**4. Analysis**

*Environmental Impact of Aviation and Sustainable Solutions*

[28]. The charges are computed in year 2016 US dollars (USD).

nents, the charges are computed in year 2016 US dollars.

within both origin and destination regions is used.

Navigation charges are based on the EUROCONTROL model using the average unit rate weighted by the number of landings in all European countries in 2008

Airport charges and ground handling charges are based on the methodology of Ploetner et al. [39]. Airport charges are composed of landing charges, passenger charges, navigation aid charges, lighting charges, terminal charges and service charges. The method was based also on data from 2008. Like other cost compo-

In the fleet model, flights could be either within a region or between two regions.

Using available data from Aircraft Commerce [40] for the initial fleet aircraft, the method recommended by the Association of European Airlines (AEA method) [31, 32] is adopted because it uses aircraft parameters such as aircraft operating weight empty, engine bypass ratio, etc. Other parameters such as aircraft price are obtained from the ownership cost model already explained. Furthermore, for the engine price [year 1989 USD], the approach by Jenkinson et al. [41] is used, which calculates engine price in year 1995 British pounds based on specific fuel consumption [lb/lbf/h] and cruise thrust [Ma]. The engine bare price [year 1989 USD] is then obtained after the price in year 1995 British pounds is first converted to year

The AEA method assumed mature levels of cost, i.e. after 5–7 years of operation. Using ageing function from Strohrmann [42], based on Dixon [43], DMC values for other years of the aircraft lifetime are determined. Furthermore, input labour rate value given by the AEA in 1989 is used and converted to 2016 USD. Due to lack of data, this is assumed to be constant over time and independent of route although DMC labour rate varies over time and with world region [44]. A limitation of the AEA method is that it does not hold for engines with thrust above 30 metric tonnes. Furthermore, since the method was developed to give comparable results to aircraft operated by airlines in 1989, the method cannot be directly used for next-generation aircraft considered in this work. Therefore, improvement factors are used which

correlate nonfuel COC of initial fleet aircraft to next-generation ones.

Aircraft Commerce (ACC). This is shown in **Figure 4**.

<sup>1</sup> Correspondence on 13 February 2018 with Aircraft Commerce.

they are obtained from maintenance providers.1

Since the AEA method for computing aircraft DMC is evaluated in year 1989 USD, an inflation factor is used to adjust the costs to year 2016 USD. Direct maintenance costs per flight cycle of representative aircraft of the initial fleet, determined using AEA method, were compared with corresponding cost values published by

Cost values published by ACC can be taken as representative of the industry since

ACC values increased with increasing MTOW. A higher difference can be expected for aircraft with first flights made after the AEA publication. From **Figure 4**, the AEA method for DMC computation produced aircraft DMC values at most 22% higher than those of ACC. Compared to cost levels given by IATA's MCTF [45], the

The difference between AEA and

For flights belonging to the latter category, the average value of airport charges

*3.3.2.3 Navigation charges*

*3.3.2.4 Airport charges*

*3.3.2.5 Direct maintenance costs*

1995 USD and then to year 1989 USD.

**154**

#### **4.1 Longer-term fleet development strategies**

If the order of implementing the last two of the three main steps of fleet renewal presented in sub-section B of the previous section is modified, two fleet renewal strategies can be defined on an FSDM route. These are shown in **Figure 5**.

The *Growth Strategy* prioritises aircraft allocation for serving demand growth and replacing aircraft retired at the end of their design lives, before replacing those that are retired because of their operating cost disadvantage. This strategy is assumed a status quo strategy used in the airline industry. This is because aircraft manufacturers claim that more than half of aircraft deliveries forecast for the next two decades are to accommodate growth in air travel demand (ATR, 65%; Embraer, 63% and 56%2 ; Boeing, 57%; Airbus, 63%) rather than replace existing aircraft (ATR, 35%; Embraer, 37% and 44%; Boeing, 43%; Airbus, 37%).3 This is also

<sup>2</sup> For 70–130 seat jet segment and turboprop segment, respectively.

<sup>3</sup> Values derived from ATR's turboprop market forecast 2016–2035, Embraer's market outlook 2017, Boeing current market outlook 2017–2036 and Airbus Global Market Forecast 2017–2036.

#### **Figure 5.** *Strategies for fleet renewal on a route.*

assumed because airlines tend to delay replacement of their older aircraft when jet fuel prices are low [48], meaning that a complete economic retirement of aircraft has not been performed industry-wide.

Conversely, a more radical strategy, the *Replacement Strategy*, allocates global aircraft production capacity first for replacing aircraft that are retired based on their operating cost disadvantage and those retired on reaching their design life limit, before serving growth in air travel demand.

#### **4.2 Calibration using growth strategy**

FSDM was calibrated to generate fleet composition results similar to Boeing's forecast for 2036 while reflecting verifying the historical development for the jet aircraft fleet.

#### *4.2.1 Assumed input*

Passenger and freight load factors from 2008 to 2016 are taken from IATA reports [49], without differentiating between route groups. While passenger load factor increased from 76% in 2008 to 80.3% in 2016, freight load factor reduced from 46% in 2008 to 43% in 2016. In 2017, freight load factor is assumed to be slightly higher due to the entry of LCCs into the cargo business and other reasons given by JADC [50]. After 2017, freight load factor is assumed to be stable at 47.7%. However, the development in freight traffic is beyond the scope of this work. In addition, JADC [50] forecasts that passenger load factor is set to increase from 80.3% in 2016 to 83.3% in 2036.

Fuel prices (with units in year 2016 US dollars) were derived for years 1968 until 2016 using US GDP deflator values [27] and US Gulf Coast Kerosene-Type Jet Fuel Spot Price [51]. It was assumed that jet fuel prices were constant until year 1990 since there was no major difference between the average US Kerosene-Type Jet Fuel Wholesale and Resale Price by Refiners between 1978 and 1990 [52].

For years 2016–2036, low- and high-fuel price scenarios by Boeing are used as shown in **Figure 6**. The scenario of fuel price that was used in the Boeing CMO was not stated. The low-fuel price forecast assumed that fuel price in 2030 would

**157**

route groups.

**Figure 6.**

*Increasing the Emission Mitigation Potential by Employing an Economically Optimised…*

be similar to 2005 price levels. On the other hand, Boeing's high-fuel price scenario assumes that fuel price in 2018 will rise close to 2008 price level while further rising beyond 2012 price level in 2030. Same RPK growth factors on route groups were assumed for both fuel price scenarios as given in the CMO report. Fuel prices after 2030 are assumed to be stable at 2.08 and 3.1452016 US dollars per gallon in the low

These fuel price scenarios differ from Airbus fuel price forecast which assumes

Maximum aircraft upgauge was assumed to be 20%. In addition, the utilisation of an aircraft type is modelled to vary between route groups, with a possibility of increasing annually. According to Boeing [54], passenger airplane utilisation increased between 2008 and 2015. A study of the growth in airplane utilisation at aircraft cluster level between year 2008 and 2014 revealed that the growth occurred mostly for turboprop commuter, jet commuter, narrow-body and midrange aircraft cluster between years 2008 and 2014, whereas long-range aircraft utilisation increased between 2012 and 2014 [55]. For a given year, an increase in airplane utilisation results in lower unit costs and trip costs because fixed ownership costs are spread over an increased number of trips [56]. Considering that portions of flight crew and cabin crew costs as well as maintenance costs are possible components of fixed costs [9], it is assumed that higher airplane utilisation results in lower direct operating costs. Therefore, the unit DOC of a particular aircraft type with the same payload varies with different levels of utilisation on different

A fleet forecast also uses an assumption on development of aircraft productivity,

which, according to Evans and Johnson [57], is influenced by load factor, average block speed, annual utilisation and number of seats per aircraft. Because the updated FSDM is not capable of modelling dynamically changing average block speed or number of seats per aircraft every year, aircraft productivity growth is

In their forecast, Boeing assumed older aircraft would have lower utilisation compared to newer aircraft [58]. Although an increase in passenger load factor is expected as explained above, additional annual ASK productivity growth of aircraft is modelled for next-generation aircraft as 0.9% as used by Evans and Johnson [57], with the exception of next-generation regional aircraft assumed to have a higher annual growth rate of 1.3%. For initial fleet aircraft, a lower annual growth rate of 0.35% is assumed according to Boeing's assumptions. The 0.35% growth rate is adopted from the assumption of Forsberg [59]. These values are considered conservative when considering the compound annual growth rates of initial fleet aircraft

productivity between 2008 and 2014 as evaluated by Bellhäuser [55].

medium fuel price close to 2010 price levels in 2025 [53] (see **Figure 6**).

*DOI: http://dx.doi.org/10.5772/intechopen.88219*

and high-fuel price forecasts, respectively.

*Historical and forecast fuel price scenarios by Airbus and Boeing [27].*

modelled as growth in annual flight frequencies.

*Increasing the Emission Mitigation Potential by Employing an Economically Optimised… DOI: http://dx.doi.org/10.5772/intechopen.88219*

**Figure 6.**

*Environmental Impact of Aviation and Sustainable Solutions*

**156**

**Figure 5.**

jet aircraft fleet.

*4.2.1 Assumed input*

80.3% in 2016 to 83.3% in 2036.

*Strategies for fleet renewal on a route.*

has not been performed industry-wide.

**4.2 Calibration using growth strategy**

limit, before serving growth in air travel demand.

assumed because airlines tend to delay replacement of their older aircraft when jet fuel prices are low [48], meaning that a complete economic retirement of aircraft

Conversely, a more radical strategy, the *Replacement Strategy*, allocates global aircraft production capacity first for replacing aircraft that are retired based on their operating cost disadvantage and those retired on reaching their design life

FSDM was calibrated to generate fleet composition results similar to Boeing's forecast for 2036 while reflecting verifying the historical development for the

Passenger and freight load factors from 2008 to 2016 are taken from IATA reports [49], without differentiating between route groups. While passenger load factor increased from 76% in 2008 to 80.3% in 2016, freight load factor reduced from 46% in 2008 to 43% in 2016. In 2017, freight load factor is assumed to be slightly higher due to the entry of LCCs into the cargo business and other reasons given by JADC [50]. After 2017, freight load factor is assumed to be stable at 47.7%. However, the development in freight traffic is beyond the scope of this work. In addition, JADC [50] forecasts that passenger load factor is set to increase from

Fuel prices (with units in year 2016 US dollars) were derived for years 1968 until 2016 using US GDP deflator values [27] and US Gulf Coast Kerosene-Type Jet Fuel Spot Price [51]. It was assumed that jet fuel prices were constant until year 1990 since there was no major difference between the average US Kerosene-Type Jet Fuel

For years 2016–2036, low- and high-fuel price scenarios by Boeing are used as shown in **Figure 6**. The scenario of fuel price that was used in the Boeing CMO was not stated. The low-fuel price forecast assumed that fuel price in 2030 would

Wholesale and Resale Price by Refiners between 1978 and 1990 [52].

*Historical and forecast fuel price scenarios by Airbus and Boeing [27].*

be similar to 2005 price levels. On the other hand, Boeing's high-fuel price scenario assumes that fuel price in 2018 will rise close to 2008 price level while further rising beyond 2012 price level in 2030. Same RPK growth factors on route groups were assumed for both fuel price scenarios as given in the CMO report. Fuel prices after 2030 are assumed to be stable at 2.08 and 3.1452016 US dollars per gallon in the low and high-fuel price forecasts, respectively.

These fuel price scenarios differ from Airbus fuel price forecast which assumes medium fuel price close to 2010 price levels in 2025 [53] (see **Figure 6**).

Maximum aircraft upgauge was assumed to be 20%. In addition, the utilisation of an aircraft type is modelled to vary between route groups, with a possibility of increasing annually. According to Boeing [54], passenger airplane utilisation increased between 2008 and 2015. A study of the growth in airplane utilisation at aircraft cluster level between year 2008 and 2014 revealed that the growth occurred mostly for turboprop commuter, jet commuter, narrow-body and midrange aircraft cluster between years 2008 and 2014, whereas long-range aircraft utilisation increased between 2012 and 2014 [55]. For a given year, an increase in airplane utilisation results in lower unit costs and trip costs because fixed ownership costs are spread over an increased number of trips [56]. Considering that portions of flight crew and cabin crew costs as well as maintenance costs are possible components of fixed costs [9], it is assumed that higher airplane utilisation results in lower direct operating costs. Therefore, the unit DOC of a particular aircraft type with the same payload varies with different levels of utilisation on different route groups.

A fleet forecast also uses an assumption on development of aircraft productivity, which, according to Evans and Johnson [57], is influenced by load factor, average block speed, annual utilisation and number of seats per aircraft. Because the updated FSDM is not capable of modelling dynamically changing average block speed or number of seats per aircraft every year, aircraft productivity growth is modelled as growth in annual flight frequencies.

In their forecast, Boeing assumed older aircraft would have lower utilisation compared to newer aircraft [58]. Although an increase in passenger load factor is expected as explained above, additional annual ASK productivity growth of aircraft is modelled for next-generation aircraft as 0.9% as used by Evans and Johnson [57], with the exception of next-generation regional aircraft assumed to have a higher annual growth rate of 1.3%. For initial fleet aircraft, a lower annual growth rate of 0.35% is assumed according to Boeing's assumptions. The 0.35% growth rate is adopted from the assumption of Forsberg [59]. These values are considered conservative when considering the compound annual growth rates of initial fleet aircraft productivity between 2008 and 2014 as evaluated by Bellhäuser [55].

Lastly, after 2022, production capacity of all aircraft types is assumed to grow at an annual rate of 4.7%, same as Boeing's projected worldwide growth rate for air passenger traffic. This growth rate is arguably reasonable because over the period from 2008 to 2016, total aircraft production capacity has also grown at an average of 4.7% per year.

#### *4.2.2 Model calibration objective*

Boeing assumed in their forecast that some trends would continue. For example, they assumed that new markets that had previously been either unreachable or unprofitable, especially those that can be served by small wide-body aircraft, would open up [58]. Although the opening up of new markets is not modelled in FSDM, the effect of liberalisation, in terms of increased air traffic, is considered. These assumptions led to a forecast that the share of wide-body aircraft would increase from 19% in 2016 to 21% in 2036. As shown in **Figure 7**, this growth is driven by the growth in small twin-aisle aircraft.

This calibration work, therefore, has an objective goal of a higher preference for wide-body aircraft over narrow-body aircraft in 2050. Boeing categorised aircraft types into three groups. However, the method used in doing this was not explained. **Table 6** shows how FSDM aircraft clusters compare to the classifications.

Boeing also categorised the B777X, A350–1000 and B787–10 as M/L-TA aircraft which would be already in operation in year 2036. In the fleet model, however, these future aircraft types are not modelled as unique representative aircraft. This is mainly because the fuel burn performance of these aircraft types cannot be determined using the BADA version used in this work. Besides this, there is uncertainty about the future production capacities of these future aircraft types. Production capacities were assumed for the B777X and included in that of the NGLR, whereas, since the other two aircraft types are related to existing FSDM representative aircraft, special production capacities are not included. As a result, for calibration purposes, the production capacity share of the B777X in the NGLR is deducted from the total delivered aircraft in this aircraft cluster and its corresponding aircraft category (i.e. the S-TA) and added to the number of aircraft belonging to the category M/L-TA.

**159**

**Table 7.**

*Increasing the Emission Mitigation Potential by Employing an Economically Optimised…*

**Boeing category FSDM aircraft cluster** Single aisle (SA) JC, NB, NGJC, NGNB Small twin aisle (S-TA) MR, NGMR, NGLR Medium/large twin aisle (M/L-TA) LRC, LRH, LR NGLRH

Fleet composition in year 2036 is dependent primarily on the choice of aircraft during introduction to fill the capacity gap. Apart from cost improvements modelled in the aircraft, aircraft preference depends on fuel price (FP), depreciation period (DP) and planning horizon (PH) assumed during aircraft evaluation. To have a simplified approach in calibration, these variables are applied without differentiating between single- and twin-aisle aircraft. Upper and lower boundaries

Because of the high number of combinations possible if intermediate values of these variables are observed, simplifications are made in the calibration process by using only combinations involving the boundary values. Moreover, further assumptions were made in terms of cost improvements due to the increase in aircraft utilisation, leading to a preference for S-TA and M/L-TA. These are shown in the Appendix (see **Table A-2**). For each fuel burn scenario, four combinations of DP

The calibration results are shown in **Figure 8** for years 2008, 2016 and 2036. Because the long-term development is of interest, yearly changes in the results are

In 2008, FSDM's total fleet size, which is taken from the ACAS database, was 3% less than that of Boeing because the latter considered more aircraft types (e.g. twin-aisle aircraft like Ilyushin IL-86 and Lockheed L-1011 and single-aisle aircraft like Sukhoi Superjet 100,Yakovlev Yak-42, Mitsubishi MRJ, Dornier 328JET, Fokker 70, F28 and BAe 146). As a result, FSDM's SA fleet size was 10% less than that of Boeing. When weighted by their 75% share in the total aircraft fleet size, a difference of 7% results. Furthermore, for the S-TA and M/L-TA aircraft categories, respectively, with approximately 12% share each, percentage differences in fleet size estimates by Boeing and FSDM of 60 and 4% were estimated. When weighted by fleet size, differences of 7 and 0% result for the S-TA and M/L-TA, respectively. Therefore, a maximum difference of 7% is estimated between the fleet sizes in each category, when weighted by fleet share, based on Boeing's data and those of the

In 2016, some of these aircraft types, especially single aisles, not considered in FSDM initial fleet were in limited service. Therefore, the share of single-aisle aircraft

**Variable Low boundary Upper boundary** Fuel price scenario Boeing low fuel price Boeing high fuel price

Depreciation period 14 years 20 years Planning horizon 7 years 15 years

*Upper and lower boundary values of calibrated variables.*

*DOI: http://dx.doi.org/10.5772/intechopen.88219*

*Comparison of Boeing CMO to FSDM aircraft classification.*

for the variables are shown in **Table 7**.

and PH are used for calibration.

*4.2.3 Calibration results*

FSDM simulation year.

not shown.

**Table 6.**

**Figure 7.**

*Fleet size and composition according to Boeing in 2016 and 2036 [58].*

*Increasing the Emission Mitigation Potential by Employing an Economically Optimised… DOI: http://dx.doi.org/10.5772/intechopen.88219*


#### **Table 6.**

*Environmental Impact of Aviation and Sustainable Solutions*

4.7% per year.

*4.2.2 Model calibration objective*

growth in small twin-aisle aircraft.

belonging to the category M/L-TA.

*Fleet size and composition according to Boeing in 2016 and 2036 [58].*

Lastly, after 2022, production capacity of all aircraft types is assumed to grow at an annual rate of 4.7%, same as Boeing's projected worldwide growth rate for air passenger traffic. This growth rate is arguably reasonable because over the period from 2008 to 2016, total aircraft production capacity has also grown at an average of

Boeing assumed in their forecast that some trends would continue. For example,

This calibration work, therefore, has an objective goal of a higher preference for wide-body aircraft over narrow-body aircraft in 2050. Boeing categorised aircraft types into three groups. However, the method used in doing this was not explained.

Boeing also categorised the B777X, A350–1000 and B787–10 as M/L-TA aircraft

**Table 6** shows how FSDM aircraft clusters compare to the classifications.

which would be already in operation in year 2036. In the fleet model, however, these future aircraft types are not modelled as unique representative aircraft. This is mainly because the fuel burn performance of these aircraft types cannot be determined using the BADA version used in this work. Besides this, there is uncertainty about the future production capacities of these future aircraft types. Production capacities were assumed for the B777X and included in that of the NGLR, whereas, since the other two aircraft types are related to existing FSDM representative aircraft, special production capacities are not included. As a result, for calibration purposes, the production capacity share of the B777X in the NGLR is deducted from the total delivered aircraft in this aircraft cluster and its corresponding aircraft category (i.e. the S-TA) and added to the number of aircraft

they assumed that new markets that had previously been either unreachable or unprofitable, especially those that can be served by small wide-body aircraft, would open up [58]. Although the opening up of new markets is not modelled in FSDM, the effect of liberalisation, in terms of increased air traffic, is considered. These assumptions led to a forecast that the share of wide-body aircraft would increase from 19% in 2016 to 21% in 2036. As shown in **Figure 7**, this growth is driven by the

**158**

**Figure 7.**

*Comparison of Boeing CMO to FSDM aircraft classification.*

Fleet composition in year 2036 is dependent primarily on the choice of aircraft during introduction to fill the capacity gap. Apart from cost improvements modelled in the aircraft, aircraft preference depends on fuel price (FP), depreciation period (DP) and planning horizon (PH) assumed during aircraft evaluation. To have a simplified approach in calibration, these variables are applied without differentiating between single- and twin-aisle aircraft. Upper and lower boundaries for the variables are shown in **Table 7**.

Because of the high number of combinations possible if intermediate values of these variables are observed, simplifications are made in the calibration process by using only combinations involving the boundary values. Moreover, further assumptions were made in terms of cost improvements due to the increase in aircraft utilisation, leading to a preference for S-TA and M/L-TA. These are shown in the Appendix (see **Table A-2**). For each fuel burn scenario, four combinations of DP and PH are used for calibration.

#### *4.2.3 Calibration results*

The calibration results are shown in **Figure 8** for years 2008, 2016 and 2036. Because the long-term development is of interest, yearly changes in the results are not shown.

In 2008, FSDM's total fleet size, which is taken from the ACAS database, was 3% less than that of Boeing because the latter considered more aircraft types (e.g. twin-aisle aircraft like Ilyushin IL-86 and Lockheed L-1011 and single-aisle aircraft like Sukhoi Superjet 100,Yakovlev Yak-42, Mitsubishi MRJ, Dornier 328JET, Fokker 70, F28 and BAe 146). As a result, FSDM's SA fleet size was 10% less than that of Boeing. When weighted by their 75% share in the total aircraft fleet size, a difference of 7% results. Furthermore, for the S-TA and M/L-TA aircraft categories, respectively, with approximately 12% share each, percentage differences in fleet size estimates by Boeing and FSDM of 60 and 4% were estimated. When weighted by fleet size, differences of 7 and 0% result for the S-TA and M/L-TA, respectively. Therefore, a maximum difference of 7% is estimated between the fleet sizes in each category, when weighted by fleet share, based on Boeing's data and those of the FSDM simulation year.

In 2016, some of these aircraft types, especially single aisles, not considered in FSDM initial fleet were in limited service. Therefore, the share of single-aisle aircraft


**Table 7.**

*Upper and lower boundary values of calibrated variables.*

between 2008 and 2016 is expected to have increased slightly over the period. As a result, in 2016, FSDM produced a slightly higher share of single-aisle aircraft compared to 2008, although the share of single-aisle aircraft did not change in Boeing's data from 2008 to 2016. In 2016, the difference between the numbers of aircraft in each category based on Boeing's data and those of the FSDM in 2016 ranges between −6 and 43%. However, when weighted by fleet share, there was a 6% maximum difference in fleet size estimates by FSDM and Boeing for each aircraft category.

In 2036, the difference in fleet size estimates by FSDM and Boeing for each aircraft category ranged from −51 to 19% depending on the combination of calibration variables used. However, when weighted by fleet share, there was a maximum difference of 17% in fleet size estimates by FSDM and Boeing for each aircraft category. The calibration results in terms of ASK show a good comparison to Boeing's forecast. In years 2008, 2016 and 2036, maximum differences in ASK of −7, −1 and −3%, respectively, were attained.

For both fuel price forecasts used, of the four combinations of DP and PH possible, the combination of low depreciation period (LDP) and high planning horizon (HPH) gives results closer to Boeing's forecast. Therefore, this combination is used in the remaining steps of this study. The most comparable result to the jet fleet composition forecast by Boeing is obtained using the low-fuel price (LFP) scenario in the LDP and HPH combination. In other words, this combination has the lowest maximum difference between the numbers of aircraft in each category based on Boeing's data and those resulting from the FSDM in 2036.

Furthermore, from **Figure 8**, it can be seen that an increase in jet fuel price from Boeing's low- to high-price scenarios leads to a "slightly" different fleet composition in 2036. This primarily results from a change in the ranking and introduction of costefficient aircraft on the route groups, leading to an increase in the number and share of narrow-body aircraft. Because, unlike wide-body aircraft, narrow-body aircraft are less sensitive to fuel price, a higher jet fuel price has less impact in increasing their

**161**

**Figure 9.**

*Passenger aircraft fuel burn and fuel efficiency 2008-2016.*

*Increasing the Emission Mitigation Potential by Employing an Economically Optimised…*

time, the comparative cost performance of the aircraft differs over time.

*4.3.1 Verification of historical fleet fuel burn and fuel efficiency*

*4.3.2 Verification of historical fleet unit cost and average age*

unit DOC. As a result, narrow-body aircraft are more competitive than their widebody counterparts are, especially when compared on the design range of the former. This result is in agreement with the claim by Rutherford [60] that aircraft with four engines like the B747 and A380 were less fuel-efficient than fuel-efficient twinjets like the A350–900 and B787–9 even on trans-Pacific routes for which the former are designed. Therefore, in a high-fuel price scenario like that of Boeing, fleet phase-in decisions will favour less of NGLRH. Furthermore, given that fuel prices change over

Based on calculation results by Wasiuk et al. [61], IATA [49, 62] and Dray et al. [63], estimates of past passenger aircraft fuel burn in million tonnes are compared with results from FSDM. FSDM estimates are in average 5% above the estimates of Dray et al. and 7% below IATA's estimate for year 2016. However, FSDM's estimate of fleet fuel burn in 2016 is 1% below that of the IATA. Using the approach explained by Dray et al. [63], IATA's values used here are reduced by 9.6% because, unlike the IATA reports, freighter aircraft are not included in this work. Another 5% was deducted to account for unscheduled flights that were included in IATA reports. For the fuel efficiency results, IATA's fuel burn data was combined with ICAO's ASK data. In 2016, FSDM exactly reproduces fuel efficiency data by the IATA and ICAO. Fuel burn performance of the global passenger aircraft from year 2008 to

Fleet unit cost development is dependent on development in fuel unit cost [64]. The developments in cost per ASK (CASK), fuel price and average aircraft age are

*DOI: http://dx.doi.org/10.5772/intechopen.88219*

**4.3 Verification using growth strategy**

2016 is shown in **Figure 9**.

shown in **Figure 10**.

*Increasing the Emission Mitigation Potential by Employing an Economically Optimised… DOI: http://dx.doi.org/10.5772/intechopen.88219*

unit DOC. As a result, narrow-body aircraft are more competitive than their widebody counterparts are, especially when compared on the design range of the former. This result is in agreement with the claim by Rutherford [60] that aircraft with four engines like the B747 and A380 were less fuel-efficient than fuel-efficient twinjets like the A350–900 and B787–9 even on trans-Pacific routes for which the former are designed. Therefore, in a high-fuel price scenario like that of Boeing, fleet phase-in decisions will favour less of NGLRH. Furthermore, given that fuel prices change over time, the comparative cost performance of the aircraft differs over time.

#### **4.3 Verification using growth strategy**

*Environmental Impact of Aviation and Sustainable Solutions*

between 2008 and 2016 is expected to have increased slightly over the period. As a result, in 2016, FSDM produced a slightly higher share of single-aisle aircraft compared to 2008, although the share of single-aisle aircraft did not change in Boeing's data from 2008 to 2016. In 2016, the difference between the numbers of aircraft in each category based on Boeing's data and those of the FSDM in 2016 ranges between −6 and 43%. However, when weighted by fleet share, there was a 6% maximum difference in fleet size estimates by FSDM and Boeing for each aircraft category. In 2036, the difference in fleet size estimates by FSDM and Boeing for each aircraft category ranged from −51 to 19% depending on the combination of calibration variables used. However, when weighted by fleet share, there was a maximum difference of 17% in fleet size estimates by FSDM and Boeing for each aircraft category. The calibration results in terms of ASK show a good comparison to Boeing's forecast. In years 2008, 2016 and 2036, maximum differences in ASK of −7, −1 and

For both fuel price forecasts used, of the four combinations of DP and PH possible, the combination of low depreciation period (LDP) and high planning horizon (HPH) gives results closer to Boeing's forecast. Therefore, this combination is used in the remaining steps of this study. The most comparable result to the jet fleet composition forecast by Boeing is obtained using the low-fuel price (LFP) scenario in the LDP and HPH combination. In other words, this combination has the lowest maximum difference between the numbers of aircraft in each category based on

Furthermore, from **Figure 8**, it can be seen that an increase in jet fuel price from Boeing's low- to high-price scenarios leads to a "slightly" different fleet composition in 2036. This primarily results from a change in the ranking and introduction of costefficient aircraft on the route groups, leading to an increase in the number and share of narrow-body aircraft. Because, unlike wide-body aircraft, narrow-body aircraft are less sensitive to fuel price, a higher jet fuel price has less impact in increasing their

**160**

**Figure 8.** *Calibration results.*

−3%, respectively, were attained.

Boeing's data and those resulting from the FSDM in 2036.

### *4.3.1 Verification of historical fleet fuel burn and fuel efficiency*

Based on calculation results by Wasiuk et al. [61], IATA [49, 62] and Dray et al. [63], estimates of past passenger aircraft fuel burn in million tonnes are compared with results from FSDM. FSDM estimates are in average 5% above the estimates of Dray et al. and 7% below IATA's estimate for year 2016. However, FSDM's estimate of fleet fuel burn in 2016 is 1% below that of the IATA. Using the approach explained by Dray et al. [63], IATA's values used here are reduced by 9.6% because, unlike the IATA reports, freighter aircraft are not included in this work. Another 5% was deducted to account for unscheduled flights that were included in IATA reports. For the fuel efficiency results, IATA's fuel burn data was combined with ICAO's ASK data. In 2016, FSDM exactly reproduces fuel efficiency data by the IATA and ICAO. Fuel burn performance of the global passenger aircraft from year 2008 to 2016 is shown in **Figure 9**.

#### *4.3.2 Verification of historical fleet unit cost and average age*

Fleet unit cost development is dependent on development in fuel unit cost [64]. The developments in cost per ASK (CASK), fuel price and average aircraft age are shown in **Figure 10**.

**Figure 9.**

*Passenger aircraft fuel burn and fuel efficiency 2008-2016.*

#### **Figure 10.**

*Fuel price, unit cost, and average age of global pax fleet: 2008-2016. Source: Own calculations [65].*

Data on aircraft unit cost used for verification was obtained from Centre for Asia Pacific Aviation [64]. A comparable unit cost drop between 2014 and 2016 can be observed for both FSDM results (25%) and CAPA data (20%). Likewise, the trend of the cost development throughout the period is comparable for both FSDM and CAPA, although absolute values are not equal. Although Groenenboom [65] recorded that the average age of passenger aircraft slightly decreased between 2010 and 2015, it does not precisely give the age for passenger aircraft. Average age of the passenger aircraft fleet depends on the rate of aircraft additions to the fleet, compared to retirements from the fleet. In addition, the average fleet age depends on jet fuel price. Lower fuel prices encourage airlines to keep older aircraft longer in service, especially when travel demand is strong [66, 67], thereby increasing the average fleet age. Therefore, a slight increase in the average age of the fleet accompanies a decrease in the price of fuel from year 2012 to 2016.

#### *4.3.3 Verification of forecast fleet fuel burn and air passenger traffic*

Next, the reliability of the model in estimating future emissions and air passenger traffic of the global passenger aircraft fleet is verified.

For forecasts until 2050, passenger load factor is assumed steady at 2036 levels. Dray et al. [63] updated AIM to AIM2015 and used the UK Department of Energy and Climate Change (DECC) historical and forecast oil price levels [68]. In this verification study, the DECC medium oil price forecast was used. A review of the historical prices [year 2016 USD per gallon] between 1990 and 2015 shows that jet fuel prices were approximately 21% above DECC oil prices. The fuel price development according to the DECC has a price level in 2036 and beyond which is even higher than Boeing's high-fuel price forecast.

Furthermore, from year 2015, RPK growth rates of 3.8% per year were used in this verification process according to the SSP2 baseline scenario of Dray et al. [63]. In the SSP2 baseline scenario, zero carbon prices were assumed, so that ETS costs were set to zero. The assumptions in aircraft utilisation, load factor and technology improvements used for arriving at Boeing's future fleet composition are retained. As a result, the basic giant-leap technological improvements assumed were similar. Incremental improvements were excluded since they did not assume incremental technological improvements. **Figure 11** shows the jet fuel price development of the SSP2 baseline scenario.

**Figure 12** shows estimates of fuel burn and air traffic in 2050 relative to 2015 from Dray et al. [63] and using FSDM. Because the long-term development is of interest, yearly changes in the results are not shown.

**163**

**Figure 12.**

*Increasing the Emission Mitigation Potential by Employing an Economically Optimised…*

Dray et al. [63] obtained a range of results in the fleet fuel burn depending on the modelled scenario for future technology. FSDM's forecast fuel burn falls between their forecast boundaries as shown in the figure. Furthermore, there is a slight difference between the relative developments of RPK for the two models. This may be due to different input assumptions on aircraft size and utilisation used in both models as noted by Dray et al. [63]. However, since the fleet size, composition and capacity forecast ability of FSDM have been tested, this difference can be neglected.

*Jet fuel price development in the SSP2 baseline scenario. Source: Own calculations, based on [63].*

Past and forecast RPK growth factors are used as given by Boeing [58]. After 2036, the annual growth rates are assumed constant at 2036 levels. Assumptions on seat and freight load factor are the same as in the verification according to Boeing's forecast. Past fuel price until 2016 and forecast prices by Airbus until 2025 are used

*Verification of forecast passenger aircraft fuel burn and traffic. Source: Own calculations, based on [63].*

**4.4 Emission mitigation potential of the retirement strategy**

*4.4.1 General input for analysis*

**Figure 11.**

*DOI: http://dx.doi.org/10.5772/intechopen.88219*

*Increasing the Emission Mitigation Potential by Employing an Economically Optimised… DOI: http://dx.doi.org/10.5772/intechopen.88219*

**Figure 11.** *Jet fuel price development in the SSP2 baseline scenario. Source: Own calculations, based on [63].*

Dray et al. [63] obtained a range of results in the fleet fuel burn depending on the modelled scenario for future technology. FSDM's forecast fuel burn falls between their forecast boundaries as shown in the figure. Furthermore, there is a slight difference between the relative developments of RPK for the two models. This may be due to different input assumptions on aircraft size and utilisation used in both models as noted by Dray et al. [63]. However, since the fleet size, composition and capacity forecast ability of FSDM have been tested, this difference can be neglected.
