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EUROCONTROL

EUROCONTROL Medium-Term Forecast

February 2011

Flight Movements 2011 - 2017

EUROCONTROL Medium-Term Forecast: IFR Flight Movements 2011-2017

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EXECUTIVE SUMMARY

This report presents the February 2011 update of the EUROCONTROL Medium-Term Forecast. The forecast considers the development of air traffic in Europe over the next 7 years.

Europe1 is forecast to have 11.6 million IFR flights in 2017, 22% more than in 2010. Traffic growth will bounce back in 2011 (above 4%), but with the underlying growth rate a little more than half of this, after allowing for the effects of the ash-cloud, weather and strikes. The average growth rate over the 7 years is 2.9% per year.

Figure 1. Summary of the forecast for the ESRA082.

Short-Term (2011-2012)

In 2010, aviation in Europe pulled out of the economic downturn and began to climb again. The total number of flights in Europe (ESRA08) in 2010 was 9.5 million, an increase of 0.8% compared to 2009. However, this annual average masks a wide monthly variation, from -11% in the April ash-cloud crisis to +4% in October, at the end of a strong Summer and the start of a weaker Winter.

For the next two years, the forecast is for a strengthened recovery of traffic growth at European level: +4.3% (1%) in 2011 and +3.4% (0.7%) in 2012. However, in the first year, underlying traffic growth will, when removing the impacts of the 2010 adverse events, be only around 2.5% for Europe. In the second year, growth will return to more typical rates, being also boosted by the leap year and by major sporting events. 2012 is the year by when European traffic is expected to exceed the peak IFR movements reached in 2008. However, traffic levels of South-Eastern European States are already above 2008 traffic levels while some States may not pass the previous peak for another five years (Figure 15).

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).

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

AAGR 2017/ 2010

H . . . . . 10,007 10,433 10,846 11,258 11,676 12,120 12,490 4.0%

B 9,561 10,043 10,083 9,413 9,493 9,902 10,240 10,479 10,760 11,045 11,337 11,571 2.9%

IFR Flight Movements

(Thousands) L . . . . . 9,813 10,100 10,238 10,435 10,643 10,861 11,004 2.1%

H . . . . . 5.4% 4.3% 4.0% 3.8% 3.7% 3.8% 3.1% 4.0%

B 3.7% 5.0% 0.4% -6.6% 0.8% 4.3% 3.4% 2.3% 2.7% 2.7% 2.6% 2.1% 2.9%

Annual Growth (compared to

previous year) L . . . . . 3.4% 2.9% 1.4% 1.9% 2.0% 2.1% 1.3% 2.1%


These disparities are also noticeable in terms of growth rates (see Figure 2) and significant risks beyond those captured in the three forecast scenarios are to be noted. The risks include:

Recent events in Tunisia and Egypt are an up-side risk for the Northern Mediterranean in terms of alternative tourist destinations, but are reducing overflights for other States.

Aviation taxes in Germany, Austria and the UK are downside risks for their local traffic.

High fuel prices and weak economies are downside risks, though in some cases the potential for stronger economic recovery is an upside risk.

Very recent data suggest the potential for slower growth in the Hungary-Romania overflight corridor, with that traffic flying instead closer to the Adriatic coast.

These overflight routings along the South-East axis remain extremely fluid; and any capacity shortage that emerges in a single State could see a jump in traffic elsewhere.

Figure 2. Average annual growth 2010-2012 for each State.

Medium-Term (2013-2017)

For the 2013-2017 period, traffic growth in Europe is expected to remain stable (between 2.1% and 2.7%). Again, some differences can be observed at State level (Figure 3): in percentage terms, traffic growth will be stronger for most of the States in Eastern Europe. However in terms of number of additional flights the main contributors will be, besides Turkey, the busiest States of Western Europe (France, Germany, Italy, UK; see Figure 16 on page 18). Details of growth per State are provided in annexes.

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Figure 3. Average annual growth 2010-2017 for each State.

For the remainder of the period, a number of effects will reduce growth:

Airport Capacity: Lower levels of traffic diminish the impact of airport constraints compared to earlier forecasts, to 100,000 IFR departures in 2017 (0.8% off total growth over 7 years).

Continuing improvements to the high-speed train network reduce growth by about 47,000 flights (0.4%) in total over 7 years, less than last year’s forecast, and concentrated in Spain, Turkey, France and Lisbon FIR.

Aviation will join the EU Emission Trading Scheme (ETS) from 2012, reducing growth by a further 0.3%.

Any user of the forecast is strongly advised to use the forecast range (low-growth to high-growth) as part of the risk-management process during planning. By 2017, the high-growth scenario has nearly 1 million more flights compared to the base-scenario (+8%); the low-growth scenario has less than a half-million fewer movements (-5%). There are also a number of other important risks that are not included in these forecast numbers, some of which have already been mentioned above. While downside risks seem set to dominate for the next few years, at this stage in the cycle upside risks also begin to grow locally, if not Europe-wide; load factors are high, and that does mean some opportunities for more rapid growth.

The EUROCONTROL Medium-Term Forecast will next be refreshed in September 2011. The first two years will be additionally reviewed through the short-term forecast in May of 2011.

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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

2011-2017

Edition Number : v1.0 Edition Date : 23/02/2011 Status : Released Issue Intended for : General Public


DOCUMENT CHARACTERISTICS

TITLE


11/01/11-01 Publications Reference:

Edition Number: v1.0 Document Identifier STATFOR Doc418 Edition Date: 23/02/2011

Abstract This medium-term forecast of IFR flight movements has been prepared by the EUROCONTROL Statistics and Forecast Service (STATFOR).

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

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

Tel Unit Contact Person(s) Claire Leleu x3346 DNM/FTA/STATFOR

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© 2011 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.

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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

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v0.4 28/01/11 Draft version All

v1.0 23/02/11 Final Draft All


CONTENTS

1. INTRODUCTION...................................................................................................8 1.1 General......................................................................................................................................8 1.2 Summary of Forecast Method...................................................................................................8

2. TRAFFIC IN 2010 .................................................................................................9

3. GROWTH IN IFR MOVEMENTS TO 2017..........................................................13 3.1 Summary of growth .................................................................................................................13 3.2 Risks to the Forecast ..............................................................................................................19 3.3 Airport constraints ...................................................................................................................20 3.4 High-Speed Train ....................................................................................................................21 3.5 Emission Trading Scheme (ETS)............................................................................................23 3.6 Comparison with earlier forecast ............................................................................................24

4. ACRONYMS .......................................................................................................26

Annex A. FORECAST METHOD...................................................................27

Annex B. TRAFFIC REGION DEFINITIONS.................................................31 B.1 ESRA08...................................................................................................................................31 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 ..................................................................................................................42 C.7 Emission Trading Scheme (ETS)............................................................................................47

Annex D. SUMMARY OF THE FORECAST FOR THE ESRA ......................49

Annex E. FUTURE TRAFFIC AND GROWTH.......................................................52 E.1 Summary of the Forecast. Annual IFR Movements 2006-2017..............................................52 E.2 Summary of the Forecast. Growth Rates 2006-2017 .............................................................57

Annex F. REFERENCES .......................................................................................62

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List of Figures. Figure 1. Summary of the forecast for the ESRA08. ................................................................. i Figure 2. Average annual growth 2010-2012 for each State.................................................... ii Figure 3. Average annual growth 2010-2017 for each State................................................... iii Figure 4. The recovery was uneven amongst the States in 2010. (Source: EUROCONTROL)

..........................................................................................................................................9 Figure 5. Main market segment distribution, 2009 and 2010. (Source: EUROCONTROL) ...10 Figure 6. Downturn, and recovery, by market segment. (Source: EUROCONTROL)............10 Figure 7. Russia is catching up the USA amongst the main partners. (Source:

EUROCONTROL) ...........................................................................................................10 Figure 8. Load factors in Europe at their highest in 2010, apart from during the ash crisis.

(Source: AEA. AEA member airlines, intra-Europe traffic)..............................................12 Figure 9. Ticket prices change in Europe (Source: Eurostat).................................................12 Figure 10. Fuel price is still a growing risk. (Source: EIA, Eurostat, IATA) ............................12 Figure 11. Summary of the forecast for Europe (ESRA08). ...................................................13 Figure 12. Forecast for 2011 in Europe is for a bounce back. (Uncertainty is typically ±1.2

percentage points, ±1.1 percentage points for States with 1 million flights). ..................14 Figure 13. Forecast for 2012 in Europe (Uncertainty is typically ±0.8 percentage points, ±0.6

percentage points for States with 1 million flights). .........................................................15 Figure 14. Number of additional movements per day for each State (2012 v 2010). .............16 Figure 15. The speed of traffic recovery from 2008 high levels is different across the States

........................................................................................................................................17 Figure 16. Number of additional movements per day for each State (2017 v 2010). .............18 Figure 17. Map of the top 40 traffic growth flows (2017 v 2010). ...........................................18 Figure 18. Impact of airport constraints..................................................................................20 Figure 19. Effect of high-speed train: reduction in IFR departures for the ESRA08 (top) and

the most affected States (bottom). ..................................................................................21 Figure 20. Improved high-Speed Train City-Pairs (Baseline Scenario)..................................22 Figure 21. Reduction in passenger demand for flights due to aviation’s participation in the

ETS. ................................................................................................................................23 Figure 22. Forecast for the ESRA08 has been revised downwards.......................................25 Figure 23. Summary of short-term forecast method...............................................................27 Figure 24. Preparation process of the Medium-Term Forecast..............................................29 Figure 25. The EUROCONTROL Statistical Reference Area. ...............................................31 Figure 26. Regions used in flow statistics. .............................................................................32 Figure 27. Since the last forecast, the economic outlook for EU27 has been revised upwards

for 2010 and remains comparable in the later years. (Source: Oxford Economics Ltd.).34 Figure 28. Summary of changes in GDP forecasts for 2010-2016: biggest revisions happen

in 2011 and 2012. (Source: Oxford Economics Ltd.) ......................................................35 Figure 29. GDP Growth by Traffic Region..............................................................................35 Figure 30. GDP Growth by Traffic Zone.................................................................................36 Figure 31. GDP Growth by Origin-Destination Zone. .............................................................36 Figure 32. Low-cost effects by Traffic Zone. ..........................................................................38 Figure 33. High-Speed Train Times in the Baseline Scenario. ..............................................40 Figure 34. Example of forecast for load factors – here for Europe. (source: AEA,

EUROCONTROL). ..........................................................................................................41 Figure 35. Load factors by Traffic Region. .............................................................................42 Figure 36. Events and Trends assumptions by Traffic Zone..................................................43 Figure 37. Airport traffic switch...............................................................................................47 Figure 38. ETS characteristics ...............................................................................................48


Figure 39. Prices ....................................................................................................................48 Figure 40. Price Elasticities per (bidirectional) Traffic Region Pair and period ......................48 Figure 41. Growth in the ESRA. .............................................................................................49 Figure 42. Traffic on the main flow categories for the ESRA. ................................................50 Figure 43. Traffic and growth on the biggest region-to-region flows through the ESRA. .......51 Figure 44. Annual traffic per traffic zone and 2011-2017 average annual growth..................52 Figure 45. Annual growth rates per traffic zone and 2011-2017 average annual growth.......57

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1. INTRODUCTION

1.1 General

This report presents the forecast of annual numbers of instrument flight rules (IFR) movements for 2011 to 2017, prepared by the EUROCONTROL Statistics and Forecast Service (STATFOR) in February 2011. This replaces the forecast issued in September 2010 (Ref. 1).

This report contains a summary and discussion of the forecast, including forecast method (Annex A), geographical definitions (Annex B), forecast assumptions (Annex C), forecast details for Europe as a whole (Annex C.1) and annual total forecasts per State (Annex E). The detail for each traffic zone (usually the same as ‘State’) is provided on the STATFOR portal (Ref. 2).

STATFOR also prepares a long-term forecast (20 years), recently updated and available in summary on the STATFOR web pages (Ref. 3). This forecast will be refreshed in September 2011. The first two years of this forecast will be updated in May 2011.

1.2 Summary of 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 a broader range of economic and industry developments into account.

The short-term forecast is a monthly forecast, derived largely from the analysis of recent trends on some 8,000 flows, but also incorporating forward-looking information from future published schedules, from economic forecasts, and describing future events (such as the Euro12 football championship).

The medium-term forecast is an annual forecast developed by growing baseline traffic (all IFR flight movements for the whole of 2010) 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. Two key features are:

The routing between airport pairs is modelled both on the patterns observed in the baseline year, and recent trends in how these patterns are changing. However, no account of future route network changes is made.

The medium-term forecast is constrained by annual airport capacities.

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 detailed in Annex C.

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 3.2.


2. TRAFFIC IN 2010

In 2010, aviation in Europe pulled out of the economic downturn and began to climb again. The total number of flights in Europe (ESRA08) in 2010 was 9.49 million, an increase of 0.8% compared to 2009. This annual average masks a wide monthly variation, from -11% in the April ash-cloud crisis to +4% in October, at the end of a strong Summer.

The first half of 2010 was rather hectic for the aircraft operators: first the bad weather conditions and occasional industrial actions; then the eruption of the Eyjafjallajökull volcano in April and May. By the middle of the year, the number of flights in Europe was still 1% below the first half of 2009. The number of flights boosted by the Summer timetable increased by 2.6% during the second half of the year (compared to the second half of the year 2009).

If the 2010 recovery was lumpy during the year, it was also uneven across the States. Flight growth was concentrated in a few States: Turkey, Italy, Ukraine and Germany were the States adding most traffic to the European network (Figure 4). The economic crisis and a series of general strikes reduced traffic in Greece overall; and the UK and Ireland both ended the year with fewer flights than the already reduced levels of 2009.

Growth was driven mainly by low-cost carriers, which saw an increase of 6.9% compared to 2009. Though their growth slowed markedly at the end of the year (Figure 6), their market share climbed 1.3 points compared to 2009, to 22.1% of all flights (Figure 5). Business aviation also contributed strongly to growth, bouncing back from 2009 with an increase of 5.5%. The other segment contributing to the growth in 2010 was all-cargo, showing a 2.7% increase on 2009, which had nevertheless a limited impact on overall growth as this segment remains small in terms of number of flights. The more important scheduled traffic segment declined by 1.1% over the year (compared to 2009). Part of this decline is related to the ash-cloud crisis and the heavy snow disruptions at major European airports in December.

The average daily traffic in Europe was around 26,000 flights a day. Overall, the 2010 traffic has followed the levels experienced in 2006 (26,200 flights a day). Capacity added by the carriers during the summer 2010 was not enough to offset the several disruptions. Indeed, 2010 saw significant flight cancellations as a result of weather, the ash-clouds, and strikes. An estimated 175,000 scheduled flights were cancelled during the year. Even if the effects of the ash-cloud are removed, the 2010 cancellations amounted to 2.5 times as many cancellations as in 2009.

Traffic flows to the top 6 extra-European partners were all back in growth in January 2010 (in terms of additional flights compared to 12 months before), except the US flow which finally recovered in August (Figure 7). Russia was a clear source of growth this year, and indeed for one month during the Summer passed the US as the main external partner for Europe.

Figure 4. The recovery was uneven amongst the States in 2010. (Source: EUROCONTROL) Units: Additional IFR movements/day compared to 2009, overflights excluded. Note: Shows only States with changes of 25 local movements/day.



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Figure 5. Main market segment distribution, 2009 and 2010. (Source: EUROCONTROL)

Figure 6. Downturn, and recovery, by market segment. (Source: EUROCONTROL)

Figure 7. Russia is catching up the USA amongst the main partners. (Source: EUROCONTROL)



During 2010, load factors have been consistently above the 2009 figures (except the blip during the ash crisis), reaching record highs especially during summer (see weeks 18 to 43 on Figure 8). Indeed, this summer was a good one for many airlines, as passenger growth exceeded the airlines’ capacity. This apparent recovery in passenger traffic was more a “return to normal” considering the low levels the passenger traffic had reached in 2009, nevertheless, the strength of demand clearly exceeded the expectations of some airlines. The Association of European Airlines (AEA) reported a load factor of 77.8% for 2010 against 75.6% in 2009.

If the cost of air travel in the EU had mostly declined in 2009, ticket prices started to climb again during the second and the third quarters of 2010—ash-crisis apart (Figure 9). Major European airlines reported recovery in yields during Q2 and Q3 a result of both the passenger-traffic recovery, filling up the front, the rear, as well as the cargo recovery and the effects of the cost-reduction programmes implemented.

For most of 2010, oil prices were stable, around €60 per barrel (/b) as shown on the left-hand side of Figure 10. However, they were stable at a high level and have climbed in the last quarter of the year to more than €70. Some European airlines reflected this increase in raising their fuel surcharges on some tickets. Moreover, the difference between oil prices and fuel prices (the gap between the two lines in Figure 10) has increased and stayed wide compared to the previous years. Fuel has not gone up as much as oil, notably because of increasing refining capacity, but this does not bode well for fuel prices which have some more leeway to climb again. The growing disparity between the prices of the two main benchmarks of oil, Brent (shown here) and Texas may also be contributing to this separation.


Figure 8. Load factors in Europe at their highest in 2010, apart from during the ash crisis. (Source: AEA. AEA member airlines, intra-Europe traffic)

Figure 9. Ticket prices change in Europe (Source: Eurostat)

Figure 10. Fuel price is still a growing risk. (Source: EIA, Eurostat, IATA)




3. GROWTH IN IFR MOVEMENTS TO 2017

3.1 Summary of growth

The forecast is for 11.6 million IFR movements in the ESRA in 2017, 22% more than in 2010. Traffic growth will bounce back in 2011 (above 4%), but with the underlying growth rate a little more than half of this, after allowing for the effects of the ash-cloud, weather and strikes. Then, the traffic growth will slowly decrease in 2012 with growth rate around 3%. In the later years, the slowdown will continue with annual growth around 2.5%, which is still below the long-term historical average. The average growth rate over the 7 years is for 2.9% per year.

Figure 11 summarises the forecast results for Europe (ESRA08 – see Annex B.1). The forecast is for 11.6 million IFR movements in 2017, 22% more than in 2010. The high-growth scenario adds a little under 1 million movements (+8%) while the low-growth scenario has about half a million movements fewer (-5%).

Figure 11. Summary of the forecast for Europe (ESRA08).

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

AAGR 2017/ 2010

H . . . . . 10,007 10,433 10,846 11,258 11,676 12,120 12,490 4.0%

B 9,561 10,043 10,083 9,413 9,493 9,902 10,240 10,479 10,760 11,045 11,337 11,571 2.9%

IFR Flight Movements

(Thousands) L . . . . . 9,813 10,100 10,238 10,435 10,643 10,861 11,004 2.1%

H . . . . . 5.4% 4.3% 4.0% 3.8% 3.7% 3.8% 3.1% 4.0%

B 3.7% 5.0% 0.4% -6.6% 0.8% 4.3% 3.4% 2.3% 2.7% 2.7% 2.6% 2.1% 2.9%

Annual Growth (compared to

previous year) L . . . . . 3.4% 2.9% 1.4% 1.9% 2.0% 2.1% 1.3% 2.1%

Short-Term outlook

For the next two years, the forecast is for a strengthened recovery at the European level but each of the two years’ growth relies on different factors. Locally, there are also some significant risks, discussed in section 3.2, which include:

The potential for a switch of tourism preferences in the short-term away from Tunisia and Egypt, is an up-side risk for the North side of the Mediterranean.

The effects of aviation taxes in Germany, Austria and the UK are downside risks for their local traffic.

High fuel prices and weak economies present downside risks, though in some cases the potential for stronger economic recovery is an upside risk.

Very recent data suggest slower growth in the Hungary-Romania overflight corridor, with an upswing along the Adriatic.



Figure 12. Forecast for 2011 in Europe is for a bounce back. (Uncertainty is typically ±1.2 percentage points, ±1.1 percentage points for States with 1 million flights).

Figure 12 shows the forecast for the whole of 2011. Compared to the weak 0.8% growth in 2010, the forecast for 2011 can be looked on as a year of bounce-back with 4.3% for Europe (1%). Around 1.9% of this growth comes directly as a compensation for the European lost flights in 2010 (ash-crisis, adverse weather and strikes). The underlying growth for 2011 in Europe is around 2.5%, which is well below the pre-recession typical trends of around 3.5%-4.5% annual rates of growth but which is also partly explained by the uneven economic recovery across the region. Most of the Eastern European States show growth rates greater than the European average, but the differences on the map between the States also strongly reflect the fact that each European State was affected differently by the events of 20103. Moreover, some States are developing at a much different rate to their neighbours, e.g. Greece where monthly traffic, since the beginning of the Winter 10/11 timetable, is lower than the same months a year earlier.

3 Some States have been more severely affected than others, notably Finland, Ireland, Germany, Sweden and UK (see

Annex C.6 for more details).


Even if the 2011 forecast is for a sustained growth thanks to the rebound from 2010, the current high load factors do present an up-side risk, as airlines continue to search for the right level of capacity to meet the post-economic crisis demand.

Figure 13. Forecast for 2012 in Europe (Uncertainty is typically ±0.8 percentage points, ±0.6 percentage points for States with 1 million flights).

Figure 13 shows the forecast for the whole 2012, which is for 3.4% growth across Europe (0.7%). There are many factors influencing 2012. Out of the European growth:

around 0.3% of the traffic growth is owing to the leap year’s additional day in 2012 (at European level), and;

around 0.1% of traffic growth is estimated to be added because of two major sporting events (Olympics in London and Euro football cup in Poland and Ukraine, see Annex C.6).

In the meantime, 2012 is also the year when aviation industry should join the EU Emission Trading Scheme (EU ETS). The effect of aviation participation in the ETS on passenger demand is now examined in the forecasts (see section 3.5). Because of the recent downturn in traffic and also the efforts made by air operators to implement cost-efficiency and environmental efficiency measures, the reduction in demand for flights in 2012 due to airlines passing the additional ETS costs onto passengers is likely to be very limited (0.1%).




When inspecting the forecast in more detail, some European States lag behind (e.g Ireland, Spain, Portugal); this is mostly linked to the slow economic recovery in these areas. Dealing with UK, the effect of the airport constraints, particularly high in the London area, slow the growth of flights for the State from 2012 (and later). See section 3.3.

Figure 14. Number of additional movements per day for each State (2012 v 2010).

The period of lost growth (covering 2008-2010) has put traffic volumes in 2012 back by nearly 4 years at European level, compared to earlier forecasts. The peak in terms of annual IFR movement reached in 2008 is now forecast to be passed again in 2012 with 10.2 million IFR movements expected in the base scenario for Europe. However, at State level, the speed of recovery in traffic is two-tier (see Figure 15). Most of Eastern States have already exceeded 2008 traffic levels whereas busiest western States (France, Germany, Benelux), will not recuperate before 2012 or even later (e.g Ireland and UK might not recover before 2015 or 2016).


Figure 15. The speed of traffic recovery from 2008 high levels is different across the States

Last but not least, the compensation for the events of 2010 has no effect anymore in 2012, even if the forecast implicitly assumes that some effects can be repeated every year (e.g. adverse weather, strikes) which constitutes a downside risk.

The upside and downside risks to the forecast are probably in balance across Europe as a whole, but between individual States, we will continue to see strong divergence.

Medium-term outlook

The forecast for 2013 is expected to be for a lower growth than in 2012 with a value of 2.3% (±1.5%). This drop is mainly due to the removal of the 2012 effects (leap-year and major events) but is also explained by the increasing weight of the capacity constraints at major airports (30,000 departures will not be accommodated across the European network in 2013, see Figure 18).

From 2014, the growth is expected to remain stable around 2.7% and until 2016 — another leap year — and then to drop slightly in 2017 (leap-year effect withdrawn and capacity constraints removing 100,000 departures to the European network).

The traffic development is not uniform across the region. By 2017, the growth (in percentage terms) is relatively stronger in the East (see Figure 3). In contrast, in terms of the number of additional movements per day, the traffic will increase the most in the big economies in the West (eg. France, UK, Germany, Italy) but also in Turkey, which is forecast to see one of the strongest traffic growth in Europe (see Figure 16).




Figure 16. Number of additional movements per day for each State (2017 v 2010).

Figure 17 shows the top 40 pairs of States or regions (outside Europe) adding the most flights to the European traffic (when compared to 2010 traffic). Of the 5,000 or more additional daily flights that are forecast, domestic traffic in Turkey is one of the main sources of growth, as well as the flow between Turkey and Former CIS region. Most of the busiest European States (Germany, UK, Italy and France) will also see their domestic traffic expanding, even if growth in each State only represents a quarter of Turkish internal growth. The flow between France and North-Africa region is also expected to develop quite significantly by 2017.

Figure 17. Map of the top 40 traffic growth flows (2017 v 2010).



3.2 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. The patchy economic recovery in Europe, as well as the entry into force of the Emission Trading Scheme or the continuing rise of oil prices, have an impact on forecast traffic growth.

The main sources of uncertainty in the forecast are:

This forecast is prepared as the recovery is becoming increasingly self-sustaining at EU level. However, progress across Member States remains uneven. The economic forecasts (Figure 30) were updated in January 2011. Nevertheless, the situation remains uncertain and the low- and high-growth scenarios may not entirely capture the risks in the economies on both side of the Atlantic.

Network changes and route choice of airlines have a large influence on the number of overflights. The future network changes are not modelled in this forecast; only the recent trends in overflights patterns are captured. This variation in overflights remains a risk in the forecast, especially for States located along the Adriatic coast (where this appears to be an up-side risk) and also through the Hungary-Romania corridor (where this appears to be a down-side risk).

Tourism trends are quite variable. The medium-term forecast aims to be accurate over the seven-year period, rather than identifying which will be the new holiday “destination of preference” in a given year. That being said, recent events in Tunisia and Egypt could see a fair proportion of European holidaymakers transferring to Mediterranean area destinations for Summer (e.g. Spain continental, Turkey). As a ‘what if?’ test, we calculate that if 20% of departures from ESRA to Tunisia and Egypt next Summer were to be transferred to Turkey, it would represent +8% additional arrivals per month in this State.

Oil prices remain changeable with oil being increasingly an item of speculation and investment. Fuel accounting for 25-35% or even more of costs of the airlines, this can have an effect on fares and cost of travel for customer. In this forecast, changes in price of oil are not factored in.

Participation of aviation in the Emission Trading Scheme is now modeled in the forecast, which will limit the traffic growth from 2012 (see Section 3.5).

Other regulatory measures such as environmental taxes (e.g introduction of the eco taxes in Germany and Austria from the beginning of the year) or new tax regimes also add to the downside risks on traffic growth. Aircraft operators’ decisions, to adapt their capacity plans to reduce their costs, have been modeled in a limited way through available4 schedule data in the forecast. Moreover, some local changes might not have been identified (transfer of operations for a specific aircraft operator from one hub/State to another).

Terrorist attacks, wars and natural disasters. The last seven years have not been quiet ones for aviation. There is no reason to believe the next seven years will be uneventful, with the risk of a further volcano eruption or other pandemic disease of the most obvious risks. The impact on air traffic could be a temporary one, or more significant.

Current high load factors highlight that, as the industry climbs out of the trough, there are up-side risks at least locally, as some aircraft operators seek to expand quickly to satisfy pockets of growing demand.

4 Last available schedule info dated January 2011, though this seems incomplete for the Summer.



3.3 Airport constraints

Due to the lower levels of traffic (2006 levels), the effect of airport constraints is low in this forecast compared to pre-recession levels. Demand will exceed capacity by around 100,000 IFR departures in 2017, which is below 1%.

This forecast uses an extended set of airport capacity figures, building on the work done for the Long-Term Forecast 2010 (Ref. 4) and Challenges of Growth 2008 (Ref. 5). This set has been enhanced with updated capacity figures and completed with 17 additional airport data (mostly in UK, Germany and Turkey) thanks to STATFOR User Group members. In total, 155 major airports are included in the capacity checks. The set of airports covered by declared capacities accounts for around 70% of the traffic in Europe.

In the baseline scenario in 2017, demand for around 100,000 departures will not be accommodated due to airport congestion (mainly in UK, Turkey and France). This will reduce the growth in departures by less than 1% percentage point across Europe as a whole over the 7 years of the forecast, though of course the effect will be stronger in the States with congested airports.

Compared to last year, the forecast traffic is lower (see section 3.6). As a result, the number of unaccommodated flights is lower than in the previous Medium-Term Forecast (excess demand of 119,000 in 2016 in MTF10, see Ref. 6, compared to 69,000 in this forecast).

Figure 18. Impact of airport constraints.

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

Change in IFR Departures (000s) Percentage Change

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

High 16.6 25.3 36.5 65.7 91.8 127.6 202.0 0.2% 0.2% 0.3% 0.6% 0.8% 1.0% 1.6%

Base 12.3 23.2 30.2 40.3 47.8 69.3 100.5 0.1% 0.2% 0.3% 0.4% 0.4% 0.6% 0.8%

Low 11.4 17.5 22.4 30.7 33.4 42.6 77.4 0.1% 0.2% 0.2% 0.3% 0.3% 0.4% 0.7%


3.4 High-Speed Train

The continuing development of the high-speed train network reduces growth in air traffic by about 47,000 IFR departures (0.4%) in total over 7 years. Spain, Turkey, France and Lisbon FIR see the largest reductions in terms of departures.

Figure 19 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; small on the scale of the network as a whole, even if on particular city-pairs the effect can be large. Because some HST projects have been delayed and also, due to the lower levels of traffic, the current expected reduction in traffic is below the MTF10 result (see Ref. 6) in which 61,000 IFR departures were assessed to be removed from the network.

Figure 19. Effect of high-speed train: reduction in IFR departures for the ESRA08 (top) and the most affected States (bottom).


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

1.0 6.4 15.6 24.6 35.1 44.9 44.8 0.0% 0.1% 0.1% 0.2% 0.3% 0.4% 0.3% High

2.4 10.4 21.2 32.2 38.3 43.7 46.8 0.0% 0.1% 0.2% 0.3% 0.3% 0.4% 0.4% Base

2.4 10.3 24.8 37.8 42.8 43.2 45.2 0.0% 0.1% 0.2% 0.4% 0.4% 0.4% 0.4% Low


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

France . 0.3 2.0 4.0 2.5 2.4 4.5 . 0.0% 0.2% 0.4% 0.2% 0.2% 0.4%

Germany . 0.2 0.4 0.5 0.6 0.6 0.6 . 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Lisbon FIR . . . 2.1 4.2 4.3 4.4 . . . 1.4% 2.8% 2.8% 2.8%

Netherlands . . . 0.4 0.9 0.9 0.9 . . . 0.1% 0.3% 0.3% 0.3%

Spain 2.3 7.4 10.9 16.7 21.7 26.8 28.6 0.3% 0.9% 1.3% 2.0% 2.5% 3.1% 3.2%

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

Turkey . 2.5 7.7 7.9 8.2 8.5 8.2 . 0.5% 1.4% 1.4% 1.3% 1.3% 1.2%

Base

UK . . . 0.3 0.4 0.4 0.4 . . . 0.0% 0.0% 0.0% 0.0%

Spain sees the largest impact of HST: a reduction of around 29,000 IFR departures in 2017 (3.2%). Turkey has around 8,000 fewer IFR departures, which is a 1.2% reduction. France and Lisbon FIR, less affected, lose around 4,500 departures (each) to HST, which does not weigh the same related to the respective State’s traffic size: only a 0.4% reduction France but a 2.8% reduction for Lisbon FIR.

The effects in Spain are mainly because of the Madrid-Valencia and Madrid-Alicante to start operations in 2011 and 2013 respectively. Both links are expected to lose around 50% of traffic (each) from 2012 and 2014 respectively. In Lisbon FIR, the international HST connection between the Lisbon and Madrid, to start operating in 2014 is likely to remove 40% of traffic from 2015.

In Turkey, the connection between Ankara and Istanbul (to start in 2012) is expected to withdraw 13% of traffic from 2013 based on the current information.

Figure 20 shows the changes in the network compared to the baseline in 2010.




Figure 20. Improved high-Speed Train City-Pairs (Baseline Scenario).

Source: Actuals from on-line timetables. Plans from UIC, TEN-T and elsewhere. Comments: Only new or improved links after 2010 are shown.

In the current economic climate, Governments are looking for areas where cuts to expenditure can be made. One possible outcome is reduced investment in these high-speed rail links, leading to delays in implementation. This is a clear upside risk to the flight forecast.



3.5 Emission Trading Scheme (ETS)

The aviation industry will join the EU ETS from 2012 as stated in the 2008 European Commission Directive5. The overall goal of the ETS being to limit the greenhouse emissions, aircraft operators of nearly all IFR flights departing from/arriving at aerodromes in any European Community Member State will need to surrender allowances equal to their CO2 emissions from aviation activities.

The model used to assess the effect of ETS on traffic demand in this forecast is described in a STATFOR study published in March6 (Ref. 7, available upon request). In summary, the method is in three steps:

1. Determination of the total amount of emissions from the aviation industry covered by the ETS,

2. Determination of the allowances in excess of those allocated for free and distribution to flights and passengers,

3. Estimation of the induced increase in fares (conversion of the excess allowances into monetary terms) and consequential reduction in demand.

The modelling has been performed on passenger flights only (excluding business aviation flights) accounting for around 85% of all IFR flights in Europe. The study took a conservative assumption of just 7% efficiency improvement per flight (e.g more efficient traffic management, changes in fleet mix and use) between now and 2017, which corresponds to 1% per year, i.e. below ICAO and IATA targets.

Figure 21 summarises the estimated reduction in passenger demand for flights due to increase in prices of tickets in consequence of airlines passing onto their customers their additional expenses because of their participation in the EU ETS.

The results shown that the reduction in demand for flights up to 2017 due to airlines passing the additional ETS costs onto passengers is likely to be very limited (around 0.3%). In result, the forecast number of flights in this forecast has not been reduced to reflect the possible impact of ETS on demand.

Figure 21. Reduction in passenger demand for flights due to aviation’s participation in the ETS.

Note: ETS relevant for passenger flights only (limited to all IFR flights arriving or departing from any Community Member State aerodrome only, business aviation excluded).

IFR movements (000s) Relative

Low Base High Low Base High

2012 -8.5 -7.2 -6.2 -0.1% -0.1% -0.1%

2013 -15.7 -12.3 -9.3 -0.2% -0.2% -0.1%

2014 -23.6 -17.7 -12.9 -0.3% -0.2% -0.2%

2015 -32.4 -22.5 -16.3 -0.4% -0.3% -0.2%

2016 -43.7 -28.7 -19.6 -0.5% -0.3% -0.2%

2017 -47.7 -30.9 -20.7 -0.6% -0.3% -0.2%

5 Directive 2008/101/EC of the European Parliament and of the Council of 19 November 2008. 6 Contact [email protected]



3.6 Comparison with earlier forecast

The new medium-term forecast starts lower than the previous forecast. The growth rates are similar in the later years and the annual average growth rate is similar to the previous forecast.

Three major differences between the two forecasts (inputs and method) have been identified:

In terms of inputs,

The actual traffic in 2010, which is used as the baseline for the MTF11, is lower than what was forecast by the MTF10b (by about 0.07 million fewer movements, 0.7%). Also, the current forecast (MTF11) compensates for “all-causes” 2010 lost flights (strike, weather, and ash-crisis) whereas the previous forecast was only including the ash-crisis related compensation flights.

Dealing with the low-cost carriers’ growth: the typical rate growth of the market segment has been revised downwards in line with more recent trends (see Annex C.2),

In terms of method,

An improved alignment of the forecast intervals between STF and MTF has been implemented in the MTF11 which resulted in a reduction the width between the high-and low-range forecasts, especially in the first years (see Annex A).

Figure 22 shows the new forecast (MTF11) for the ESRA08 compared with the forecast from end September (MTF10b, Ref. 1).

As the baseline traffic is lower than expected, the first year of the forecast (2011) is also lower (about 0.11 million fewer movements, around 1%). The width of the forecast range in the first years is narrower in the MTF11 (reduced by nearly a third in 2011 and by nearly half in 2012 compared to MTF10b) explained by both the nearness of the years to forecast and the improvement in the method.

The downwards shift due to lower traffic levels in the baseline year (2010), together with the slow-down in low-cost carriers’ growth inputs, shifts the MTF11 slightly further down across the horizon (about 0.17 million fewer movements in 2016, corresponding to 1.5% difference between the two forecasts).

In this forecast, the growth is nevertheless less limited by the airport constraints (due to lower forecast number of movements, see 3.3). This allows the stronger growth seen in the high-growth scenario, compared to the previous forecast.

After the 2009 downturn and the weak 2010 growth, the traffic growth is still expected to recover in the following years. The growth in the final periods of the forecast horizon is similar in both forecasts (around 2.7%-2.8%). The growth trend in the MTF11 is comparable to result overall in annual average growth rates similar to those forecast in the MTF10b (2.9%).


Figure 22. Forecast for the ESRA08 has been revised downwards

Note: MTF10b: grey lines, MTF11: blue lines. For both forecasts: base scenario: solid lines, low/high scenario: dashed lines.

Though the scope of the medium-term forecast is a traffic outlook for 7 years ahead, a longer term perspective is certainly also of interest. The last long-term forecast, LTF10 (see Ref. 4) was published in December 2010 and was based on the MTF10b (see Ref. 1), the latest medium-term forecast then available.




4. ACRONYMS

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) 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 SES Single European Sky SID STATFOR Interactive Dashboard 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. 8) 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 (0 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. The main changes this year are:

Update of the calculation of the forecast width: the STF and MTF forecast ranges are now aligned at an earlier stage in the calculations. This resulted in reducing the width of the forecast ranges in the short-term forecast.

Introduction of an economic indicator (GDP) used as an explanatory variable in the time-series forecast of the STF.

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.



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

History (10+ years)Calibration data from STATFOR Archives, CFMU, PRISME-Fleet, National Sources, OAG...

Supply-Side Demand-Side

Scenario Inputs- Economic Growth- High-Speed Train- Airport capacity- Events & Trends- Low-cost growth- Load factor change- Demographic change

Passenger All-Cargo

Historical Flight RoutingsCalibration data from CFMU & National Sources (including overflights)

Airport Capacity

• Short- and Medium-Term Reports, Tables and Maps• Dashboard• Additional views (eg emissions)

Review

by S

TA

TF

OR

User G

roupForeca

stR

outings

Airport-Pair History

Historical growth

Airport-Pair History

Historical growth Economic Growth

High-Speed Train

Network change

Low-Cost Growth

Airport-PairForecast

MilitarySmall

TrendConstant

MilitarySmall

TrendConstant

BizAv

Trend& Economy

Initial MTF Forecast

Short-Term ForecastSTF alignment

Forecast Flights per Airspace

The forecast is built from three main datasets.

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

A baseline from CFMU and National sources that includes routing information;

The set of scenario inputs.

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 derive

d from UN population forecasts.

using estimates of routing

r year) are kept constant.

of the forecast follows the , with a maximum

explanatory variable for this

d. -side is used if traffic matches one of the standard histories (circular

ft load factors, then grown according to GDP growth and the elasticity for

educed if there has ted for

o rport-to-airport

: base-year airport

The medium-term forecast is prepared in two stages: first the airport-pair forecast;then the traffic per volume of airspace, calculated from thisfrom 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 pe

Growth of military flight movements in the first yearaverage of the last three years for the traffic zone as a wholechange 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 State.

For other traffic, the use of supply-side or demand-side approach is considereSupplyflights, 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, aircratype andthis 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 rbeen a reduction in journey times by HST since the baseline year, adjuslow-cost growth in the traffic zone and for any network change assumptions (bytraffic 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 tcalculate how many flights are generated in each airspace by these aiflights. 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 examplemovements 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 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 MTF09 report. 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 Figure 43). 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.9).

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 2011 to 2017 for Functional Airspace Blocks (FAB). A FAB is blocks of airspace based on operational7 requirements regardless of the State boundaries (Figure 14). FAB initiatives (definitions) are constantly evolving according to the targets defined to improve the performance of the European air traffic management network. Compared to the last publication (MTF10b), the following FAB modifications took place:

NUAC has been renamed to DK-SE (and overlapping with NEFAB). DK-SE results are now presented, independently of NEFAB ones,

The Greenland area (Sondrestrøm FIR) as well as BODO International have been taken out of NEFAB (and out of any FAB).

Figure 14. Map of FAB initiatives dated January 2011. (Source: PRU).

7 http://ec.europa.eu/transport/air/single_european_sky/functional_airspace_blocks_en.htm



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 (OE). Data in this draft originate from the January 2011 update of the GDP forecast. These are shown for specific States8 in Figure 30 and Figure 31. 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 29.

The high- and low-growth scenarios are based on fixed offsets9 from these forecasts.

The economic recovery has taken hold in 2010 in EU27, reporting a most likely GDP growth rate of 1.8%. As a consequence, most of the States have seen their GDP growth revised upwards in 2010, compared to the MTF10 update (MTF10b, see Ref. 1). From 2011 onwards, the current GDP forecast remains quite comparable to the last one at European level.

Figure 27. Since the last forecast, the economic outlook for EU27 has been revised upwards for 2010 and remains comparable in the later years. (Source: Oxford Economics Ltd.)

Figure 28 shows the differences in GDP data per State considered in MTF10b and in this update. The biggest downward revisions are observed for Ireland in 2011 and 2012. The biggest upwards revision is for Sweden in 2011, where the economy recovered strongly in 2010 (+5.3%). Between 2012 and 2016, GDP forecast updates are small (between -2% and +1% changes, depending on the States), even if most States have been revised downwards.

8 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. 9 +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 28. Summary of changes in GDP forecasts for 2010-2016: biggest revisions happen in 2011 and 2012. (Source: Oxford Economics Ltd.)

In 2010, the pace of the recovery has been gradual and rather uneven across member states. Most economies seem to have pulled out of the crisis with biggest GDP growth for Turkey (8.3% ), Sweden (5.3%), Ukraine (4.3%) and Poland (4%). Of the 3 big economies, Germany recorded the best GDP growth (3.5%) in 2010, a significant way ahead of UK (1.6%) and France (1.5%) as shown in Figure 30. It is to be noted that 2010 GDP data are not significant generation of the MTF11 flight forecast.

Germany is expected to see its growth rate slowing down in 2011 and 2012 down to 1.7% whereas UK and France rates are forecast to slowly accelerate during the same period (respectively to 2.6% and 2%). The growth will slowly accelerate in the following years to revert back close to 2007 levels from 2013 for UK (average annual growth typically around 3%-3.5%). The recovery will be slower for France and Germany; they will both show weaker average annual growth rates (around 2%) from 2013. Dealing with the European economies severely affected by the economic crisis, Ireland and Portugal are not expected to recover before 2012 and Greece before 2013.

Figure 29. GDP Growth by Traffic Region.

Source: 1993-2004 from STATFOR records. 2005 onwards from Oxford Economics Ltd, Jan 11. Comments: Real GDP Growth. Units: Growth per year. Data last updated: 04/02/2011

Actual Base

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 ESRA East 4.3% -2.5% 2.2% 2.9% 4.1% 4.2% 3.9% 3.8% 4.1% 4.1% ESRA Mediterranean -0.1% -4.3% 1.4% 1.3% 2.0% 2.3% 2.4% 2.5% 2.8% 2.8% ESRA North-West 0.4% -4.1% 2.3% 2.0% 2.1% 2.4% 2.4% 2.3% 2.4% 2.4% Far-East 0.2% -4.2% 5.3% 2.3% 3.2% 3.1% 2.9% 2.6% 2.9% 2.9% Former CIS Region 5.6% -6.4% 4.2% 4.9% 5.6% 5.5% 4.8% 4.5% 5.1% 5.1% Mid-Atlantic 1.7% -4.7% 3.9% 4.2% 5.2% 4.4% 3.8% 3.6% 4.2% 4.2% Middle-East 5.9% 1.6% 4.4% 4.6% 4.9% 4.8% 4.7% 4.6% 5.3% 5.3% North Atlantic 0.0% -2.6% 2.9% 3.2% 3.5% 3.6% 3.5% 3.3% 3.4% 3.4% North-Africa 5.1% 3.5% 5.2% 5.3% 5.5% 5.4% 5.3% 5.0% 5.7% 5.7% Oceania 2.2% 1.1% 2.7% 3.3% 3.8% 3.3% 3.3% 3.2% 3.7% 3.7% Other -1.2% -16% 0.6% 3.7% 4.9% 5.8% 5.9% 5.6% 5.8% 5.8% South-Atlantic 5.8% -0.0% 5.3% 4.2% 4.6% 4.3% 3.9% 3.5% 3.9% 3.9% Southern Africa 5.9% 3.6% 5.7% 6.2% 6.5% 5.9% 5.6% 5.2% 6.1% 6.1%




Figure 30. GDP Growth by Traffic Zone.

Source: 1993-2004 from STATFOR records. 2005 onwards from Oxford Economics Ltd, Jan11. Comments: Real GDP Growth in local currency. Units: Growth per year. Data last updated: 04/02/2011

Actual Base

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Albania 6.8% 3.3% 2.8% 3.3% 3.8% 4.2% 4.5% 4.5% 4.5% 4.5% Armenia 6.9% -14% 2.2% 3.5% 6.0% 5.5% 5.0% 5.0% 5.0% 5.0% Austria 2.2% -3.9% 1.9% 2.0% 2.0% 2.1% 2.1% 2.0% 1.8% 1.8% Azerbaijan 11% 9.3% 4.5% 3.0% 5.8% 5.2% 5.2% 5.2% 5.2% 5.2% Belarus 10% 0.2% 5.9% 4.0% 4.6% 4.0% 3.7% 3.5% 3.3% 3.3% Belgium/Luxembourg 0.8% -2.6% 2.1% 2.1% 2.4% 2.7% 2.2% 2.0% 1.9% 1.9% Bosnia-Herzegovina 5.4% -3.5% 0.3% 3.7% 6.1% 5.7% 5.0% 5.0% 5.0% 5.0% Bulgaria 6.3% -4.9% -0.3% 2.5% 5.9% 6.9% 6.8% 6.0% 5.5% 5.5% Canary Islands 0.9% -3.7% -0.2% 0.6% 1.4% 1.7% 1.9% 1.9% 1.7% 1.7% Croatia 2.4% -5.8% -1.1% 1.8% 3.9% 4.3% 4.2% 3.9% 2.9% 2.9% Cyprus 3.6% -1.7% 0.6% 1.9% 2.8% 3.8% 3.4% 3.4% 3.4% 3.4% Czech Republic 2.3% -4.0% 2.3% 2.6% 3.3% 3.2% 2.8% 2.7% 2.6% 2.6% Denmark -1.1% -5.2% 2.2% 2.5% 2.2% 2.2% 2.2% 2.3% 2.2% 2.2% Estonia -5.1% -14% 2.4% 4.3% 4.8% 5.0% 5.0% 4.7% 4.3% 4.3% FYROM 5.0% -0.8% 1.1% 2.9% 4.7% 4.2% 4.0% 4.0% 3.3% 3.3% Finland 1.0% -8.1% 3.0% 2.6% 2.9% 3.0% 3.0% 3.0% 2.8% 2.8% France 0.1% -2.5% 1.5% 1.8% 2.0% 2.1% 2.0% 2.0% 1.8% 1.8% Georgia 2.4% -4.0% 4.8% 5.0% 7.0% 6.0% 6.0% 6.0% 5.1% 5.1% Germany 0.7% -4.6% 3.5% 2.2% 1.7% 2.1% 2.1% 2.0% 1.8% 1.8% Greece 1.3% -2.3% -4.1% -3.4% -0.1% 1.5% 2.0% 2.0% 1.6% 1.6% Hungary 0.6% -6.5% 1.1% 2.8% 3.6% 3.3% 3.3% 3.5% 3.1% 3.1% Iceland 1.0% -6.8% -3.5% 2.0% 2.8% 3.1% 3.0% 2.8% 2.6% 2.6% Ireland -3.5% -7.6% -0.8% -2.3% 1.1% 2.2% 2.5% 3.1% 3.2% 3.2% Italy -1.3% -5.0% 1.0% 1.0% 1.1% 1.2% 1.4% 1.8% 1.4% 1.4% Latvia -4.2% -18% -0.5% 3.1% 4.8% 5.8% 6.2% 5.6% 4.9% 4.9% Lisbon FIR 0.0% -2.5% 1.3% -0.7% 0.6% 1.1% 1.7% 1.8% 1.6% 1.6% Lithuania 2.9% -15% 0.5% 3.7% 5.0% 6.2% 6.2% 6.1% 5.0% 5.0% Malta 2.6% -2.1% 2.2% 2.1% 2.8% 3.0% 3.0% 3.0% 3.0% 3.0% Moldova 7.3% -6.5% 2.2% 3.4% 5.5% 6.0% 5.5% 5.5% 5.5% 5.5% Netherlands 1.9% -3.9% 1.7% 1.7% 2.1% 2.2% 2.0% 1.8% 1.6% 1.6% Norway 0.6% -1.3% -0.1% 2.6% 2.8% 2.7% 2.5% 2.4% 2.3% 2.3% Poland 5.0% 1.7% 4.0% 3.9% 4.4% 4.2% 3.9% 3.7% 3.1% 3.1% Romania 7.5% -6.9% -2.1% 0.1% 4.8% 5.9% 5.8% 5.7% 5.6% 5.6% Santa Maria FIR 0.0% -2.5% 1.3% -0.7% 0.6% 1.1% 1.7% 1.8% 1.6% 1.6% Serbia&Montenegro 5.5% 2.0% 1.5% 3.0% 4.5% 5.0% 5.0% 5.0% 5.0% 5.0% Slovakia 6.0% -4.8% 4.0% 2.9% 4.7% 4.7% 3.8% 2.9% 2.6% 2.6% Slovenia 3.5% -8.4% 1.3% 2.0% 2.3% 2.4% 3.1% 3.5% 3.4% 3.4% Spain 0.9% -3.7% -0.2% 0.6% 1.4% 1.7% 1.9% 1.9% 1.7% 1.7% Sweden -0.8% -5.2% 5.3% 4.3% 2.9% 2.8% 2.5% 2.4% 2.2% 2.2% Switzerland 1.9% -1.9% 2.7% 1.7% 1.8% 2.1% 2.0% 1.9% 1.8% 1.8% Turkey 0.9% -4.7% 8.3% 5.9% 6.9% 6.5% 6.0% 5.6% 5.4% 5.4% UK -0.1% -4.9% 1.6% 2.0% 2.6% 3.0% 3.0% 2.8% 2.4% 2.4% Ukraine 2.3% -15% 4.3% 4.7% 6.7% 7.5% 6.5% 6.0% 5.2% 5.2%

Figure 31. GDP Growth by Origin-Destination Zone.

Source: 1993-2004 from STATFOR records. 2005 onwards from Oxford Economics Ltd, Jan 11. Comments: Real GDP Growth in local currency. Units: Growth per year. Data last updated: 04/02/2011

Actual Base

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017China 9.8% 8.9% 10% 9.2% 9.1% 8.8% 8.7% 8.6% 8.3% 8.3%India 7.3% 6.8% 8.8% 8.2% 9.0% 8.8% 8.4% 8.0% 7.5% 7.5%


C.2 Low-Cost Carrier Growth

The low-cost carrier growth model is unchanged from the previous forecast. However, the typical rate of growth of the low-cost carriers’ market share for the ESRA has been revised downwards compared to the previous forecast. Until last year, a typical rate of 2.3 percentage points per year was used (annual change in low-cost market share). Based on recent internal analyses, this figure was about right for the 2002-2007 period. For 2008-2010, the rate appears to have slowed, with a base figure of 1 percentage point looking more likely. Moreover, this downwards revision is confirmed in the news by some low-cost carriers announcements (slow down in growth) and in particular from the Winter 10/11 timetable.

We have used the market share of low-cost carriers in each traffic zone in December 2010. This has been augmented by a rate of growth of the market share derived from a base growth for the whole ESRA of 1 percentage points per year (0.5 and 2.5 p.p. in the low- and high-growth scenarios respectively) and adjusted for observed higher State-to-State variation. The low-cost carriers are defined by means of a list (Ref.10), more detailed data on this segment are available in STATFOR web page (Ref. 2).

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. Figure 32 shows the market share of low-cost carriers in 2010 and 2017 (actual for 2010 and assumptions for 2017), and the percentage additional growth in movements generated by the low-cost carriers by 2017.




Figure 32. Low-cost effects by Traffic Zone.

Source: STATFOR Analysis and modeling. Comments: Represents additional growth for Low-Cost, but only the baseline year is a true statistic for low-cost market share. Units: Percentage Additional Growth Due to Low-Cost Growth. Data last updated: 14/01/2011

Actual Low Base High

2010 2017 2017 2017Albania 39% 37% 41% 43%Armenia 0% 1% 1% 3%Austria 22% 24% 26% 31%Azerbaijan 0% 1% 1% 3%Belarus 0% 1% 1% 3%Belgium/Luxembourg 16% 17% 18% 21%Bosnia-Herzegovina 1% 2% 2% 4%Bulgaria 18% 22% 26% 39%Canary Islands 31% 33% 35% 42%Croatia 7% 9% 11% 17%Cyprus 14% 17% 19% 27%Czech Republic 18% 22% 25% 36%Denmark 13% 14% 16% 20%Estonia 7% 9% 11% 16%FYROM 0% 1% 2% 5%Finland 23% 24% 26% 30%France 17% 19% 21% 27%Georgia 0% 1% 2% 5%Germany 29% 31% 33% 38%Greece 4% 6% 8% 14%Hungary 17% 19% 21% 26%Iceland 0% 2% 4% 9%Ireland 52% 55% 59% 70%Italy 36% 36% 40% 45%Latvia 12% 14% 16% 21%Lisbon FIR 24% 26% 28% 33%Lithuania 21% 25% 28% 39%Malta 28% 32% 36% 49%Moldova 4% 4% 5% 6%Netherlands 18% 19% 20% 23%Norway 21% 23% 25% 30%Poland 28% 31% 33% 41%Romania 24% 28% 31% 42%Santa Maria FIR 4% 5% 6% 8%Serbia&Montenegro 8% 10% 12% 18%Slovakia 37% 40% 42% 50%Slovenia 4% 6% 8% 13%Spain 36% 36% 39% 45%Sweden 18% 19% 21% 25%Switzerland 17% 18% 20% 24%Turkey 14% 16% 18% 24%UK 43% 44% 46% 49%Ukraine 0% 1% 1% 3%



C.3 High-Speed Train Network Development

The information on the high-speed rail network between 2010 and 2017 is based on information provided by the European Commission10 (TEN-T priority projects), the Union Internationale des Chemins de Fer and 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 33 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.

For the 2011 medium-term forecast, the travel times have been updated, and the years when the projects come into operation have been reviewed. There are a number of adjustments as a result. Several projects, entered in service in the end of 2009 have been removed (TAV projects in Italy, HSL Zuid improvements in France, Netherlands and Belgium). On the other hand, some projects have been added :

in Turkey, some new HST links from Istanbul/Ankara to main cities (Konya, Sivas and Bursa).

Projects in France (High speed links in Bretagne added with first effects in 2017).

Projects trans-states :

o High speed lines between Spain and France (e.g Madrid-Bordeaux by 2017),

o German operator to operate faster on already built links between Germany and UK (e.g. Frankfurt to London or Köln to London by 2013).

10 Progress Report 2010, Implementation of the Priority Projects, European Commission (TEN-T, Trans-European Transport Network), June 2010.



Figure 33. High-Speed Train Times in the Baseline Scenario.

Source: Actuals from on-line timetables. Plans from UIC, TEN-T and elsewhere. Comments: HST projects to be finalised 2011-2017. Units: Travel time (minutes). Data last updated: 14/01/2011.

Rail Time (mins)

2010 2011 2012 2013 2014 2015 2016 2017 Konya B 630 75 . . . . . . Sivas B 720 . . . . . . 180

Ankara

Bursa B 240 . . . . . . 150 Lyon B 225 . . 145 . . . . Marseille B 340 . . 240 . . . . Nimes B 350 . . 250 . . . .

Bale Mulhouse

Paris B 210 . . 180 . . . . Barcelona Lyon B 460 . . . . . . 180 Bilbao San Sebastian B 130 . . . . . 40 .

Bale Mulhouse B 180 . . . 80 . . . Frankfurt B 390 . . 290 . . . .

Dijon

Strasbourg B 220 . . 130 . . . . Erfurt Leipzig B 70 . . . . . 39 .

Bern B 230 . 200 . . . . . Bale Mulhouse B 170 . 140 . . . . . London B 370 . . . 310 . . .

Frankfurt

Paris B 230 . 215 . . . . . Geneva Paris B 202 185 . . . . . .

Ankara B 330 . 180 . . . . . Konya B 745 . 210 . . . . . Sivas B 1260 . . . . . . 300

Istanbul

Bursa B 240 . . . . . . 140 Bern B 180 . 150 . . . . . Karlsruhe Bale Mulhouse B 110 . . 80 . . . . Bale Mulhouse B 230 . 200 . . . . . Koln/Bonn London B 283 . . . 240 . . .

Lisboa Madrid B 690 . . . 165 . . . Alicante B 360 . . 120 . . . . Bilbao B 300 . . . . 135 . . Bordeaux B 750 . . . . . . 238 Murcia B 250 . . . . 120 . . San Sebastian B 300 . . . . . 135 . Valencia B 210 93 . . . . . .

Madrid

Vitoria B 220 . . . . 120 . . Munchen Berlin B 360 . . . . . . 240 Porto Vigo B 480 . . . . . . 60

Barcelona B 440 . . 335 . . . . Brest B 250 . . . . . . 180 Montpellier B 210 . . 180 . . . .

Paris

Rennes B 127 . . . . . . 90 Barcelona B 165 . . 50 . . . . Perpignan Madrid B 500 . . 230 . . . .

Schiphol London B 287 . . . 240 . . . Luxembourg B 130 . . . . . . 85 Lille B 200 . . . . . . 115 Lyon B 280 . . 200 . . . . Marseille B 380 . . 270 . . . .

Strasbourg

Paris B 140 . . . . . . 110 Bilbao B 210 . . . . 75 . . San Sebastian B 240 . . . . . 65 .

Valladolid

Vitoria B 140 . . . . 65 . . Bilbao B 140 . . . . 30 . . Vitoria San Sebastian B 100 . . . . . 30 .



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 155 airports has been used, coming from either Long-Term forecast assumptions (see Ref. 4) or from 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.

C.5 Load Factors

Figure 35 summarises the assumptions about the development of load factors. Simple forecasts have been made using the recent trends in the annual data provided by the Association of European Airlines (AEA).

It could be argued that load factors for other market segments (e.g. 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.

The forecast is based on the previous increasing trend as shown in Figure 34 but it is combined with assumptions about maximum limits that can be maintained by the airlines in the medium-term11. According to IATA statistics, global industry load factors have increased by 2.8 percentage points in 2010 to 78.4% (compared to 2009). The load factors forecasts for 7 regions have been capped by the ICAO CAEP assumptions out of the 11 regions considered (see Figure 35).

Figure 34. Example of forecast for load factors – here for Europe. (source: AEA, EUROCONTROL).

11 Cross-checked against ICAO CAEP/8 FESG Forecast Inputs and Assumptions (unpublished).



The lower bound on the forecast of load factors is assumed to correspond to the low-growth scenario. 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.

Figure 35. Load factors by Traffic Region.

Source: Actual: AEA, IATA. Forecast: STATFOR analysis and modelling. Comments: Data for 2010 based on year to November. Units: Percentage Load Factor for this Traffic Region. Data last updated: 06/02/2011

Actual Low Base High

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2017 2017 2017ESRA East 64% 63% 65% 65% 65% 67% 68% 69% 68% 68% 70% 71% 73% 74%ESRA Mediterranean 64% 63% 65% 65% 65% 67% 68% 69% 68% 68% 70% 71% 73% 74%ESRA North-West 64% 63% 65% 65% 65% 67% 68% 69% 68% 68% 70% 71% 73% 74%Far-East 79% 77% 81% 76% 78% 80% 81% 83% 81% 81% 84% 85% 85% 85%Former CIS Region 64% 63% 65% 65% 65% 67% 68% 69% 68% 68% 70% 71% 73% 74%Mid-Atlantic 80% 79% 79% 79% 80% 82% 82% 84% 83% 82% 82% 85% 85% 85%Middle-East 67% 63% 68% 69% 70% 74% 71% 75% 74% 69% 72% 74% 74% 74%North Atlantic 77% 74% 80% 80% 81% 82% 81% 82% 81% 82% 84% 85% 85% 85%North-Africa 71% 65% 69% 65% 69% 67% 68% 69% 70% 68% 70% 68% 70% 73%Oceania 79% 77% 81% 76% 78% 80% 81% 83% 81% 81% 84% 85% 85% 85%Other 64% 63% 65% 65% 65% 67% 68% 69% 68% 68% 70% 71% 73% 74%South-Atlantic 80% 74% 76% 82% 83% 83% 86% 85% 81% 80% 85% 85% 85% 85%Southern Africa 74% 77% 76% 76% 78% 78% 78% 79% 78% 78% 78% 80% 80% 80%

C.6 Events and Trends

Events

‘Events and Trends’ usually refers to future events. The forecast method means that local traffic (excluding overflights) is the focus for such adjustments, with the possibility to account for diverging trends in domestic and external flights by adjusting internals or arrivals and departures separately. Overflight traffic, calculated using both the airport-to-airport routing and current trends, will be consequently adjusted too. In the short-term method, the overflights may be directly adjusted (this has been done only for the effects of the events in 2010).

Figure 36 summarises the events and trends assumptions for this forecast. The assumptions are expressed as 'cumulative' change: so a 1.02 figure in the year 2012 only would mean increase growth by 2% in 2012 and decrease it in 2013 (with a total cumulative effect of 0 over the full period of the forecast).

The events in this forecast are:

2010 cancellations: during the Icelandic ash-cloud episodes12 in April and May, the strikes in 2010 as well as the snow disruptions in December, European traffic was disrupted for several days. Adjustments have been made on 2011 traffic for almost all States to compensate for the lost flights in the 2010 baseline data. The STATFOR adjustments correct the 2010 ‘one-off’ events only as, we assume that such events (adverse weather, strike) can be repeated every year.

EU accession: Following the recent slowdown in the growth of Romania and Bulgaria, the EU accession boost to traffic (due to joining in January 2007) is now smaller than the one used in MTF10. Croatia and Iceland are expected to join the

12 Ash cloud of April and May 2010: impact on air traffic, STATFOR Doc394, June 2010.


EU around 2012. The effect of air transport and market liberalisation is factored in by additional 5% growth in 2012, halving in the subsequent years.

EU visa-free measures: after EC relaxation on visa requirements for Albanian and Bosnian citizens travelling to EU, the arrival-departures traffic for these States is expected to grow by additional 1% in 2011 and 0.5% in the following year. The same visa-free measures which took effect in 2010 for Serbia&Montenegro and FYROM are expected to increase traffic only by 0.5% from 2011.

For the London Olympics in 2012, the MTF11 assumptions have been based on both the London 2012 Airport Capacity Study report (see Ref. 11) and on NATS data provision. Olympics in 2012, considering the restrictions in airspace and runway capacity in the London area, are estimated to generate around 5,200 additional flights on traffic (ie around 0.3% additional arrival-departure traffic and 0.4% additional internal traffic for the UK).

For the EURO 2012 (European football Championship), the assumptions are based on the experience of the EURO 2008 in Austria and Switzerland:

o 1% additional arrival-departures and 0.5% additional internals for Poland,

o 3% additional arrival-departures and 0.5% additional internals for Ukraine.

No adjustments have been added to model the influence of the forthcoming aviation taxes to be introduced in Germany and Austria from 2011 or regarding the increase of airport tax duty in the UK from November 2010. The effects on traffic are quite difficult to estimate; however they are to a limited extent captured in the STF (schedule data) and hence will influence the MTF as forecast results are aligned.

Figure 36. Events and Trends assumptions by Traffic Zone.

Source: STATFOR analysis and modelling Comments: Consolidation includes: trends from STF, volcano impact; and future events such as EU Accession, Euro football cup or London Olympics. Units: Growth index (Baseline Year=1.0). Data last updated: 12/12/2010

2011 2012 2013 2014 2015 2016 2017 H 1.037 1.042 1.042 1.042 1.042 1.042 1.042 B 1.027 1.032 1.032 1.032 1.032 1.032 1.032

Albania Total: Arr/Dep

L 1.017 1.022 1.022 1.022 1.022 1.022 1.022 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

Armenia Total: Arr/Dep

L 1.010 1.010 1.010 1.010 1.010 1.010 1.010 H 1.007 1.007 1.007 1.007 1.007 1.007 1.007 B 1.007 1.007 1.007 1.007 1.007 1.007 1.007

Total: Internal

L 1.007 1.007 1.007 1.007 1.007 1.007 1.007 H 1.017 1.017 1.017 1.017 1.017 1.017 1.017 B 1.017 1.017 1.017 1.017 1.017 1.017 1.017

Austria

Total: Arr/Dep

L 1.017 1.017 1.017 1.017 1.017 1.017 1.017 H 1.015 1.015 1.015 1.015 1.015 1.015 1.015 B 1.015 1.015 1.015 1.015 1.015 1.015 1.015

Azerbaijan Total: Arr/Dep

L 1.015 1.015 1.015 1.015 1.015 1.015 1.015 H 1.007 1.007 1.007 1.007 1.007 1.007 1.007 B 1.007 1.007 1.007 1.007 1.007 1.007 1.007

Belarus Total: Arr/Dep

L 1.007 1.007 1.007 1.007 1.007 1.007 1.007 H 1.021 1.021 1.021 1.021 1.021 1.021 1.021 B 1.021 1.021 1.021 1.021 1.021 1.021 1.021

Belgium/Luxembourg Total: Arr/Dep

L 1.021 1.021 1.021 1.021 1.021 1.021 1.021 H 1.029 1.039 1.039 1.039 1.039 1.039 1.039 B 1.019 1.024 1.024 1.024 1.024 1.024 1.024

Bosnia-Herzegovina Total: Arr/Dep

L 1.009 1.009 1.009 1.009 1.009 1.009 1.009




2011 2012 2013 2014 2015 2016 2017 H 1.031 1.036 1.036 1.036 1.036 1.036 1.036 B 1.021 1.026 1.026 1.026 1.026 1.026 1.026

Total: Internal

L 1.011 1.016 1.016 1.016 1.016 1.016 1.016 H 1.028 1.033 1.033 1.033 1.033 1.033 1.033 B 1.018 1.023 1.023 1.023 1.023 1.023 1.023

Bulgaria

Total: Arr/Dep

L 1.008 1.013 1.013 1.013 1.013 1.013 1.013 H 1.018 1.018 1.018 1.018 1.018 1.018 1.018 B 1.018 1.018 1.018 1.018 1.018 1.018 1.018

Total: Internal

L 1.018 1.018 1.018 1.018 1.018 1.018 1.018 H 1.020 1.020 1.020 1.020 1.020 1.020 1.020 B 1.020 1.020 1.020 1.020 1.020 1.020 1.020

Canary Islands

Total: Arr/Dep

L 1.020 1.020 1.020 1.020 1.020 1.020 1.020 H 1.009 1.079 1.114 1.124 1.129 1.129 1.129 B 1.009 1.059 1.084 1.094 1.099 1.099 1.099

Total: Internal

L 1.009 1.039 1.054 1.064 1.069 1.069 1.069 H 1.017 1.087 1.122 1.132 1.137 1.137 1.137 B 1.017 1.067 1.092 1.102 1.107 1.107 1.107

Croatia

Total: Arr/Dep

L 1.017 1.047 1.062 1.072 1.077 1.077 1.077 H 1.016 1.016 1.016 1.016 1.016 1.016 1.016 B 1.016 1.016 1.016 1.016 1.016 1.016 1.016

Total: Internal

L 1.016 1.016 1.016 1.016 1.016 1.016 1.016 H 1.016 1.016 1.016 1.016 1.016 1.016 1.016 B 1.016 1.016 1.016 1.016 1.016 1.016 1.016

Cyprus

Total: Arr/Dep

L 1.016 1.016 1.016 1.016 1.016 1.016 1.016 H 1.015 1.015 1.015 1.015 1.015 1.015 1.015 B 1.015 1.015 1.015 1.015 1.015 1.015 1.015

Czech Republic Total: Arr/Dep

L 1.015 1.015 1.015 1.015 1.015 1.015 1.015 H 1.022 1.022 1.022 1.022 1.022 1.022 1.022 B 1.022 1.022 1.022 1.022 1.022 1.022 1.022

Total: Internal

L 1.022 1.022 1.022 1.022 1.022 1.022 1.022 H 1.022 1.022 1.022 1.022 1.022 1.022 1.022 B 1.022 1.022 1.022 1.022 1.022 1.022 1.022

Denmark

Total: Arr/Dep

L 1.022 1.022 1.022 1.022 1.022 1.022 1.022 H 1.038 1.038 1.038 1.038 1.038 1.038 1.038 B 1.038 1.038 1.038 1.038 1.038 1.038 1.038

Total: Internal

L 1.038 1.038 1.038 1.038 1.038 1.038 1.038 H 1.021 1.021 1.021 1.021 1.021 1.021 1.021 B 1.021 1.021 1.021 1.021 1.021 1.021 1.021

Estonia

Total: Arr/Dep

L 1.021 1.021 1.021 1.021 1.021 1.021 1.021 H 1.022 1.022 1.022 1.022 1.022 1.022 1.022 B 1.017 1.017 1.017 1.017 1.017 1.017 1.017

FYROM Total: Arr/Dep

L 1.012 1.012 1.012 1.012 1.012 1.012 1.012 H 1.039 1.039 1.039 1.039 1.039 1.039 1.039 B 1.039 1.039 1.039 1.039 1.039 1.039 1.039

Total: Internal

L 1.039 1.039 1.039 1.039 1.039 1.039 1.039 H 1.029 1.029 1.029 1.029 1.029 1.029 1.029 B 1.029 1.029 1.029 1.029 1.029 1.029 1.029

Finland

Total: Arr/Dep

L 1.029 1.029 1.029 1.029 1.029 1.029 1.029 H 1.029 1.029 1.029 1.029 1.029 1.029 1.029 B 1.029 1.029 1.029 1.029 1.029 1.029 1.029

Total: Internal

L 1.029 1.029 1.029 1.029 1.029 1.029 1.029 H 1.025 1.025 1.025 1.025 1.025 1.025 1.025 B 1.025 1.025 1.025 1.025 1.025 1.025 1.025

France

Total: Arr/Dep

L 1.025 1.025 1.025 1.025 1.025 1.025 1.025 H 1.011 1.011 1.011 1.011 1.011 1.011 1.011 B 1.011 1.011 1.011 1.011 1.011 1.011 1.011

Georgia Total: Arr/Dep

L 1.011 1.011 1.011 1.011 1.011 1.011 1.011 H 1.032 1.032 1.032 1.032 1.032 1.032 1.032 B 1.032 1.032 1.032 1.032 1.032 1.032 1.032

Total: Internal

L 1.032 1.032 1.032 1.032 1.032 1.032 1.032 H 1.022 1.022 1.022 1.022 1.022 1.022 1.022 B 1.022 1.022 1.022 1.022 1.022 1.022 1.022

Germany

Total: Arr/Dep

L 1.022 1.022 1.022 1.022 1.022 1.022 1.022


2011 2012 2013 2014 2015 2016 2017 H 1.013 1.013 1.013 1.013 1.013 1.013 1.013 B 1.013 1.013 1.013 1.013 1.013 1.013 1.013

Total: Internal

L 1.013 1.013 1.013 1.013 1.013 1.013 1.013 H 1.013 1.013 1.013 1.013 1.013 1.013 1.013 B 1.013 1.013 1.013 1.013 1.013 1.013 1.013

Greece

Total: Arr/Dep

L 1.013 1.013 1.013 1.013 1.013 1.013 1.013 H 1.013 1.013 1.013 1.013 1.013 1.013 1.013 B 1.013 1.013 1.013 1.013 1.013 1.013 1.013

Hungary Total: Arr/Dep

L 1.013 1.013 1.013 1.013 1.013 1.013 1.013 H 1.082 1.117 1.1271.012 1.132 1.132 1.132 B 1.012 1.062 1.087 1.097 1.102 1.102 1.102

Iceland Total: Arr/Dep

L 1.012 1.042 1.057 1.067 1.072 1.072 1.072 H 1.038 1.038 1.038 1.038 1.038 1.038 1.038 B 1.038 1.038 1.038 1.038 1.038 1.038 1.038

Total: Internal

L 1.038 1.038 1.038 1.038 1.038 1.038 1.038 H 1.029 1.029 1.029 1.029 1.029 1.029 1.029 B 1.029 1.029 1.029 1.029 1.029 1.029 1.029

Ireland

Total: Arr/Dep

L 1.029 1.029 1.029 1.029 1.029 1.029 1.029 H 1.006 1.006 1.006 1.006 1.006 1.006 1.006 B 1.006 1.006 1.006 1.006 1.006 1.006 1.006

Total: Internal

L 1.006 1.006 1.006 1.006 1.006 1.006 1.006 H 1.015 1.015 1.015 1.015 1.015 1.015 1.015 B 1.015 1.015 1.015 1.015 1.015 1.015 1.015

Italy

Total: Arr/Dep

L 1.015 1.015 1.015 1.015 1.015 1.015 1.015 H 1.015 1.015 1.015 1.015 1.015 1.015 1.015 B 1.015 1.015 1.015 1.015 1.015 1.015 1.015

Latvia Total: Arr/Dep

L 1.015 1.015 1.015 1.015 1.015 1.015 1.015 H 1.008 1.008 1.008 1.008 1.008 1.008 1.008 B 1.008 1.008 1.008 1.008 1.008 1.008 1.008

Total: Internal

L 1.008 1.008 1.008 1.008 1.008 1.008 1.008 H 1.021 1.021 1.021 1.021 1.021 1.021 1.021 B 1.021 1.021 1.021 1.021 1.021 1.021 1.021

Lisbon FIR

Total: Arr/Dep

L 1.021 1.021 1.021 1.021 1.021 1.021 1.021 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

Lithuania Total: Arr/Dep

L 1.010 1.010 1.010 1.010 1.010 1.010 1.010 H 1.013 1.013 1.013 1.013 1.013 1.013 1.013 B 1.013 1.013 1.013 1.013 1.013 1.013 1.013

Malta Total: Arr/Dep

L 1.013 1.013 1.013 1.013 1.013 1.013 1.013 H 1.018 1.018 1.018 1.018 1.018 1.018 1.018 B 1.018 1.018 1.018 1.018 1.018 1.018 1.018

Moldova Total: Arr/Dep

L 1.018 1.018 1.018 1.018 1.018 1.018 1.018 H 1.018 1.018 1.018 1.018 1.018 1.018 1.018 B 1.018 1.018 1.018 1.018 1.018 1.018 1.018

Netherlands Total: Arr/Dep

L 1.018 1.018 1.018 1.018 1.018 1.018 1.018 H 1.015 1.015 1.015 1.015 1.015 1.015 1.015 B 1.015 1.015 1.015 1.015 1.015 1.015 1.015

Total: Internal

L 1.015 1.015 1.015 1.015 1.015 1.015 1.015 H 1.021 1.021 1.021 1.021 1.021 1.021 1.021 B 1.021 1.021 1.021 1.021 1.021 1.021 1.021

Norway

Total: Arr/Dep

L 1.021 1.021 1.021 1.021 1.021 1.021 1.021 H 1.009 1.017 1.009 1.009 1.009 1.009 1.009 B 1.009 1.014 1.009 1.009 1.009 1.009 1.009

Total: Internal

L 1.009 1.012 1.009 1.009 1.009 1.009 1.009 H 1.019 1.033 1.019 1.019 1.019 1.019 1.019 B 1.019 1.029 1.019 1.019 1.019 1.019 1.019

Poland

Total: Arr/Dep

L 1.019 1.025 1.019 1.019 1.019 1.019 1.019 H 1.025 1.035 1.035 1.035 1.035 1.035 1.035 B 1.015 1.020 1.020 1.020 1.020 1.020 1.020

Total: Internal

L 1.005 1.005 1.005 1.005 1.005 1.005 1.005 H 1.031 1.041 1.041 1.041 1.041 1.041 1.041 B 1.021 1.026 1.026 1.026 1.026 1.026 1.026

Romania

Total: Arr/Dep

L 1.011 1.011 1.011 1.011 1.011 1.011 1.011




2011 2012 2013 2014 2015 2016 2017 H 1.031 1.031 1.031 1.031 1.031 1.031 1.031 B 1.031 1.031 1.031 1.031 1.031 1.031 1.031

Total: Internal

L 1.031 1.031 1.031 1.031 1.031 1.031 1.031 H 1.007 1.007 1.007 1.007 1.007 1.007 1.007 B 1.007 1.007 1.007 1.007 1.007 1.007 1.007

Santa Maria FIR

Total: Arr/Dep

L 1.007 1.007 1.007 1.007 1.007 1.007 1.007 H 1.014 1.014 1.014 1.014 1.014 1.014 1.014 B 1.014 1.014 1.014 1.014 1.014 1.014 1.014

Total: Internal

L 1.014 1.014 1.014 1.014 1.014 1.014 1.014 H 1.022 1.022 1.022 1.022 1.022 1.022 1.022 B 1.017 1.017 1.017 1.017 1.017 1.017 1.017

Serbia&Montenegro

Total: Arr/Dep

L 1.012 1.012 1.012 1.012 1.012 1.012 1.012 H 1.006 1.006 1.006 1.006 1.006 1.006 1.006 B 1.006 1.006 1.006 1.006 1.006 1.006 1.006

Total: Internal

L 1.006 1.006 1.006 1.006 1.006 1.006 1.006 H 1.009 1.009 1.009 1.009 1.009 1.009 1.009 B 1.009 1.009 1.009 1.009 1.009 1.009 1.009

Slovakia

Total: Arr/Dep

L 1.009 1.009 1.009 1.009 1.009 1.009 1.009 H 1.011 1.011 1.011 1.011 1.011 1.011 1.011 B 1.011 1.011 1.011 1.011 1.011 1.011 1.011

Slovenia Total: Arr/Dep

L 1.011 1.011 1.011 1.011 1.011 1.011 1.011 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 1.019 1.019 1.019 1.019 1.019 1.019 1.019 B 1.019 1.019 1.019 1.019 1.019 1.019 1.019

Spain

Total: Arr/Dep

L 1.019 1.019 1.019 1.019 1.019 1.019 1.019 H 1.030 1.030 1.030 1.030 1.030 1.030 1.030 B 1.030 1.030 1.030 1.030 1.030 1.030 1.030

Total: Internal

L 1.030 1.030 1.030 1.030 1.030 1.030 1.030 H 1.023 1.023 1.023 1.023 1.023 1.023 1.023 B 1.023 1.023 1.023 1.023 1.023 1.023 1.023

Sweden

Total: Arr/Dep

L 1.023 1.023 1.023 1.023 1.023 1.023 1.023 H 1.013 1.013 1.013 1.013 1.013 1.013 1.013 B 1.013 1.013 1.013 1.013 1.013 1.013 1.013

Total: Internal

L 1.013 1.013 1.013 1.013 1.013 1.013 1.013 H 1.024 1.024 1.024 1.024 1.024 1.024 1.024 B 1.024 1.024 1.024 1.024 1.024 1.024 1.024

Switzerland

Total: Arr/Dep

L 1.024 1.024 1.024 1.024 1.024 1.024 1.024 H 1.002 1.002 1.002 1.002 1.002 1.002 1.002 B 1.002 1.002 1.002 1.002 1.002 1.002 1.002

Total: Internal

L 1.002 1.002 1.002 1.002 1.002 1.002 1.002 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

Turkey

Total: Arr/Dep

L 1.010 1.010 1.010 1.010 1.010 1.010 1.010 H 1.030 1.034 1.030 1.030 1.030 1.030 1.030 B 1.030 1.034 1.030 1.030 1.030 1.030 1.030

Total: Internal

L 1.030 1.034 1.030 1.030 1.030 1.030 1.030 H 1.024 1.027 1.024 1.024 1.024 1.024 1.024 B 1.024 1.027 1.024 1.024 1.024 1.024 1.024

UK

Total: Arr/Dep

L 1.024 1.027 1.024 1.024 1.024 1.024 1.024 H . 1.007 . . . . . B . 1.005 . . . . .

Total: Internal

L . 1.002 . . . . . H 1.006 1.041 1.006 1.006 1.006 1.006 1.006 B 1.006 1.036 1.006 1.006 1.006 1.006 1.006

Ukraine

Total: Arr/Dep

L 1.006 1.031 1.006 1.006 1.006 1.006 1.006

Leap year effect

Adjustments to the overall number of movements have been made to allow for the 2012 and 2016 leap years.



Airport switches

The Medium-Term Forecast in 2007 was the first MTF to adopt a technique which allows traffic to be switched between airports (see Figure 37). Similarly as in 2010 the following assumptions have been used for this forecast:

The closure of Berlin/Tegel (EDDT), with traffic moving to Berlin/Schönefeld (EDDB): shift of traffic starting in 2011, completed13 in 2012.

The growth of Istanbul/Sabiha Gokcen (LTFJ) as a secondary airport relieving Istanbul/Ataturk (LTBA). This is achieved by switching 1% of LTBA local traffic to LTFJ in the first year, increasing gradually to 10% in 2017.

The move of Fedex hub from Frankfurt (EDDF) to Cologne (EDDK) to be completed in 2011.

Figure 37. Airport traffic switch.

Source: EUROCONTROL Data and analysis Comments: Updated from MTF10 inputs Units: Airport Traffic Switching. Data last updated: 15/12/2010

Low Base High

2011 2012 2017 2011 2012 2017 2011 2012 2017Traffic Type Traffic Between And Region Move To

EDDT - EDDB 25% 100% . 25% 100% . 25% 100% .All LTBA ESRA Mediterranean LTFJ 1% . 10% 1% . 10% 1% . 10%

ESRA North-West EDDK 22% . . 22% . . 22% . .ESRA Mediterranean EDDK 15% . . 15% . . 15% . .

Cargo EDDF

North Atlantic EDDK 22% . . 22% . . 22% . .

C.7 Emission Trading Scheme (ETS)

The aviation industry will join the EU ETS from 2012. The effects of the ETS on traffic demand are now included in the STATFOR forecasts (see Ref. 7 for details of the method).

The air operators will need to surrender allowances for their CO2 emissions from 2012. Some of the allowances will be given for free; the rest will need to be bought from auctions or on the free market. The amount of allowances allocated to the airlines free of charge will be decided upon by the European Commission and will be calculated as a percentage of the historical aviation emissions as follows: the total amount of emission allowances allocated to the airlines shall be capped at a level of 95-97% of the historical aviation emissions (see Figure 38 for the respective years’ figures), 15% of the allocated allowances shall be auctioned, the rest of the allocated allowances shall be distributed free of charge14.

13 It is not clear when the switch and completion will happen, but the assumptions for MTF11 input are based on the above mentioned dates. 14 Directive 2008/101/EC gives more detail on the allocation of allowances. Mainly, it states the procedure for the operators to apply for the free allocations and the method for distributing them; it specifies the role of the Member States in auctioning of allowance. These being intended to be distributed for free are not further considered in the cost modelling.



Figure 38. ETS characteristics

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: 15/12/2010

2010 2011 2012 2013 2014 2015 2016 2017Low 100% 100% 97% 95% 95% 95% 95% 95%Base 100% 100% 97% 95% 95% 95% 95% 95%

Cap

High 100% 100% 97% 95% 95% 95% 95% 95%Low 0% 0% 15% 15% 15% 15% 15% 15%Base 0% 0% 15% 15% 15% 15% 15% 15%

Auction

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

Applying the percentages shown in Figure 38 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 emissions15, the total amount the operators will 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 39. The prices start at €11-21 per tonne of CO2 in 2012 and increase to €29-54 in 2017. They correspond to current market situation: in November 2010, the price of carbon contracts for emissions in 2012 is around €15 per tonne (source: Point Carbon).

Figure 39. Prices

Source: STATFOR Analysis and modelling Comments: Price in EUR per tonne CO2 Units: Price in EUR per tonne of CO2. Data last updated: 15/12/2010.

2010 2011 2012 2013 2014 2015 2016 2017Low 0 0 11 15 20 24 28 29Base 0 0 16 22 28 34 40 41High 0 0 21 29 36 44 52 54

There is no distinction between the price of CO2 on the free market and in auctions as these are expected to be relatively close. 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 40 by a set of flow specific price elasticities derived largely from an IATA publication (see Ref. 12).

Figure 40. 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: 15/12/2010

ESRA North-West

ESRA Mediterranean

ESRA East Other

Former CIS Region

North Atlantic

Mid-Atlantic

South-Atlantic

North-Africa

Southern Africa

Middle-East

Far-East Oceania

ESRA North-West -0.92 -0.84 -0.84 -0.84 -0.70 -0.70 -0.60 -0.60 -0.54 -0.54 -0.60 -0.72 -0.70ESRA Mediterranean -0.84 -0.92 -0.84 -0.84 -0.70 -0.70 -0.60 -0.60 -0.54 -0.54 -0.60 -0.72 -0.70ESRA East -0.84 -0.84 -0.92 -0.84 -0.59 -0.70 -0.60 -0.60 -0.54 -0.54 -0.60 -0.72 -0.70Other -0.84 -0.84 -0.84 -0.92 -0.59 -0.70 -0.70 -0.70 -0.54 -0.54 -0.70 -0.72 -0.70Former CIS Region -0.70 -0.70 -0.59 -0.59 -0.63 -0.36 -0.70 -0.70 -0.57 -0.57 -0.70 -0.36 -0.36North Atlantic -0.72 -0.72 -0.72 -0.72 -0.36 -0.70 -0.70 -0.70 -0.36 -0.36 -0.70 -0.66 -0.70Mid-Atlantic -0.70 -0.70 -0.70 -0.70 -0.36 -0.75 -0.70 -0.70 -0.36 -0.36 -0.70 -0.70 -0.83South-Atlantic -0.70 -0.70 -0.70 -0.70 -0.36 -0.83 -0.70 -0.70 -0.36 -0.36 -0.70 -0.70 -0.75North-Africa -0.60 -0.60 -0.60 -0.70 -0.70 -0.70 -0.36 -0.36 -0.70 -0.70 -0.70 -0.70 -0.70Southern Africa -0.60 -0.60 -0.60 -0.70 -0.70 -0.70 -0.36 -0.40 -0.70 -0.70 -0.70 -0.70 -0.70Middle-East -0.54 -0.54 -0.54 -0.54 -0.57 -0.36 -0.70 -0.70 -0.63 -0.57 -0.70 -0.36 -0.36Far-East -0.54 -0.54 -0.54 -0.54 -0.57 -0.36 -0.70 -0.70 -0.57 -0.63 -0.36 -0.36 -0.36Oceania -0.60 -0.60 -0.60 -0.70 -0.70 -0.70 -0.70 -0.70 -0.70 -0.36 -0.70 -0.70 -0.70

15 See Ref. 7 for future emissions calculation.


Figure 41. Growth in the ESRA.

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

t: IFR Flight Movements 2011-2017

ANNEX D. SUMMARY OF THE FORECAST FOR THE ESRA

EUROCONTROL Medium-Term Forecas


Figure 42. Traffic on the main flow categories for the ESRA.

IFR Movements(000s) Annual Growth

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

AAGR 2017/ 2010

H . . . . . 7,911 8,218 8,499 8,778 9,060 9,355 9,582 . . . . . 4.6% 3.9% 3.4% 3.3% 3.2% 3.3% 2.4% 3.4%

B 7,903 8,241 8,182 7,602 7,562 7,829 8,066 8,215 8,397 8,584 8,770 8,909 3.4% 4.3% -0.7% -7.1% -0.5% 3.5% 3.0% 1.8% 2.2% 2.2% 2.2% 1.6% 2.4%

Total: Internal

L . . . . . 7,760 7,956 8,028 8,148 8,279 8,415 8,482 . . . . . 2.6% 2.5% 0.9% 1.5% 1.6% 1.6% 0.8% 1.7%

H . . . . . 1,964 2,077 2,200 2,326 2,453 2,592 2,726 . . . . . 8.2% 5.7% 6.0% 5.7% 5.5% 5.7% 5.2% 6.0%

B 1,590 1,721 1,807 1,711 1,815 1,944 2,038 2,122 2,214 2,306 2,405 2,493 4.7% 8.2% 5.0% -5.3% 6.1% 7.1% 4.8% 4.1% 4.3% 4.2% 4.3% 3.7% 4.6%

Total: Arr/Dep

L . . . . . 1,927 2,012 2,073 2,144 2,215 2,293 2,362 . . . . . 6.1% 4.4% 3.0% 3.4% 3.3% 3.5% 3.0% 3.8%

H . . . . . 132 139 147 155 163 172 182 . . . . . 14% 5.7% 5.5% 5.4% 5.3% 5.8% 5.4% 6.7%

B 67 81 94 100 116 129 136 142 148 155 162 169 14% 20% 16% 7.1% 15% 12% 5.2% 4.5% 4.6% 4.4% 4.8% 4.4% 5.6%

Total: Overflight

L . . . . . 126 132 137 143 148 154 160 . . . . . 9.3% 4.5% 3.8% 4.0% 3.8% 4.1% 3.7% 4.7%

H . . . . . 10,007 10,433 10,846 11,258 11,676 12,120 12,490 . . . . . 5.4% 4.3% 4.0% 3.8% 3.7% 3.8% 3.1% 4.0%

B 9,561 10,043 10,083 9,413 9,493 9,902 10,240 10,479 10,760 11,045 11,337 11,571 3.7% 5.0% 0.4% -6.6% 0.8% 4.3% 3.4% 2.3% 2.7% 2.7% 2.6% 2.1% 2.9%

Grand Total

L . . . . . 9,813 10,100 10,238 10,435 10,643 10,861 11,004 . . . . . 3.4% 2.9% 1.4% 1.9% 2.0% 2.1% 1.3% 2.1%





Figure 43. Traffic and growth on the biggest region-to-region flows through the ESRA.

IFR Movements(000s) Annual Growth

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

AAGR 2017/ 2010

H . . . . . 3738.6 3851.8 3944.1 4033.2 4125.4 4220.2 4277.3 . . . . . 4.6% 3.0% 2.4% 2.3% 2.3% 2.3% 1.4% 2.6%

B 3934.1 3995.6 3973.1 3665.3 3573.2 3705.2 3795.0 3840.9 3897.2 3957.0 4012.7 4048.5 1.4% 1.6% -0.6% -7.7% -2.5% 3.7% 2.4% 1.2% 1.5% 1.5% 1.4% 0.9% 1.8%

1 ESRA North-W ESRA North-W

L . . . . . 3675.7 3754.7 3776.2 3813.8 3856.2 3898.3 3899.6 . . . . . 2.9% 2.1% 0.6% 1.0% 1.1% 1.1% 0.0% 1.3%

H . . . . . 1673.3 1738.7 1801.7 1860.9 1919.3 1981.0 2025.3 . . . . . 6.1% 3.9% 3.6% 3.3% 3.1% 3.2% 2.2% 3.6%

B 1606.0 1709.5 1684.3 1548.8 1576.8 1654.8 1705.4 1738.1 1777.3 1816.8 1855.0 1882.1 2.3% 6.4% -1.5% -8.0% 1.8% 4.9% 3.1% 1.9% 2.3% 2.2% 2.1% 1.5% 2.6%

2 ESRA Mediter ESRA North-W

L . . . . . 1638.6 1679.7 1694.7 1720.2 1745.9 1772.3 1782.4 . . . . . 3.9% 2.5% 0.9% 1.5% 1.5% 1.5% 0.6% 1.8%

H . . . . . 1508.6 1563.3 1629.0 1694.9 1760.0 1830.7 1893.6 . . . . . 2.8% 3.6% 4.2% 4.0% 3.8% 4.0% 3.4% 3.7%

B 1471.4 1573.3 1518.2 1445.0 1466.9 1492.5 1528.7 1561.9 1604.9 1648.6 1696.5 1734.8 7.0% 6.9% -3.5% -4.8% 1.5% 1.7% 2.4% 2.2% 2.7% 2.7% 2.9% 2.3% 2.4%

3 ESRA Mediter ESRA Mediter

L . . . . . 1479.9 1502.5 1511.4 1535.1 1565.3 1599.6 1628.5 . . . . . 0.9% 1.5% 0.6% 1.6% 2.0% 2.2% 1.8% 1.5%

H . . . . . 538.3 575.3 602.3 631.4 661.2 691.8 719.5 . . . . . 5.4% 6.9% 4.7% 4.8% 4.7% 4.6% 4.0% 5.0%

B 495.5 528.6 560.0 513.5 510.6 530.7 561.9 577.7 596.7 615.6 633.9 649.8 8.5% 6.7% 5.9% -8.3% -0.6% 3.9% 5.9% 2.8% 3.3% 3.2% 3.0% 2.5% 3.5%

4 ESRA East ESRA North-W

L . . . . . 524.6 553.0 563.7 578.0 592.0 605.4 615.5 . . . . . 2.7% 5.4% 1.9% 2.5% 2.4% 2.3% 1.7% 2.7%

H . . . . . 327.1 336.3 347.9 359.9 372.4 386.2 399.4 . . . . . 6.2% 2.8% 3.5% 3.4% 3.5% 3.7% 3.4% 3.8%

B 321.2 339.6 341.4 309.1 308.1 324.6 331.5 338.5 346.3 354.0 362.3 369.6 2.8% 5.7% 0.5% -9.5% -0.3% 5.4% 2.1% 2.1% 2.3% 2.2% 2.3% 2.0% 2.6%

5 ESRA North-W North Atlant

L . . . . . 322.1 328.2 332.9 338.4 343.9 349.9 354.7 . . . . . 4.5% 1.9% 1.4% 1.7% 1.6% 1.7% 1.3% 2.0%

H . . . . . 242.4 258.3 273.1 289.0 305.3 322.2 337.7 . . . . . 3.2% 6.6% 5.7% 5.8% 5.7% 5.5% 4.8% 5.3%

B 227.4 240.2 248.5 237.8 234.9 239.3 251.9 261.1 271.7 282.2 292.6 301.8 6.2% 5.6% 3.5% -4.3% -1.2% 1.9% 5.3% 3.7% 4.0% 3.9% 3.7% 3.1% 3.6%

6 ESRA East ESRA East

L . . . . . 236.8 247.6 254.4 262.5 270.7 278.5 285.1 . . . . . 0.8% 4.6% 2.7% 3.2% 3.1% 2.9% 2.4% 2.8%

H . . . . . 251.0 266.3 282.9 298.8 315.3 334.4 353.4 . . . . . 9.8% 6.1% 6.2% 5.6% 5.5% 6.0% 5.7% 6.4%

B 185.6 202.5 210.9 212.5 228.5 248.8 262.1 274.0 287.1 299.9 314.0 327.9 5.0% 9.1% 4.1% 0.8% 7.5% 8.9% 5.3% 4.5% 4.8% 4.5% 4.7% 4.4% 5.3%

7 ESRA North-W North-Africa

L . . . . . 247.1 259.5 269.1 280.1 291.1 304.0 315.5 . . . . . 8.1% 5.0% 3.7% 4.1% 3.9% 4.5% 3.8% 4.7%

H . . . . . 210.2 230.2 248.7 268.4 288.4 309.3 328.9 . . . . . 5.4% 9.5% 8.1% 7.9% 7.5% 7.2% 6.3% 7.4%

B 168.8 194.1 198.0 191.5 199.3 206.9 223.6 235.5 249.6 264.2 279.0 291.5 15% 15% 2.0% -3.3% 4.1% 3.8% 8.1% 5.3% 6.0% 5.9% 5.6% 4.5% 5.6%

8 ESRA East ESRA Mediter

L . . . . . 204.2 218.9 227.4 238.1 249.3 260.4 270.8 . . . . . 2.4% 7.2% 3.9% 4.7% 4.7% 4.5% 4.0% 4.5%




ANNEX E. FUTURE TRAFFIC AND GROWTH

E.1 Summary of the Forecast. Annual IFR Movements 2006-2017.

Figure 44. Annual traffic per traffic zone and 2011-2017 average annual growth.

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

AAGR

2017/

2010

H . . . . . 202 219 231 242 252 264 275 6.1%

B 119 142 148 161 181 199 215 222 230 238 246 253 4.9%

Albania

L . . . . . 197 212 216 222 228 235 240 4.1%

H . . . . . 59 63 68 74 79 85 91 8.0%

B 43 48 52 48 53 58 62 66 70 74 79 83 6.7%

Armenia

L . . . . . 57 61 64 67 71 75 78 5.7%

H . . . . . 1,205 1,273 1,333 1,393 1,452 1,514 1,570 4.7%

B 1,092 1,180 1,204 1,113 1,137 1,191 1,247 1,283 1,322 1,362 1,402 1,435 3.4%

Austria

L . . . . . 1,179 1,229 1,251 1,280 1,309 1,338 1,360 2.6%

H . . . . . 127 133 144 155 167 180 192 6.9%

B 92 95 108 108 120 125 130 138 147 156 167 176 5.6%

Azerbaijan

L . . . . . 124 128 135 142 149 158 166 4.7%

H . . . . . 220 233 250 266 282 299 315 7.0%

B 146 173 199 182 196 218 228 239 251 263 275 285 5.5%

Belarus

L . . . . . 216 225 233 242 251 260 268 4.5%

H . . . . . 1,105 1,143 1,184 1,224 1,264 1,306 1,344 3.8%

B 1,056 1,100 1,108 1,020 1,035 1,093 1,124 1,148 1,175 1,201 1,229 1,252 2.8%

Belgium/Luxembourg

L . . . . . 1,083 1,109 1,124 1,143 1,162 1,183 1,197 2.1%

H . . . . . 278 302 320 338 356 375 393 6.7%

B 168 201 218 224 250 275 295 306 319 332 345 356 5.2%

Bosnia-Herzegovina

L . . . . . 272 290 298 308 317 327 335 4.3%

H . . . . . 538 573 615 656 695 737 775 6.3%

B 402 444 478 477 504 531 560 587 618 648 679 703 4.9%

Bulgaria

L . . . . . 526 551 572 596 620 644 666 4.1%

H . . . . . 290 303 314 326 338 351 362 4.0%

B 303 308 307 267 275 287 296 301 307 313 319 324 2.4%

Canary Islands

L . . . . . 284 291 292 296 299 302 304 1.4%

H . . . . . 497 537 569 600 630 661 689 6.0%

B 339 398 422 422 459 491 525 545 567 589 610 628 4.6%

Croatia

L . . . . . 486 516 530 547 563 579 592 3.7%

H . . . . . 308 327 347 368 388 411 432 6.1%

B 217 242 272 268 285 303 320 333 348 363 379 393 4.7%

Cyprus

L . . . . . 300 314 324 335 347 359 370 3.8%

H . . . . . 716 754 790 827 865 904 940 5.0%

B 612 646 682 648 668 707 739 761 785 808 833 854 3.6%

Czech Republic

L . . . . . 700 729 743 761 778 796 810 2.8%



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

AAGR

2017/

2010

H . . . . . 633 661 681 701 722 744 764 3.6%

B 602 631 629 576 595 626 650 661 674 688 701 712 2.6%

Denmark

L . . . . . 621 643 649 659 669 679 686 2.1%

H . . . . . 170 180 190 201 212 224 235 6.0%

B 137 153 174 153 156 168 176 183 191 198 206 214 4.6%

Estonia

L . . . . . 166 173 178 185 191 197 202 3.7%

H . . . . . 130 136 143 150 158 165 172 4.7%

B 118 123 125 125 125 128 132 137 142 147 153 157 3.3%

FYROM

L . . . . . 127 130 133 138 142 146 149 2.6%

H . . . . . 262 271 279 288 297 306 315 3.8%

B 246 245 261 240 242 259 266 271 277 283 289 294 2.8%

Finland

L . . . . . 257 263 267 271 276 280 283 2.3%

H . . . . . 2,978 3,096 3,201 3,297 3,395 3,502 3,587 3.6%

B 2,854 3,025 3,020 2,801 2,794 2,946 3,041 3,097 3,164 3,231 3,297 3,348 2.6%

France

L . . . . . 2,919 3,000 3,028 3,073 3,120 3,171 3,194 1.9%

H . . . . . 108 116 125 135 144 154 164 8.2%

B 73 80 80 77 94 107 113 120 128 136 144 151 6.9%

Georgia

L . . . . . 106 112 117 123 130 136 143 6.1%

H . . . . . 3,160 3,301 3,429 3,558 3,689 3,828 3,951 4.1%

B 2,971 3,108 3,151 2,930 2,981 3,124 3,240 3,313 3,397 3,481 3,568 3,638 2.9%

Germany

L . . . . . 3,094 3,197 3,241 3,302 3,362 3,424 3,469 2.2%

H . . . . . 670 706 741 776 811 848 882 4.3%

B 566 621 643 638 655 662 691 712 738 762 788 809 3.1%

Greece

L . . . . . 655 678 692 711 729 748 764 2.2%

H . . . . . 651 684 723 763 803 846 886 5.2%

B 605 615 622 608 622 643 668 694 721 749 778 803 3.7%

Hungary

L . . . . . 636 658 676 698 719 741 759 2.9%

H . . . . . 107 115 121 127 133 139 145 5.2%

B 100 105 110 101 102 106 113 117 121 125 130 134 4.0%

Iceland

L . . . . . 105 111 114 117 121 124 127 3.2%

H . . . . . 548 569 592 616 642 671 697 4.5%

B 565 598 601 530 513 543 558 571 586 601 617 632 3.0%

Ireland

L . . . . . 538 551 559 569 579 591 598 2.2%

H . . . . . 1,807 1,879 1,961 2,043 2,127 2,217 2,299 4.3%

B 1,641 1,779 1,736 1,647 1,712 1,788 1,843 1,887 1,941 1,997 2,053 2,100 3.0%

Italy

L . . . . . 1,772 1,814 1,834 1,867 1,902 1,939 1,966 2.0%

H . . . . . 235 249 264 280 296 312 328 6.2%

B 176 202 225 206 214 232 244 254 265 277 288 298 4.8%

Latvia

L . . . . . 230 240 248 257 266 274 282 4.0%

H . . . . . 454 470 486 503 516 530 546 3.5%

B 401 427 438 406 429 448 460 468 474 480 491 499 2.2%

Lisbon FIR

L . . . . . 444 452 452 455 461 468 471 1.3%



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

AAGR

2017/

2010

H . . . . . 223 237 251 265 279 294 307 5.9%

B 173 195 219 192 206 221 233 242 252 261 271 279 4.4%

Lithuania

L . . . . . 218 229 236 244 251 258 264 3.6%

H . . . . . 104 111 118 125 132 141 149 6.7%

B 76 82 84 85 95 103 108 113 118 123 129 135 5.1%

Malta

L . . . . . 102 107 110 114 118 122 126 4.1%

H . . . . . 63 69 75 80 86 92 98 8.8%

B 28 35 41 44 54 63 68 72 76 80 85 89 7.2%

Moldova

L . . . . . 62 67 70 73 77 80 83 6.3%

H . . . . . 1,082 1,123 1,158 1,193 1,229 1,266 1,294 3.6%

B 1,056 1,108 1,090 996 1,013 1,071 1,106 1,127 1,150 1,173 1,198 1,218 2.7%

Netherlands

L . . . . . 1,062 1,094 1,106 1,123 1,139 1,157 1,165 2.0%

H . . . . . 566 585 602 619 637 657 674 3.3%

B 513 536 550 526 537 561 576 584 595 605 616 624 2.2%

Norway

L . . . . . 558 570 575 582 589 597 601 1.6%

H . . . . . 648 701 738 778 819 862 902 6.0%

B 491 556 612 566 599 641 687 710 737 765 792 816 4.5%

Poland

L . . . . . 634 678 694 715 736 756 773 3.7%

H . . . . . 492 521 558 595 634 675 716 6.2%

B 416 432 444 434 470 486 508 532 559 586 614 639 4.5%

Romania

L . . . . . 481 500 518 538 559 581 601 3.6%

H . . . . . 122 128 134 139 145 152 158 4.3%

B 107 109 116 113 118 121 126 129 133 138 142 146 3.1%

Santa Maria FIR

L . . . . . 120 124 126 129 132 135 137 2.2%

H . . . . . 586 628 665 701 737 775 808 5.8%

B 393 458 497 513 543 578 614 637 663 689 716 738 4.5%

Serbia&Montenegro

L . . . . . 572 604 621 641 661 682 699 3.7%

H . . . . . 389 412 435 458 482 507 530 5.3%

B 330 324 345 337 370 385 403 417 434 450 467 481 3.8%

Slovakia

L . . . . . 381 397 407 420 432 445 455 3.0%

H . . . . . 350 370 390 409 427 447 464 5.1%

B 267 306 327 313 328 345 361 374 387 400 414 424 3.7%

Slovenia

L . . . . . 341 356 364 374 384 394 402 2.9%

H . . . . . 1,695 1,759 1,821 1,885 1,946 2,013 2,069 3.7%

B 1,641 1,779 1,747 1,581 1,608 1,674 1,721 1,753 1,788 1,823 1,861 1,895 2.4%

Spain

L . . . . . 1,656 1,692 1,700 1,716 1,737 1,764 1,777 1.4%

H . . . . . 698 735 761 787 819 847 873 4.0%

B 689 708 736 654 664 692 723 740 758 781 799 815 3.0%

Sweden

L . . . . . 686 716 727 741 760 774 786 2.4%

H . . . . . 1,078 1,119 1,158 1,196 1,233 1,274 1,310 3.6%

B 1,032 1,093 1,096 1,018 1,025 1,066 1,099 1,120 1,145 1,170 1,196 1,216 2.5%

Switzerland

L . . . . . 1,055 1,084 1,095 1,113 1,130 1,149 1,162 1.8%


2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2017

AAGR

2017/

2010 2016

H . . . . . 1,047 1,129 1,218 1,302 1,386 1,478 1,556 7.1%

B 693 757 822 857 965 1,035 1,104 1,167 1,240 1,312 1,389 1,448 6.0%

Turkey

L . . . . . 1,026 1,089 1,138 1,199 1,261 1,326 1,387 5.3%

H . . . . . 474 512 547 585 623 663 702 7.3%

B 345 373 406 378 429 469 502 527 557 586 617 644 6.0%

Ukraine

L . . . . . 465 495 514 539 563 587 610 5.1%

H . . . . . 2,290 2,365 2,433 2,501 2,568 2,639 2,674 3.0%

B 2,465 2,550 2,514 2,278 2,181 2,272 2,330 2,371 2,421 2,469 2,516 2,555 2.3%

UK

L . . . . . 2,253 2,304 2,327 2,361 2,395 2,430 2,430 1.6%

H . . . . . 9,882 10,298 10,703 11,106 11,514 11,949 12,311 4.0%

B 9,439 9,916 9,954 9,301 9,367 9,779 10,108 10,342 10,615 10,894 11,179 11,407 2.9%

ESRA02

L . . . . . 9,690 9,970 10,104 10,295 10,497 10,711 10,849 2.1%

H . . . . . 9,277 9,657 10,022 10,388 10,757 11,148 11,473 3.9%

B 8,937 9,441 9,470 8,787 8,805 9,179 9,479 9,686 9,925 10,170 10,417 10,617 2.7%

EU27

L . . . . . 9,094 9,348 9,460 9,622 9,795 9,976 10,083 2.0%

H . . . . . 10,007 10,433 10,846 11,258 11,676 12,120 12,490 4.0%

B 9,561 10,043 10,083 9,413 9,493 9,902 10,240 10,479 10,760 11,045 11,337 11,571 2.9%

ESRA08

L . . . . . 9,813 10,100 10,238 10,435 10,643 10,861 11,004 2.1%

H . . . . . 9,658 10,051 10,430 10,811 11,194 11,600 11,939 3.8%

B 9,337 9,794 9,833 9,152 9,171 9,557 9,867 10,082 10,331 10,585 10,842 11,050 2.7%

SES

L . . . . . 9,470 9,732 9,850 10,019 10,199 10,387 10,501 2.0%



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

AAGR

2017/

2010

H . . . . . 730 780 821 866 913 960 1,005 5.8%

B . . 704 644 679 722 763 790 821 852 883 910 4.3%

Baltic

L . . . . . 715 753 772 796 820 843 862 3.5%

H . . . . . 2,434 2,547 2,668 2,789 2,912 3,044 3,165 4.6%

B . . 2,310 2,225 2,308 2,407 2,495 2,565 2,648 2,732 2,819 2,891 3.3%

Blue Med

L . . . . . 2,384 2,453 2,492 2,547 2,605 2,666 2,713 2.3%

H . . . . . 787 841 900 960 1,019 1,082 1,141 6.5%

B . . 690 687 734 777 821 860 903 946 991 1,028 4.9%

Danube

L . . . . . 769 809 837 871 905 939 971 4.1%

H . . . . . 1,977 2,092 2,197 2,303 2,410 2,522 2,624 5.0%

B . . 1,904 1,806 1,864 1,953 2,047 2,111 2,183 2,254 2,326 2,387 3.6%

FAB CE

L . . . . . 1,933 2,017 2,059 2,112 2,164 2,217 2,259 2.8%

H . . . . . 5,762 5,991 6,204 6,410 6,619 6,842 7,027 3.7%

B . . 5,816 5,406 5,431 5,701 5,886 6,005 6,144 6,283 6,423 6,534 2.7%

FAB EC

L . . . . . 5,648 5,809 5,877 5,975 6,072 6,177 6,237 2.0%

H . . . . . 1,609 1,675 1,733 1,790 1,854 1,916 1,973 3.8%

B . . 1,616 1,487 1,519 1,594 1,648 1,681 1,719 1,760 1,799 1,831 2.7%

NEFAB

L . . . . . 1,581 1,630 1,651 1,679 1,711 1,740 1,760 2.1%

H . . . . . 1,915 1,985 2,055 2,127 2,195 2,271 2,335 3.6%

B . . 1,969 1,786 1,820 1,892 1,942 1,977 2,017 2,057 2,100 2,138 2.3%

SW Portugal - Spain

L . . . . . 1,872 1,910 1,919 1,938 1,962 1,991 2,006 1.4%

H . . . . . 2,327 2,403 2,474 2,545 2,615 2,690 2,729 3.0%

B . 2,559 2,316 2,216 2,308 2,367 2,409 2,460 2,510 2,559 2,599 2.3%

UK-Ireland

L . . . . . 2,288 2,340 2,364 2,399 2,433 2,470 2,470 1.6%

H . . . . . 1,010 1,055 1,090 1,124 1,164 1,202 1,236 3.8%

B . . 1,031 931 953 1,001 1,038 1,059 1,082 1,109 1,134 1,153 2.8%

DK-SE

L . . . . . 992 1,027 1,040 1,057 1,079 1,098 1,111 2.2%

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



E.2 Summary of the Forecast. Growth Rates 2006-2017

Figure 45. Annual growth rates per traffic zone and 2011-2017 average annual growth.

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

AAGR 2017/ 2010

H . . . . . 11% 8.9% 5.1% 4.7% 4.5% 4.7% 4.0% 6.1%

B 2.7% 19% 4.5% 8.9% 12% 9.8% 7.9% 3.3% 3.6% 3.5% 3.5% 2.8% 4.9%

Albania

L . . . . . 8.6% 7.4% 2.2% 2.8% 2.7% 2.8% 2.2% 4.1%

H . . . . . 11% 8.0% 8.1% 7.5% 7.3% 7.4% 6.8% 8.0%

B 2.6% 11% 8.0% -6.7% 9.3% 9.5% 7.0% 6.2% 6.3% 6.1% 6.1% 5.5% 6.7%

Armenia

L . . . . . 8.2% 6.4% 5.2% 5.3% 5.2% 5.2% 4.7% 5.7%

H . . . . . 6.0% 5.6% 4.7% 4.5% 4.3% 4.3% 3.7% 4.7%

B 4.1% 8.1% 2.0% -7.6% 2.2% 4.7% 4.7% 2.9% 3.1% 3.0% 2.9% 2.3% 3.4%

Austria

L . . . . . 3.7% 4.2% 1.8% 2.3% 2.2% 2.2% 1.6% 2.6%

H . . . . . 5.5% 4.9% 8.2% 7.8% 7.5% 7.7% 6.9% 6.9%

B . 4.3% 13% 0.5% 11% 4.2% 3.9% 6.2% 6.4% 6.3% 6.6% 5.6% 5.6%

Azerbaijan

L . . . . . 3.1% 3.4% 5.1% 5.4% 5.3% 5.5% 5.0% 4.7%

H . . . . . 12% 5.8% 7.2% 6.5% 6.2% 5.9% 5.4% 7.0%

B 13% 18% 16% -8.6% 7.7% 11% 4.8% 5.0% 4.9% 4.6% 4.4% 3.8% 5.5%

Belarus

L . . . . . 9.9% 4.2% 3.7% 4.0% 3.7% 3.5% 3.0% 4.5%

H . . . . . 6.7% 3.5% 3.5% 3.3% 3.3% 3.3% 2.9% 3.8%

B 4.9% 4.2% 0.7% -7.9% 1.5% 5.6% 2.8% 2.1% 2.3% 2.3% 2.3% 1.9% 2.8%

Belgium/Luxembourg

L . . . . . 4.6% 2.5% 1.3% 1.7% 1.7% 1.7% 1.2% 2.1%

H . . . . . 11% 8.5% 6.1% 5.6% 5.3% 5.4% 4.7% 6.7%

B 5.5% 19% 8.5% 3.1% 11% 9.9% 7.3% 4.0% 4.2% 4.0% 3.9% 3.2% 5.2%

Bosnia-Herzegovina

L . . . . . 8.7% 6.7% 2.8% 3.3% 3.1% 3.1% 2.5% 4.3%

H . . . . . 6.8% 6.6% 7.3% 6.7% 6.0% 6.0% 5.1% 6.3%

B 1.6% 11% 7.7% -0.2% 5.6% 5.5% 5.4% 5.0% 5.2% 4.8% 4.8% 3.6% 4.9%

Bulgaria

L . . . . . 4.4% 4.8% 3.8% 4.2% 4.0% 4.0% 3.4% 4.1%

H . . . . . 5.5% 4.2% 3.8% 3.7% 3.7% 3.8% 3.1% 4.0%

B 4.0% 1.7% -0.2% -13% 3.2% 4.2% 3.1% 1.7% 2.1% 2.0% 2.1% 1.6% 2.4%

Canary Islands

L . . . . . 3.1% 2.5% 0.5% 1.2% 1.1% 1.2% 0.5% 1.4%

H . . . . . 8.5% 7.9% 6.0% 5.3% 5.0% 5.0% 4.3% 6.0%

B 2.4% 17% 6.0% 0.1% 8.7% 7.1% 6.8% 3.9% 4.0% 3.8% 3.6% 2.9% 4.6%

Croatia

L . . . . . 6.0% 6.2% 2.7% 3.2% 3.0% 2.9% 2.3% 3.7%

H . . . . . 8.1% 6.4% 6.1% 5.9% 5.5% 5.8% 5.1% 6.1%

B 4.1% 12% 12% -1.7% 6.4% 6.6% 5.5% 4.2% 4.4% 4.2% 4.4% 3.6% 4.7%

Cyprus

L . . . . . 5.2% 4.8% 3.2% 3.5% 3.3% 3.5% 3.1% 3.8%




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

AAGR 2017/ 2010

H . . . . . 7.1% 5.4% 4.8% 4.7% 4.5% 4.6% 4.0% 5.0%

B 2.4% 5.6% 5.5% -5.0% 3.2% 5.8% 4.5% 2.9% 3.2% 3.0% 3.0% 2.5% 3.6%

Czech Republic

L . . . . . 4.8% 4.1% 1.9% 2.4% 2.3% 2.3% 1.7% 2.8%

H . . . . . 6.4% 4.4% 3.0% 2.9% 3.0% 3.1% 2.7% 3.6%

B 2.9% 4.8% -0.3% -8.5% 3.3% 5.3% 3.8% 1.7% 2.0% 2.0% 2.0% 1.6% 2.6%

Denmark

L . . . . . 4.4% 3.6% 1.0% 1.4% 1.5% 1.5% 1.1% 2.1%

H . . . . . 8.8% 5.8% 5.9% 5.7% 5.5% 5.4% 5.0% 6.0%

B -5.7% 12% 13% -12% 2.1% 7.4% 4.9% 4.0% 4.2% 4.1% 4.0% 3.5% 4.6%

Estonia

L . . . . . 6.2% 4.5% 3.0% 3.4% 3.3% 3.2% 2.7% 3.7%

H . . . . . 4.2% 4.3% 5.3% 5.0% 4.9% 4.9% 4.2% 4.7%

B 6.8% 3.9% 2.0% -0.1% -0.1% 2.7% 3.2% 3.5% 3.8% 3.7% 3.6% 3.0% 3.3%

FYROM

L . . . . . 1.5% 2.6% 2.6% 3.1% 2.9% 2.9% 2.4% 2.6%

H . . . . . 8.4% 3.3% 3.1% 3.1% 3.2% 3.2% 2.7% 3.8%

B 1.4% -0.3% 6.3% -7.7% 0.6% 7.2% 2.7% 1.8% 2.1% 2.1% 2.1% 1.6% 2.8%

Finland

L . . . . . 6.3% 2.5% 1.2% 1.6% 1.7% 1.6% 1.1% 2.3%

H . . . . . 6.6% 4.0% 3.4% 3.0% 3.0% 3.2% 2.4% 3.6%

B 3.9% 6.0% -0.2% -7.3% -0.2% 5.5% 3.2% 1.8% 2.2% 2.1% 2.0% 1.5% 2.6%

France

L . . . . . 4.5% 2.8% 1.0% 1.5% 1.5% 1.6% 0.7% 1.9%

H . . . . . 15% 7.0% 8.0% 7.6% 7.1% 7.0% 6.0% 8.2%

B -1.2% 9.7% -0.0% -3.6% 22% 13% 6.1% 6.1% 6.3% 6.1% 6.1% 4.9% 6.9%

Georgia

L . . . . . 12% 5.7% 5.0% 5.3% 5.2% 5.1% 4.6% 6.1%

H . . . . . 6.0% 4.5% 3.9% 3.8% 3.7% 3.8% 3.2% 4.1%

B 4.2% 4.6% 1.4% -7.0% 1.7% 4.8% 3.7% 2.3% 2.5% 2.5% 2.5% 2.0% 2.9%

Germany

L . . . . . 3.8% 3.4% 1.4% 1.9% 1.8% 1.9% 1.3% 2.2%

H . . . . . 2.4% 5.3% 5.0% 4.8% 4.4% 4.6% 3.9% 4.3%

B 3.2% 9.9% 3.4% -0.8% 2.6% 1.2% 4.3% 3.1% 3.5% 3.3% 3.4% 2.7% 3.1%

Greece

L . . . . . 0.1% 3.4% 2.1% 2.7% 2.6% 2.7% 2.1% 2.2%

H . . . . . 4.7% 5.0% 5.8% 5.5% 5.3% 5.3% 4.7% 5.2%

B 4.4% 1.8% 1.1% -2.3% 2.4% 3.4% 4.0% 3.8% 4.0% 3.8% 3.9% 3.2% 3.7%

Hungary

L . . . . . 2.3% 3.5% 2.7% 3.1% 3.0% 3.1% 2.5% 2.9%

H . . . . . 5.4% 7.4% 5.3% 4.8% 4.7% 4.8% 4.5% 5.2%

B 4.1% 5.7% 4.2% -7.8% 0.6% 4.3% 6.4% 3.6% 3.6% 3.4% 3.5% 3.1% 4.0%

Iceland

L . . . . . 3.3% 6.0% 2.6% 2.8% 2.6% 2.7% 2.3% 3.2%

H . . . . . 6.8% 3.7% 4.0% 4.1% 4.2% 4.5% 3.9% 4.5%

B 5.4% 5.9% 0.5% -12% -3.1% 5.8% 2.8% 2.3% 2.6% 2.6% 2.7% 2.3% 3.0%

Ireland

L . . . . . 4.8% 2.4% 1.4% 1.8% 1.8% 2.0% 1.3% 2.2%



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

AAGR 2017/ 2010

H . . . . . 5.6% 4.0% 4.4% 4.2% 4.1% 4.2% 3.7% 4.3%

B 4.0% 8.4% -2.4% -5.1% 3.9% 4.5% 3.1% 2.4% 2.8% 2.9% 2.8% 2.3% 3.0%

Italy

L . . . . . 3.5% 2.4% 1.1% 1.8% 1.9% 2.0% 1.4% 2.0%

H . . . . . 9.6% 5.9% 6.2% 5.9% 5.7% 5.5% 5.1% 6.2%

B 13% 15% 11% -8.4% 4.0% 8.2% 5.0% 4.2% 4.5% 4.3% 4.0% 3.5% 4.8%

Latvia

L . . . . . 7.1% 4.7% 3.2% 3.7% 3.5% 3.2% 2.8% 4.0%

H . . . . . 5.7% 3.5% 3.5% 3.5% 2.6% 2.7% 3.0% 3.5%

B 5.7% 6.3% 2.7% -7.2% 5.6% 4.4% 2.5% 1.7% 1.4% 1.3% 2.2% 1.7% 2.2%

Lisbon FIR

L . . . . . 3.4% 1.9% -0.2% 0.7% 1.4% 1.5% 0.7% 1.3%

H . . . . . 8.5% 6.3% 5.9% 5.5% 5.3% 5.1% 4.6% 5.9%

B 6.0% 13% 12% -12% 7.3% 7.2% 5.4% 3.9% 4.1% 3.9% 3.6% 3.1% 4.4%

Lithuania

L . . . . . 6.1% 5.1% 2.9% 3.3% 3.1% 2.8% 2.3% 3.6%

H . . . . . 9.7% 6.3% 6.3% 6.1% 6.0% 6.5% 5.9% 6.7%

B 0.2% 8.1% 3.4% 0.7% 12% 8.3% 5.4% 4.2% 4.5% 4.4% 4.8% 4.2% 5.1%

Malta

L . . . . . 7.1% 4.8% 3.1% 3.5% 3.4% 3.8% 3.3% 4.1%

H . . . . . 17% 9.3% 7.6% 7.4% 7.1% 7.1% 6.6% 8.8%

B 7.8% 25% 18% 6.7% 24% 15% 8.3% 5.5% 5.8% 5.6% 5.6% 5.0% 7.2%

Moldova

L . . . . . 14% 7.9% 4.3% 4.8% 4.6% 4.6% 4.2% 6.3%

H . . . . . 6.8% 3.8% 3.1% 3.0% 3.0% 3.0% 2.2% 3.6%

B 6.0% 4.9% -1.6% -8.6% 1.7% 5.8% 3.2% 1.8% 2.1% 2.0% 2.1% 1.7% 2.7%

Netherlands

L . . . . . 4.9% 3.0% 1.1% 1.5% 1.4% 1.6% 0.7% 2.0%

H . . . . . 5.4% 3.3% 2.9% 2.9% 2.9% 3.1% 2.6% 3.3%

B 6.1% 4.5% 2.6% -4.4% 2.2% 4.5% 2.6% 1.5% 1.8% 1.7% 1.8% 1.3% 2.2%

Norway

L . . . . . 3.8% 2.2% 0.9% 1.2% 1.2% 1.3% 0.8% 1.6%

H . . . . . 8.3% 8.3% 5.2% 5.4% 5.3% 5.2% 4.7% 6.0%

B 16% 13% 10% -7.6% 5.8% 7.0% 7.3% 3.3% 3.9% 3.7% 3.5% 3.1% 4.5%

Poland

L . . . . . 6.0% 6.8% 2.4% 3.0% 2.9% 2.7% 2.2% 3.7%

H . . . . . 4.9% 5.8% 7.0% 6.8% 6.5% 6.6% 6.0% 6.2%

B 1.3% 4.0% 2.7% -2.3% 8.2% 3.5% 4.6% 4.7% 5.0% 4.8% 4.9% 4.1% 4.5%

Romania

L . . . . . 2.4% 4.0% 3.5% 4.0% 3.9% 3.9% 3.4% 3.6%

H . . . . . 4.0% 4.6% 4.3% 4.2% 4.2% 4.5% 4.0% 4.3%

B 0.9% 1.7% 6.5% -2.6% 4.5% 2.8% 3.8% 2.9% 3.2% 3.0% 3.2% 2.8% 3.1%

Santa Maria FIR

L . . . . . 1.7% 3.3% 1.8% 2.3% 2.2% 2.5% 1.8% 2.2%

H . . . . . 7.8% 7.1% 5.9% 5.4% 5.1% 5.2% 4.3% 5.8%

B 8.7% 16% 8.6% 3.3% 5.9% 6.4% 6.1% 3.9% 4.1% 3.9% 3.9% 3.1% 4.5%

Serbia&Montenegro

L . . . . . 5.3% 5.6% 2.8% 3.3% 3.1% 3.1% 2.6% 3.7%


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

AAGR 2017/ 2010

H . . . . . 5.2% 5.7% 5.6% 5.4% 5.2% 5.2% 4.6% 5.3%

B 3.8% -1.6% 6.4% -2.4% 9.9% 3.9% 4.7% 3.6% 3.9% 3.7% 3.7% 3.1% 3.8%

Slovakia

L . . . . . 2.8% 4.2% 2.6% 3.1% 2.9% 2.9% 2.4% 3.0%

H . . . . . 6.5% 5.7% 5.4% 4.9% 4.6% 4.6% 3.8% 5.1%

B 3.7% 15% 6.8% -4.2% 4.8% 5.1% 4.7% 3.5% 3.6% 3.4% 3.3% 2.6% 3.7%

Slovenia

L . . . . . 4.0% 4.1% 2.4% 2.8% 2.7% 2.6% 2.0% 2.9%

H . . . . . 5.4% 3.8% 3.6% 3.5% 3.2% 3.4% 2.8% 3.7%

B 5.1% 8.4% -1.8% -9.5% 1.8% 4.1% 2.8% 1.8% 2.0% 2.0% 2.1% 1.8% 2.4%

Spain

L . . . . . 3.0% 2.2% 0.5% 0.9% 1.2% 1.5% 0.7% 1.4%

H . . . . . 5.1% 5.2% 3.5% 3.5% 4.0% 3.5% 3.1% 4.0%

B 3.8% 2.8% 3.9% -11% 1.5% 4.1% 4.6% 2.3% 2.4% 3.0% 2.4% 2.0% 3.0%

Sweden

L . . . . . 3.3% 4.3% 1.6% 1.9% 2.5% 1.9% 1.5% 2.4%

H . . . . . 5.1% 3.8% 3.5% 3.3% 3.1% 3.3% 2.8% 3.6%

B 2.4% 5.9% 0.3% -7.1% 0.7% 4.0% 3.1% 1.9% 2.2% 2.2% 2.2% 1.7% 2.5%

Switzerland

L . . . . . 2.9% 2.7% 1.1% 1.6% 1.6% 1.7% 1.1% 1.8%

H . . . . . 8.5% 7.9% 7.9% 6.9% 6.5% 6.6% 5.3% 7.1%

B 8.0% 9.2% 8.6% 4.2% 13% 7.3% 6.7% 5.6% 6.3% 5.8% 5.9% 4.2% 6.0%

Turkey

L . . . . . 6.3% 6.1% 4.5% 5.4% 5.1% 5.2% 4.6% 5.3%

H . . . . . 10% 8.0% 6.8% 7.0% 6.6% 6.4% 5.8% 7.3%

B 1.3% 8.2% 8.7% -6.9% 14% 9.2% 7.0% 5.0% 5.6% 5.3% 5.2% 4.5% 6.0%

Ukraine

L . . . . . 8.2% 6.5% 4.0% 4.7% 4.5% 4.3% 3.9% 5.1%

H . . . . . 5.0% 3.2% 2.9% 2.8% 2.7% 2.8% 1.3% 3.0%

B 3.4% 3.5% -1.4% -9.4% -4.3% 4.2% 2.6% 1.7% 2.1% 2.0% 1.9% 1.5% 2.3%

UK

L . . . . . 3.3% 2.3% 1.0% 1.5% 1.4% 1.5% -0.0% 1.6%

H . . . . . 5.5% 4.2% 3.9% 3.8% 3.7% 3.8% 3.0% 4.0%

B 3.9% 5.1% 0.4% -6.6% 0.7% 4.4% 3.4% 2.3% 2.6% 2.6% 2.6% 2.0% 2.9%

ESRA02

L . . . . . 3.4% 2.9% 1.3% 1.9% 2.0% 2.0% 1.3% 2.1%

H . . . . . 5.4% 4.1% 3.8% 3.7% 3.5% 3.6% 2.9% 3.9%

B 3.5% 5.6% 0.3% -7.2% 0.2% 4.2% 3.3% 2.2% 2.5% 2.5% 2.4% 1.9% 2.7%

EU27

L . . . . . 3.3% 2.8% 1.2% 1.7% 1.8% 1.8% 1.1% 2.0%

H . . . . . 5.4% 4.3% 4.0% 3.8% 3.7% 3.8% 3.1% 4.0%

B 3.7% 5.0% 0.4% -6.6% 0.8% 4.3% 3.4% 2.3% 2.7% 2.7% 2.6% 2.1% 2.9%

ESRA08

L . . . . . 3.4% 2.9% 1.4% 1.9% 2.0% 2.1% 1.3% 2.1%

H . . . . . 5.3% 4.1% 3.8% 3.6% 3.5% 3.6% 2.9% 3.8%

B 3.6% 4.9% 0.4% -6.9% 0.2% 4.2% 3.2% 2.2% 2.5% 2.5% 2.4% 1.9% 2.7%

SES

L . . . . . 3.3% 2.8% 1.2% 1.7% 1.8% 1.8% 1.1% 2.0%



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

AAGR 2017/ 2010

H . . . . . 7.5% 6.8% 5.3% 5.5% 5.3% 5.2% 4.7% 5.8%

B . . . -8.6% 5.5% 6.3% 5.8% 3.5% 3.9% 3.8% 3.6% 3.1% 4.3%

Baltic

L . . . . . 5.2% 5.3% 2.5% 3.1% 3.0% 2.8% 2.3% 3.5%

H . . . . . 5.5% 4.6% 4.7% 4.6% 4.4% 4.5% 4.0% 4.6%

B . . . -3.7% 3.7% 4.3% 3.7% 2.8% 3.2% 3.2% 3.2% 2.6% 3.3%

Blue Med

L . . . . . 3.3% 2.9% 1.6% 2.2% 2.3% 2.3% 1.8% 2.3%

H . . . . . 7.3% 6.9% 7.0% 6.6% 6.2% 6.2% 5.4% 6.5%

B . . . -0.4% 6.7% 5.9% 5.7% 4.7% 5.0% 4.7% 4.8% 3.8% 4.9%

Danube

L . . . . . 4.8% 5.2% 3.6% 4.0% 3.9% 3.9% 3.3% 4.1%

H . . . . . 6.1% 5.8% 5.1% 4.8% 4.6% 4.6% 4.0% 5.0%

B . . . -5.1% 3.2% 4.8% 4.8% 3.1% 3.4% 3.3% 3.2% 2.6% 3.6%

FAB CE

L . . . . . 3.7% 4.3% 2.1% 2.6% 2.5% 2.4% 1.9% 2.8%

H . . . . . 6.1% 4.0% 3.6% 3.3% 3.2% 3.4% 2.7% 3.7%

B . . . -7.0% 0.5% 5.0% 3.2% 2.0% 2.3% 2.3% 2.2% 1.7% 2.7%

FAB EC

L . . . . . 4.0% 2.9% 1.2% 1.7% 1.6% 1.7% 1.0% 2.0%

H . . . . . 5.9% 4.1% 3.4% 3.3% 3.5% 3.4% 2.9% 3.8%

B . . . -8.0% 2.2% 4.9% 3.4% 2.0% 2.2% 2.4% 2.2% 1.7% 2.7%

NEFAB

L . . . . . 4.1% 3.1% 1.3% 1.7% 1.9% 1.7% 1.2% 2.1%

H . . . . . 5.2% 3.6% 3.5% 3.5% 3.2% 3.5% 2.8% 3.6%

B . . . -9.3% 1.9% 4.0% 2.7% 1.8% 2.0% 2.0% 2.1% 1.8% 2.3%

SW Portugal - Spain

L . . . . . 2.9% 2.0% 0.5% 1.0% 1.2% 1.5% 0.7% 1.4%

H . . . . . 5.0% 3.3% 3.0% 2.9% 2.8% 2.9% 1.5% 3.0%

B . . 3E8% -9.5% -4.3% 4.1% 2.6% 1.8% 2.1% 2.0% 1.9% 1.6% 2.3%

UK-Ireland

L . . . . . 3.3% 2.3% 1.0% 1.5% 1.4% 1.5% 0.0% 1.6%

H . . . . . 6.0% 4.4% 3.3% 3.2% 3.6% 3.2% 2.8% 3.8%

B . . . -9.7% 2.4% 5.0% 3.8% 2.0% 2.2% 2.6% 2.2% 1.7% 2.8%

DK-SE

L . . . . . 4.1% 3.5% 1.3% 1.7% 2.1% 1.7% 1.2% 2.2%





ANNEX F. REFERENCES

Electronic versions of Refs. 1, 4, 5, 6, are available on www.eurocontrol.int/statfor.

1 EUROCONTROL Medium-Term Forecast Update, Flight Movements (2010-2016), EUROCONTROL STATFOR, September 2010, STATFOR Doc404 [MTF10b].

2 STATFOR Interactive Dashboard (SID) accessible at www.eurocontrol.int/statfor/sid

3 www.eurocontrol.int/statfor

4 EUROCONTROL Long-Term Forecast Update, Flight Movements (2010-2030), EUROCONTROL STATFOR Doc415, December 2010 [LTF10].

5 Challenges of Growth 2008, Summary Report, EUROCONTROL, November 2008.

6 EUROCONTROL Medium-Term Forecast, Flight Movements (2010-2016), EUROCONTROL STATFOR Doc378, February 2010 [MTF10].

7 Estimated effects of ETS on passenger demand for flights in 2012-2016, EUROCONTROL STATFOR Doc365, March 2010.

8 EUROCONTROL Glossary for Flight Statistics & Forecasts, EUROCONTROL STATFOR Doc87, January 2005.

9 STATFOR Geographical Hierarchy, EUROCONTROL STATFOR Doc172, February 2009.

10 Low-Cost Panel v5, EUROCONTROL STATFOR Doc258, September 2007.

11 Air Traffic Review and Airport Capacity Assessment associated with the London 2012 Olympics and Paralympics, UK Department of Transport, prepared by Atkins Ltd, February 2010.

12 Estimating Air Travel Demand Elasticities, Prepared by InterVISTAS Consulting Inc. for IATA, December 2007.

http://www.eurocontrol.int/statfor

http://www.eurocontrol.int/statfor

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

© 2011 - 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

EUROCONTROL.

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