In this chapter we present a brief overview of the approach for the passenger and flight forecast, the fleet model, and the emission modelling. For the time period 2014–2035 we create a single forecast, and from 2035 to 2050 two scenarios are employed. These differ basically in terms of aviation technology and market development. The scenario assumptions have two effects: first, aviation technology development determines the technology of future aircraft. This in turn influences the development of future airfares, which have an effect on passenger demand and, finally, flight volume. For the time period up to 2035 we assume a decline in real airfares, i.e., inflation-adjusted airfares, of 1.5% per year based on data analysis of Sabre AirVision Market Intelligence and inflation data from World Development Indicators [
2]. For the time period 2035–2050, we have therefore created two scenarios: in the High Scenario, we assume a more favorable technological and market development, which results in a decline of real airfares of 2.0% per year. On the other hand, in the Low Scenario, we assume a decline of 0.5% per year because of a less favorable development. At this stage, the technological development is rather generic, i.e., it is not related to specific aircraft types. As a result, the two scenarios differ in terms of their demand development, and we simply call them the “High Scenario” and “Low Scenario” in
Section 2.1.
These two types of scenarios can be combined, e.g., the CS2 Scenario with the High Scenario or the Reference Scenario with the Low Scenario. In total, we have four combinations, and they are important for the assessment of the emission reduction potential of CS2 concept aircraft. The emission reduction potential of CS2 aircraft is assessed relative to the state-of-the-art aircraft of 2014 in order to identify the contribution of CS2 technology. The results of the Reference Scenario are not reported on their own; they serve as a reference point for the results of the CS2 Scenario.
2.1. Passenger and Flight Forecasts
In this section we briefly cover the methodology of the passenger and flight forecasts. For a more detailed description, the reader is referred to [
2].
Figure 1 illustrates the model approach:
In the first step, unconstrained passenger demand and flight volume is forecast for each airport pair.
In the next step, for each airport pair, the flight volume is compared with the current and expected airport capacity. The forecast passenger and flight volume, as well as the constraint situation at airports, will influence the average future aircraft size, which is forecast for each airport pair.
In the final step, the expected passenger and flight volume is balanced with airport capacity and aircraft size development to yield the constrained passenger and flight volume. This might result in some unaccommodated passenger demand and flight volume, depending on the severity of airport capacity constraints and the potential of employing larger aircraft.
The relationships between passenger demand, aircraft size and airport capacity are particularly important in a world in which future airport capacity tends to fall short of demand, at least at some airports. Regarding capacity constraints, we mainly focus on the runway system, as this is the most critical element for overall airport capacity in the long term. Especially in Western countries, the enlargement of the runway system requires a plannig approval procedure which involves the public, who are typically opposed to such plans because of environmental concerns, e.g. increased noise levels. On the other hand, other aspects of airport capacity, such as terminal capacity, can be enlarged as part of the airports’ business decisions without the involvement of the public [
3]. Employing bigger aircraft is an effective option to mitigate capacity shortages; however, in particular at hub airports, existing network carriers rather tend to increase flight frequency in order to restrict competitors’ access to core markets [
4,
5]. On the other hand, there are certainly limits to ever increasing aircraft size and transporting more and more passengers per flight. In the case of a high share of feeder traffic, in particular to smaller airports, frequencies are typically higher [
6].
Figure 2 displays the relationships between the passenger demand (annual passenger volume), airport capacity (annual flight volume) and average aircraft size (passengers per aircraft) on a very general level: in order to accommodate the forecast passenger demand, airport capacity in terms of flight movements is required, in combination with a pre-determined average aircraft size (which is endogenous to the model). Both the average aircraft size and the airport capacity limit the maximum number of passengers that can be handled. Aircraft size and airport capacity can be substituted for each other to some degree: if the airport capacity is insufficient to serve a given passenger demand, increasing aircraft size can compensate for a lack of airport capacity to serve that demand. A lack in terms of aircraft size can be compensated for by more airport capacity, so that more flights—but with, on average, fewer passengers per flight—can be handled. However, as already explained earlier, typically airport capacity is the bottleneck.
The model accounts for these interrelations between passenger demand, airport capacity and aircraft size by adjusting all three elements in a constrained forecast. In contrast, an unconstrained forecast always assumes the best case regarding the future development of airport capacity. Moreover, unconstrained forecasts underestimate the long-term increase of average aircraft size, i.e., the average number of passengers transported per flight, as a measure to compensate for the shortfall in airport capacity.
Shifting traffic to neighbour airports can be another option to mitigate capacity constraints. Gelhausen [
8] shows that, from a passenger’s perspective, such a shift can theoretically happen in a decentralised or multi-airport environment. Gudmundsson et al. [
9] identify spillover effects between London Heathrow and Gatwick, and to a lesser degree between Heathrow and Stansted (and even to the more remote airports Birmingham and Manchester). However, if they control for low-cost carrier effects, they only find spillover effects between Gatwick and London City airports in European traffic. Redondi and Gudmundsson [
10] find significant spillover effects of constraints at London Heathrow and Frankfurt airports in stop-over traffic in European and Gulf hub airports. Dennis [
11], on the other hand, argues that shifting flights to less-congested airports is simply not compatible with the demands of passengers and hub airlines. Currently, this is a very difficult topic to implement in a sound way, especially on a global level, and therefore certainly needs more research. The main questions are: What share of excess traffic is shifted to which airports? Is excess traffic only partially or fully shifted to neighbour airports? Currently, it seems that the potential of shifting traffic to neighbour airports is rather limited, and the model currently does not contain a (full) shift of excess traffic to airports in the vicinity.
In order to illustrate this point in more detail, we have chosen the London area, which serves as a prime example of a region with a heavily constrained hub airport (London Heathrow), surrounded by another major airport (Gatwick) and two secondary airports (Stansted and Luton) with ample capacity reserves. We have excluded London City airport from the analysis, as it has only limited means of absorbing excess traffic from London Heathrow because of its restricted location in the London Royal Docks, and it can be served only by small aircraft.
London Heathrow has already been constrained for about 20 years, and there has been a long debate about its expansion [
12].
Figure 3 illustrates the development of passenger volume and aircraft movements at these four airports, as well as for the London area and the UK between 2006 and 2019 relative to the volumes of 2006. If we look at the UK or the London area as a whole, aircraft movements have remained more or less constant in the UK, and increased by 10% in the London area. Passenger volumes, on the other hand, increased by 33% between 2006 and 2019, both in the UK and in the London area. Of the smaller airports, only Luton increased its flight volume significantly, by around 50%, and in the case of Stansted there was even a decline in the number of flights. The flight volume remained constant at Heathrow, and increased at Gatwick by about 30% since 2006; however, this was much less than passenger volume, which increased by almost 60% since 2006. As we can see, a large share of the passenger volume growth has been accommodated by increasing the number of passengers per flight.
Our forecast model consists of four distinct sub-modules:
Air passenger demand, which is origin–destination (OD) passenger flows, and the total passenger flows including transfer passengers, between countries as well as airports.
Airport capacity and capacity utilisation.
Airport capacity enlargements and limits.
Average aircraft size: the average number of passengers per flight.
2.1.1. Air Passenger Demand
Well-known long-term forecasts of global air traffic are typically conducted by aircraft manufacturers such as Airbus [
13], Boeing [
14], and supranational organisations like the ICAO [
15]. In the academic literature, the gravity model is still the workhorse of spatial demand modelling. One of the first gravity models employed in air transport research was developed by Harvey [
16] to analyse airline traffic patterns in the USA. Grosche et al. [
17] employed two gravity models to estimate the air passenger volume of city pairs without any air service. Tusi and Fung [
18] analysed passenger flows at Hong Kong International Airport, and focused on a single airport. Matsumoto [
19] and Shen [
20] based their gravity models on network analysis: Matsumoto’s model was used to estimate passenger and cargo flows between large cities such as Tokyo, London, Paris and New York, while Shen’s was used to analyse inter-city airline passenger flows in a 25-node US network. Bhadra and Kee [
21] employed a gravity model to analyse demand characteristics, such as the fare and income elasticities of the US origin–destination market over time. Endo [
22] developed a gravity model to analyse the impact of a bilateral aviation policy between the USA and Japan on passenger air transport, while Hazledine [
23] used a gravity model to analyse border effects in international air travel. The gravity model has also been applied in air freight modelling: Alexander and Merkert [
24] developed a gravity model to evaluate the air freight market in Australia. Later, Alexander and Merkert [
25] analysed US international air freight markets in light of the 2008/09 financial crisis. Baier et al. [
26] developed a gravity model with airport fixed effects to model global air freight between airports.
The air passenger demand model that is employed in this paper is also based on the gravity model. Whilst variables such as distance, population and gross domestic product (GDP) are common in gravity models, we expect further insights from the inclusion of an airfare variable. The model was estimated on Sabre AirVison Market Intelligence data [
27], which includes information on actual airfares paid by air passengers. From the model estimation, we found airfare elasticity to be −1.11%, i.e., if airfare decreases by 1% then OD air travel demand rises by 1.11%; it is thus slightly elastic. For better discrimination between different types of origin and destination, we included variables such as tourism receipts and expenditure, and population density. Tourism receipts and expenditures are measures for the tourism affinity of the destination and origin country. They both have a positive influence on OD demand; however, tourism receipts have a higher impact. Population density, which is only relevant for domestic air travel, is a measure for mode substitutability: the higher the population density is, the better developed and more relevant the road and rail network for domestic travel is typically in terms of travel time. Take for example the US, which has a population density of about 36 people per km
2, and Germany, which has a population density of about 232 people per km
2. The rail and road networks are relatively better developed in Germany compared to the US. For example, travelling from Hamburg to Munich by car or train is more relevant than for the journey from New York City to Los Angeles: for the trip from Hamburg to Munich, you need about one and a half hours by plane (excluding access and egress times), around eight hours by car, and five and a half hours by train (again, excluding access and egress times). For the trip from New York City to Los Angeles, you need nearly six hours by plane, 41 h by car, and more than two and a half days by train. Of course, the distance is much longer between New York City and Los Angeles than between Hamburg and Munich (about 600 vs. 4000 km great circle distance), but cars and trains are relatively faster for Hamburg–Munich, and the planes are relatively faster in the case of New York City–Los Angeles, even though the relative advantage is substantially larger for planes than for cars and trains. As a result, population density has a slightly negative impact on OD air travel demand. The model contains further variables of lesser importance, like distance and country ties [
2].
For model estimation, we employed a Poisson pseudo-maximum likelihood (PPML) estimator to produce better and more reliable forecasts [
28]. As a result, a series of relevant demand elasticities (see
Figure 4) were estimated, e.g., if the GDP per capita increases in the origin country by 1%, then OD passenger demand rises by 0.45%, and if GDP per capita rises in the destination region by 1%, the OD demand grows by 0.23%.
Due to a lack of consistent global data, there is no differentiation between business and leisure passengers. There are only individual surveys covering particular markets like Germany and the UK. We do have information about the share of ticket categories for each airport pair, e.g., from Sabre AirVision Market Intelligence, but it is not possible to infer the share of business travellers from the share of business tickets, as many business travellers choose the economy class. For example, in Germany, 69% of domestic air travel in 2003 was business, and 28% of all European and intercontinental trips from Germany were for business purposes [
29]. However, based on the analyses of such surveys, Mason [
30] estimates the share of business passengers to be about 30%, but it is not possible to break down this number to the country or airport pair level, which is needed for consistent model estimation.
2.1.2. Airport Capacity and Capacity Utilisation
The global airport capacity model is based upon data envelopment analysis (DEA) and regression analysis. DEA is a non-parametric empirical method used in operations research to estimate production frontiers employing linear programming techniques. DEA is a standard tool for efficiency analyses, and for the benchmarking of so-called decision-making units (DMUs) [
31,
32]. DEA allows us to compare DMUs which differ in their input and output structures. Examples of such DMUs include hospitals, energy production, or the cost-/profit centers of large organisations and, in our case, airports. The model analyses airport capacity utilisation with one main question in mind: Which airport achieves what output given a particular input structure? In our model approach, this enables us to compute the current and future annual service volumes of airports worldwide. The model allows the forecasting of the 5% peak hour volume and the average hourly volume of an airport in a situation of the highest possible capacity utilisation.
The model produces robust results using input information which is generally available, e.g., the runway system, as this typically limits overall airport capacity in the long term. For our generic airport capacity model, we aimed for an as-high-as-possible degree of accuracy on average on the global level, but we do not pretend that it is as accurate as detailed airport capacity analyses for specific airports. Nevertheless, the results are surprisingly accurate on the airport level. For a comparison with the US and some European and Asian airports, the reader is referred to [
2].
Figure 5 provides an overview of the generic airport capacity model process:
The first step is the use of the aforementioned DEA to estimate the current airport capacity for airports of interest.
In a second step, the average number of aircraft movements per runway and per operating hour at the highest possible level of capacity utilisation for each airport is calculated.
The last step is to perform a regression analysis based on the results of the DEA.
2.1.3. Airport Capacity Enlargements and Limits
With the model of forecasting the realisation probabilities of airport capacity enlargements and limits, we have introduced an approach which incorporates the enlargement of limited airport capacity in air transport forecasts. If the forecast number of flights exceeds the current airport capacity, the model analyses whether adding new runways is possible, and if this is the case, how long this process is expected to take. This analysis is conducted for each airport and each new runway at that airport. The model is based on the idea that there is a particular degree of opposition to airport expansion from the population living in proximity to the airport. This depends on factors such as noise annoyance, pollution, welfare level, economic opportunities, participation level, and intermodal substitution. The degree of opposition may range from almost none to such an intense opposition that airport expansion is virtually impossible. As a result, the model enables us to estimate the probability of the realisation of a new runway, which can be transformed into an expected value in terms of delay. The approach used is a probabilistic one based on Markov chains [
33] and discrete choice theory [
34]. The Markov chain comprises of two situations that an airport can face (see
Figure 6):
Entry into situation two is triggered by the underlying demand forecast and the current capacity of the airport. If the airport is in situation two, the realisation probability (RP) of an additional runway corresponds to the transition probability from situation two to situation one. Eventually, based on the probability of delay, we can calculate the expected delay of runway capacity expansion, as it is the inverse of the transition probability minus one, because 100% RP is defined as no delay in the model with, theoretically, the new runway being instantly available. The realisation probability of runway expansion is modelled by means of a binary logit model [
35]. Of course, it is difficult to define an exact threshold above which an airport is capacity constrained. It is a more or less a smooth transition from an unconstrained into a constrained situation, where participation in the general traffic growth becomes increasingly difficult. We can assume that an (artificial) threshold that separates constrained from unconstrained airports lies in a range of 75% capacity utilisation [
36]. In the light of this definition, there were ten of about 4000 airports which were capacity constrained in 2008, such as Shanghai (SHA), London Heathrow (LHR) and New York LaGuardia (LGA) [
36]. Until 2050, we expect the number of airports to increase to 24 in the Low Scenario and 36 in the High Scenario. These calculations already include new runways that have been built since 2008 (e.g., the fourth runway at Frankfurt airport in 2011) or are expected to be realised by 2050 according to the model’s forecast, shifting traffic to off-peak hours and employing larger aircraft.
2.1.4. Average Aircraft Size: Average Number of Passengers per Flight
The fourth sub-model is on the forecast of passengers per flight by airport pair. As in the case of airport capacities, the approach is highly problem-specific, and cannot be a substitute for a detailed flight route analysis in terms of aircraft fleet characteristics and their future development. The method is very similar to the modelling of airport capacities. The basic analysis is a DEA, which is further refined by regression analyses to generalise the results, so that they can be used for forecasting purposes.
The average number of passengers per flight between an airport pair is determined by the passenger volume of that airport pair, the flight distance, and the constraint situation at the origin and destination airport [
2]. The results serve as an input for the more elaborate fleet modelling presented in the next section.
Figure 7 summarises the various steps with regard to the aircraft size model. First, a DEA is performed for all of the airport pairs under consideration to obtain the current values of passenger capacity potential, i.e., the maximum number of passengers that can theoretically be transported per year, and its utilisation. In the next step, these values are updated on the basis of the passenger demand forecast by means of the passenger capacity potential utilisation model and the passenger capacity potential model. Finally, we can calculate the future number of passengers per flight, which is translated into the average aircraft size by applying a seat load factor.
2.2. Aircraft Fleet Forecast
Aircraft fleet modelling is a complex task that has received a substantial amount of research in the past. A fundamental question is the trade-off between aircraft size and flight frequency on a route with particular characteristics, such as distance, passenger volume and the competitive situation: airlines can basically choose between smaller aircraft with a corresponding higher flight frequency and larger aircraft with a lower flight frequency to serve a given passenger volume. This has already been discussed in
Section 2.1 (see
Figure 2). For example, according to Wei and Hansen [
37], airlines prefer to increase flight frequency instead of aircraft size to attract more passengers. On the other hand, Presto et al. [
38] analysed four different frequency regulation strategies: they performed differently with regard to air traffic flow management delay, cash operating costs of airlines, the net travel time balance of passengers, and the fuel consumption of aircraft. Route characteristics that influence the trade-off between flight frequency and aircraft size are typically passenger volume and flight distance [
39], in addition to airline competition [
40], whether the origin or destination airport is a hub, what kind of airports are connected (e.g., hub-to-hub or hub-to-spoke) and the season of travel [
41]. Pai [
42] conducted, for the US market, a much more detailed analysis regarding the choice of aircraft. He included market demographics (e.g., population and income), airport characteristics (e.g., runway length), airline characteristics (e.g., low-cost carriers) and route characteristics (e.g., distance). In order to actually implement an aircraft choice model, Bhadra [
41,
43,
44] employs a multinomial logit model. Kölker et al. [
39] developed an approach based on categories: they identified a distribution of aircraft size according to passenger volume and distance.
Typically, an external passenger demand volume forecast is needed for aircraft choice models. As the case may be, the passenger volume forecast has to be broken down to the airport or route level to apply the aircraft choice model. Therefore, applying these models for a global forecast can be challenging. A particular strength of our approach is the integrated modelling of passenger volume, number of flights and fleet mix on the airport pair level irrespective of the number of airports included. Compared to the approaches discussed, we include the variables of the passenger volume, flight distance and airport capacity utilisation (see
Section 2.1), as well as the aircraft distribution of the base year of the forecast. The number of passengers per flight for each airport pair serves as the major input for the fleet modelling. Applying a seat load factor transforms the passengers per flight into seats per flight. The fleet modelling concerns the distribution of aircraft types on each airport pair based on this input. In detail, the passenger aircraft fleet forecast is based on the following inputs and assumptions:
The passenger traffic forecast, including the future number of passengers and flights per airport pair (
Section 2.1).
The seat load factor forecast for the conversion of the passengers per flight to the seats offered per flight.
The base year, i.e., 2014 flight schedules as a list of flight operations by airport pair and aircraft type.
The base-year fleet data.
Aircraft retirement curves.
Aircraft utilisation assumptions.
A list of available aircraft (production window = the time between entry into service and the out-of-production date of an aircraft type) in each seat category.
The base-year fleet data originate from Cirium’s Fleets Analyzer [
45], and the schedules are taken from OAG [
46] and Innovata [
47]. The aircraft that make up the base-year fleet are limited to Cirium’s definition of primary usage “Passenger”, “Combi/Mixed (Passenger/Cargo)”, “Quick-Change/Convertible (Passenger/Cargo)”, and a limited number of aircraft used as “Business-Air Taxi/Air Charter”, where operators also carry out scheduled flights. This limitation is in line with the objective of conducting a forecast with regard to the mainliner, regional and commuter airline fleet which is operating scheduled passenger services. With regard to this limitation, we exclude aircraft used as business/VIP aircraft, aircraft in private use, cargo aircraft, and other uses where aircraft are operating unscheduled services. In terms of the number of such aircraft, about 33,000 business/air taxi aircraft are excluded from this analysis, which only account for a fraction of the emissions of scheduled passenger flights. Moreover, as only passenger traffic is the scope of the analysis, approximately 3200 cargo aircraft in service in 2014 are not considered.
Table 1 provides an overview of the aircraft included in the analysis for the base year 2014. The total number of aircraft in the base-year fleet is 24,017 (as of mid-year 2014).
For subsequent years, the connection between the fleet and the schedule is established as follows: schedule data provide information on the aircraft types and variants being operated on a particular flight. This, however, does not include individual aircraft. In the forecast years, with regard to each aircraft, the survival probability, taken from the respective retirement curve model, is applied, resulting in the average percentage of surviving aircraft for each aircraft type. This average survival percentage is applied to the frequencies of the base-year flight schedule, such that the number of flights that can be operated with the surviving fleet of the base and the previous years’ aircraft can be operated, and the number of flights for which new aircraft will be required can be estimated.
The seat load factor forecast is employed for the conversion of the number of passengers per flight to the number of seats offered. Here, a global model using an s-curve is fitted to empirical data from Sabre AirVision Market Intelligence [
27] and ICAO [
48,
49] using empirical data from 1996 onwards (see
Figure 8). From this model, we forecast a maximum seat load factor of 88% in 2050 for all routes.
For aircraft retirement modelling, the approach of International Civil Aviation Organization Committee on Aviation Environmental Protection (ICAO CAEP)/12 was employed (see
Figure 9). Here, aircraft are subdivided into the following categories: turboprop, regional jet, narrowbody jet and widebody jet. Each type has different coefficients which influence the probability of retirement for a particular aircraft age.
The aircraft utilisation model is needed for the calculation of the number of aircraft that is required to serve the forecast schedule. It is specified as:
The total annual aircraft utilisation for each aircraft is estimated by the annual distance flown as a function of the average mission distance with
a and
b being parameters that are estimated with empirical data. Utilisation typically depends on the average flight length. The longer the flights are, on average, the higher the aircraft utilisation typically is, because the total number of annual turn-arounds with the associated ground times is smaller. The data used to calibrate the model, as shown in
Figure 10, originate from ADS-B data collected by Flightradar24 [
50] in 2019. ADS-B coverage can be considered to be very high globally, especially because equipage has become mandatory for most operation types. In Europe, about 91% of flights are covered by ADS-B as of early 2022 [
51].
The accuracy of the model is shown in
Table 2, in which we applied the model to the flight schedule for the year 2019. The model is able to estimate the number of aircraft in the global fleet with an accuracy of 96%. However, forecast results should be taken with a pinch of salt in the case of aircraft-specific deliveries, because there are significant deviations in some cases, e.g., Boeing 757 and 767. A negative value for the deviation means that more aircraft than forecast are needed for a given flight schedule. Nevertheless, in total, i.e., for all aircraft types, this more or less levels out. Furthermore, this only affects deliveries of particular aircraft types but not the total CO
2 and NO
x emissions produced by aircraft in service in this paper. Emissions are calculated by the number of flights per airport pair and aircraft type.
Table 3 shows the aircraft types used for the Reference Scenario. Only aircraft that were available for order or already in service no later than 2014 are used in the Reference Scenario. They represent state-of-the-art aircraft technology for 2014. Originally, in CS2, only six reference aircraft were included. However, the modelling approach is based upon the 14 ICAO seat classes. The ICAO forecast [
15] is one of the leading forecasts in the field, and thus is a point of reference for our study. Furthermore, 14 seat classes are a more appropriate resolution for fleet modelling from our point of view. In order to make the reference aircraft compatible with the modelling approach, additional aircraft were added. In the Reference Scenario, these aircraft are employed during the whole forecasting period from 2014 to 2050. A drawback of the model approach is that aircraft types are assigned permanently to a particular seat category for the full forecast period. Over time, aircraft of an already existing type enter the market, or existing aircraft are upgraded with more seats. The Airbus A320neo is such an example, which was assigned to the 151–175 seats category in 2014, but the aircraft in service hit the upper limit of the class in 2022 [
48].
The list of available aircraft in the CS2 Scenario is determined by the environmental goals breakdown table (
Table 4). Aircraft with advanced and ultra-advanced CS2 technologies gradually enter the market according to the entry into service schedule (
Table 5).
As with the reference aircraft, there are only eight original CS2 aircraft classes (see
Table 5). In order to fill all of the ICAO seat classes, additional aircraft with similar characteristics to the original CS2 aircraft were introduced. An overview of the aircraft and in-production windows is shown in
Table 5.
2.3. Emission Modelling
For the calculation of air transport emissions, various modelling techniques can be employed. In this paper we use the commercial flight performance software Piano-X (Project Interactive Analysis and Optimization) [
52] for all reference aircraft for which no emission profiles were provided by the System Platform Developers (SPDs). It is based on the aircraft analysis tool Piano 5, and features flight performance indicators such as fuel burn, CO
2 and NO
x emissions for a set of more than 500 aircraft types. It is widely used in the aviation community, and various studies have validated the accuracy of the tool [
53,
54]. Generally, the results of calculations using Piano-X have shown a very good alignment with actual fuel consumption and emissions values, as a comparison with the flight data recorder data shows. For CS2 concept aircraft, wherever available, emissions profiles provided by the SPDs were used. In all other cases, reference aircraft with specific reduction factors were employed.
Figure 11 provides an overview of the emissions modelling at the air transport system level. The main inputs are the flight volume and fleet forecast by airport, and the emission profiles from SPD models and Piano-X. The emission profiles are provided by the SPD models and Piano-X. Piano-X is employed for already existing aircraft and the SPD models for the reference and CS2 aircraft. The aircraft models contain encrypted databases from the manufacturers concerning engine thermodynamic and aerodynamic data as well as engine NO
x and fuel flow values. For the calculation of NO
x, the Boeing Fuel Flow Method2 [
55] is employed. The aircraft performance module first calculates a trajectory and then the emissions.
The final step is to calculate the global emission inventory using the inputs from the aircraft models for every airport pair.
The main objective of the TE within CS2 is to assess the contribution of aircraft and engine technology to the environmental goals; therefore, we focused on these effects and omitted other contributing factors, which nevertheless play a role in the total fuel consumption and aircraft emissions. For example, we did not consider exact flight routes, but based all of our calculations on great circle distances between origin and destination airports. Any additional distance covered in the terminal maneuvering area around airports, as well as additional cruise distance flown due to detours because of weather, wind or air traffic management restrictions, as well as voluntary detours by airlines in order to save air navigation fees (a typical behavior observed in the EUROCONTROL area, where unit rates can differ substantially in different neighboring countries) are not considered in our analysis. Furthermore, the impact of sub-optimal flight levels or the degradation of aircraft due to age or contamination are not considered. This was to avoid any confounding effects which might negatively influence the identification of aircraft/engine technology effects on emissions.