHARACTERISTICS

TITLE


12/02/27-44 Publications Reference:

Edition Number: Document Identifier v1.0

STATFOR Doc455 Edition Date: 28/02/2012

Abstract This report presents the 2012 update of the EUROCONTROL 7-year forecast of instrument flight rules (IFR) flights in Europe in 2012-2018. It has been prepared by the EUROCONTROL Statistics and Forecast Service (STATFOR) in November-February 2012 and it replaces the Medium-Term flight forecast of October 2011 and the Short-Term flight forecast of December 2011.

This document contains a description and discussion of the main results. The details of the forecasts for individual States are available on STATFOR website (www.eurocontrol.int/statfor).

Keywords STATFOR Forecast Medium-Term Traffic Flow Movements Flight Movements Trends

Contact Person(s) Tel Unit Claire Leleu X3346 DNM/STATFOR

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Draft EATMP Stakeholders Extranet Proposed Issue Restricted Audience Internet (www.eurocontrol.int/statfor) Released Issue Printed copies of the document can be obtained from

the EUROCONTROL Publication and Documentation service (see Page iii).

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Publications EUROCONTROL Headquarters 96 Rue de la Fusée B-1130 BRUSSELS Tel: +32 (0)2 729 4715 Fax: +32 (0)2 729 5149 E-mail: [email protected]

DOCUMENT APPROVAL

The following table identifies all management authorities who have successively approved the present issue of this document.

© 2012 The European Organisation for the Safety of Air Navigation (EUROCONTROL). This document is published by EUROCONTROL for information purposes. It may be copied in whole or in part, provided that EUROCONTROL is mentioned as the source and to the extent justified by the non-commercial use (not for sale). The information in this document may not be modified without prior written permission from EUROCONTROL. The use of the document is at the user’s sole risk and responsibility. EUROCONTROL expressly disclaims any and all warranties with respect to any content within the document, express or implied.

The Statistics and Forecasts Service (STATFOR) is ISO 9001:2008 certified.

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mailto:[email protected]


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DOCUMENT CHANGE RECORD

The following table records the complete history of the successive editions of the present document. EDITION NUMBER

EDITION DATE

INFOCENTRE REFERENCE

REASON FOR CHANGE PAGES

AFFECTED

v0.1 20/01/12 Draft version All

v1.0 28/02/12 Final version All


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CONTENTS

1. INTRODUCTION...................................................................................................8

1.1 General......................................................................................................................................8

1.2 Summary of Forecast Method...................................................................................................8

2. TRAFFIC IN 2011 .................................................................................................9

3. FORECAST INPUTS AND ASSUMPTIONS ......................................................14

4. GROWTH IN IFR Flight MOVEMENTS TO 2018...............................................17

4.1 Summary of growth .................................................................................................................17

4.2 Other risks to the Forecast......................................................................................................22

4.3 Airport constraints ...................................................................................................................23

4.4 High-Speed Train ....................................................................................................................24

4.5 Emissions Trading Scheme (ETS)..........................................................................................25

4.6 Comparison with earlier forecasts...........................................................................................26

5. GLOSSARY ........................................................................................................27

Annex A. FORECAST METHOD...................................................................28

Annex B. TRAFFIC REGION DEFINITIONS.................................................32

B.1 ESRA08...................................................................................................................................32

B.2 Traffic regions .........................................................................................................................32

B.3 Functional Airspace Blocks.....................................................................................................33

Annex C. SUMMARY OF FORECAST ASSUMPTIONS...............................34

C.1 Economic Growth....................................................................................................................34

C.2 Low-Cost Carrier Growth ........................................................................................................37

C.3 High-Speed Train Network Development ...............................................................................39

C.4 Airport Capacity.......................................................................................................................41

C.5 Load Factors ...........................................................................................................................41

C.6 Events and Trends ..................................................................................................................43

C.7 Airport Traffic Switch ...............................................................................................................45

C.8 Emissions Trading Scheme ....................................................................................................46

Annex D. SUMMARY OF THE FORECAST FOR THE ESRA ......................48

Annex E. FUTURE TRAFFIC AND GROWTH.......................................................51

E.1 Summary of the Forecast. Annual IFR Movements 2006-2018..............................................51

E.2 Summary of the Forecast. Growth Rates 2006-2018 .............................................................56


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Annex F. REFERENCES .......................................................................................61 List of Figures. Figure 1. Summary of the forecast for the ESRA08. ................................................................. i Figure 2. Average annual growth 2011-2013 for each State.................................................... ii Figure 3. Average annual growth 2011-2018 for each State................................................... iii Figure 4. Actual traffic compared with the forecast of December 2010....................................9 Figure 5. Growth in 2011 was particularly strong around the Baltic, in Turkey, the

Netherlands and Ukraine. ...............................................................................................10 Figure 6. European neighbours: Strong Russian growth balances North African downturn. .11 Figure 7. Load factors in Europe remained near record levels. (intra-Europe traffic) ............11 Figure 8. Oil prices remained mostly above $100/bl in 2011. ................................................11 Figure 9. Traditional scheduled carriers took over as the fastest-growing market segment ..12 Figure 10. Market shares of the main segments hardly changed in 2011..............................12 Figure 11. GDP forecast have seen strong downward revisions. ..........................................14 Figure 12. Underlying growth for busiest States shows further falls in 2012..........................15 Figure 13. Summary of the forecast for Europe (ESRA08). ...................................................17 Figure 14. Forecast for 2012 in Europe is for a widespread decline. (Uncertainty is typically

1.0% percentage points)................................................................................................18 Figure 15. Forecast for 2013 in Europe is for a weak growth. (Uncertainty is typically 0.9

percentage points) ..........................................................................................................19 Figure 16. Number of additional movements per day for each State (2013 vs 2011). ...........19 Figure 17. Number of additional movements per day for each State (2018 v 2011). .............21 Figure 18. IFR flight growth in Europe is now persistently below the pre-2009 long-term

trend. ...............................................................................................................................21 Figure 19. Impact of airport constraints..................................................................................23 Figure 20. Impact of high-speed train: reduction in IFR departures for ESRA08 (top) and

most affected States (bottom). ........................................................................................24 Figure 21. Impact of ETS: reduction in IFR departures for ESRA08 due to aviation’s

participation in the scheme. ............................................................................................25 Figure 22. Forecast for the ESRA08 has been revised downwards (MTF11b dashed lines,

MTF12 solid lines)...........................................................................................................26 Figure 23. Summary of short-term forecast method...............................................................28 Figure 24. Preparation process of the Medium-Term Forecast..............................................30 Figure 25. The EUROCONTROL Statistical Reference Area. ...............................................32 Figure 26. Regions used in flow statistics. .............................................................................33 Figure 27. FAB initiatives as of January 2011. (Source: EUROCONTROL PRU) .................33 Figure 28. Changes per State in economic forecast for 2012 for this flight forecast. .............34 Figure 29. Since the previous forecast, the economic outlook for EU27 has significantly

worsened.........................................................................................................................35 Figure 30. Summary of changes in GDP forecasts for 2012-2018: nearly all States see

reduction in 2012 and 2013.............................................................................................35 Figure 31. GDP Growth by Traffic Zone.................................................................................36 Figure 32. GDP Growth by Origin-Destination Zone ..............................................................37 Figure 33. GDP Growth by Traffic Region..............................................................................37


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Figure 34. Low-Cost effects by Traffic Zone ..........................................................................38 Figure 35. High-Speed Train Times .......................................................................................39 Figure 36. Example load factor forecast, for Europe.............................................................42 Figure 37. Load factors by Traffic Region ..............................................................................42 Figure 38. Events and Trends assumptions by Traffic Zone..................................................44 Figure 39. Airport traffic switch...............................................................................................46 Figure 40. ETS assumptions ..................................................................................................46 Figure 41. Prices ....................................................................................................................47 Figure 42. Price Elasticities per (bidirectional) Traffic Region Pair and period ......................47 Figure 43. Growth in Europe ..................................................................................................48 Figure 44. Traffic on the main flow categories for the ESRA .................................................49 Figure 45. Traffic and growth on the biggest region-to-region flows through the ESRA. .......50 Figure 46. Annual traffic per traffic zone and 2012-2018 average annual growth..................51 Figure 47. Annual growth rates per traffic zone and 2012-2018 average annual growth.......56


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1. INTRODUCTION

1.1 General

This report presents the forecast of annual numbers of instrument flight rules (IFR) flight movements for 2012 to 2018, prepared by the EUROCONTROL Statistics and Forecast Service (STATFOR) in December 2011-February 2012. This replaces the forecast issued in September 2011 (Ref. 1).

This report contains a summary and discussion of the forecast, starting with a discussion of traffic in 2011 (section 2), the assumptions made in the forecast scenarios (section 3) and some highlights from the forecast results (section 4). More details are provided in the annexes: forecast method (Annex A), geographical definitions (Annex B), forecast assumptions (Annex C), forecast details for Europe as a whole (Annex D) and annual total forecasts per State (Annex E). The detail for each traffic zone (usually the same as ‘State’) is provided on STATFOR website (Ref. 2).

STATFOR also prepares a long-term forecast (20 years) available in summary on the STATFOR web pages (Ref. 3).

1.2 Summary of Forecast Method

The EUROCONTROL 7-year flight forecast grows airport-pair traffic using models of recent trends, and of future economic and industry developments.

The 7-year forecast is a blend of two forecasting methods: time-series modelling of recent traffic trends; and a model of future flight demand based on economic growth and other developments. The trend-based modelling provides better accuracy in the first years, so is more influential in the blend in the first two years. The economic and industry modelling has more influence on the results for the later years.

The time-series modelling looks at recent and longer-term patterns of growth and seasonality, taking into account information about one-off events, about economic growth and available data on the airline schedules for the coming months.

The economic model is developed by growing baseline traffic (all IFR flight movements for the last calendar year) taking into account factors such as economic growth, past patterns of supply, the growth of low-cost carriers, and the influence of high-speed trains.

The results of these processes are then constrained by annual airport capacities and converted from airport-to-airport counts into flights through airspace volumes using the flight patterns in the last 12 months and recent trends in how these patterns are changing. No account of future route network changes is made (see section 4.2). More detail of the method is given in Annex A.

Three ‘scenarios’ are used to capture the likely range of growth of flight movements. They comprise: the low-growth and high-growth scenario which vary economic growth, load factors and other variables in order to capture the most-likely range; and the baseline forecast which is a guidance figure within this range. The scenarios are discussed in section 3 and detailed in annex B.1.

Experience in recent years has shown the need to take the whole forecast range (from low-growth to high-growth) into account. For this new forecast, the main areas of risk are discussed in section 4.1.



2. TRAFFIC IN 2011

2011 followed a trend of weak traffic growth, becoming weaker still with the new Winter 11/12 timetable. Overall, growth appeared stronger than it really was due to cancellations in 2010. There were some hotspots, notably in the East and Scandinavia.

Any discussion of 2011 traffic trends must start in 2010, which was a weak year for European air traffic, but the particular legacy of interest for 2011 was the 170,000 flights that were cancelled due to the ash-cloud, snow and strikes. Since 2011 saw fewer than 25,000 cancellations3, we saw ‘bounce-back’ events throughout 2011 but particularly in April 2011 and December 2011. Indeed April saw growth jumping to an unlikely-sounding 16%. Overall, that difference of around 150,000 cancellations inflated the flight growth of 2011 by some 1.5% for Europe4.

This bounce-back is a substantial part of the overall growth in flights of 3.1% in 2011. It means that the underlying trend during the year was only for between 1.5% and 2% growth, as aviation in Europe remained under the pressure of an unfavourable economic situation. Looking back, we can identify three phases of the year (Figure 4):

For the first few months, the weak underlying growth was masked by cancellations at the start of 2010, and by a hesitant optimism in the industry that was due to high load factors and an apparent improvement particular at the premium end of the market. Reductions in flights to Tunisia and particularly Egypt prevented any recovery from lifting the growth trend.

Over the Summer, the underlying trend changed little, though it was slightly above the expectation of the May forecast. However, the mood of optimism began to disappear as economic forecasts and industry-sentiment indicators declined, and in our own industry the signs also turned downwards, with cargo load factors leading the way.

The Winter 11/12 timetable saw a sharp deceleration, as several carriers cut back to below the flight numbers of the previous Winter and warned of weak forward bookings.

Figure 4. Actual traffic compared with the forecast of December 2010.

3 This preliminary estimate undercounts the effects of events in Tunisia, Egypt and Libya (see below). 4 Here ‘Europe’ is used to mean ESRA08. See Annex B for definition.


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This annual average masks a geographically varied situation (Figure 5). Strong growth was seen around the Baltic (extending also to Norway and the Netherlands) and in Turkey and the Ukraine. Figure 5 just covers the period May to November to avoid the confusion of the ash-cloud and snow bounce-back, but this still exaggerates French growth due to the strike cancellations in the period in 2010. Notably Germany, which as a large State would be expected to be to the left-hand side, does not appear at all in Figure 5 due to static traffic in part as a result of airlines’ response to a new departure tax.

In spite of its open skies agreement with the EU, Morocco saw a downturn in arrivals/departures in parallel with that elsewhere in North Africa, if not as large. The strength of the Canary Islands, which appears to be growing internally and as a replacement Winter sun destination, however, boosted Morocco’s overflights.

Further afield, the effects of the Arab Spring are also evident in Figure 6: declines of 80-90 departures/day to Egypt and Tunisia together over the Summer months. This is 160-180 flights/day in both directions (the units of Figure 5), meaning that this nearly cancelled out the Turkish growth. Indeed, taken over the whole year, this suggests perhaps 50,000 flights fewer than expected5. By the same arithmetic, the growth to/from Russia of perhaps 140 flights/day places the Russian flow as the second-largest contributor to growth overall in Europe, after Turkey.

Figure 5. Growth in 2011 was particularly strong around the Baltic, in Turkey, the Netherlands and Ukraine.

5 These are not necessarily cancellations in the same sense as those caused by snow or strike, so don’t get captured by the cancellation calculations.


Figure 6. European neighbours: Strong Russian growth balances North African downturn.

Figure 7. Load factors in Europe remained near record levels. (intra-Europe traffic)

Figure 8. Oil prices remained mostly above $100/bl in 2011.



Figure 9. Traditional scheduled carriers took over as the fastest-growing market segment

Figure 10. Market shares of the main segments hardly changed in 2011.

For commercial carriers, load factors remained near record levels (Figure 7), although they began to weaken in the Autumn, especially on the high-yield Asian flow. Oil prices have now spent 12 months near €80/barrel (Figure 8) and the weakening euro of recent months suggests that fuel prices will become more difficult yet.

2011 saw a marked change in the relationships between the main market segments, as their growth patterns (Figure 9) became more synchronised than recently seen:

The traditional scheduled carriers grew the fastest, at 4.1%. This is the first time they have out-grown the low-cost carriers in percentage terms since the early 1990s.

The low-cost carriers had a year of much more moderate growth plans than in the past, managing 3.9% flight growth. Some did better out of the strength around the Baltic, but the Winter saw a sharp scaling back.

The (tourist) charter segment had a particularly difficult year (“non-scheduled” in Figure 9), being the most affected by the drop in traffic to the winter leisure markets of Tunisia and Egypt; although some were insulated a little from this by their past diversification into scheduled operations. It declined by 5.8%.



All-cargo saw warning signs in tonnage and load factors over the Summer; and the flights finally went into decline in the Autumn. Over the year as a whole it achieved 2.9% flight growth.

Business aviation (2.3% increase) saw a similar evolution, but a slightly lower annual figure as there was less bounce in April, since it was least affected by ash in April 2010.

The result of all of this was that their respective market shares hardly changed (Figure 10); charter lost 0.5% points, which the traditional segment gained when compared to 2011 figures.



3. FORECAST INPUTS AND ASSUMPTIONS

The forecast is driven by past traffic trends and by scenario assumptions for the future. The downward revision in the new forecast starts from a deceleration in flight growth of around 5 percentage points since the end of the Summer timetable. While this is partly explained by a cut in forecast economic growth (equivalent to a 2.5%-3% drop in flights) it is an earlier and deeper decline than economics alone would suggest.

The forecast is derived from historical traffic data and a set of scenario assumptions (listed in Annex C). The downward revision in the forecast can be understood from these data. In particular, since the 7-year forecast was last published in October the inputs have changed in two important respects: GDP and flight growth. These changes are behind a strong downward revision, even if our judgement is that this revision may now have left us with up-side risks.

Firstly GDP forecasts, especially for 2012, have seen strong downward revision during 2011, and particularly since the Summer (Figure 11). The last GDP forecast that we used was for 1.7% growth in 2012 at the EU level, but this is now cut to 0% for this update. This 1.7% revision alone would be expected to cut the flight growth forecast by between 2.5% and 3%. The weaker economic situation continues into 2013 (more detail on this in Annex C.1.)

Figure 11. GDP forecast have seen strong downward revisions.

There was a sharp deceleration in flight growth from the end of October (Figure 4), which had been flagged in advance by some carriers, but the extent of the downturn was beyond expectations. This downturn had some limited influence on the December forecast, whose input data included one month of the declining traffic. If anything, this deceleration is even stronger since the beginning of 2012, with some operators making further cuts, in addition to the collapse of Malev and Spanair (which had 1% of European flights between them in 2011). Figure 12 uses the median daily growth in the month, to give an approximate idea of the underlying trend largely insulated from spikes due to weather and strikes. For the busiest five States, the downward shift is perhaps 5% points since October, which is more than would be expected from economic reasons alone. Now with three months of this downturn feeding into the short-term forecast, the effect on the forecast is much more marked.



Figure 12. Underlying growth for busiest States shows further falls in 2012.

Amongst the explanations for a decline in flights that is sooner and deeper than the economic factors would imply are two factors which are not fully represented in the forecast, but clearly weigh on the outlook for 2012. They are: the fuel price, which has been quoted by many airlines as causing difficulties, and now seems to be climbing further due to tensions between Europe and Iran, with the Brent crude price at record levels in euro terms; and the weakness of national airlines, with a large number of States looking to reduce or dispose of their stakes, leaving limited scope for further re-capitalisation. We estimate that airlines with more than 6% of 2011 European flights are up for sale, in bankruptcy protection or else struggling to finance their operations.

Some of the growth areas seen last Summer (Figure 5) have lost all or most of their strength over the Winter: notably Canaries, Finland and Sweden. This weakness has been picked up in the forecast. However, here there is an upside risk, with limited visibility in the schedule data for the Summer of whether Canaries will return to being a destination of choice as an alternative to North Africa.

The traffic disruption that followed the Arab Spring is represented in the forecasts both as historical data and as assumptions for the continuing recovery. Twelve months on from the sharpest downturn to Egypt, we are seeing some recovery in the overflights for States such as Cyprus and Turkey. The timing and extent of this recovery is uncertain; our assessment is that this probably represents an upside rather than a downside risk.

Other inputs include:

Low-cost. For this forecast we have reviewed the assumptions about low-cost market share growth to take account of the fact that this market share is now growing more slowly, if at all in recent years. These are discussed in detail in Annex C.2. The effect is most marked for larger States, where the market share grows more slowly than in previous forecasts, or indeed in the low-growth scenario might see a small contraction.

High-Speed Rail: In spite of the economic downturn, high-speed rail developments continue to complete and deliver travel time savings. Over the next 7 years, there are an increasing number of cross-border connections or improvements, such as Lisbon-Madrid, Amsterdam-London, Madrid-Bordeaux, Paris-Barcelona, Zurich-Paris. (Annex C.3)

-5%

Airport capacity: These numbers have been refreshed, following data provided by STATFOR User Group members, or by airports directly to EUROCONTROL.



The main change has been the addition of capacity for the third airport in Istanbul, which leads to faster growth from 2017. (Annex C.4)

Load factors: Our forecasts generally project these to increase further, although only by a couple of percentage points in the case of intra-Europe traffic. After growth in load factors levelled off last year, the question must be asked whether they have now reached a practical maximum. If so, we may be underestimating flight growth. (Annex C.5)

Events and trends: As well as Euro12, Olympics, Croatia & Icelandic accession to the EU and relaxation of visa requirements in the Balkans, we have included a scenario for the continuing recovery of traffic to/from Egypt, Tunisia and Libya. Twelve months on, the sharp decline is dropping out of the tables. Other possible scenarios certainly exist, and the knock-on effects of this for Malta and others require further validation. (Annex C.6)

Emissions trading: This began in 2012, and is modelled as in previous forecasts. (Annex C.8)



4. GROWTH IN IFR FLIGHT MOVEMENTS TO 2018

4.1 Summary of growth

As a result of the current poor economic outlook, which is for stagnation or weak growth, and the current traffic situation, which saw cutbacks in flights this Winter, we now forecast a decline in 2012, followed by a weak recovery in 2013. Over the remaining years of the forecast, flight growth is stronger at around 3% per year. This, however, reflects a long-term growth trend that is forecast to be lower than in the past. Total traffic reaches 11.3 Million flights in 2018.

Figure 13. Summary of the forecast for Europe (ESRA08).

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011 2006

. . . . . . 9,759 9,999 10,414 10,805 11,229 11,693 12,057 3.0% H

9,561 B 10,043 10,083 9,413 9,493 9,784 9,658 9,803 10,078 10,372 10,686 11,002 11,305 2.1% IFR Movements

(thousands)

. . . . . . 9,565 9,626 9,778 9,968 10,182 10,365 10,560 1.1% L

. . . . . . -0.3% 2.5% 4.1% 3.8% 3.9% 4.1% 3.1% 3.0% H

3.7% B 5.0% 0.4% -6.6% 0.8% 3.1% -1.3% 1.5% 2.8% 2.9% 3.0% 3.0% 2.8% 2.1% Annual Growth (compared to previous year)

. L . . . . . -2.2% 0.6% 1.6% 1.9% 2.2% 1.8% 1.9% 1.1%

Short-Term outlook (2012-2013)

As a result of the poor economic outlook, which is for stagnation or weak growth, and the current traffic situation, which saw cutbacks in flights this Winter, we now forecast a decline in 2012, followed by a weak recovery in 2013 (Figure 13).

The previous section has described how this update of the forecast is particularly influenced by the drop in flights of 5 percentage points this Winter and a cut in the economic growth forecast for 2012 from 1.7% to 0%. This drop in flight growth is sooner and deeper than the economic forecast would suggest. These two factors have strongly influenced the short-term outlook for flights which is that European flights in 2012 are expected to decline by -1.3% (+/-1%) (Figure 14). This decline is widespread amongst the busiest states in Western Europe and spreads out to Mediterranean countries (except Malta).

On top of these factors, the 2012 European flight forecast also reflects

a slow recovery of traffic following the end of the Arab Spring,

the flat to negative prospects for Summer capacities.

On the Eastern side, traffic growth remains positive in nearly all emerging markets though at slower rates than the past two years.

Growth forecasts for all States are provided in Annex E, and in more detail on the STATFOR interactive dashboard (Ref. 2).



Current growth trends are strongly influenced at the regional and local levels by a number of factors and events. These imply specific risks to the forecast:

The forecast includes a scenario for recovery of Egypt traffic. However, variations in this would change the recovery in overflights, in particular for Cyprus and Turkey, but indeed for much of South-East Europe. We believe this to be on balance an upside risk, with recovery faster than currently forecast.

The recent failures of Spanair and Malev have cut 1% off total flights, with much larger effects locally. On the one hand, we have assumed some limited substitution by other carriers, on the other there is an upside risk of more extensive replacement which would improve traffic growth in Spain, Canaries and Hungary.

Information on capacity for the Summer remains incomplete, and airlines continue to review their plans. We assess this on balance to be an upside risk, of stronger growth than currently in the forecast.

The restoration of Libyan overflights has significant implications for Malta and, to a lesser extent, Italy. This we also assess to be on balance a downside risk for the short-term, with a less complete restoration of flights.

Growth in flights to/from Russia has been an important contributor to growth, especially for States in central and North-East Europe (section 2), if this growth continues, and particularly if the Russia-Egypt flow recovers more strongly, then there is a risk of faster growth overall.

Figure 14. Forecast for 2012 in Europe is for a widespread decline. (Uncertainty is typically 1.0% percentage points)



The economic downward revisions extend into 2013, which is now expected to see a 1.5% traffic growth (±1%) across Europe (Figure 15).

Figure 15. Forecast for 2013 in Europe is for a weak growth. (Uncertainty is typically 0.9 percentage points)

Figure 16. Number of additional movements per day for each State (2013 vs 2011).




Medium-Term outlook (to 2018)

Over the remaining years of the forecast, flight growth is stronger at around 3% per year. This reflects a long-term growth trend that is forecast to be lower than in the past, reaching 11.3 Million flights in 2018.

After 2013, growth is expected to follow a trend of about 3% growth per year (Figure 13). The previous peak annual traffic (10.1 Million flights in 2008) will be reached again in 2014. This means a 6-7 year hiatus in flight growth, longer than the 4-year hiatus after the 2000 and 1980 peaks6. Due to the weak start in 2012 and into 2013, the average growth between 2011 and 2018 is just 2.1%. The high- and low-growth scenarios are each around 1 percentage point from this.

As in recent years, the traffic development is not uniform across the region as Figure 3 in the executive summary illustrates. Growth (in percentage terms) is clearly stronger in the East (see Annex E.2). In contrast, in terms of the number of additional movements per day, the traffic will increase the most in the busy States Western Europe (eg. Germany) but also in Turkey and Ukraine, which are forecast to see some of the strongest traffic growth in Europe (see Figure 17).

Figure 18 illustrates that flight growth is now well below the pre-2009 long-term trend line, and is forecast to remain below that trend. There are some upside risks, but there are also good reasons for the future long-term trends to be lower than that observed in the past. In particular, we can identify two major factors that have contributed to strong growth in the last 20 years, but which now appear to be running out of steam:

Deregulation of air transport in Europe around 1990 allowed supply to match demand more accurately, as well as contributing to the growth of the low-cost market segment. However, for several North-West European states, there is increasing evidence through long-term stagnation or decline of shorter-haul markets that the number of under-served markets is limited. Nevertheless, deregulation of medium- or longer-haul remains a possible source of growth.

The expansion of the EU in 2004 and 2007 extended the area of air transport deregulation, but more importantly introduced free trade and free movement of people and businesses. This provided a one-off boost to demand which has now run its course, though there are still some States that could be included.

6 The over-expansion in the late 1990s and the escalation of oil prices in 1980.



Figure 17. Number of additional movements per day for each State (2018 v 2011).

Figure 18. IFR flight growth in Europe is now persistently below the pre-2009 long-term trend.

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4.2 Other risks to the Forecast

Users of the forecast are strongly advised to use the forecast range (low-growth to high-growth) as an indicator of risk. A number of specific risks, particularly for the short-term, have just been discussed (section 4.1). More generally, we identify:

This forecast is prepared as the economic forecasts in Europe have again been revised downwards. Nevertheless, there has been some relatively good news and it is possible that we are reaching the end of the downward revisions. The uncertainty in economic growth has a direct impact on flight growth for Europe as a whole. Traffic growth is riding on the outcome of the crisis as well as the speed of the recovery.

In some States, the economic situation remains severe, with the potential for significant falls in GDP. These risks are highly uneven across Europe.

Network changes and the route choice of airlines have a large influence on the number of overflights. The recent events in North Africa have an important impact on overflight figures for Malta and Cyprus mainly, but also in Italy and the Balkans. We have included only one of a number of different recovery patterns.

More generally, recent refinements in the forecast method will better capture the recent trends in overflights, but future network changes (eg. routings) are not modelled by the forecast.

Tourism trends are quite variable and sensitive to the political climate in the destination country. Although the growth at destinations that can substitute for North Africa, such as Canaries, was strong last year, it is now much less so. A further burst of growth, as a substitute, would be possible but is not included in the forecast.

Oil prices have been relatively stable, but at a very high level, for the last 12 months. With fuel accounting for 30-40% or even more of costs of the airlines, this has a significant effect on fares and cost of travel for customer. The jump in recent weeks of the price of crude oil in euro terms to record levels has not been explicitly factored into this forecast.

Participation of aviation in the Emissions Trading Scheme is integrated into the forecast. However, recent experience has shown that national taxes in particular can rapidly change the local outlook for flight growth.

Terrorist attacks, wars and natural disasters, socio-economic changes and epidemics. The last seven years have not been quiet ones for aviation. There is no reason to believe the next seven years will be uneventful. The impact on air traffic could be a temporary one, or more significant.



4.3 Airport constraints

Constraints at airports mean that demand for some 134,000 flights cannot be accommodated by 2018, which means a 1.2% reduction in growth over 2011-2018. This is a similar total to last year’s forecast, even though the new forecast is lower. The location of the constrained airports also differs, with the new airport for Istanbul making a significant difference in Turkey.

This forecast uses a refreshed set of airport capacity figures covering some 159 airports, building on:

The work done for the Long-Term Forecast 2010 (Ref. 9) and Challenges of Growth 2008 (Ref. 4),

The data submitted directly by STATFOR User Group members,

Data compiled by the EUROCONTROL Airport unit from submissions from around 30 airports.

In the baseline scenario in 2018, demand for around 134,000 departures will not be accommodated due to airport congestion (Figure 19, mainly in UK, Netherlands, France and Germany). This will reduce the growth in departures by 1.2% across Europe as a whole between 2011 and 2018. The reduction in growth will, of course, be more marked in the States with congested airports, and is more significant in the high-growth scenario.

There has been a geographical shift in the location of the most constrained airports as some capacity figures have been revised downwards (the Netherlands, Germany, UK, Hungary, Switzerland and Austria) while others have been revised upwards (Italy, Belgium and Sweden). In particular, capacity in Turkey has been boosted to simulate the presence of a third airport in the capital from 2017 (see Annex C.4). Overall, and despite a lower traffic forecast, the number of unaccommodated flights is similar to the one in the previous Medium-Term Forecast (excess demand of around 100,000 departures in 2017 in MTF11, see Ref. 5 compared to 98,000 in this forecast).

Figure 19. Impact of airport constraints.

Unit: Reduction in IFR Departures when airport constraints are taken into account

Change in IFR Departures (000s) Percentage Change

2013 2012 2014 2015 2016 2017 2018 2012 2013 2014 2015 2016 2017 2018

High 24.3 27.5 80.6 140.0 210.8 197.7 342.7 0.2% 0.3% 0.8% 1.3% 1.8% 1.6% 2.8%

Base 16.2 17.9 42.9 73.3 120.2 97.9 134.0 0.2% 0.2% 0.4% 0.7% 1.1% 0.9% 1.2%

Low 10.3 11.5 21.6 43.5 70.5 56.3 69.3 0.1% 0.1% 0.2% 0.4% 0.7% 0.5% 0.6%



4.4 High-Speed Train

Expansion of the high-speed train network reduces flight growth by just 0.4% over 7 years, though the local effects are more significant. This is a slightly weaker impact than in the previous forecast.

In the forecast model, an improvement in travel times for rail leads to a reduction in demand for air travel. Figure 20 summarises the number of IFR departures that are lost to rail because of improvements in the high speed train (HST) network. The effect is around 0.4% in total over the 7 years of the forecast; which is small on the scale of the network as a whole. However, on specific city-pairs, the effect can be quite large, especially at the end of the horizon. Because some HST projects have been delayed (not only due to the economic crisis but for some of them, because of environmental and political issues), and owing to the lower levels of traffic, the current reduction in traffic is below the MTF11 results (see Ref. 1) in which around 47,000 IFR departures were assessed to be removed in 2017 from the network. It is now expected that a reduction of the same order would only happen 2018. The HST network is assumed to grow more slowly in the high flight-growth scenario, hence lower values for the ‘High’ in the Figure 18.

Figure 20. Impact of high-speed train: reduction in IFR departures for ESRA08 (top) and most affected States (bottom).

Unit: Reduction in IFR Departures when airport High-Speed train network development in Europe is taken into account


2013 2012 2014 2015 2016 2017 2018 2012 2013 2014 2015 2016 2017 2018

High . 4.7 16.1 25.7 29.2 34.2 44.4 0.0% 0.0% 0.2% 0.2% 0.3% 0.3% 0.4%

Base . 11.9 19.0 23.8 28.1 38.9 47.4 0.0% 0.1% 0.2% 0.2% 0.3% 0.3% 0.4%

Low . 11.6 20.4 25.8 29.5 42.3 49.2 0.0% 0.1% 0.2% 0.3% 0.3% 0.4% 0.5%

Change in IFR Departures (000s) Percentage Change 2013 2014 2015 2016 2017 2018 2012 2013 2014 2015 2016 2017 2018 2012

. 2.2 4.4 4.5 4.5 5.3 7.8 . 0.2% 0.4% 0.4% 0.4% 0.5% 0.7%France

. 0.3 0.6 0.9 0.9 3.1 5.3 . 0.0% 0.1% 0.1% 0.1% 0.2% 0.4%Germany

. . 1.7 3.4 3.6 3.5 3.6 . . 1.3% 2.5% 2.5% 2.5% 2.5%Lisbon FIR

. . 0.8 . . . . . . 0.3% . . . .Netherlands

. 4.0 8.1 12.2 15.7 16.8 16.9 0.6% 1.1% 1.7% 2.1% 2.2% 2.2%Spain

. 0.3 0.4 0.3 0.2 . . . 0.1% 0.1% 0.1% 0.1% . .Switzerland

0.1 Turkey 5.2 3.8 4.4 5.0 11.8 16.2 0.0% 1.0% 0.7% 0.7% 0.8% 1.6% 2.1%

Base

. UK . 0.5 0.8 0.8 0.7 0.2 . . 0.0% 0.1% 0.1% 0.1% 0.0%

As far as States are concerned, Spain and Turkey will see the largest impacts: reduction of 17,000 and 16,000 IFR departures respectively in 2018 which correspond to around 2% of traffic in each state. The effects are mainly due to the Madrid-Bilbao and Madrid-Alicante lines in Spain and Istanbul-Ankara and Istanbul-Konya for Turkey. France and Germany are less affected with around 8,000 and 5,000 (respectively) fewer IFR departures.




4.5 Emissions Trading Scheme (ETS)

The forecast models the impact of compliance with the EU Emissions Trading Scheme. This is expected to reduce flights by around 25,000 by 2018, which is a 0.2% reduction in growth over the whole period.

This air traffic forecast includes an Emissions Trading Scheme (ETS) component to account for the integration of aviation7 within the EU cap-and-trade scheme since the beginning of 2012. The scheme affects all aircraft operators regardless of where they are based provided that they operate flights departing from or arriving at an aerodrome located in the EU. Aircraft operators have now to surrender one allowance for every tonne of CO2 emitted on a flight8 to/from Europe. Under current rules, EU gives the aircraft operators permits to cover 85% of the allowances (for free); the rest needs to be bought from auctions or on the free market. The component implemented in this forecast estimates the induced increase in fares (conversion of the excess allowances into monetary terms) and the resulting reduction in demand.

Figure 21 summarises the estimated number of IFR departures that are lost because of the entry into force of the ETS. In 2018, the forecast number of flights is expected to be reduced by 25,000 IFR departures, corresponding to a 0.2% reduction in percentage terms for Europe

Figure 21. Impact of ETS: reduction in IFR departures for ESRA08 due to aviation’s participation in the scheme.

Unit: Reduction in IFR Departures when EU-ETS is taken into account


2012 2013 2014 2015 2016 2017 2018 2012 2013 2014 2015 2016 2017 2018

High 2.6 7.0 10.6 14.6 26.3 38.5 41.3 0.0% 0.1% 0.1% 0.1% 0.2% 0.3% 0.3%

Base 1.4 4.6 7.5 11.4 16.2 24.6 24.7 0.0% 0.0% 0.1% 0.1% 0.1% 0.2% 0.2%

Low 0.9 3.4 5.8 9.1 12.9 18.2 19.2 0.0% 0.0% 0.1% 0.1% 0.1% 0.2% 0.2%

7 Directive 2008/101/EC of the European Parliament and of the Council of 19 November 2008. 8 The modelling has been performed on passenger flights only.


4.6 Comparison with earlier forecasts

A combination of lower-than-expected flight growth in 2011, downward revision for economic growth in 2012 from 1.7% to 0%, lingering into weak growth in 2013, and fewer airport constraints pushes the forecast back by about two years.

Figure 22 compares this new forecast (MTF12) with the previous MTF (MTF11b), the mid-year update of the MTF published in October 2011 (Ref. 1).

The actual traffic in 2011, which is used as the baseline for the MTF12, is lower than what was forecast even in late 2011. In particular, the previous MTF under-estimated the downturn in flights at the start of the Winter 2011-2012 timetable, so the new medium-term forecast starts lower than the previous forecast expected to be.

The MTF11b was based on an expectation of economic growth in 2012 of 1.7% in Europe. For the MTF12, this has been revised sharply downwards to 0%. Coupled with the weak Winter traffic, we now forecast decline in 2012, which therefore shifts traffic volumes at least one year to the right. Recovery to the 2008 peak is probably delayed until 2014.

The growth rates for 2013 are weaker than previously forecast, with the weak Italian and Spanish economies, for example, weighing on flight growth.

From 2014-2018 growth rates are higher in MTF12 compared to MTF11b. Slower growth in the early years reduces the effects of airport capacity constraints, and indeed the new forecast includes additional capacity in Istanbul, representing the planned new airport.

The combination of these factors leaves the current forecast some two years behind the previous. In the high-growth scenario, the number of flights could match the previous baseline forecast around 2015.

Figure 22. Forecast for the ESRA08 has been revised downwards (MTF11b dashed lines, MTF12 solid lines).



5. GLOSSARY

AAGR Average annual growth ACC Area Control Centre AEA Association of European Airlines B (in tables) Baseline Scenario CFMU Eurocontrol Central Flow Management Unit CRCO Eurocontrol Central Route Charges Office ESRA Eurocontrol Statistical Reference Area (see Annex B) EU27 European Union (27 States) EU European Union (abbreviation for EU27) FIR Flight Information Region GDP Gross Domestic Product H (in tables) High-Growth Scenario HST High-Speed Train IFR Instrument Flight Rules L (in tables) Low-Growth Scenario MTF Medium-Term Forecast NM Network Manager SES Single European Sky STATFOR EUROCONTROL Statistics and Forecast Service STF Short-Term Forecast TR Traffic Region (a grouping of TZs) TZ Traffic Zone (≈State, except for Spain, Portugal, Belgium and

Luxembourg, Serbia and Montenegro) UIR Upper Flight Information Region

Detailed explanations of the above terms are available in EUROCONTROL Glossary for Flight Statistics & Forecasts (Ref. 6) and on the STATFOR web pages (Ref. 3).




ANNEX A. FORECAST METHOD

The forecast reported here combines the EUROCONTROL Short- and Medium-Term Forecast methods. The short-term forecast is largely driven by analysis of recent trends. The medium-term forecast takes into account a broader range of economic and industry developments.

Short-Term Forecast Method

The short-term forecast focuses on time-series modeling of traffic trends month-by-month. The final result is in terms of numbers of flights per month per pair of zones or regions: within Europe origin-destination zones are used (groups of airports often smaller than States); outside of Europe, large regions are used (groups of States). Four separate forecasts (with differing horizons and time and geographical resolution) contribute to the forecast as a whole (see Figure 23):

The State-flow forecast method is the method developed in the legacy code. It has been used for several years for published short-term forecasts. It forecasts each State separately, and within the State, separate forecasts for a few main ‘flows’: internals, overflights etc.

The zone or region-pair forecast is largely based on time-series methods for some 8000 series.

The schedule method uses data from published schedules for future months, and comparisons of previous schedules with actual flights.

The first years of the medium-term forecast (see below) also contribute a view of future traffic.

The combined forecast is then capped by airport capacities, using the same method as used in the medium- and long-term forecasts. Overflights are calculated using both the trends identified in the short-term forecast, and the base-year flight patterns used previously in the medium-term forecast. The result is a single forecast from which short- and medium-term views can be reported.

Figure 23. Summary of short-term forecast method.

Historical Archive:Monthly Airport-Pair Data

Short-term Forecast Medium-Term Forecast(Parts of method only)

Zone/Region-PairForecast Method

ScheduleForecast Method

Historical Archive:Published Schedule

Combine

Supporting Data:-Events-Calendars

State-FlowForecast Method

Apply annual capacities

Initial Airport-PairForecast

Aligned Airport-PairForecast

Final Airport-Pair Forecast

FinalForecast

ForecastOverflights



Medium-Term Forecast Method

STATFOR produces medium-term (seven years ahead) forecasts of annual numbers of IFR flight movements for volumes of airspace called ‘traffic zones’. Traffic zones are typically States, but Spain and Portugal are split into two, and Belgium & Luxembourg and Serbia & Montenegro are combined. Larger aggregate regions, such as the 27 EU States, are also included. For each traffic zone, forecasts are published for the main region-to-region flows (Annex B.2 defines these traffic regions) on the STATFOR Interactive Dashboard (Ref. 2). Traffic flows are also categorised as internals (within the traffic zone), arrival in or departure from the traffic zone, and overflights (neither departing from nor landing in the traffic zone, but passing through its airspace).

The forecast is published annually, at the beginning of the year and refreshed mid-year to support the capacity planning process. Key features of the method are:

Development of a core, airport-pair forecast which, at each update of the medium-term forecast, is shared by medium- and short-term forecasts;

The demand-side model focuses on economic growth and its influence on demand from travellers and shippers;

A supply-side model is used to forecast growth directly on airport pairs, and replaces the traditional demand-side model when appropriate;

Specific models for some market segments, such as business aviation and a low-cost effect;

The forecast is constrained by annual airport capacity.

The overflights are calculated using the airport-to-airport routing in the baseline year, plus more recent trends captured by the short-term forecast method.

The forecast method is continuously being refined to improve the quality of the outputs. For this forecast, the emissions trading scheme is integrated into the forecasting process, and the supply-side modeling has been adapted to handle better the recent cases of decline over several years for some flows. The forecast process is summarised in Figure 24.

The review body for STATFOR is the STATFOR User Group. This has members from civil aviation authorities and air navigation service providers, and from other industry organisations. Participants are typically actively involved in statistics or forecasting. The STATFOR User Group meets once or twice per year. It reviews the inputs to the medium-term forecast and the resulting draft forecast. The aim of the review process is to produce a forecast which is consistent on a European level and acceptable to member States. This does not necessarily mean the forecast is the same as that produced nationally.

The forecast is built from three main datasets.

A historical database of the STATFOR monthly statistics (derived from CRCO, Network Manager9 and National sources) for the last ten or more years at airport-pair level;

9 Network Manager as the new name of the former CFMU


A baseline from Network Manager and National sources that includes routing information;

The set of scenario inputs.

Figure 24. Preparation process of the Medium-Term Forecast.

The Medium-Term Forecast uses three scenarios which differ in terms of the assumptions. The low-growth and high-growth scenarios between them capture the most-likely range of future growth in flight movements; the baseline scenario indicates a likely position within this range. The main parts of the scenario data are:

Economic growth, summarised as GDP growth forecasts in real prices in local currency; (Annex C.1)

Low-cost growth, which adds additional flight movements, on top of economic growth to reflect new flight movements generated by low-cost airlines; (Annex C.2)

High-speed train network, summarised as changes in rail travel time on city pairs served by high-speed links, compared to the baseline year; (Annex C.3)

Airport capacity, in movements per year for major airports; (Annex C.4)

Load factors, which are assumed to change linearly from a current level to a future level that can vary with region and scenario; (Annex C.5)

Network change, a percentage adjustment to arrival and departure movements per traffic zone, which can be used - given supporting data - to represent in the model the effects of consolidation, irregularities in the baseline, or local one-off effects; (Annex C.6)



Demographic change, which has a very small impact in the demand-side model. These data are derived from UN population forecasts.

Emission Trading Scheme characteristics, prices of CO2 allowances, cap on historical CO2 emissions and the level of auctioning in the ETS determine the additional costs of airlines from aviation participation in the ETS. The MTF model expects that these are passed onto passengers and therefore reduces the passengers' demand accordingly. (Annex C.8)

The medium-term forecast is prepared in two stages: first the airport-pair forecast; then the traffic per volume of airspace, calculated from this using estimates of routing from airport to airport. Published tables, reports, graphs and maps are derived from the second of these, for short- and medium-term forecast alike.

Each airport pair is grown as follows:

All-cargo flights are grown based on GDP growth.

Small airport pairs (< 25 flights per year) are kept constant.

Growth of military flight movements in the first year of the forecast follows the average of the last three years for the traffic zone as a whole, with a maximum change of 5%, and is kept constant afterwards.

Business aviation flights are grown based on observed trend at a State level together with economic growth if this is a useful explanatory variable for this State.

For other traffic, the use of supply-side or demand-side approach is considered. Supply-side is used if traffic matches one of the standard histories (circular flights, long-term stable or declining traffic, direct relationship to GDP) and if demand exceeds the supply. Otherwise, it is demand that drives and limits the growth.

In the demand-side, passenger numbers are estimated from flight counts, aircraft type and load factors, then grown according to GDP growth and the elasticity for this region-pair, then converted back to a number of flights using a number of seats-to-flights relationships calibrated on historical data.

The first years of the forecast are aligned with the short-term forecast.

The growth of movements on an airport-pair may then be reduced if there has been a reduction in journey times by HST since the baseline year, adjusted for low-cost growth in the traffic zone and for any network change assumptions (by traffic zone, airport, or airport pair) and capped by airport capacity.

The resulting growth per airport pair is the ‘airport-pair’ forecast. The final step is to calculate how many flights are generated in each airspace by these airport-to-airport flights. This is done using a combination of the routings through airspace observed in the baseline year and trends in overflight growth per traffic zone.

At each stage, the results are validated using any available data, such as from the STATFOR User Group or from the Industry Monitor. For example: base-year airport movements are compared with statistics published by airports; first-year growth is compared with known airline plans; long-term growth is compared with other forecasts in terms of flights or passengers. Such comparisons are typically a matter of judgement, rather than a precise numerical correlation.



ANNEX B. TRAFFIC REGION DEFINITIONS

B.1 ESRA08

The EUROCONTROL Statistical Reference Area (ESRA) is designed to include as much as possible of the European Civil Aviation Conference (ECAC) area for which data are available from a range of sources within the Agency. It is used for high-level reports from the Agency, when referring to 'total Europe'. The ESRA changes only slowly with time; a region is added to the ESRA only when there is a full year's data from all sources, so that growth calculations are possible. ‘ESRA08’ was introduced in the Medium-Term Forecast report issued in february 2009. It is now used as a basis for comparison at European level in the forecasts. Note that the EUROCONTROL forecast includes also regions outside of the ESRA (eg. Armenia and Latvia) though still within ECAC.

Figure 25. The EUROCONTROL Statistical Reference Area.

Traffic zones are represented by an aggregate of FIRs & UIR of States. These do not take delegation of airspace into account. For individual States, the differences between charging areas and ACCs can have a big impact on overflight counts (and thus on total counts where the total is dominated by overflights). For the ESRA as a whole, there is only a small proportion of overflights, so that the difference between an FIR and an ACC definition is small.

B.2 Traffic regions

For this forecast, traffic flows are described as being to or from one of a number of traffic regions listed in Figure 26 (for example in Annex D). Each region is made up of a number of traffic zones. Traffic zones are indicated in the table for brevity by the first letters of the ICAO location codes. The traffic regions are defined for statistical convenience and do not reflect an official position of the EUROCONTROL Agency.

The ESRA was defined in the previous section. For flow purposes, this is split into a “North-West” region mostly of mature air traffic markets, a “Mediterranean” region stretching from the Canaries to Turkey and with a significant tourist element, and an



Eastern region. The ‘Other region’ includes the Baltic States and Oceanic. The Former CIS Region includes Armenia and Azerbaijan (members of ECAC). In time these will join the ESRA. More details are available in STATFOR Geographical Hierarchy document (Ref. 7).

Figure 26. Regions used in flow statistics.

ICAO region/country

ESRA

ESRA1 ESRA North-West EB, ED, EF, EG, EH, EI, EK, EL, EN, ES, ET, LF, LN, LO, LS

ESRA2 ESRA Mediterranean GC, LC, LE, LG, LI, LM, LP, LT

ESRA3 ESRA East BK, EP, LA, LB, LD, LH, LJ, LK, LQ, LR, LU, LW, LY, LZ, UK

World 1 North Atlantic BG, BI, C, EK, K, P (except KG)

World 2 Middle-East O, L

World 3 North-Africa DA, DT, GM, HE, HL, HS

World 4 Southern Africa D, F, G, H, MR (except DA, DT, HE, HL, HS, GM, GE, and ESRA)

World 5 Far-East R, V, Z (except ZZZZ)

World 6 Oceania KG, N, P, Y (except NO)

World 7 Mid-Atlantic M, T (except MR)

World 8 South-Atlantic S

World 9 Former CIS Region U (except areas in ESRA)

Other Other EE, EN, EV, EY, GE, GM, LP, LX, Swanwick Oc., Bodo Oc., Santa Maria FIR

B.3 Functional Airspace Blocks

On top of the traffic zones, this report also presents the forecast of IFR movements from 2012 to 2018 for Functional Airspace Blocks (FAB). A FAB is a block of airspace based on operational requirements regardless of the State boundaries (Figure 27). FAB initiatives (definitions) are constantly evolving according to the targets defined to improve the performance of the European air traffic management network. The FAB used in this reports refers to the January 2011 definition.

Figure 27. FAB initiatives as of January 2011. (Source: EUROCONTROL PRU)




ANNEX C. SUMMARY OF FORECAST ASSUMPTIONS

C.1 Economic Growth

Forecasts of growth in gross domestic product (GDP) come from a single coherent source, Oxford Economics Ltd. Data in this report originate from the January 2012 update of the GDP forecast. These are shown for specific States10 in Figure 31 and Figure 32. For all other States, the growth of the traffic region is used. Traffic regions are listed in Figure 26, and their economic growth in Figure 33.

The high- and low-growth scenarios are based on fixed offsets11 from these forecasts.

In this update, economic growth for 2012 is strongly revised downwards, as was already shown in Figure 11. Figure 28 shows that this revision applies to all States in the scope of the flight forecast. Beyond 2012, Figure 11 illustrates that 2013 has also been revised downwards, comparing the forecast used for the MTF11b (flight forecast published in October 2011, but economic forecast from August), with the new forecast (MTF12). There are some marginal upward revisions from 2015 onwards.

Figure 30 shows the differences in GDP forecast per State for each year. Nearly all States see downward revisions in 2013 and a majority do in 2014.

Figure 28. Changes per State in economic forecast for 2012 for this flight forecast.

10 In fact the forecasts refer to ‘traffic zones’. These are typically States, represented by its FIR. However, Belgium & Luxembourg are combined, as are Serbia and Montenegro. Spain and Portugal are each divided into 2. 11 +1%, -0.8% for early years and big States, +1.5%, -0.8% for early years and small States, +0.5%, -0.5% for late years and big States, +0.8%, -0.8% for late years and small States.


Figure 29. Since the previous forecast, the economic outlook for EU27 has significantly worsened.

Figure 30. Summary of changes in GDP forecasts for 2012-2018: nearly all States see reduction in 2012 and 2013.



Figure 31. GDP Growth by Traffic Zone

Source: 2010-2018 from Oxford Economics Ltd, Jan2012 Comments: Real GDP Growth in local currency. Units: Growth per year. Data last updated: 06/02/2012

Actual Base

2010 2009 2011 2012 2013 2014 2015 2016 2017 2018

Albania 2.8% 3.8% 3.0% 4.2% 4.2% 4.5% 4.5% 4.5% 4.5% 4.5%

Armenia -14% 2.1% 4.0% 4.3% 4.3% 4.6% 5.0% 5.0% 5.0% 5.0%

Austria -3.4% 2.3% 3.0% 1.0% 2.1% 2.2% 2.1% 2.1% 1.9% 1.9%

Azerbaijan 9.3% 5.0% 0.4% 4.0% 5.0% 5.5% 5.5% 5.5% 5.5% 5.5%

Belarus 0.2% 7.6% 5.0% 1.5% 4.3% 3.7% 3.3% 3.3% 3.3% 3.3%

Belgium/Luxembourg -2.7% 2.3% 2.0% 0.9% 2.9% 2.5% 2.1% 2.1% 1.9% 1.9%

Bosnia-Herzegovina -3.0% 0.5% 2.2% 2.1% 5.0% 5.5% 5.5% 5.5% 5.3% 5.3%

Bulgaria -5.6% 0.3% 2.2% 2.4% 5.3% 5.8% 5.9% 5.9% 5.7% 5.7%

Canary Islands -3.7% -0.1% 0.7% 0.2% 1.6% 1.7% 1.8% 1.8% 1.7% 1.7%

Croatia -6.0% -1.2% 0.4% 1.2% 2.0% 3.2% 3.6% 3.6% 3.2% 3.2%

Cyprus -1.7% 1.0% 1.1% 0.6% 1.5% 2.2% 3.5% 3.5% 3.5% 3.5%

Czech Republic -4.0% 2.2% 2.0% 1.4% 2.8% 3.1% 3.1% 3.1% 2.9% 2.9%

Denmark -5.2% 1.7% 1.1% 1.2% 2.2% 2.3% 2.3% 2.3% 2.3% 2.3%

Estonia -14% 3.1% 7.2% 2.5% 4.2% 4.8% 4.7% 4.7% 4.5% 4.5%

FYROM -1.0% 1.7% 4.2% 2.8% 3.0% 4.5% 4.0% 4.0% 3.7% 3.7%

Finland -8.3% 3.6% 3.0% 1.3% 2.8% 3.1% 2.7% 2.7% 2.6% 2.6%

France -2.6% 1.4% 1.6% 0.3% 1.6% 2.0% 2.0% 2.0% 1.9% 1.9%

Georgia -3.8% 6.4% 5.6% 4.9% 5.5% 5.5% 5.2% 5.2% 4.9% 4.9%

Germany -4.7% 3.6% 2.9% 0.9% 2.0% 2.1% 2.0% 2.0% 1.9% 1.9%

Greece -2.3% -4.4% -6.2% -6.1% -2.0% -0.0% 1.8% 1.8% 2.1% 2.1%

Hungary -6.5% 1.1% 1.0% 0.7% 2.7% 3.4% 3.4% 3.4% 3.2% 3.2%

Iceland -6.9% -4.0% 2.1% 1.8% 2.9% 3.0% 2.8% 2.8% 2.7% 2.7%

Ireland -7.0% -0.4% 1.2% 0.5% 3.1% 3.4% 3.7% 3.7% 3.9% 3.9%

Italy -5.2% 1.2% 0.6% -0.7% 0.2% 0.9% 1.3% 1.3% 1.4% 1.4%

Latvia -18% -0.3% 4.3% 3.5% 5.8% 6.2% 5.7% 5.7% 5.3% 5.3%

Lisbon FIR -2.5% 1.3% -1.3% -2.9% 0.9% 1.7% 1.6% 1.6% 1.7% 1.7%

Lithuania -15% 1.3% 5.8% 3.7% 6.2% 6.3% 6.2% 6.2% 5.6% 5.6%

Malta -3.3% 2.7% 2.0% 1.5% 2.6% 3.0% 3.0% 3.0% 3.0% 3.0%

Moldova -7.3% 6.9% 6.5% 4.0% 5.5% 5.5% 5.5% 5.5% 5.5% 5.5%

Netherlands -3.5% 1.6% 1.8% 0.7% 2.0% 2.2% 1.9% 1.9% 1.8% 1.8%

Norway -1.6% 0.3% 1.2% 1.8% 2.2% 2.4% 2.3% 2.3% 2.2% 2.2%

Poland 1.6% 3.8% 4.2% 2.8% 4.1% 4.1% 3.8% 3.8% 3.5% 3.5%

Romania -6.9% -1.5% 1.2% 2.8% 5.2% 5.8% 5.7% 5.7% 5.6% 5.6%

Russian Federation -7.8% 4.0% 3.8% 4.4% 4.3% 3.6% 3.5% 3.5% 3.5% 3.5%

Santa Maria FIR -2.5% 1.3% -1.3% -2.9% 0.9% 1.7% 1.6% 1.6% 1.7% 1.7%

Serbia&Montenegro -3.1% 1.7% 2.7% 2.8% 5.0% 5.5% 5.5% 5.5% 5.3% 5.3%

Slovakia -4.8% 4.0% 2.9% 1.1% 3.5% 4.3% 4.7% 4.7% 4.0% 4.0%

Slovenia -8.4% 1.3% 1.1% 1.0% 2.2% 3.1% 3.5% 3.5% 3.4% 3.4%

Spain -3.7% -0.1% 0.7% 0.2% 1.6% 1.7% 1.8% 1.8% 1.7% 1.7%

Sweden -5.3% 5.4% 4.2% 1.5% 2.5% 2.1% 1.9% 1.9% 1.9% 1.9%

Switzerland -1.9% 2.7% 1.9% 0.6% 1.9% 2.0% 2.0% 2.0% 1.9% 1.9%

Turkey -4.8% 9.0% 7.7% 2.2% 5.6% 6.0% 5.5% 5.5% 5.4% 5.4%

UK -4.9% 1.8% 0.9% 1.0% 2.6% 2.8% 2.7% 2.7% 2.5% 2.5%

Ukraine -15% 4.2% 4.8% 4.4% 5.9% 6.5% 6.3% 6.3% 5.8% 5.8%



Figure 32. GDP Growth by Origin-Destination Zone

Source: 1993-2004 from STATFOR records. 2005 onwards from Oxford Economics Ltd, Jan 12. Comments: Real GDP Growth in local currency. Units: Growth per year. Data last updated: 07/02/2012

Actual Base

2010 2009 2011 2012 2013 2014 2015 2016 2017 2018

China 9.2% 10% 9.1% 8.5% 8.8% 8.8% 8.3% 7.9% 7.5% 7.5%

India 7.0% 8.7% 7.1% 6.5% 8.7% 9.2% 8.6% 7.9% 7.6% 7.6%

Figure 33. GDP Growth by Traffic Region

Source: 2005 onwards updated from Oxford Economics Jan12 Comments: Real GDP Growth. Units: Growth per year. Data last updated: 07/02/2012

Actual Base

20102009 2011 2012 2013 2014 2015 2016 2017 2018

ESRA East -3.6% 2.5% 3.1% 1.3% 2.7% 4.1% 4.4% 4.1% 4.6% 4.6%

ESRA Mediterranean -4.4% 1.7% 1.2% -0.8% 0.8% 1.8% 2.4% 2.7% 2.8% 2.8%

ESRA North-West -4.1% 2.4% 2.0% 0.3% 1.8% 2.3% 2.3% 2.2% 2.4% 2.4%

Far-East -3.7% 5.6% 0.9% 2.3% 4.0% 3.3% 2.7% 2.4% 2.9% 2.9%

Former CIS Region -5.4% 4.8% 4.6% 4.1% 4.4% 4.8% 4.3% 4.1% 5.0% 5.0%

Mid-Atlantic -4.7% 4.3% 3.6% 3.5% 4.6% 4.6% 4.0% 3.5% 4.3% 4.3%

Middle-East 1.2% 4.2% 4.9% 3.5% 4.6% 4.6% 4.6% 4.5% 5.3% 5.3%

North Atlantic -3.4% 3.0% 1.8% 2.5% 2.7% 3.0% 3.0% 2.8% 3.5% 3.5%

North-Africa 1.6% 4.1% -12% 9.2% 6.7% 5.8% 4.9% 4.7% 5.2% 5.2%

Oceania 1.2% 2.5% 1.9% 3.0% 3.8% 3.3% 2.6% 2.2% 2.6% 2.6%

Other -16% 1.1% 6.2% 2.4% 4.9% 5.9% 5.7% 5.3% 5.9% 5.9%

South-Atlantic 0.0% 6.0% 6.6% 3.9% 4.0% 4.3% 3.7% 3.4% 4.0% 4.0%

Southern Africa 3.7% 6.1% 5.6% 5.9% 6.1% 6.0% 5.4% 5.2% 6.2% 6.2%

C.2 Low-Cost Carrier Growth

The starting point is the market share of low-cost carriers in each traffic zone in September 2011. The low-cost carriers are defined by means of a list (Ref. 8), more detailed data on this segment are available in STATFOR web page (Ref. 3). For the purposes of this analysis only, the market share is defined as the share of passenger flights (i.e. excluding the military, cargo and business aviation since these are forecasted separately, see Annex A).

The observed market share in 2011 is assumed to continue to change according to low-, base- and high- scenarios. These scenarios are based on observed average annual growth across all States in the ESRA over 2005-2011, which is 0.9 percentage points per year, with a high rate of 2.9 points and low of -0.1.

These rates are rather different to previously used, as it is apparent that growth in the low-cost market has for the most part slowed down considerably. The low value indicates that in 25% of historical observations we saw an annual decrease in market share: as a projection this becomes a State seeing a 1% share decline for low-cost over 7 years. The high value was seen for 25% of observations (TZ and year), but the data suggest that it is only really applicable in States with less traffic, so we used half this rate for States with more than 25,000 departures in the month.



The evidence is that low-cost carrier market share growth is partly new, generated traffic (for example attracted by the price), and partly replacement or re-badging of existing traffic. In the forecast, approximately half of the difference between the baseline and future values shown in Figure 34 is converted to extra growth in flights.

Figure 34. Low-Cost effects by Traffic Zone

Source: STATFOR Analysis and modelling Comments: Represents additional growth for Low-Cost, but only the baseline year is a true statistic for low-co Units: Percentage Additional Growth Due to Low-Cost Growth. Data last updated: 06/02/2012

Actual Low Base High

2011 2018 2018 2018

Albania 41% 38% 42% 46%

Armenia 0% 0% 2% 6%

Austria 21% 21% 24% 27%

Azerbaijan 0% 0% 2% 6%

Belarus 0% 0% 2% 6%

Belgium/Luxembourg 15% 15% 19% 21%

Bosnia-Herzegovina 7% 11%7% 19%

Bulgaria 15% 14% 21% 36%

Canary Islands 34% 34% 38% 44%

Croatia 20% 20% 23% 31%

Cyprus 23% 22% 29% 44%

Czech Republic 26% 25% 32% 47%

Denmark 13% 13% 17% 19%

Estonia 11% 11% 14% 21%

FYROM 6% 6% 7% 8%

Finland 21% 21% 23% 27%

France 18% 18% 21% 24%

Georgia 0% 0% 2% 6%

Germany 29% 32%29% 34%

Greece 23% 23% 26% 28%

Hungary 15% 15% 18% 25%

Iceland 2% 2% 5% 12%

Ireland 47% 46% 53% 68%

Italy 37% 36% 40% 42%

Latvia 11% 10% 17% 32%

Lisbon FIR 32% 32% 35% 42%

Lithuania 26% 25% 32% 47%

Malta 31% 30% 37% 52%

Moldova 3% 3% 7% 15%

Netherlands 22% 22% 25% 28%

Norway 22% 22% 25% 28%

Poland 25% 24% 31% 46%

Romania 26% 25% 32% 47%

Santa Maria FIR 0% 0% 2% 6%

Serbia&Montenegro 9% 9% 13% 21%

Slovakia 24% 23% 30% 45%

Slovenia 3% 2% 9% 24%

Spain 46% 41% 46% 50%

Sweden 20% 20% 23% 26%

Switzerland 16% 16% 20% 22%




2011 2018 2018 2018

Turkey 19% 19% 22% 25%

UK 46% 46% 48% 50%

Ukraine 0% 0% 4% 12%

C.3 High-Speed Train Network Development

The information on improvements in the high-speed train (HST) network between 2011 and 2018 on is based on information provided by the Union Internationale des Chemins de Fer and many websites of a number of specific HST projects. The model converts improved rail travel times into increased market share for rail, and thus fewer passengers travelling by air.

Figure 35 indicates the changes in rail travel time in the baseline scenario. In the low- and high-growth scenarios, the times remain the same, but they happen earlier and later, respectively. The distance indicated is based on an average location of airports associated with the city, not on city-centre locations. It is also a direct distance, so underestimates the distance in particular where the sea intervenes, such as London-Amsterdam or Istanbul-Bursa.

For the 2012 medium-term forecast, the travel times have been updated, and the years when the projects come into operation have been reviewed. In practice, there have been few changes, since, in the medium-term the HST projects are largely already in progress and appear to have been relatively little affected by the economic slowdown.

Figure 35. High-Speed Train Times

Source: Actuals from on-line timetables. Plans from UIC, TEN-T and elsewhere. Comments: HST projects to be finalised 2012-2018. Units: Travel time (minutes). Data last updated: 03/02/2012 Distances estimated from airport locations.

Distance Rail Time (mins) Speed (km/h)

2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017Km

B 355 720 . . . . . 120 30 . . . . . 177Sivas

BBURSA (MIL 317 240 . . . . . 130 79 . . . . . 146

Ankara

B 519 900 . . . . . 200 35 . . . . . 156IZMIR-ADNA

B 282 225 . 145 . . . . 75 . 117 . . . .Lyon

B 486 340 . 240 . . . . 86 . 121 . . . .Marseille

BNimes 484 350 . 250 . . . . 83 . 116 . . . .

Bale Mulho

BParis 398 210 . . 180 . . . 114 . . 133 . . .

Barcelona Lyon B 530 460 . . . . . 180 69 . . . . . 177

Bilbao B 91 130 . . . . 40 . 42 . . . . 136 .SAN SEBAST

B 195 180 . . 80 . . . 65 . . 146 . . .Bale Mulho

BFrankfurt 386 390 . 290 . . . . 59 . 80 . . . .

DIJON/DARO

BStrasbourg 242 220 . 130 . . . . 66 . 112 . . . .

Erfurt Leipzig B 102 70 . . . . 39 . 87 . . . . 156 .




Distance Rail Time (mins) Speed (km/h)

Km 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017

Bern B 348 230 200 . . . . . 91 105 . . . . .

Bale Mulho B 274 170 140 . . . . . 97 118 . . . . .

London B 641 370 . . 310 . . . 104 . . 124 . . .

Frankfurt

Paris B 444 230 215 . . . . . 116 124 . . . . .

Geneva Paris B 397 202 185 . . . . . 118 129 . . . . .

Ankara B 335 330 180 . . . . . 61 112 . . . . .

Konya B 454 745 210 . . . . . 37 130 . . . . .

Sivas B 682 1260 . . . . . 300 32 . . . . . 136

Istanbul

BURSA (MIL B 84 240 . . . . . 135 21 . . . . . 37

Bern B 239 180 150 . . . . . 80 96 . . . . .Karlsruhe

Bale Mulho B 166 110 . . . 69 . . 91 . . . 144 . .

Bale Mulho B 361 230 200 . . . . . 94 108 . . . . .Koln/Bonn

London B 516 283 . . 240 . . . 109 . . 129 . . .

LISBOA Madrid B 507 690 . . 165 . . . 44 . . 184 . . .

ALICANTE B 361 360 . 120 . . . . 60 . 180 . . . .

Bilbao B 327 300 . . . 135 . . 65 . . . 145 . .

Bordeaux B 543 750 . . . . . 238 43 . . . . . 137

MURCIA SAN B 386 250 . . . 120 . . 93 . . . 193 . .

SAN SEBAST B 363 300 . . . . 135 . 73 . . . . 161 .

Valencia B 295 210 93 . . . . . 84 190 . . . . .

Madrid

Vitoria B 286 220 . . . 120 . . 78 . . . 143 . .

Munchen Berlin B 481 360 . . . . . 240 80 . . . . . 120

PORTO Vigo B 108 480 . . . . 60 . 13 . . . . 108 .

Barcelona B 824 440 . 335 . . . . 112 . 148 . . . .

Brest B 511 250 . . . . . 180 123 . . . . . 170

Montpellie B 600 210 . 180 . . . . 171 . 200 . . . .

Paris

Rennes B 326 127 . . . . . 90 154 . . . . . 217

Barcelona B 158 165 . 50 . . . . 57 . 190 . . . .Perpignan

Madrid B 605 500 . 230 . . . . 73 . 158 . . . .

SCHIPHOL A London B 359 287 . . 240 . . . 75 . . 90 . . .

LUXEMBOURG B 162 130 . . . . . 85 75 . . . . . 114

Lille B 405 200 . . . . . 115 121 . . . . . 211

Lyon B 373 280 . 200 . . . . 80 . 112 . . . .

Marseille B 589 380 . 270 . . . . 93 . 131 . . . .

Strasbourg

Paris B 381 140 . . . . . 110 163 . . . . . 208

Bilbao B 238 210 . . . 75 . . 68 . . . 190 . .

SAN SEBAST B 310 240 . . . . 65 . 78 . . . . 286 .

VALLADOLID

Vitoria B 218 140 . . . 65 . . 93 . . . 201 . .

Bilbao B 49 140 . . . 30 . . 21 . . . 98 . .Vitoria

SAN SEBAST B 92 100 . . . . 30 . 55 . . . . 184 .

ZURICH Paris B 469 270 . 240 . . . . 104 . 117 . . . .


C.4 Airport Capacity

The forecast takes airport capacity into account. The final stage of this is that demand in excess of the expected capacity cannot be accommodated and is lost. For this medium-term forecast, information on capacity of 159 airports has been used, coming from either long-term forecast assumptions (see Ref. 9) or from airports, civil aviation authorities and air navigation service providers when this data is available.

These capacities are treated as confidential and not reported in this document. Nevertheless, we have included for the first time an assumption that the proposed third Istanbul airport, due for completion in 2016, will provide additional capacity from 2017. The effects of this are clearly observable in the growth of Turkey and its neighbours.

C.5 Load Factors

Figure 37 summarises the assumptions about the development of load factors. Time-series forecast have been made using the recent trends in the annual data provided by the Association of European Airlines (AEA).

Figure 36 shows an example of this forecast. The lower bound of the forecast is used for the “low-growth” forecast. This makes sense in terms of the air transport business, but the side effect of this is to bring close the overall low- and high-growth traffic forecasts, because in the high-growth scenario, more of the growth in passengers is absorbed in load factor growth and less in growth in flight-frequencies.

It could be argued that load factors for other market segments (eg regional or low-cost carriers) are different from those provided by the AEA. Whilst this is true, it is the change in load factors which has the main influence on the forecast, so if the absolute load factors are different but the changes are similar, then the inputs will still be appropriate.

High fuel prices amongst other things continue to pressure airlines to keep load factors high. As shown in Figure 7, load factors have been slipping slightly back from their historic highs in recent months. The assumption here is that the upward movement will resume, even if the net change over 7 years within the EU for example is a modest 2% points.



Figure 36. Example load factor forecast, for Europe.

Figure 37. Load factors by Traffic Region

Source: Actual: AEA, IATA. Forecast: STATFOR analysis and modelling. Comments: Data for 2018 based on year to December Units: Percentage Load Factor for this Traffic Region. Data last updated: 06/02/2012


2001 2000 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2018 2018 2018

ESRA East 64.4% 62.5% 64.5% 65.0% 65.3% 66.9% 68.4% 68.9% 67.7% 67.7% 69.1% 70.9% 71.8% 73.3% 74.8%

ESRA Mediterranean 64.4% 62.5% 64.5% 65.0% 65.3% 66.9% 68.4% 68.9% 67.7% 67.7% 69.1% 70.9% 71.8% 73.3% 74.8%

ESRA North-West 64.4% 62.5% 64.5% 65.0% 65.3% 66.9% 68.4% 68.9% 67.7% 67.7% 69.1% 70.9% 71.8% 73.3% 74.8%

Far-East 78.7% 76.6% 80.7% 76.3% 78.0% 79.7% 80.7% 83.2% 80.7% 80.6% 83.6% 81.6% 85.0% 85.0% 85.0%

Former CIS Region 64.4% 62.5% 64.5% 65.0% 65.3% 66.9% 68.4% 68.9% 67.7% 67.7% 69.1% 70.9% 71.8% 73.3% 74.8%

Mid-Atlantic 79.8% 78.9% 79.1% 79.4% 80.3% 81.7% 82.1% 84.3% 83.3% 81.7% 82.2% 81.7% 85.0% 85.0% 85.0%

Middle-East 67.1% 63.3% 67.5% 69.2% 69.6% 73.5% 70.8% 74.6% 73.5% 69.3% 71.3% 70.0% 74.0% 74.0% 74.0%

North Atlantic 77.4% 73.6% 79.7% 79.5% 81.3% 82.2% 81.1% 81.7% 80.9% 82.0% 83.8% 82.8% 85.0% 85.0% 85.0%

North-Africa 70.5% 65.2% 68.8% 65.3% 68.7% 67.0% 68.4% 69.4% 70.4% 67.9% 69.8% 67.7% 67.2% 69.8% 72.4%

Oceania 78.7% 76.6% 80.7% 76.3% 78.0% 79.7% 80.7% 83.2% 80.7% 80.6% 83.6% 81.6% 85.0% 85.0% 85.0%

Other 64.4% 62.5% 64.5% 65.0% 65.3% 66.9% 68.4% 68.9% 67.7% 67.7% 69.1% 70.9% 71.8% 73.3% 74.8%

South-Atlantic 79.7% 73.8% 76.1% 82.2% 82.9% 82.5% 86.3% 85.1% 81.0% 80.2% 84.9% 85.9% 85.0% 85.0% 85.0%

Southern Africa 74.3% 77.4% 76.4% 75.9% 78.2% 77.9% 77.9% 79.2% 77.8% 77.6% 77.4% 76.9% 79.6% 80.0% 80.0%



C.6 Events and Trends

‘Events and Trends’ are used to represent future events. The forecast method means that only the local traffic (excluding overflights) can be adjusted in this way but with the possibility to account for diverging trends in domestic and external flights by adjusting internals or arrivals and departures separately.

The assumptions are expressed as 'cumulative' change: so a 1.01 figure in the year 2012 only would mean increase growth by 1% in 2012 and decrease it in 2013 (with a total cumulative effect of 0 over the full period of the forecast). Thus, for one-time events, a value in the corresponding year is provided. To show a lasting effect over the whole forecast period, the flight adjustment factor is present in all years until the end of the forecast.

The events in this forecast are:

For the London Olympics in 2012, the MTF12 assumptions are based on data prepared for the Olympics planners via UK NATS.

The boost to local traffic of Poland and Ukraine from the Euro2012 football championship is also included.

The collapse of Spanair and Malev is represented by reductions in local traffic for Canary Islands, Spain and Hungary (and implicitly on all flows connecting with these States).

EU accession: Croatia and Iceland are expected to join the EU around 2013. The effect of air transport and market liberalisation is factored in for Croatia by an additional 5% growth in 2013, with a declining effect in subsequent years. Iceland is expected to see an effect half this size.

The Arab Spring: civil unrest in Tunisia, Egypt and Libya continues to affect flights across Europe. Figure 38 shows adjustments to the arrival-departure flows to allow for these effects.

The available data on airline schedules was not robust enough to be used en masse for forecasting the Summer 2012 traffic; instead some small amendments were made to the big three - France, Germany, UK – to produce better alignment with the schedules. This was just 0.4% at annual level, shown here.

EU visa-free measures: the boost to growth after EU relaxation of visa requirements for Serbian, Montenegrin and Macedonian citizens travelling is assumed to have finished, while a small effect lingers for flights from Albania and Bosnia-Herzegovina.

Adjustments to the overall number of movements have been made to allow for the 2012 and 2016 leap years, which leads to approximately 0.3% points of extra growth in 2012 and 2016, and a corresponding fall in 2013 and 2017.

This section does not list a number of adjustments made to the historical data to provide a clean baseline from which to forecast, since they are more technical in nature. The large adjustments made for the ash clouds of April-May 2010, and weather cancellations of December 2010 are now only of historical interest. The number of flight cancellations in 2011 was much smaller than 2010, but historical adjustments were made for 2011 including to North African flows, and to the overflights of States neighbouring Greece which saw additional traffic during social unrest there, principally in September and October 2011. These neighbours will therefore see a dip in growth in those months in 2012.



Figure 38. Events and Trends assumptions by Traffic Zone

Source: STATFOR analysis and modelling Comments: Consolidation includes: trends from STF and future events such as EU Accession, major sport events a Units: Growth index (Baseline Year=1.0). Data last updated: 20/02/2012

2012 2013 2014 2015 2016 2017 2018

H 1.010 1.010 1.010 1.010 1.010 1.010 1.010

B 1.010 1.010 1.010 1.010 1.010 1.010 1.010

Albania Total: Arr/Dep

L 1.010 1.010 1.010 1.010 1.010 1.010 1.010

H 1.010 1.010 1.010 1.010 1.010 1.010 1.010

B 1.010 1.010 1.010 1.010 1.010 1.010 1.010

Bosnia-Herzegovina Total: Arr/Dep

L 1.010 1.010 1.010 1.010 1.010 1.010 1.010

H 0.963 0.950 0.950 0.950 0.950 0.950 0.950

B 0.963 0.950 0.950 0.950 0.950 0.950 0.950

Canary Islands Total: Arr/Dep

L 0.963 0.950 0.950 0.950 0.950 0.950 0.950

H . 1.050 1.075 1.085 1.090 1.090 1.090

B . 1.050 1.075 1.085 1.090 1.090 1.090

Croatia Total: Arr/Dep

L . 1.050 1.075 1.085 1.090 1.090 1.090

H 0.844 . . . . . .

B 0.844 . . . . . .

Egypt Total: Arr/Dep

L 0.844 . . . . . .

H 1.004 1.004 1.004 1.004 1.004 1.004 1.004

B 1.004 1.004 1.004 1.004 1.004 1.004 1.004

Total: Internal

L 1.004 1.004 1.004 1.004 1.004 1.004 1.004

H 1.004 1.004 1.004 1.004 1.004 1.004 1.004

B 1.004 1.004 1.004 1.004 1.004 1.004 1.004

France

Total: Arr/Dep

L 1.004 1.004 1.004 1.004 1.004 1.004 1.004

H 1.004 1.004 1.004 1.004 1.004 1.004 1.004

B 1.004 1.004 1.004 1.004 1.004 1.004 1.004

Total: Internal

L 1.004 1.004 1.004 1.004 1.004 1.004 1.004

H 1.004 1.004 1.004 1.004 1.004 1.004 1.004

B 1.004 1.004 1.004 1.004 1.004 1.004 1.004

Germany

Total: Arr/Dep

L 1.004 1.004 1.004 1.004 1.004 1.004 1.004

H 0.800 0.700 0.700 0.700 0.700 0.700 0.700

B 0.800 0.700 0.700 0.700 0.700 0.700 0.700

Hungary Total: Arr/Dep

L 0.800 0.700 0.700 0.700 0.700 0.700 0.700

H . 1.025 1.035 1.040 1.040 1.040 1.040

B . 1.025 1.035 1.040 1.040 1.040 1.040

Iceland Total: Arr/Dep

L . 1.025 1.035 1.040 1.040 1.040 1.040

H 1.031 1.239 1.239 1.239 1.239 1.239 1.239

B 1.031 1.239 1.239 1.239 1.239 1.239 1.239

Libya Total: Arr/Dep

L 1.031 1.239 1.239 1.239 1.239 1.239 1.239

H 1.007 . . . . . .

B 1.004 . . . . . .

Total: Internal

L 1.002 . . . . . .

H 1.016 . . . . . .

B 1.012 . . . . . .

Poland

Total: Arr/Dep

L 1.008 . . . . . .




2012 2013 2014 2015 2016 2017 2018

H 0.925 0.900 0.900 0.900 0.900 0.900 0.900

B 0.925 0.900 0.900 0.900 0.900 0.900 0.900

Total: Internal

L 0.925 0.900 0.900 0.900 0.900 0.900 0.900

H 0.985 0.980 0.980 0.980 0.980 0.980 0.980

B 0.985 0.980 0.980 0.980 0.980 0.980 0.980

Spain

Total: Arr/Dep

L 0.985 0.980 0.980 0.980 0.980 0.980 0.980

H 1.148 1.149 1.149 1.149 1.149 1.149 1.149

B 1.148 1.149 1.149 1.149 1.149 1.149 1.149

Tunisia Total: Arr/Dep

L 1.148 1.149 1.149 1.149 1.149 1.149 1.149

H 1.009 1.004 1.004 1.004 1.004 1.004 1.004

B 1.009 1.004 1.004 1.004 1.004 1.004 1.004

Total: Internal

L 1.009 1.004 1.004 1.004 1.004 1.004 1.004

H 1.006 1.004 1.004 1.004 1.004 1.004 1.004

B 1.006 1.004 1.004 1.004 1.004 1.004 1.004

UK

Total: Arr/Dep

L 1.006 1.004 1.004 1.004 1.004 1.004 1.004

H 1.007 . . . . . .

B 1.005 . . . . . .

Total: Internal

L 1.002 . . . . . .

H 1.033 . . . . . .

B 1.028 . . . . . .

Ukraine

Total: Arr/Dep

L 1.023 . . . . . .

C.7 Airport Traffic Switch

Traffic can be switched between airports for two reasons: to allow for the opening or closure of an airport (as in the two examples here), in which case the overall number of flights is unaffected; or to shift flights from a constrained larger airport to a faster-growing secondary airport, in which case the overall number of flights can be increased because the number of flights that cannot be accommodated at a constrained airport typically decreases.

In recent forecasts we had switched traffic from Istanbul/Ataturk to Istanbul/Sabiha Gokcen. A review of the effects of this showed that it led to better forecasting of the latter, but under-estimating the former, and thus under-estimating the capacity constraints. After discussion, this traffic switching was discontinued. In any case, new capacity is expected to become available in Istanbul (see annex C.4).

The remaining traffic switches allow for the closure of two airports (see Figure 39).

The closure of Berlin/Tegel (EDDT), with traffic moving to Berlin/Schönefeld (becoming Berlin/Brandenburg) from 3 June 2012. Working assumption concerning transfer of Berlin/Tegel traffic to Berlin/Schönefeld: 50% in 2012 and 100% in 2013).

The closure of Bucharest/Baneasa airport, and its traffic moving to Bucharest/Otopeni International Airport. It is not yet clear whether this will be a permanent situation, but overall traffic growth should be unaffected.



Figure 39. Airport traffic switch

Source: EUROCONTROL Data and analysis Comments: Updated from MTF12 inputs Units: Airport Traffic Switching. Data last updated: 16/02/2012

Low Base High

2012 2013 2012 2013 2012 2013

Traffic Type Traffic Between And Region Move To

EDDT - EDDB 50% 100% 50% 100% 50% 100% All

LRBS - LROP 75% 100% 75% 100% 75% 100%

C.8 Emissions Trading Scheme

The aviation industry joined the EU ETS from 1st January 2012. The effects of the ETS on traffic demand are now included in the STATFOR forecasts (see Ref. 10 for details on the method).

The air operators need to surrender allowances for their CO2 emissions. This year, the EU gives the airlines permits to cover 85% of the allowances (for free); the rest needs to be bought from auctions or, on the free market. For each airline, the amount of allowances allocated to the airlines free of charge is determined as a percentage of the historical aviation emissions. The total amount of emission allowances allocated from 2012 is capped at a level of 95%-97% of the historical aviation, depending on the horizon (see Figure 40).

Figure 40. ETS assumptions

Source: STATFOR Analysis and modelling Comments: ETS characteristics (cap on historical emissions and level of auctioning, in %) Units: Cap on historical 2004-06 emissions and level of auctioning, in %. Data last updated: 12/12/2011

2010 2011 2012 2013 2014 2015 2016 2017 2018

Low 100% 100% 97% 95% 95% 95% 95% 95% 95%

Base 100% 100% 97% 95% 95% 95% 95% 95% 95%

Cap

High 100% 100% 97% 95% 95% 95% 95% 95% 95%

Low 0% 0% 15% 15% 15% 15% 15% 15% 15%

Base 0% 0% 15% 15% 15% 15% 15% 15% 15%

Auction

High 0% 0% 15% 15% 15% 15% 15% 15% 15%

Applying the percentages defined in Figure 40 on the total historical amount of emissions, the total amount of emission allowances that the aircraft operators will get free of charge is calculated. Subtracting the latter to the estimate of total amount of future emissions12, the total amount the operators need to buy either at an auction or on the free market is then assessed. This amount of allowances to be purchased (expressed per passenger per airport-pair) is converted into price terms with three scenarios for future prices of CO2 as shown in Figure 41. The prices start at €8-13 per tonne of CO2 in 2012 and increase to €30-55 in 2018. They correspond to current

12 See Ref. 10 for future emissions calculation.



market situation: in December 2011, the price of carbon contracts for emissions in 2012 were around €8 per tonne13. They are lower than the prices used in MTF11.

Figure 41. Prices

Source: STATFOR Analysis and modelling Comments: Price in EUR per tonne CO2 Units: Price in EUR per tonne of CO2. Data last updated: 09/02/2012

2011 2012 2013 2014 2015 2016 2017 2018

Low 0 8 10 12 15 19 24 30

Base 0 10 13 16 20 25 32 40

High 0 13 17 21 27 34 43 55

The airlines are expected to transfer the additional CO2 costs (i.e. the allowances purchased) fully to their customers, the passengers. This will imply an increase in fares, and consequently a reduction in demand from price-sensitive passengers. The sensitivity of demand for air travel to changes in the price of the tickets is described in Figure 42 by a set of flow specific price elasticities derived largely from an IATA publication (see Ref. 11).

Figure 42. Price Elasticities per (bidirectional) Traffic Region Pair and period

Source: STATFOR Analysis and modelling Comments: Demand price elasticities (based on LTF08 figures) Units: Multiplier (Elasticity). Data last updated: 13/01/2012

ESRA North-West

ESRA Mediterran

ean ESRA East Other

Former CIS

Region North

AtlanticMid-

AtlanticSouth-Atlantic

North-Africa

Southern Africa

Middle-East

Far-East Oceania

ESRA North-West -0.9 -0.8 -0.8 -0.8 -0.7 -0.7 -0.7 -0.7 -0.6 -0.6 -0.5 -0.5 -0.6

ESRA Mediterranean -0.8 -0.9 -0.8 -0.8 -0.7 -0.7 -0.7 -0.7 -0.6 -0.6 -0.5 -0.5 -0.6

ESRA East -0.8 -0.8 -0.9 -0.8 -0.6 -0.7 -0.7 -0.7 -0.6 -0.6 -0.5 -0.5 -0.6

Other -0.8 -0.8 -0.8 -0.9 -0.6 -0.7 -0.7 -0.7 -0.7 -0.7 -0.5 -0.5 -0.7

Former CIS Region -0.7 -0.7 -0.6 -0.6 -0.6 -0.4 -0.4 -0.4 -0.7 -0.7 -0.6 -0.6 -0.7

North Atlantic -0.7 -0.7 -0.7 -0.7 -0.4 -0.7 -0.7 -0.7 -0.7 -0.7 -0.4 -0.4 -0.7

Mid-Atlantic -0.7 -0.7 -0.7 -0.7 -0.4 -0.7 -0.8 -0.8 -0.7 -0.7 -0.4 -0.4 -0.7

South-Atlantic -0.7 -0.7 -0.7 -0.7 -0.4 -0.7 -0.8 -0.8 -0.7 -0.7 -0.4 -0.4 -0.7

North-Africa -0.6 -0.6 -0.6 -0.7 -0.7 -0.7 -0.7 -0.7 -0.4 -0.4 -0.7 -0.7 -0.7

Southern Africa -0.6 -0.6 -0.6 -0.7 -0.7 -0.7 -0.7 -0.7 -0.4 -0.4 -0.7 -0.7 -0.7

Middle-East -0.5 -0.5 -0.5 -0.5 -0.6 -0.4 -0.4 -0.4 -0.7 -0.7 -0.6 -0.6 -0.7

Far-East -0.5 -0.5 -0.5 -0.5 -0.6 -0.4 -0.4 -0.4 -0.7 -0.7 -0.6 -0.6 -0.4

Oceania -0.6 -0.6 -0.6 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.4 -0.7

13 Source : Point Carbon



ANNEX D. SUMMARY OF THE FORECAST FOR THE ESRA

Units: IFR Movements (thousands) and growth compared to the previous year.

Figure 43. Growth in Europe


Figure 44. Traffic on the main flow categories for the ESRA

IFR Movements(000s) Annual Growth



2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

. . . . . 7,673 7,801 8,084 8,351 8,643 8,947 9,169 . . . . . -1.5% 1.7% 3.6% 3.3% 3.5% 3.5% 2.5% 2.4% H

8,241B 8,182 7,602 7,562 7,790 7,594 7,648 7,827 8,026 8,243 8,445 8,638 4.3% -0.7% -7.1% -0.5% 3.0% -2.5% 0.7% 2.3% 2.5% 2.7% 2.4% 2.3% 1.5%

Total: Internal

L . . . . . 7,521 7,512 7,596 7,717 7,861 7,969 8,086 . . . . . -3.4% -0.1% 1.1% 1.6% 1.9% 1.4% 1.5% 0.5%

. . . . . 1,962 2,068 2,192 2,309 2,433 2,584 2,717 . . . . . 4.2% 5.4% 6.0% 5.3% 5.3% 6.2% 5.1% 5.4% H

1,721B 1,807 1,711 1,815 1,883 1,942 2,029 2,120 2,209 2,299 2,406 2,509 8.2% 5.0% -5.3% 6.1% 3.7% 3.2% 4.5% 4.5% 4.2% 4.1% 4.7% 4.3% 4.2%

Total: Arr/Dep

L . . . . . 1,924 1,993 2,056 2,120 2,184 2,255 2,326 . . . . . 2.2% 3.6% 3.2% 3.1% 3.1% 3.2% 3.2% 3.1%

. . . . . 125 129 137 145 153 161 171 . . . . . 12% 3.6% 6.0% 5.6% 5.5% 5.5% 5.9% 6.3% H

81B 94 100 116 112 122 125 132 138 144 151 158 20% 16% 7.1% 15% -3.4% 9.4% 2.7% 5.0% 4.6% 4.6% 4.5% 4.9% 5.1%

Total: Overflight

. . . . . 120 121 126 131 136 141 147 . . . . . 7.1% 1.3% 4.2% 3.9% 3.9% 3.8% 4.1% 4.0% L

. . . . . 9,759 9,999 10,414 10,805 11,229 11,693 12,057 . . . . . -0.3% 2.5% 4.1% 3.8% 3.9% 4.1% 3.1% 3.0% H

10,043B 10,083 9,413 9,493 9,784 9,658 9,803 10,078 10,372 10,686 11,002 11,305 5.0% 0.4% -6.6% 0.8% 3.1% -1.3% 1.5% 2.8% 2.9% 3.0% 3.0% 2.8% 2.1%

Grand Total

.L . . . . 9,565 9,626 9,778 9,968 10,182 10,365 10,560 . . . . . -2.2% 0.6% 1.6% 1.9% 2.2% 1.8% 1.9% 1.1%



Figure 45. Traffic and growth on the biggest region-to-region flows through the ESRA.

IFR Movements(000s) Annual Growth

2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

H . . 3656.1 3717.6 3817.5 3898.8 3982.2 4044.1 4082.4 . . -0.6% 1.7% 2.7% 2.1% 2.1% 1.6% 0.9% 1.5%

B 3573.2 3679.4 3627.4 3661.7 3724.5 3791.0 3857.9 3897.0 3942.2 -2.5% 3.0% -1.4% 0.9% 1.7% 1.8% 1.8% 1.0% 1.2% 1.0%

1 ESRA North-W ESRA North-W

L . . 3598.1 3607.6 3632.7 3668.4 3710.5 3725.9 3751.0 . . -2.2% 0.3% 0.7% 1.0% 1.1% 0.4% 0.7% 0.3%

H . . 1654.7 1679.8 1742.3 1800.4 1864.1 1927.1 1958.3 . . -1.2% 1.5% 3.7% 3.3% 3.5% 3.4% 1.6% 2.3%

B 1576.8 1674.8 1636.3 1643.7 1681.2 1725.1 1773.1 1816.2 1856.9 1.8% 6.2% -2.3% 0.5% 2.3% 2.6% 2.8% 2.4% 2.2% 1.5%

2 ESRA Mediter ESRA North-W

L . . 1618.8 1610.7 1624.5 1650.0 1681.0 1702.4 1725.7 . . -3.3% -0.5% 0.9% 1.6% 1.9% 1.3% 1.4% 0.4%

H . . 1414.9 1428.0 1478.7 1536.9 1606.8 1706.2 1778.8 . . -4.4% 0.9% 3.6% 3.9% 4.5% 6.2% 4.3% 2.7%

B 1466.9 1480.4 1397.5 1395.0 1429.4 1471.5 1525.6 1596.9 1655.1 1.5% 0.9% -5.6% -0.2% 2.5% 2.9% 3.7% 4.7% 3.6% 1.6%

3 ESRA Mediter ESRA Mediter

L . . 1383.2 1366.9 1382.3 1409.2 1447.4 1487.9 1524.8 . . -6.6% -1.2% 1.1% 1.9% 2.7% 2.8% 2.5% 0.4%

H . . 518.9 533.4 566.4 597.7 631.0 660.6 691.3 . . -0.3% 2.8% 6.2% 5.5% 5.6% 4.7% 4.7% 4.1%

B 510.6 520.3 511.7 519.0 539.5 561.1 582.4 600.0 620.0 -0.6% 1.9% -1.6% 1.4% 4.0% 4.0% 3.8% 3.0% 3.3% 2.5%

4 ESRA East ESRA North-W

L . . 505.6 508.1 520.8 535.2 549.3 559.5 571.8 . . -2.8% 0.5% 2.5% 2.8% 2.6% 1.9% 2.2% 1.4%

H . . 320.7 330.4 343.8 356.8 370.1 383.1 396.1 . . -0.6% 3.0% 4.1% 3.8% 3.7% 3.5% 3.4% 3.0%

B 308.1 322.6 318.1 325.3 334.7 344.3 353.1 361.5 371.1 -0.3% 4.7% -1.4% 2.2% 2.9% 2.9% 2.6% 2.4% 2.7% 2.0%

5 ESRA North-W North Atlant

L . . 315.6 320.4 326.0 331.8 337.5 342.6 348.8 . . -2.1% 1.5% 1.7% 1.8% 1.7% 1.5% 1.8% 1.1%

H . . 227.7 234.3 252.5 271.4 291.8 311.5 334.2 . . -0.1% 2.9% 7.8% 7.5% 7.5% 6.8% 7.3% 5.6%

B 234.9 227.8 224.1 227.3 238.2 249.9 262.0 272.8 284.9 -1.2% -3.0% -1.6% 1.4% 4.8% 4.9% 4.8% 4.1% 4.4% 3.2%

6 ESRA East ESRA East

L . . 221.5 222.8 230.3 238.7 247.2 254.3 262.5 . . -2.7% 0.6% 3.4% 3.6% 3.6% 2.9% 3.2% 2.0%

H . . 200.4 208.2 226.7 245.9 267.4 298.0 323.8 . . -3.2% 3.9% 8.9% 8.4% 8.7% 11% 8.7% 6.6%

B 199.3 207.0 196.9 201.5 213.7 227.1 242.0 261.8 279.2 4.1% 3.8% -4.8% 2.3% 6.1% 6.3% 6.5% 8.2% 6.7% 4.4%

7 ESRA East ESRA Mediter

L . . 194.1 196.3 205.1 215.2 226.0 238.5 250.4 . . -6.2% 1.2% 4.5% 4.9% 5.0% 5.5% 5.0% 2.8%

H . . 220.4 240.5 255.3 267.3 279.6 291.2 302.1 . . 11% 9.1% 6.1% 4.7% 4.6% 4.2% 3.7% 6.2%

B 228.5 198.2 219.1 238.0 249.1 259.3 270.0 280.1 290.9 7.5% -13% 11% 8.6% 4.7% 4.1% 4.1% 3.7% 3.9% 5.6%

8 ESRA North-W North-Africa

L . . 216.9 234.0 242.2 249.8 258.0 265.6 274.4 . . 9.4% 7.8% 3.5% 3.1% 3.3% 3.0% 3.3% 4.8%

I




ANNEX E. FUTURE TRAFFIC AND GROWTH

E.1 Summary of the Forecast. Annual IFR Movements 2006-2018.

Figure 46. Annual traffic per traffic zone and 2012-2018 average annual growth.

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

H . . . . . . 209 226 237 247 257 270 278 5.0%

B 119 142 148 161 181 197 206 222 229 237 245 255 264 4.2%

Albania

L . . . . . . 204 217 222 227 233 240 246 3.2%

H . . . . . . 64 70 76 82 88 96 103 8.8%

B 43 48 52 48 53 57 64 68 73 78 83 88 94 7.4%

Armenia

L . . . . . . 63 67 71 74 78 83 87 6.2%

H . . . . . . 1,154 1,200 1,254 1,300 1,354 1,410 1,456 3.4%

B 1,092 1,180 1,204 1,113 1,137 1,154 1,143 1,174 1,212 1,252 1,292 1,332 1,373 2.5%

Austria

L . . . . . . 1,132 1,152 1,173 1,199 1,226 1,250 1,275 1.4%

H . . . . . . 131 141 153 165 178 197 213 8.1%

B 92 95 108 108 120 124 130 138 148 158 168 182 195 6.7%

Azerbaijan

L . . . . . . 129 136 144 152 160 170 180 5.5%

H . . . . . . 246 262 282 299 317 336 356 6.8%

B 146 173 199 182 196 225 243 256 270 283 296 309 322 5.3%

Belarus

L . . . . . . 241 251 260 269 279 287 296 4.0%

H . . . . . . 1,110 1,140 1,184 1,224 1,267 1,304 1,337 2.9%

B 1,056 1,100 1,108 1,020 1,035 1,091 1,098 1,118 1,147 1,178 1,209 1,235 1,264 2.1%

Belgium/Luxembourg

L . . . . . . 1,087 1,098 1,112 1,131 1,153 1,169 1,187 1.2%

H . . . . . . 285 307 324 341 359 380 397 5.3%

B 168 201 218 224 250 276 282 300 311 324 337 353 367 4.2%

Bosnia-Herzegovina

L . . . . . . 278 294 301 310 320 329 339 3.0%

H . . . . . . 559 591 627 661 698 765 809 6.0%

B 402 444 478 477 504 539 552 578 605 631 658 705 740 4.6%

Bulgaria

L . . . . . . 547 566 587 607 627 656 681 3.4%

H . . . . . . 303 309 320 333 347 358 370 3.1%

B 303 308 307 267 275 298 299 301 306 313 321 326 332 1.6%

Canary Islands

L . . . . . . 296 295 295 298 303 303 305 0.4%

H . . . . . . 509 527 556 583 613 648 673 4.4%

B 339 398 422 422 459 497 503 515 534 555 577 602 625 3.3%

Croatia

L . . . . . . 497 504 517 531 547 563 578 2.2%



2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

H . . . . . . 300 310 327 344 362 388 408 5.5%

B 217 242 272 268 285 281 295 302 313 325 338 355 370 4.0%

Cyprus

L . . . . . . 291 294 302 310 319 330 340 2.7%

H . . . . . . 716 740 781 818 859 901 940 4.4%

B 612 646 682 648 668 695 707 723 750 776 803 828 853 3.0%

Czech Republic

L . . . . . . 700 709 725 743 761 775 790 1.8%

H . . . . . . 625 644 666 686 706 724 739 2.4%

B 602 631 629 576 595 625 619 632 647 663 677 689 702 1.7%

Denmark

L . . . . . . 613 621 629 637 647 653 661 0.8%

H . . . . . . 188 197 210 223 235 247 260 5.6%

B 137 153 174 153 156 178 186 193 202 211 220 228 237 4.2%

Estonia

L . . . . . . 184 189 195 202 208 214 220 3.1%

H . . . . . . 127 129 135 141 148 156 161 3.8%

B 118 123 125 125 125 124 125 125 130 134 140 146 151 2.8%

FYROM

L . . . . . . 123 122 125 128 133 136 140 1.7%

H . . . . . . 266 271 283 295 306 315 326 2.9%

B 246 245 261 240 242 267 263 266 274 282 290 295 302 1.8%

Finland

L . . . . . . 261 262 266 273 278 281 285 0.9%

H . . . . . . 2,976 3,027 3,139 3,241 3,349 3,436 3,493 2.4%

B 2,854 3,025 3,020 2,801 2,794 2,968 2,945 2,968 3,037 3,114 3,198 3,258 3,322 1.6%

France

L . . . . . . 2,914 2,913 2,942 2,988 3,040 3,070 3,110 0.7%

H . . . . . . 117 126 136 146 157 174 187 7.9%

B 73 80 80 77 94 110 115 123 131 139 148 161 172 6.6%

Georgia

L . . . . . . 114 120 127 134 141 150 158 5.4%

H . . . . . . 3,073 3,143 3,271 3,385 3,507 3,621 3,727 2.8%

B 2,971 3,108 3,151 2,930 2,981 3,078 3,041 3,082 3,167 3,255 3,344 3,419 3,501 1.9%

Germany

L . . . . . . 3,010 3,026 3,071 3,126 3,186 3,227 3,277 0.9%

H . . . . . . 668 682 710 739 774 810 839 3.6%

B 566 621 643 638 655 656 660 668 686 709 736 764 790 2.7%

Greece

L . . . . . . 653 655 665 680 700 718 737 1.7%

H . . . . . . 615 627 663 698 736 782 823 4.2%

B 605 615 622 608 622 617 608 613 638 664 691 723 753 2.9%

Hungary

L . . . . . . 601 601 618 636 656 676 696 1.7%

H . . . . . . 119 126 133 139 146 153 161 5.5%

B 100 105 110 101 102 111 118 123 128 133 138 143 148 4.2%

Iceland

L . . . . . . 116 121 125 128 131 134 138 3.2%



2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

H . . . . . . 523 542 571 602 634 667 697 4.2%

B 565 598 601 530 513 523 517 529 548 567 587 607 628 2.7%

Ireland

L . . . . . . 512 520 530 543 556 568 581 1.5%

H . . . . . . 1,722 1,761 1,833 1,904 1,985 2,063 2,129 3.0%

B 1,641 1,779 1,736 1,647 1,712 1,726 1,704 1,725 1,771 1,824 1,885 1,940 1,997 2.1%

Italy

L . . . . . . 1,686 1,691 1,711 1,743 1,782 1,813 1,847 1.0%

H . . . . . . 238 251 270 287 305 324 343 5.5%

B 176 202 225 206 214 235 234 244 257 269 281 293 305 3.8%

Latvia

L . . . . . . 232 239 247 256 265 272 280 2.5%

H . . . . . . 453 464 486 502 520 537 553 3.0%

B 401 427 438 406 429 450 448 454 461 470 483 493 504 1.6%

Lisbon FIR

L . . . . . . 443 444 445 448 456 460 466 0.5%

H . . . . . . 239 254 272 288 304 321 338 5.4%

B 173 195 219 192 206 233 236 247 259 271 282 292 303 3.8%

Lithuania

L . . . . . . 234 242 250 258 266 272 279 2.6%

H . . . . . . 94 106 112 117 124 132 138 8.0%

B 76 82 84 85 95 81 93 104 107 111 116 121 126 6.6%

Malta

L . . . . . . 91 101 104 107 110 114 117 5.5%

H . . . . . . 67 72 78 83 89 96 104 8.0%

B 28 35 41 44 54 60 65 70 74 78 83 88 93 6.3%

Moldova

L . . . . . . 65 69 72 75 78 82 85 5.0%

H . . . . . . 350 364 385 405 427 446 467 4.1%

B 303 323 331 312 339 352 347 357 371 385 401 414 429 2.9%

Morocco14

L . . . . . . 343 349 358 368 379 387 397 1.7%

H . . . . . . 1,104 1,129 1,167 1,193 1,220 1,244 1,266 2.2%

B 1,056 1,108 1,090 996 1,013 1,085 1,093 1,109 1,136 1,164 1,182 1,197 1,215 1.6%

Netherlands

L . . . . . . 1,083 1,090 1,104 1,120 1,138 1,152 1,168 1.1%

H . . . . . . 585 600 619 636 655 671 687 2.9%

B 513 536 550 526 537 563 581 591 604 616 629 640 652 2.1%

Norway

L . . . . . . 576 583 588 595 603 607 613 1.2%

H . . . . . . 702 740 788 834 883 930 982 6.0%

B 491 556 612 566 599 655 694 722 753 785 816 845 876 4.2%

Poland

L . . . . . . 686 709 728 750 770 788 807 3.0%

14 This report includes a forecast for Morocco which should be treated with extreme care. First of all, traffic counts presented here are those recorded by Network Management system of EUROCONTROL—some internal flights may be missing, flights departing from Ceuta-Melilla are considered as Moroccan overflights. Work is in progress to clarify these data. Second, forecast shall be treated as less certain than for longer-established regions in Europe for which we have a better understanding of local markets.


2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

. . . . . . 497 517 553 589 629 681 727 5.9%H

416 B 432 444 434 470 487 491 504 530 556 583 617 649 4.2%

Romania

. . . . . . 486 494 512 531 551 574 596 2.9%L

. . . . . . 131 135 142 149 156 162 167 4.5%H

107 B 109 116 113 118 123 129 132 137 142 147 151 156 3.5%

Santa Maria FIR

. . . . . . 128 129 132 135 139 141 145 2.4%L

. . . . . . 571 603 635 666 699 743 775 4.8%H

393 B 458 497 513 543 558 564 590 612 636 661 693 721 3.7%

Serbia&Montenegro

. . . . . . 558 578 593 610 628 649 668 2.6%L

. . . . . . 390 401 424 446 470 498 523 4.6%H

330 B 324 345 337 370 382 386 391 408 424 441 459 477 3.2%

Slovakia

. . . . . . 381 384 395 406 418 429 442 2.1%L

. . . . . . 361 374 393 410 430 452 469 4.2%H

267 B 306 327 313 328 353 356 365 378 392 406 422 436 3.1%

Slovenia

. . . . . . 352 358 366 375 385 395 404 2.0%L

. . . . . . 1,592 1,604 1,667 1,726 1,794 1,847 1,897 1.9%H

1,641 B 1,779 1,747 1,581 1,608 1,665 1,571 1,564 1,593 1,631 1,677 1,708 1,745 0.7%

Spain

. . . . . . 1,552 1,528 1,532 1,550 1,576 1,587 1,604 -0.5%L

. . . . . . 731 753 784 812 841 867 894 3.1%H

689 B 708 736 654 664 724 725 740 762 783 803 820 840 2.1%

Sweden

. . . . . . 719 728 741 754 767 776 787 1.2%L

. . . . . . 1,060 1,088 1,128 1,165 1,202 1,233 1,258 2.4%H

1,032 B 1,093 1,096 1,018 1,025 1,063 1,048 1,066 1,092 1,121 1,152 1,177 1,201 1.8%

Switzerland

. . . . . . 1,037 1,045 1,057 1,075 1,095 1,109 1,125 0.8%L

. . . . . . 1,096 1,158 1,230 1,303 1,381 1,557 1,656 6.9%H

693 B 757 822 857 965 1,039 1,083 1,135 1,201 1,263 1,328 1,461 1,553 5.9%

Turkey

. . . . . . 1,073 1,113 1,172 1,224 1,277 1,364 1,433 4.7%L

. . . . . . 472 498 538 578 619 668 715 6.7%H

345 B 373 406 378 429 453 466 487 517 548 579 615 649 5.3%

Ukraine

. . . . . . 462 478 502 526 551 576 602 4.2%L

. . . . . . 2,222 2,263 2,348 2,429 2,506 2,577 2,584 2.1%H

2,465 B 2,550 2,514 2,278 2,181 2,241 2,203 2,225 2,279 2,338 2,398 2,447 2,502 1.6%

UK

. . . . . . 2,185 2,192 2,222 2,260 2,301 2,333 2,369 0.8%L

. . . . . . 9,628 9,869 10,270 10,648 11,057 11,507 11,856 3.0%H

9,439 B 9,916 9,954 9,301 9,367 9,641 9,529 9,677 9,944 10,228 10,532 10,840 11,135 2.1%

ESRA02

. L . . . . . 9,437 9,504 9,649 9,830 10,038 10,214 10,403 1.1%



2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

. . . . . . 8,991 9,201 9,570 9,913 10,284 10,647 10,949 2.8%H

8,937 B 9,441 9,470 8,787 8,805 9,036 8,898 9,018 9,249 9,502 9,771 10,008 10,254 1.8%

EU27

. . . . . . 8,810 8,854 8,969 9,122 9,300 9,430 9,582 0.8%L

. . . . . . 9,759 9,999 10,414 10,805 11,229 11,693 12,057 3.0%H

9,561 B 10,043 10,083 9,413 9,493 9,784 9,658 9,803 10,078 10,372 10,686 11,002 11,305 2.1%

ESRA08

. . . . . . 9,565 9,626 9,778 9,968 10,182 10,365 10,560 1.1%L

. . . . . . 9,371 9,592 9,976 10,332 10,718 11,093 11,409 2.8%H

9,337 B 9,794 9,833 9,152 9,171 9,407 9,275 9,404 9,645 9,907 10,187 10,433 10,688 1.8%

SES

. . . . . . 9,185 9,235 9,355 9,514 9,699 9,834 9,991 0.9%L

. . . . . . 786 821 875 928 982 1,037 1,095 5.7%H

. B . 704 644 679 741 776 800 836 872 907 940 975 4.0%

Baltic

. . . . . . 767 785 808 832 856 876 898 2.8%L

. . . . . . 2,336 2,411 2,515 2,619 2,738 2,862 2,967 3.7%H

. B . 2,310 2,225 2,308 2,296 2,311 2,361 2,429 2,507 2,597 2,685 2,772 2.7%

Blue Med

. . . . . . 2,286 2,314 2,348 2,398 2,459 2,512 2,568 1.6%L

. . . . . . 787 826 880 931 988 1,075 1,141 6.0%H

. B . 690 687 734 758 778 807 846 884 924 984 1,034 4.5%

Danube

. . . . . . 770 791 820 848 878 916 950 3.3%L

. . . . . . 1,938 2,008 2,111 2,206 2,311 2,425 2,522 4.0%H

. B . 1,904 1,806 1,864 1,914 1,917 1,963 2,033 2,105 2,180 2,256 2,331 2.9%

FAB CE

. . . . . . 1,897 1,925 1,968 2,016 2,067 2,112 2,159 1.7%L

. . . . . . 5,663 5,794 6,013 6,206 6,409 6,589 6,729 2.5%H

. B . 5,816 5,406 5,431 5,671 5,607 5,686 5,827 5,978 6,133 6,254 6,384 1.7%

FAB EC

. . . . . . 5,551 5,584 5,652 5,743 5,847 5,913 5,995 0.8%L

. . . . . . 1,651 1,695 1,762 1,822 1,884 1,940 1,997 3.0%H

. B . 1,616 1,487 1,519 1,619 1,636 1,667 1,710 1,754 1,797 1,832 1,873 2.1%

NEFAB

. . . . . . 1,622 1,640 1,664 1,690 1,716 1,734 1,756 1.2%L

. . . . . . 1,819 1,836 1,908 1,977 2,054 2,116 2,173 2.1%H

. B . 1,969 1,786 1,820 1,882 1,796 1,790 1,823 1,867 1,919 1,954 1,996 0.8%

SW Portugal - Spai

. . . . . . 1,774 1,750 1,755 1,775 1,805 1,817 1,837 -0.3%L

. . . . . . 2,260 2,304 2,393 2,477 2,558 2,634 2,646 2.2%H

. B 2,559 2,316 2,216 2,272 2,240 2,265 2,321 2,381 2,443 2,494 2,551 1.7%

UK-Ireland

. . . . . . 2,221 2,231 2,262 2,301 2,343 2,376 2,413 0.9%L

. . . . . . 1,020 1,048 1,089 1,123 1,159 1,192 1,224 2.8%H

. B . 1,031 931 953 1,008 1,011 1,031 1,058 1,084 1,110 1,131 1,156 2.0%

DK-SE

. L . . . . . 1,002 1,014 1,029 1,044 1,060 1,071 1,085 1.1%

Units: IFR movements (thousands).

Source: AP2 forecast dated: 21Feb12



E.2 Summary of the Forecast. Growth Rates 2006-2018

Figure 47. Annual growth rates per traffic zone and 2012-2018 average annual growth.

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

. . . . . . 5.8% 8.4% 4.5% 4.2% 4.4% 5.0% 3.0% 5.0%H

2.7%B 19% 4.5% 8.9% 12% 8.7% 4.6% 7.4% 3.2% 3.5% 3.7% 3.9% 3.5% 4.2%

Albania

. . . . . . 3.4% 6.6% 2.0% 2.5% 2.8% 2.6% 2.6% 3.2%L

. . . . . . 13% 8.2% 8.8% 8.0% 7.8% 8.4% 7.8% 8.8%H

2.6%B 11% 8.0% -6.7% 9.3% 8.1% 11% 7.0% 6.9% 6.7% 6.4% 6.8% 6.6% 7.4%

Armenia

. . . . . . 10% 6.1% 5.6% 5.6% 5.4% 5.4% 5.4% 6.2%L

. . . . . . 0.0% 4.0% 4.5% 3.7% 4.1% 4.1% 3.3% 3.4%H

4.1%B 8.1% 2.0% -7.6% 2.2% 1.5% -1.0% 2.7% 3.2% 3.2% 3.3% 3.1% 3.0% 2.5%

Austria

. . . . . . -2.0% 1.8% 1.9% 2.2% 2.3% 1.9% 2.0% 1.4%L

. . . . . . 6.3% 7.5% 8.4% 7.6% 7.7% 11% 8.2% 8.1%H

.B 4.3% 13% 0.5% 11% 2.8% 5.2% 6.5% 6.9% 6.5% 6.4% 8.6% 7.2% 6.7%

Azerbaijan

. . . . . . 4.2% 5.6% 5.8% 5.4% 5.4% 6.4% 5.8% 5.5%L

. . . . . . 9.6% 6.5% 7.4% 6.2% 6.1% 5.9% 5.9% 6.8%H

13%B 18% 16% -8.6% 7.7% 15% 8.3% 5.2% 5.3% 4.9% 4.6% 4.4% 4.3% 5.3%

Belarus

. . . . . . 7.2% 4.2% 3.7% 3.5% 3.4% 3.0% 3.2% 4.0%L

. . . . . . 1.7% 2.7% 3.9% 3.4% 3.4% 3.0% 2.5% 2.9%H

4.9%B 4.2% 0.7% -7.9% 1.5% 5.4% 0.6% 1.8% 2.6% 2.7% 2.6% 2.1% 2.3% 2.1%

Belgium/Luxembourg

. . . . . . -0.4% 1.0% 1.3% 1.7% 1.9% 1.3% 1.6% 1.2%L

. . . . . . 3.5% 7.6% 5.6% 5.1% 5.3% 6.0% 4.3% 5.3%H

5.5%B 19% 8.5% 3.1% 11% 10% 2.2% 6.4% 3.9% 4.1% 4.1% 4.5% 4.0% 4.2%

Bosnia-Herzegovina

. . . . . . 1.0% 5.5% 2.6% 2.9% 3.1% 3.0% 2.9% 3.0%L

. . . . . . 3.6% 5.7% 6.1% 5.5% 5.6% 9.5% 5.8% 6.0%H

1.6%B 11% 7.7% -0.2% 5.6% 7.1% 2.4% 4.6% 4.7% 4.3% 4.3% 7.1% 5.1% 4.6%

Bulgaria

. . . . . . 1.3% 3.6% 3.6% 3.4% 3.3% 4.7% 3.8% 3.4%L

. . . . . . 1.8% 1.9% 3.8% 3.9% 4.4% 3.1% 3.2% 3.1%H

4.0%B 1.7% -0.2% -13% 3.2% 8.2% 0.5% 0.6% 1.7% 2.3% 2.6% 1.5% 1.9% 1.6%

Canary Islands

. . . . . . -0.6% -0.4% 0.1% 1.0% 1.5% 0.3% 0.7% 0.4%L

. . . . . . 2.3% 3.6% 5.4% 4.9% 5.1% 5.7% 3.9% 4.4%H

2.4%B 17% 6.0% 0.1% 8.7% 8.4% 1.1% 2.4% 3.8% 3.9% 4.0% 4.3% 3.8% 3.3%

Croatia

. . . . . . -0.1% 1.5% 2.5% 2.8% 3.0% 2.8% 2.8% 2.2%L

. . . . . . 6.5% 3.5% 5.5% 5.1% 5.3% 7.1% 5.3% 5.5%H

4.1%B 12% 12% -1.7% 6.4% -1.2% 4.8% 2.4% 3.8% 3.7% 3.9% 5.1% 4.2% 4.0%

Cyprus

.L . . . . . 3.4% 1.2% 2.7% 2.7% 2.9% 3.4% 3.0% 2.7%



2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

. . . . . . 2.9% 3.4% 5.5% 4.8% 5.0% 4.9% 4.4% 4.4%H

2.4%B 5.6% 5.5% -5.0% 3.2% 4.0% 1.7% 2.2% 3.7% 3.6% 3.4% 3.0% 3.0% 3.0%

Czech Republic

. . . . . . 0.7% 1.3% 2.3% 2.4% 2.4% 1.8% 2.0% 1.8%L

. . . . . . 0.0% 3.0% 3.5% 2.9% 3.0% 2.4% 2.2% 2.4%H

2.9%B 4.8% -0.3% -8.5% 3.3% 5.1% -0.9% 2.2% 2.4% 2.4% 2.2% 1.7% 1.9% 1.7%

Denmark

. . . . . . -1.9% 1.4% 1.2% 1.4% 1.5% 0.9% 1.2% 0.8%L

. . . . . . 5.5% 4.9% 6.6% 5.9% 5.6% 5.1% 5.4% 5.6%H

-5.7%B 12% 13% -12% 2.1% 14% 4.2% 3.7% 4.7% 4.7% 4.2% 3.6% 3.9% 4.2%

Estonia

. . . . . . 3.2% 2.8% 3.2% 3.5% 3.2% 2.6% 2.9% 3.1%L

. . . . . . 1.8% 1.5% 5.0% 4.6% 4.8% 5.2% 3.7% 3.8%H

6.8%B 3.9% 2.0% -0.1% -0.1% -0.4% 0.4% 0.2% 3.5% 3.8% 4.1% 4.0% 3.7% 2.8%

FYROM

. . . . . . -0.9% -0.8% 2.2% 2.8% 3.2% 2.8% 2.8% 1.7%L

. . . . . . -0.5% 2.1% 4.3% 4.1% 3.8% 3.0% 3.4% 2.9%H

1.4%B -0.3% 6.3% -7.7% 0.6% 11% -1.5% 1.1% 2.8% 3.1% 2.7% 1.9% 2.3% 1.8%

Finland

. . . . . . -2.4% 0.4% 1.6% 2.3% 1.9% 1.1% 1.5% 0.9%L

. . . . . . 0.3% 1.7% 3.7% 3.3% 3.3% 2.6% 1.6% 2.4%H

3.9%B 6.0% -0.2% -7.3% -0.2% 6.2% -0.8% 0.8% 2.3% 2.6% 2.7% 1.9% 2.0% 1.6%

France

. . . . . . -1.8% -0.1% 1.0% 1.6% 1.8% 1.0% 1.3% 0.7%L

. . . . . . 6.3% 7.6% 8.4% 7.4% 7.4% 11% 7.6% 7.9%H

-1.2%B 9.7% -0.0% -3.6% 22% 16% 5.0% 6.5% 6.8% 6.3% 6.1% 8.6% 6.8% 6.6%

Georgia

. . . . . . 3.7% 5.7% 5.8% 5.4% 5.1% 6.3% 5.5% 5.4%L

. . . . . . -0.2% 2.3% 4.1% 3.5% 3.6% 3.3% 2.9% 2.8%H

4.2%B 4.6% 1.4% -7.0% 1.7% 3.2% -1.2% 1.3% 2.8% 2.8% 2.7% 2.3% 2.4% 1.9%

Germany

. . . . . . -2.2% 0.5% 1.5% 1.8% 1.9% 1.3% 1.5% 0.9%L

. . . . . . 1.9% 2.2% 4.1% 4.1% 4.6% 4.8% 3.5% 3.6%H

3.2%B 9.9% 3.4% -0.8% 2.6% 0.2% 0.7% 1.2% 2.7% 3.3% 3.8% 3.8% 3.5% 2.7%

Greece

. . . . . . -0.4% 0.3% 1.5% 2.3% 2.9% 2.6% 2.6% 1.7%L

. . . . . . -0.3% 2.1% 5.7% 5.2% 5.4% 6.3% 5.2% 4.2%H

4.4%B 1.8% 1.1% -2.3% 2.4% -0.8% -1.5% 0.8% 4.1% 4.1% 4.0% 4.6% 4.1% 2.9%

Hungary

. . . . . . -2.5% -0.1% 2.9% 3.0% 3.0% 3.1% 3.0% 1.7%L

. . . . . . 7.3% 5.8% 5.5% 5.0% 4.9% 5.0% 4.8% 5.5%H

4.1%B 5.7% 4.2% -7.8% 0.6% 9.0% 6.1% 4.8% 4.0% 3.8% 3.6% 3.6% 3.8% 4.2%

Iceland

. . . . . . 5.0% 4.0% 2.8% 2.7% 2.6% 2.5% 2.8% 3.2%L

. . . . . . 0.0% 3.5% 5.5% 5.3% 5.4% 5.3% 4.4% 4.2%H

5.4%B 5.9% 0.5% -12% -3.1% 1.9% -1.1% 2.4% 3.5% 3.5% 3.5% 3.3% 3.5% 2.7%

Ireland

.L . . . . . -2.1% 1.5% 2.0% 2.3% 2.4% 2.2% 2.4% 1.5%



2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

. . . . . . -0.2% 2.2% 4.1% 3.9% 4.2% 3.9% 3.2% 3.0%H

4.0%B 8.4% -2.4% -5.1% 3.9% 0.8% -1.3% 1.3% 2.7% 3.0% 3.3% 2.9% 2.9% 2.1%

Italy

. . . . . . -2.3% 0.3% 1.2% 1.9% 2.3% 1.7% 1.9% 1.0%L

. . . . . . 1.1% 5.4% 7.5% 6.6% 6.3% 5.9% 5.9% 5.5%H

13%B 15% 11% -8.4% 4.0% 9.8% -0.4% 4.0% 5.2% 5.0% 4.4% 4.0% 4.2% 3.8%

Latvia

. . . . . . -1.5% 3.0% 3.6% 3.7% 3.3% 2.7% 3.0% 2.5%L

. . . . . . 0.7% 2.4% 4.8% 3.3% 3.4% 3.4% 3.0% 3.0%H

5.7%B 6.3% 2.7% -7.2% 5.6% 4.8% -0.5% 1.3% 1.6% 1.9% 2.9% 2.0% 2.3% 1.6%

Lisbon FIR

. . . . . . -1.6% 0.3% 0.1% 0.7% 1.8% 0.9% 1.3% 0.5%L

. . . . . . 2.6% 6.1% 7.0% 6.0% 5.7% 5.4% 5.4% 5.4%H

6.0%B 13% 12% -12% 7.3% 13% 1.2% 4.7% 4.8% 4.5% 4.1% 3.7% 3.8% 3.8%

Lithuania

. . . . . . 0.0% 3.7% 3.3% 3.2% 3.0% 2.4% 2.6% 2.6%L

. . . . . . 16% 13% 5.3% 5.1% 5.3% 6.4% 5.2% 8.0%H

0.2%B 8.1% 3.4% 0.7% 12% -15% 15% 12% 3.8% 3.7% 3.8% 4.6% 4.2% 6.6%

Malta

. . . . . . 13% 11% 2.8% 2.7% 2.9% 3.2% 3.1% 5.5%L

. . . . . . 10% 8.1% 7.9% 7.2% 7.3% 8.1% 7.4% 8.0%H

7.8%B 25% 18% 6.7% 24% 11% 8.4% 6.7% 6.0% 5.7% 5.6% 6.0% 5.8% 6.3%

Moldova

. . . . . . 7.2% 5.8% 4.4% 4.5% 4.5% 4.5% 4.5% 5.0%L

. . . . . . -0.6% 3.9% 6.0% 5.2% 5.3% 4.6% 4.5% 4.1%H

10%B 6.6% 2.5% -5.8% 8.6% 3.9% -1.6% 2.9% 3.9% 3.9% 4.0% 3.3% 3.6% 2.9%

Morocco14

. . . . . . -2.7% 2.0% 2.4% 2.8% 3.0% 2.2% 2.6% 1.7%L

. . . . . . 1.7% 2.3% 3.4% 2.2% 2.2% 2.0% 1.8% 2.2%H

6.0%B 4.9% -1.6% -8.6% 1.7% 7.2% 0.8% 1.4% 2.4% 2.5% 1.6% 1.3% 1.5% 1.6%

Netherlands

. . . . . . -0.2% 0.6% 1.2% 1.5% 1.7% 1.2% 1.4% 1.1%L

. . . . . . 3.8% 2.5% 3.3% 2.8% 2.9% 2.4% 2.4% 2.9%H

6.1%B 4.5% 2.6% -4.4% 2.2% 4.9% 3.1% 1.8% 2.1% 2.1% 2.1% 1.6% 1.9% 2.1%

Norway

. . . . . . 2.3% 1.1% 1.0% 1.1% 1.3% 0.7% 1.0% 1.2%L

. . . . . . 7.3% 5.3% 6.5% 5.9% 5.8% 5.4% 5.6% 6.0%H

16%B 13% 10% -7.6% 5.8% 9.4% 6.0% 4.1% 4.3% 4.2% 4.0% 3.5% 3.7% 4.2%

Poland

. . . . . . 4.9% 3.2% 2.8% 2.9% 2.8% 2.2% 2.5% 3.0%L

. . . . . . 1.9% 4.1% 7.0% 6.5% 6.7% 8.3% 6.8% 5.9%H

1.3%B 4.0% 2.7% -2.3% 8.2% 3.8% 0.7% 2.7% 5.0% 4.9% 4.9% 6.0% 5.2% 4.2%

Romania

. . . . . . -0.4% 1.8% 3.7% 3.7% 3.7% 4.1% 3.8% 2.9%L

. . . . . . 6.3% 3.5% 5.4% 4.6% 4.6% 4.1% 3.2% 4.5%H

0.9%B 1.7% 6.5% -2.6% 4.5% 4.3% 5.0% 2.4% 3.6% 3.4% 3.7% 2.8% 3.4% 3.5%

Santa Maria FIR

.L . . . . . 3.8% 1.5% 2.2% 2.1% 2.6% 1.9% 2.4% 2.4%



2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

. . . . . . 2.3% 5.7% 5.3% 4.8% 5.0% 6.3% 4.2% 4.8%H

8.7%B 16% 8.6% 3.3% 5.9% 2.7% 1.1% 4.5% 3.8% 3.9% 4.0% 4.8% 4.0% 3.7%

Serbia&Montenegro

. . . . . . 0.0% 3.6% 2.6% 2.8% 3.0% 3.2% 3.0% 2.6%L

. . . . . . 2.2% 2.8% 5.8% 5.2% 5.4% 5.8% 5.1% 4.6%H

3.8%B -1.6% 6.4% -2.4% 9.9% 3.1% 1.0% 1.5% 4.1% 4.0% 3.9% 4.2% 3.9% 3.2%

Slovakia

. . . . . . -0.1% 0.6% 2.8% 2.9% 2.9% 2.8% 2.8% 2.1%L

. . . . . . 2.3% 3.6% 5.1% 4.5% 4.7% 5.2% 3.9% 4.2%H

3.7%B 15% 6.8% -4.2% 4.8% 7.5% 1.0% 2.4% 3.6% 3.6% 3.6% 3.9% 3.5% 3.1%

Slovenia

. . . . . . -0.1% 1.5% 2.3% 2.5% 2.7% 2.5% 2.5% 2.0%L

. . . . . . -4.4% 0.8% 3.9% 3.6% 3.9% 2.9% 2.7% 1.9%H

5.1%B 8.4% -1.8% -9.5% 1.8% 3.6% -5.6% -0.5% 1.9% 2.4% 2.8% 1.8% 2.2% 0.7%

Spain

. . . . . . -6.8% -1.6% 0.2% 1.2% 1.7% 0.7% 1.1% -0.5%L

. . . . . . 1.0% 2.9% 4.2% 3.5% 3.5% 3.1% 3.2% 3.1%H

3.8%B 2.8% 3.9% -11% 1.5% 9.1% 0.1% 2.1% 2.9% 2.7% 2.6% 2.2% 2.4% 2.1%

Sweden

. . . . . . -0.8% 1.3% 1.7% 1.7% 1.7% 1.2% 1.5% 1.2%L

. . . . . . -0.2% 2.6% 3.7% 3.3% 3.2% 2.6% 2.0% 2.4%H

2.4%B 5.9% 0.3% -7.1% 0.7% 3.6% -1.3% 1.7% 2.4% 2.7% 2.7% 2.2% 2.0% 1.8%

Switzerland

. . . . . . -2.4% 0.8% 1.1% 1.7% 1.8% 1.3% 1.5% 0.8%L

. . . . . . 5.5% 5.6% 6.2% 5.9% 6.0% 13% 6.3% 6.9%H

8.0%B 9.2% 8.6% 4.2% 13% 7.6% 4.3% 4.7% 5.9% 5.1% 5.1% 10% 6.3% 5.9%

Turkey

. . . . . . 3.3% 3.8% 5.3% 4.5% 4.3% 6.8% 5.1% 4.7%L

. . . . . . 4.1% 5.5% 8.1% 7.3% 7.2% 7.9% 7.0% 6.7%H

1.3%B 8.2% 8.7% -6.9% 14% 5.5% 2.9% 4.4% 6.2% 6.0% 5.7% 6.1% 5.7% 5.3%

Ukraine

. . . . . . 1.9% 3.6% 4.9% 4.9% 4.7% 4.6% 4.5% 4.2%L

. . . . . . -0.8% 1.8% 3.8% 3.4% 3.2% 2.8% 0.3% 2.1%H

3.4%B 3.5% -1.4% -9.4% -4.3% 2.8% -1.7% 1.0% 2.4% 2.6% 2.6% 2.1% 2.2% 1.6%

UK

. . . . . . -2.5% 0.3% 1.4% 1.7% 1.8% 1.4% 1.6% 0.8%L

. . . . . . -0.1% 2.5% 4.1% 3.7% 3.8% 4.1% 3.0% 3.0%H

3.9%B 5.1% 0.4% -6.6% 0.7% 2.9% -1.2% 1.6% 2.7% 2.9% 3.0% 2.9% 2.7% 2.1%

ESRA02

. . . . . . -2.1% 0.7% 1.5% 1.9% 2.1% 1.8% 1.8% 1.1%L

. . . . . . -0.5% 2.3% 4.0% 3.6% 3.7% 3.5% 2.8% 2.8%H

3.5%B 5.6% 0.3% -7.2% 0.2% 2.6% -1.5% 1.4% 2.6% 2.7% 2.8% 2.4% 2.5% 1.8%

EU27

. . . . . . -2.5% 0.5% 1.3% 1.7% 2.0% 1.4% 1.6% 0.8%L

. . . . . . -0.3% 2.5% 4.1% 3.8% 3.9% 4.1% 3.1% 3.0%H

3.7%B 5.0% 0.4% -6.6% 0.8% 3.1% -1.3% 1.5% 2.8% 2.9% 3.0% 3.0% 2.8% 2.1%

ESRA08

.L . . . . . -2.2% 0.6% 1.6% 1.9% 2.2% 1.8% 1.9% 1.1%



2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

AAGR 2018/ 2011

. . . . . . -0.4% 2.4% 4.0% 3.6% 3.7% 3.5% 2.8% 2.8%H

3.6%B 4.9% 0.4% -6.9% 0.2% 2.6% -1.4% 1.4% 2.6% 2.7% 2.8% 2.4% 2.5% 1.8%

SES

. . . . . . -2.4% 0.5% 1.3% 1.7% 1.9% 1.4% 1.6% 0.9%L

. . . . . . 6.0% 4.4% 6.7% 6.0% 5.9% 5.5% 5.6% 5.7%H

.B . . -8.6% 5.5% 9.2% 4.6% 3.2% 4.4% 4.3% 4.0% 3.6% 3.7% 4.0%

Baltic

. . . . . . 3.5% 2.3% 2.9% 3.0% 2.9% 2.3% 2.5% 2.8%L

. . . . . . 1.8% 3.2% 4.3% 4.1% 4.5% 4.5% 3.6% 3.7%H

.B . . -3.7% 3.7% -0.5% 0.6% 2.2% 2.9% 3.2% 3.6% 3.4% 3.2% 2.7%

Blue Med

. . . . . . -0.5% 1.3% 1.5% 2.1% 2.5% 2.1% 2.2% 1.6%L

. . . . . . 3.8% 5.0% 6.5% 5.9% 6.1% 8.8% 6.2% 6.0%H

.B . . -0.4% 6.7% 3.3% 2.6% 3.8% 4.8% 4.6% 4.5% 6.5% 5.0% 4.5%

Danube

. . . . . . 1.5% 2.8% 3.6% 3.5% 3.5% 4.3% 3.8% 3.3%L

. . . . . . 1.3% 3.6% 5.1% 4.5% 4.8% 4.9% 4.0% 4.0%H

.B . . -5.1% 3.2% 2.7% 0.2% 2.4% 3.5% 3.6% 3.6% 3.5% 3.3% 2.9%

FAB CE

. . . . . . -0.9% 1.5% 2.2% 2.5% 2.5% 2.2% 2.2% 1.7%L

. . . . . . -0.1% 2.3% 3.8% 3.2% 3.3% 2.8% 2.1% 2.5%H

.B . . -7.0% 0.5% 4.4% -1.1% 1.4% 2.5% 2.6% 2.6% 2.0% 2.1% 1.7%

FAB EC

. . . . . . -2.1% 0.6% 1.2% 1.6% 1.8% 1.1% 1.4% 0.8%L

. . . . . . 2.0% 2.7% 4.0% 3.4% 3.4% 2.9% 2.9% 3.0%H

.B . . -8.0% 2.2% 6.6% 1.1% 1.9% 2.6% 2.6% 2.5% 1.9% 2.2% 2.1%

NEFAB

. . . . . . 0.2% 1.1% 1.4% 1.6% 1.6% 1.0% 1.3% 1.2%L

. . . . . . -3.3% 0.9% 3.9% 3.6% 3.9% 3.0% 2.7% 2.1%H

.B . . -9.3% 1.9% 3.4% -4.6% -0.3% 1.9% 2.4% 2.8% 1.8% 2.2% 0.8%

SW Portugal - Spai

. . . . . . -5.7% -1.4% 0.3% 1.1% 1.7% 0.7% 1.1% -0.3%L

. . . . . . -0.5% 2.0% 3.8% 3.5% 3.3% 3.0% 0.5% 2.2%H

.B . 3E8% -9.5% -4.3% 2.5% -1.4% 1.1% 2.5% 2.6% 2.6% 2.1% 2.3% 1.7%

UK-Ireland

. . . . . . -2.2% 0.4% 1.4% 1.7% 1.9% 1.4% 1.6% 0.9%L

. . . . . . 1.2% 2.8% 3.8% 3.2% 3.2% 2.8% 2.7% 2.8%H

.B . . -9.7% 2.4% 5.7% 0.3% 2.0% 2.6% 2.5% 2.4% 1.9% 2.2% 2.0%

DK-SE

.L . . . . . -0.6% 1.3% 1.4% 1.5% 1.6% 1.0% 1.3% 1.1%

Units: IFR movements growth compared to the previous year.

Source: AP2 forecast dated: 21Feb12




ANNEX F. REFERENCES

Electronic versions of most references are available on www.eurocontrol.int/statfor.

1 EUROCONTROL Medium-Term Forecast Update, Flight Movements (2011-2017) EUROCONTROL STATFOR, September 2011, [MTF11b].

2 STATFOR Interactive Dashboard accessible at www.eurocontrol.int/statfor/sid

3 www.eurocontrol.int/statfor

4 Challenges of Growth 2008, Summary Report, EUROCONTROL, November 2008.

5 EUROCONTROL Medium-Term Forecast, Flight Movements (2011-2017) EUROCONTROL STATFOR February 2011, [MTF11]

6 EUROCONTROL Glossary for Flight Statistics & Forecasts, EUROCONTROL STATFOR, January 2005.

7 STATFOR Geographical Hierarchy, STATFOR Doc172, February 2009.

8 Low-Cost Panel v5, EUROCONTROL STATFOR, September 2007.

9 EUROCONTROL Long-Term Forecast, Flight Movements 2010-2030, December 2010.

10 Estimated effects of ETS on passenger demand for flights in 2012-2016, EUROCONTROL STATFOR Doc365, March 2010.

11 Estimating Air Travel Demand Elasticities, Prepared by InterVISTAS Consulting Inc. for IATA, December 2007.



EUROCONTROL

For further information, please contact: STATFOR, the EUROCONTROL Statistics and Forecast [email protected]://www.eurocontrol.int/statfor

The Statistics and Forecasts Service (STATFOR) is ISO 9001:2008 certified

© 2012 - European Organisation for the Safety of Air Navigation (EUROCONTROL)

This document is published by EUROCONTROL for information purposes. It may

be copied in whole or in part, provided that EUROCONTROL is mentioned as the

source and it is not used for commercial purposes (i.e. for financial gain). The infor-

mation in this document may not be modified without prior written permission from

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