Aviation and the Global Atmosphere

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9.4. Long-Term Emissions Scenarios

Long-term projections to the year 2050 producing 3-D emissions data have been made by the Forecasting and Economic Analysis Subgroup of CAEP, using the NASA studies as a base (CAEP/4-FESG, 1998), and by DTI using the ANCAT studies as a base (Newton and Falk, 1997). Long-term projections of total demand, fuel consumption, and emissions (but not providing 3-D data) have also been made by EDF (Vedantham and Oppenheimer, 1994, 1998), WWF (Barrett, 1994), and MIT (Schafer and Victor, 1997).

Table 9-9: Summary of IPCC GDP scenarios used in FESG model.
  Average Annual Global GDP Growth
Rate Scenario 1990-2025 1990-2100
IS92a 2.9% 2.3%

Predictions of traffic demand and resulting emissions beyond 2015 become increasingly uncertain because the probability for unforeseeable major changes in key factors influencing the results steadily increases. The best approach for insight into the evolution of long-term futures is the application of scenarios. A scenario is simply a set of assumptions devised to reflect the possible development of a particular situation over time. These assumptions are used as inputs to a model that describes the manner in which an activity might develop over time. A range of possible futures can be described by a set of independent scenarios. The results of the scenario are difficult to judge in terms of confidence level: They are simply the outcome of input assumptions. However, scenarios can be objectively judged as implausible by showing that their assumptions or outcomes conflict with industry trends or with invariant rules and laws that might reasonably be expected to remain unchanged during the scenario time period or by revealing internal inconsistencies or incompatibilities with other dominating external developments. Investigation of the consequences and implications of scenarios can be used to support a subjective assessment regarding which of the remaining possible scenarios might be more plausible than others.

Table 9-10: Traffic projections and 5-year average growth rates from FESG (CAEP/4 - FESG Report 4, 1998).
Fa Fa Fc Fc Fe Fe
  Demand Growth Rate Demand Growth Rate Demand Growth Rate
Year (109 RPK) (%) (109 RPK) (%) (109 RPK) (%)
1995 2,536.6a 2,536.6a 2,536.6a
2000 3,238.0 5.0 3,068.8 3.9 3,336.1 5.6
2005 3,981.4 4.2 3,591.9 3.2 4,322.4 5.3
2010 4,782.6 3.7 4,103.0 2.7 5,491.7 4.9
2015 5,638.6 3.3 4,596.1 2.3 6,876.2 4.6
2020 6,552.9 3.1 5,070.7 2.0 8,302.4 3.8
2025 7,533.6 2.8 5,530.7 1.8 9,908.5 3.6
2030 8,592.7 2.7 5,981.4 1.6 11,727.0 3.4
2035 9,744.9 2.5 6,429.8 1.5 13,794.9 3.3
2040 11,006.8 2.5 6,881.5 1.4 16,155.8 3.2
2045 12,396.5 2.4 7,342.5 1.3 18,864.2 3.1
2050 13,933.5 2.4 7,817.2 1.3 21,978.2 3.1

Figure 9-16: ICAO/FESG traffic demand scenarios to
2050 (based on IPCC IS92a, IS92c, and IS92e).


Figure 9-17: Average traffic growth rates from FESG


Figure 9-18: Fuel efficiency trends to 2050
corresponding to the two ICCAIA technology scenarios
for the FESG high traffic demand case.


Figure 9-19: Fleet average trends in EI(NOx) showing
projections for the two ICCAIA technology scenarios.

9.4.1. FESG 2050 Scenarios Development of Traffic Projection Model

In developing long-term traffic scenarios, various models of traffic demand were considered (CAEP/4-FESG, 1998), particularly those incorporating a market maturity concept. Under this concept, historical traffic growth rates in excess of economic growth are considered unlikely to continue indefinitely, and traffic growth will eventually approach a rate equal to GDP growth as the various global markets approached maturity. Based on this assumption, a single global model of traffic demand per unit of GDP was developed, based on a logistics growth curve function:

t = time
RPK = revenue passenger-km
GDP = gross domestic product

The parameters in the model equation were estimated from historic values of RPK/GDP for the period 1960 through 1995. No constraints were imposed on the values the parameters could take. Further details of the modeling process appear in CAEP/4-FESG (1998). Table 9-9 lists the GDP growth assumptions used in developing these scenarios (Leggett et al., 1992). The key assumptions of this approach follow:

  • The world can be treated as a single, gradually maturing aviation market that is the sum of regional markets at various stages of maturity.
  • Historical values of world demand and GDP over time provide sufficient information about the stage of development of the industry to provide reliable estimates of market maturity.
  • Business and personal travel sectors can be combined.
  • Global traffic growth is driven primarily by global GDP; as markets mature, overall passenger growth rates will eventually grow in line with GDP growth.
  • Fuel will be available, and fuel prices will not increase greatly relative to other costs.
  • Whatever aviation technological or regulatory changes occur, they will have no significant impact on ticket prices, demand, or service availability.
  • Infrastructure will be sufficient to handle demand.
  • There will be no significant impacts from other travel modes (e.g., high-speed rail) or alternative technologies (e.g., telecommunications).

Perhaps the most critical assumption of this methodology was that historical global traffic totals contained sufficient information about the maturity of the industry as a whole to provide a reasonable basis upon which long-term aviation trends could be projected. There is a question of whether the signals of recent years (i.e., that overall traffic growth is slowing) are sufficiently robust to provide a reliable indication of future long-term growth. A related concern is that historical world traffic totals are dominated by OECD experience, thus may not adequately capture the potential for growth in other, less-developed regions (CAEP/4-FESG, 1998). To a large extent, the FESG scenarios for 2050 reflect assumptions of no fundamental change in overall revenue/cost structure trends of the aviation industry and no fundamental changes in the trends in technology or society. They also assume that the growth of air traffic demand will not be significantly constrained by other limiting factors. Sections 9.6.5 and 9.6.6 examine the availability of infrastructure and fuel with regard to the plausibility of all of the long-term scenario projections.

Growth rates from the model were applied to 1995 reported world traffic demand (Boeing, 1996)-together with GDP growth rates from the IPCC IS92a, IS92e, and IS92c scenarios-to produce FESG base case (Fa), high (Fe), and low (Fc) scenarios of scheduled traffic demand. The high case (Fe) was adjusted slightly to match the NASA traffic forecast for 2015 on which the NASA 2015 emission inventory was based. The basis for the NASA 2015 traffic forecasts were GDP forecasts that were similar to the IS92e GDP scenario (Boeing, 1996). The resulting traffic demand and average growth rate for the three 2050 scenarios are illustrated in Figures 9-16 and 9-17 and listed in Table 9-10. The traffic demand scenarios have been labeled Fa through Fe for brevity; these labels, when combined with the appropriate technology assumption designator (1 or 2; see Section, form the complete designator for the FESG scenarios used throughout the rest of this report.

Global traffic from the model projections was apportioned over 45 regional traffic flows with a separate market share model because certain regions grow faster than others, and the correct distribution of traffic is important in the calculation of the effects of emissions on the atmosphere. In this procedure, regional traffic flows were expressed as a share of the global market; using the market share and historical growth patterns ensures consistency between regional flows and the global forecast. The underlying assumption of this procedure is that each regional share approaches its ultimate share of the total market asymptotically. Mature markets tend to have declining shares approaching an asymptotic value, whereas developing markets tend to increase their shares. Adjustments of traffic flows were made so that the "top-down" traffic projections of the FESG global model were matched by a reasonable "bottom-up" distribution of regional traffic flows. These traffic flows include all traffic in all regions, and regional variations in growth rates are highlighted. Factors that affect the operations of military and general aviation aircraft were also estimated, and projections were made of the growth of these sectors (CAEP/4-FESG, 1998).

Table 9-11: ICCAIA NOx and fuel-efficiency technology assumptions for 2050.
Technology Scenario Fuel Efficiency Increase by 2050 LTO NOx Levels
Design for fuel efficiency and NOx reduction Average of production aircraft will be 40-50% better relative to 1997 levels Fleet average will be 10-30% below CAEP/2 limit by 2050; fleet average EI(NOx) = 15.5 in 2050
Design for aggressive NOx reduction Average of production aircraft will be 30-40% better relative to 1997 levels Average of production aircraft will be 30-50% below CAEP/2 limit by 2020 and 50-70% below CAEP/2 limit by 2050; fleet average

Table 9-12: Projected scheduled fleet fuel efficiency (Sutkus, 1997).
  Scheduled Fleet Fuel
Efficiency (ASK kg-1 Fuel)
2015 NASA Inventory 41.8
Traffic Scenario Technology
Scenario 1
Scenario 2
Demand scenario Fa 53.6 51.8
Demand scenario Fc 53.1 51.4
Demand scenario Fe 54.0 52.0

Table 9-13: Results of FESG year 2050 scenarios calculations.
Sector Fa1 Fa2 Fc1 Fc2 Fe1 Fe2
Calculated Fuel Burned (Tg)
Scheduled 396.1 410.8 224.0 232.3 620.0 643.9
Charter 21.4 22.2 12.1 12.6 33.5 34.8
FSU/China 30.3 31.4 8.8 9.1 67.5 70.1
General Aviation 8.8 8.8 8.8 8.8 8.8 8.8
Civil Subtotal 456.6 473.2 253.8 262.8 729.8 757.7
Military 14.4 14.4 14.4 14.4 14.4 14.4
Global Total 471.0 487.6 268.2 277.2 744.3 772.1
Calculated CO2 Emissions (Tg C)
Scheduled 340.7 353.3 192.7 199.7 533.2 553.7
Charter 18.4 19.1 10.4 10.8 28.8 29.9
FSU/China 26.0 27.0 7.5 7.8 58.1 60.3
General Aviation 7.6 7.6 7.6 7.6 7.6 7.6
Civil Subtotal 392.7 407.0 218.2 226.0 627.7 651.6
Military 12.4 12.4 12.4 12.4 12.4 12.4
Global Total 405.1 419.4 230.6 238.4 640.1 664.0
Calculated NOx Emissions (Tg as NO2)
Scheduled 6.1 4.7 3.5 2.7 9.6 7.4
Charter 0.4 0.3 0.2 0.2 0.6 0.4
FSU/China 0.5 0.3 0.1 0.1 1.0 0.8
General Aviation 0.1 0.1 0.1 0.1 0.1 0.1
Civil Subtotal 7.0 5.4 3.9 3.0 11.3 8.7
Military 0.1 0.1 0.1 0.1 0.1 0.1
Global Total 7.2 5.5 4.0 3.1 11.4 8.8
Calculated Fleet Average EI(NOx) [g NOx (as NO2) kg-1 fuel burned]
Scheduled 15.5 11.5 15.5 11.5 15.5 11.5
Charter 16.7 12.4 16.7 12.4 16.8 12.4
FSU/China 14.9 11.1 14.9 11.1 14.9 11.0
General Aviation 9.0 9.0 9.0 9.0 9.0 9.0
Civil Subtotal 15.4 11.5 15.3 11.4 15.4 11.5
Military 8.7 8.7 8.7 8.7 8.7 8.7
Global Total 15.2 11.4 15.0 11.3 15.3 11.4

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