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CO2 emissions associated with hubbing activities in air transport: an international comparison
Journal of Transport Geography 34 (2014) 185–193
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Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo
CO2 emissions associated with hubbing activities in air transport: an international comparison Becky P.Y. Loo a,⇑, Linna Li a, Voula Psaraki b, Ioanna Pagoni b a b
Department of Geography, The University of Hong Kong, Pokfulam, Hong Kong Faculty of Civil Engineering, National Technical University of Athens, 5 Iroon Polytechniou Street, Zografou Campus, 15773 Athens, Greece
a r t i c l e Keywords: CO2 emissions Air transport Airspace Hubbing Greece Hong Kong
i n f o
a b s t r a c t Hubbing is an important operational practice in air transport. Many studies have been conducted to examine the benefits and impacts of hubbing from an economic perspective. However, its impact on CO2 emissions, especially across different air spaces, is not well understood. This paper explores the impact of hubbing activities in air transport from an environmental perspective. With a detailed methodology and data from the Greek and Hong Kong/Sanya flight information regions (FIRs), three levels of CO2 emissions are estimated: airport-based, airspace-based and flight-based. After contrasting the CO2 emission efficiencies of Athens International Airport (AIA) and the Hong Kong International Airport (HKIA), aircraft type and flight distance are examined to explain their emission efficiency differences. It is found that HKIA is associated with poorer CO2 emission efficiency at the airport and airspace levels because of the larger aircraft and longer flight distance. However, when CO2 emission efficiency at the flight level is considered, HKIA, with a higher passenger load factor, performs better. Major international hub airports should implement additional environmental measures to minimize the impact of hubbing activities on CO2 emissions at the airport and airspace levels. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Hubs are special nodes in a network to facilitate connectivity between interacting places (O’Kelly, 1998). Hubbing is a practice of airlines to accommodate passengers or goods going through intermediate centers, with a purpose to maximize the number of potential connections available to the airlines (Graham, 1995). As a consequence of airline and airport deregulation and privatization, it has become a global trend in air transport (Nero and Black, 1998). Most studies about hubbing activities in air transport focused on the economic dimension. Despite a few doubts about the effect of lower unit costs (Morrell and Lu, 2007), it is believed that airline hubbing can alter airport operations and lead to economies of density, and particularly, economies of scope (Graham, 1995; Nero, 1999). Through the consolidation of passengers from multiple spoke cities across an airspace, larger aircraft can be employed to fly routes more frequently at higher load factor, thus reducing the cost per passenger. Meanwhile, aircraft arrivals and departures as well as passengers at the hub airports can increase accordingly. However, the impact of hubbing on the environment, especially CO2 emissions, has received little attention. This paper ⇑ Corresponding author. Address: Room 1034, Department of Geography, The Jockey Club Tower, The University of Hong Kong, Pokfulam, Hong Kong. Tel.: +852 3917 7024; fax: +852 2559 8994. E-mail address: [email protected] (B.P.Y. Loo). 0966-6923/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jtrangeo.2013.12.006
aims to explore the relationship between CO2 emissions and air traffic activities by using two illustrative cases of hub airports with different mixes of connecting and local traffic and different scales. In particular, it shows that the environmental impact of airport hubs needs to be understood geographically at different levels. The CO2 emission efficiency or environmental impact of airport hubs will differ depending on the geographical scale that one is looking at. Currently, aviation is the second largest contributor of CO2 emissions in the transport sector, accounting for about 2% of the global anthropogenic CO2 emissions (Penner et al., 1999). Considering the rapid growth of air traffic, which is the highest amongst all transport modes (Chapman, 2007), CO2 emissions of the air industry is projected to increase greatly in the future. Most studies about reducing air transport CO2 emissions focus on the aircraft and engine technology, alternative fuels, air traffic management, and regulatory and economic measures (Penner et al., 1999; Lee, 2000). Nonetheless, hubbing, as an important operational practice in air transport, can also have significant impact on CO2 emissions and warrants more research. On the one hand, hubbing can lead to higher CO2 emissions at airport hubs by increasing the volume of air transport activities. On the other hand, it may influence CO2 emission efficiency by changing load factors, aircraft size utilized and flight distance. Several studies have discussed the relationship between CO2 emissions and aircraft size at different flight
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distances (Wit et al., 2002; Jardine, 2005; Williams and Noland, 2006; Department of Environment, 2008; Givoni and Rietveld, 2010). Usually, larger aircraft types have higher CO2 per flightkm but smaller aircraft types with lower emissions per flight-km cannot operate long-haul routes (Williams and Noland, 2006). However, airlines usually choose to use small aircraft types and increase flight frequency, the large aircraft types thereby are limited on long haul routes (Givoni and Rietveld, 2010). Methodologically, load factor should be considered as a factor; and CO2 emission per passenger-km is an important indicator of CO2 emission efficiency (Williams and Noland, 2006; Department of Environment, 2008). Research also found that with the same aircraft type, short-haul air flights tend to have higher CO2 emission per seat-kilometer than medium or long-haul flights (Department of Environment, 2008). It can be explained by the larger component of landing and take-off cycle, which requires more fuel burn for average distance than cruise cycle, in short flights than medium or long flights. When the distance gets longer than about 5000 km, the weight of extra fuel that must be carried may slightly increase the emission factor (Jardine, 2005). However, studies which directly examine CO2 emissions associated with hubbing activities in air transport are still limited. One study found that CO2 emission per passenger of hub and spoke networks is significantly higher than the pointto-point networks in all cases, because of larger aircraft types used at many major hubs due to scarce slots (Morrell and Lu, 2007). Another study, however, shows that the use of larger aircraft with higher load factors by the hub-and-spoke system may be beneficial for reducing the total CO2 emission but deteriorate the environment situation of the hubs (Schipper and Rietveld, 1997). It is also found that hub location could also influence fuel cost and CO2 emission. O’Kelly (2012) shows that a high degree of connectivity could reduce fuel cost while a pure single assignment hub network could increase fuel cost. In this well developed literature context, there is now a need for some comparative analysis, showing empirical evidence on emissions at different scale and type of airport hub. Therefore, two hub airports with different mixes of connecting and local traffic of very different scale are chosen to study the relationship between CO2 emissions and air traffic activities. Comprehensive analysis of this issue calls for an enormous array of data, including aircraft types, flight paths and flight distances, so that the choice of airports in this study was constrained by data availability. It was also recognized that an understanding of local airport operations, and access to supplementary data on flight details would be valuable. Hence, the case studies are the authors’ respective home airports: Hong Kong International Airport (HKIA), as an international hub, and Athens International Airport (AIA), as a national hub. HKIA is commonly recognized as an international hub airport and has over 100 airlines operating flights to about 170 locations worldwide, including 49 destinations in mainland China. Its position as an international airport hub has been established (Matsumoto, 2004). In 2010, its total passenger number reached 50.9 million and total cargo was 4.1 million tonnes. Its total air traffic movement was 30.7 million, of which 24.8 million was for passenger, 5.1 million was for cargo (Hong Kong International Airport, 2012). The percentage of transit passenger in arrivals was about 3% in the period of 2001–2005 (Civil Aviation Department Hong Kong, 2002–2005). For AIA, its total number of passengers in 2010 was 15.4 million, of which 5.6 million was domestic and 9.8 million was international. Its total aircraft movement was 19.2 million, of which 16.7 million was for passenger and 0.9 million was for cargo (Athens International Airport, 2012). The passengers of AIA accounted for 40% of the total passengers in all airports in Greece in 2010 (Hellenic Civil Aviation Authority, 2011). It is both a domestic hub and tourist airport (Papatheodorou and Arvanitis, 2009). Transit passengers
accounted for 0.6% of its total passengers (Hellenic Civil Aviation Authority, 2011). Recognizing the differences between these two airports, this study attempts to quantify and systematically compare the impact of an international hub and a national hub on CO2 emissions at the three geographical levels of the airport site, the airspace region, and the entire atmosphere. The following section introduces an improved methodology, based on Pagoni and Psaraki (2010a, 2010b, 2013) and Psaraki et al. (2012), for the estimation of CO2 emissions at three different levels. Then, the estimation results are presented and contrasted. After that, emission efficiencies of different aircraft types and flight distances are analyzed to understand the environmental impact of the airport hubs better. The paper ends with some discussion and concluding remarks.
2. Methodology When estimating the CO2 emissions, researchers typically only considered the emissions generated by aircraft during the Landing/Take-off (LTO) cycle (Kesgin, 2006; Schürmann et al., 2007; Turgut and Rosen, 2010). This study adopts a three-level approach to estimate CO2 emissions of airport hubs: airport-based, airspacebased and flight-based. CO2 emissions for the entire flights departing from or arriving at the study airspaces are computed using Eq. (1) below. Subsequently, emissions for each of the three levels are determined. Airport-based CO2 emissions include those generated during the LTO cycle. Therefore, each flight is divided in two main parts: the LTO cycle, which includes operations below 3000 feet and is, therefore, related to airport emissions, and the climb-cruise-descent cycle (called cruise thereafter), which includes operations above 3000 feet. The LTO cycle is further divided in five phases: taxi-out, take-off, climb-out, approach landing and taxi-in. The rate of fuel consumption (and the resulting CO2 emissions) is proportional to the drag and has to be balanced by the thrust of the engine. The take-off phase requires full engine thrust, and thus more fuel (European Environment Agency, 2009). As the aircraft ascends to higher altitudes, drag decreases, so does the rate of fuel burn. CO2 emissions within a given airspace are next computed. Two airspaces are studied, the Flight Information Regions (FIR) of Greece and the FIR of Hong Kong/Sanya as defined by ICAO. Airspace-based emissions include those generated during the flight segments contained in these airspaces. For this reason, flights are divided into two categories: intra-zonal flights that depart from and arrive at airports of the same airspace, and inter-zonal flights whose origin and destination airports are not in the same airspace. In the case of the Greek airspace, the distinction coincides with domestic and international flights respectively. This is not true in the Hong Kong/Sanya airspace which is part of the larger China’s airspace. Intra-zonal and inter-zonal flights are illustrated in Fig. 1. Flights from airport 1 to airport 2 are considered as intra-zonal, since both airports belong to the same airspace. In contrast, flights from airport 1 to airport 3 are inter-zonal flights, since airport 3 is not located in the same airspace under study. For the inter-zonal flight departing from Airport 1 in Fig. 1, the emissions include those emitted during take-off (at the airport 1), climb out and the part of the cruise segment that extends until the exit waypoint. Similar considerations apply to inter-zonal flights arriving at the study FIR. At the third level, computed emissions are related to the actual flight distance of the entire flight, whether intra-zonal or inter-zonal. Obviously, for intra-zonal flights, airspace-based and flight-based emissions are the same. Air traffic data were obtained from the Civil Aviation Authorities in Greece and China. The data include flight code, origin and
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Fig. 1. Definition of intra-zonal and inter-zonal flights.
destination airports, aircraft types, distances flown and taxi times in the study airports. Data describing the Flight Information Region (FIR) boundaries (i.e. exit and entry waypoints) were used to define the part of flight within the study FIR. The following formulas are applied to obtain CO2 emissions for any time interval.
ECO2 ¼
! 8 n n X 4 X X > > > FC d;a;cruise þ ne;a FC e;mj tmj for jets > EICO2 > < i¼1 i¼1 j¼1 n X 5 > X > > > FC d;a;mj EI > CO 2 :
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the flight plan is not available, the flight distance is approximated by the Great Circle Distance (GCD), which is the shortest distance between two points on the surface of a sphere. A factor depending on the GCD and the airspace characteristics is then applied to account for delays, airspace constraints, detours around weather and estimates of the actual distance flown. Finally, time spent in each LTO sub-phase is treated in the following way: taxi times (taxi-in and taxi-out times) are defined for each specific airport within the study areas, while times for climb-out, approach landing and take-off are specified using the typical LTO cycle defined by ICAO (European Environment Agency, 2009). Thus, take-off lasts 0.7 min, climb-out 2.2 min and approach landing 4 min. Once fuel burn and CO2 emissions at the specific airports, airspaces and the entire flights are determined, emission efficiencies are calculated by dividing the CO2 emissions by the product of passenger carried and flight distance. Comparisons of emission efficiencies are made and explained by taking into account the different aircraft types used. It is hoped that a better understanding of the environmental impact of hubbing activities at different levels can be gained. 3. CO2 emissions based on airports, airspaces and entire flights
3.1. Background information of Greek and Hong Kong/Sanya airspaces
for turboprops
i¼1 j¼1
ð1Þ ECO2 is the mass of CO2 emissions for n flights, EICO2 is the emission index of CO2 (which is 3.157 kg CO2/kg fuel), FCd,a,cruise is the fuel consumption during cruise of aircraft type a for flight distance d,ne,a denotes the number of engines for aircraft type a, FCe,mj is the fuel burn rate of engine type e for mj mode of LTO, tmj is the time spent in mode mj of LTO cycle and FCd,a,mj is the fuel consumption of aircraft type a for flight distance d during mj flight mode (j = 1, . . . , 5). In this study, the fuel burn rate for the LTO cycle (FCe,mj) is obtained from the ICAO Engine Exhaust Emissions databank (International Civil Aviation Organization, 2012) and the fuel burn rate during the cruise phase (FCd,a,cruise) is taken from the EMEP CORINAIR database (European Environment Agency, 2009). The ICAO databank does not provide fuel burn rates for turboprop aircraft. Thus, the EMEP CORINAIR database is considered both for the cruise and the LTO cycle to estimate FCd,a,mj for turboprops. Both databases provide aircraft fuel consumption data based on several flight characteristics and have been widely used for emissions modeling (Cristea et al., 2013; Symeonidis et al., 2004). To calculate CO2 emissions during LTO using the ICAO databank, the engine type used has to be determined for each aircraft. The model incorporates a sub-model that maps the actual aircraft type to the engine type used. Both databanks provide the fuel burn rates for specific aircraft types. Thus, a classification of aircraft types to their equivalent ones based on aircraft technical specifications from aircraft manufacturers and other sources was necessary for turboprops (European Environment Agency, 2009; EUROCONTROL, 2011). In a few cases where information on equivalent aircraft types is not available, the aircraft is substituted with an aircraft type of similar characteristics, that is, maximum take-off weight, size, cruise speed and range. Flight distance is based on the route operated per flight defined by the flight plan, which is obtained from the Civil Aviation Authorities. For inter-zonal flights, the route for each flight within an airspace is determined using the boundary waypoints (exit and entry) of the respective FIR. When
In this section, the computational model described above is applied to the Greek airspace and the Hong Kong/Sanya airspace in 2010. The Greek airport system consists of 38 active civilian airports, the majority of which serve both intra-zonal and inter-zonal flights. Fig. 2 shows an overview of the Greek airspace along with the boundary waypoints (red triangles1) and the main airports (red circles). Many Greek airports are located on islands. The Greek air traffic exhibits seasonal demand volatility. During the low season, many airports cease to operate as coordinated airports. They handle almost exclusively domestic flights and accommodate much lower traffic levels than those they have been planned for. During the high season, especially from June to August, tourist flows, mainly interzonal ones, reach their peak and most airports situated on Greek islands are being used. Air traffic in the high season accounts for 60% of the total annual traffic, as indicated in Fig. 3. The Hong Kong/Sanya airspace consists of two FIRs: Hong Kong FIR and Sanya FIR. In contrast to the Greek airspace, most of the area covered by the airspace consists of water body rather than landmass. There are four civilian airports in the Hong Kong/Sanya region: Hong Kong International Airport, Macau International Airport, Haikou Meilan International Airport and Sanya Phoenix International Airport. All these four airports are located on islands and serve both intra-zonal and inter-zonal flights, most of which are inter-zonal flights. Fig. 4 presents the Hong Kong/Sanya airspace along with the boundary waypoints (red triangles) and the airports (red circles). As shown in Fig. 5, Hong Kong/Sanya airspace shows no distinct difference in aircraft movement by month and features no strong seasonal patterns. Air traffic statistics are from the statistics of HKIA (2010), Macau International Airport (2011) and Meilan International Airport (2010). The daily traffic samples in the Hong Kong/Sanya and Greek airspaces form the basis for the calculation of fuel burn and CO2 emissions. To reflect seasonal variations in air traffic volumes, the Greek air traffic data were recorded both for the low and high seasons by selecting two typical days per season. The air traffic in Hong Kong/ Sanya (Fig. 5) features no strong seasonal patterns. Thus, one 1 For interpretation of color in Figs. 2 and 4, the reader is referred to the web version of this article.
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Aircraft Movements (Year 2010)
Fig. 2. The Greek FIR with its boundary waypoints and airports.
Greek airspace 60,000
Low season 22 % of total traffic
High season 60% of total traffic
Low season 18 % of total traffic
50,000 40,000 30,000 20,000 10,000 0 Jan
Feb
Mar
Apr
May
Intra-zone
Jun
Jul
Inter-zone
Aug
Sep
Oct
Nov
Dec
Total
Fig. 3. Seasonal and intra/inter-zonal breakdowns of aircraft movements in the Greek FIR (based on HCAA, 2010).
typical day was taken as representative for the annual air traffic operations. The characteristics of these traffic samples are shown in Table 1. Interpolation factors are applied to determine annual
indexes for 2010. It is assumed that aircraft fleet composition and route lengths on the sample days are typical and representative over 2010. In the estimation, light aircraft (with a seating capacity of up to 10 passengers) primarily used for personal and business flying were omitted. Table 1 shows that intra-zonal flights in the Hong Kong/Sanya airspace, i.e. flights between the four airports within the territory, accounted for only 1% of the total traffic and inter-zonal flights dominated the total air traffic, due to the fact that most of the airspace is above the South China Sea with a few large tourist attractions only. On the other hand, the annual intra-zonal flights over the Greek airspace accounted for 30% of the total traffic, due to the more dispersed human settlements and tourist attractions over the airspace. 3.2. Estimation results of CO2 emissions Fuel burn and CO2 emissions are computed for the two main flight cycles: cruise and LTO. Emission characteristics are reported
Fig. 4. The Hong Kong/Sanya airspace with its boundary waypoints and airports.
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times as in AIA and it carried 3.1 times passengers as that of Athens. The load factor was higher in Hong Kong (0.78) than in Athens (0.72). At the airport level, the fuel burn and CO2 emissions of Hong Kong were 3.29 and 3.52 times higher than those of Athens. Considering the CO2 emissions within the airspaces, the absolute volume of CO2 emissions generated by HKIA was 2.49 times as that of AIA. Finally, the total volume of CO2 emissions of entire flights generated by HKIA was 7.56 times as that of AIA. The aircraft fleet mix was certainly a major factor. Due to its international hub status, flights from and to HKIA are mainly served by large aircraft, as opposed to smaller ones in AIA. Another factor that influences aircraft emissions around the airports is the time spent in LTO sub-modes. For climb out, approach landing and take-off modes, ICAO typical times-in-mode were assumed for many previous studies (Woodmansey and Patterson, 1994; Kalivoda and Kudrna, 1997; Kesgin, 2006; Turgut and Rosen, 2010). In this study, taxiing times incorporated into the calculation procedure are airport-specific. The taxi time was 21 min for HKIA and 12 min for AIA in 2010. This difference accounts for much of the different emission levels between the two airports. Generally, the taxi time depends a lot on the airport size and the volume of air passenger and cargo that it handles. Taking into consideration passenger load factors, the average CO2 emission per passenger carried (tn/pax) was 2.26 times higher at HKIA than that of AIA. However, based on the passengers carried and the total CO2 emitted to the entire atmosphere (both within and outside the respective airspace), the picture changes. The average emission efficiency of HKIA was better than that of AIA. The average CO2 emission per passenger-km was 0.11 and 0.14 for HKIA and AIA respectively.
Aircraft Movements (Year 2010)
40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 Jan
Feb
Mar
Apr May
Hong Kong
Jun
Jul
Macau
Aug
Sep
Haikou
Oct
Nov
Dec
Total
Fig. 5. Seasonal breakdowns of aircraft movements in the Hong Kong/Sanya airspace (Sanya airport data not available).
within the study airspace and their airports as well as the entire flights. Emissions are calculated using Eq. (1) given the aircraft or engine type, the distance of the entire flight, the time-in-mode (during LTO), etc. Especially for aircraft emissions within a given airspace more analytical process is required. For inter-zonal flights, we first calculate the emissions for the entire flight and then further computations are done to determine emissions only for the segment of flight within the study FIR (see Fig. 1). For intra-zonal flight, the distance of the entire flight is used. Emissions based on airports are computed by summing the LTO cycles. Emissions of the entire flights are computed by adding both cruise and LTO cycles of the whole flight distance. Our estimates on fuel burn and CO2 emissions are presented in Table 2. The total fuel burn and CO2 emissions were much higher in the Hong Kong/Sanya airspace in comparison to the Greek airspace. The number of flights in the Hong Kong/Sanya airspace was 1.36 times as in the Greek airspace. CO2 emissions in the Hong Kong/Sanya airspace were 1.27, 1.68 and 3.95 times higher than in the Greek case at the airport, airspace and flight levels. The total distance (of the entire flights) associated with all flights at the Hong Kong/Sanya airspace was 2.06 times as that of Greek flights, but the emissions of the former was 3.95 times higher. In order to examine the CO2 emissions associated with hubbing activities in air transport more closely, AIA and HKIA are considered separately in Table 3. The number of flights in HKIA was 1.5
4. CO2 emissions and hubbing activities in air transport
4.1. Emission efficiency and aircraft size In passenger transport, the economics of using larger aircraft has been well established (Graham, 1995; Nero, 1999). However, for CO2 emissions, the effects of using larger aircraft are not as clearly established because larger aircraft may be less efficient in CO2 emissions in terms of CO2 emission per passenger-km (Williams and Noland, 2006). Notably, it may be influenced by the aircraft technology and the load factor of the flights
Table 1 Characteristics of the Greek and Hong Kong/Sanya airspaces. Characteristics
Greece
Hong Kong/Sanya 2
163,270 nm2 4
Study Area Number of civil airports within study airspace
159,920 nm 38
Study year Annual passenger flights (year 2010) – Total – Intra-zonal – Inter-zonal
2010
2010
306,814 90,617 (30%) 216,197 (70%)
416,100 4380 (1%) 411,720 (99%)
Day of air traffic sample Daily passenger flights in traffic samplea
Two typical days per season Low-density
High-density
430 190 240
1410 329 1081
Low-density
High-density
172,496 74,077 98,418
624,291 119,007 505,284
– Total – Intra-zonal – Inter-zonal Daily distances in traffic sample within study airspacea – Total [km] – Intra-zonal [km] – Inter-zonal [km]
One typical day 1140 12 1128
287,981 7088 280,893
a Greece: Daily flights and distances during each season are given as averages of the two typical days of the same season. The low-density season is from January to April and from October to December (153 days). The high-density season is from May to September (212 days).
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Table 2 Annual aircraft fuel burn and CO2 emissions of the Greek and Hong Kong/Sanya airspaces (flight-based). Study airspace (2010) Greece
Hong Kong/ Sanya
306,814
416,100
Air traffic (number of flights) Fuel burn Airport level: At airports within airspace [Mt] – Total – Intra-zonal – Inter-zonal
0.25 0.06 0.19
0.31 0.00 0.31
Airspace level: Within study airspace [Mt] – Total – Intra-zonal – Inter-zonal
0.52 0.12 0.40
0.87 0.01 0.86
Flight level: Over entire flights [Mt] – Total – Intra-zonal – Inter-zonal
1.76 0.12 1.63
6.95 0.01 6.94
CO2 emissions Airport level: At airports within airspace [Mt] – Total – Intra-zonal – Inter-zonal
0.78 0.18 0.60
0.99 0.01 0.98
Airspace level: Within study airspace [Mt] – Total – Intra-zonal – Inter-zonal
1.64 0.39 1.25
2.76 0.04 2.72
Flight level: Over entire flights [Mt] – Total – Intra-zonal – Inter-zonal
5.55 0.39 5.16
21.95 0.04 21.91
Distance flown within study airspace [km] Distance flown of entire flights [km]
132,085,537 485,839,372
105,112,939 1,001,892,547
Greek FIR (high season)
Greek FIR (low season)
50 40 30 20 10 0 0-50
Table 3 Air traffic activities and CO2 emissions in Athens and Hong Kong (passenger-based). Study airspace (2010) Athens (AIA)
Hong Kong (HKIA)
Air traffic (number of LTOs) Passenger number Average load factor
160,304 15,733,114 0.72
236,520 48,954,165 0.78
Fuel burn – Airport level [Mt] – Airspace level [Mt] – Flight level [Mt]
0.07 0.27 0.79
0.23 0.66 5.98
0.21 0.84 2.50 0.14
0.74 2.09 18.89 0.33
0.14
0.11
CO2 emissions – Airport level [Mt] – Airspace level [Mt] – Flight level [Mt] Average CO2 emission per passenger carried (tn/pax) Average CO2 emission per pax km (tn/ pax/1000 km)
Hong Kong/Sanya FIRs 60
51-100 101-150 151-200 201-250 251-300 301-350 351-400 401-450 451-500
Aircraft passenger capacity Fig. 6. Distribution of aircraft passenger capacities in the Greek and Hong Kong/ Sanya FIRs.
less than 150 seats and 89% were less than 200 seats; in the low season, about 49% of its aircraft were less than 150 seats and 97% were less than 200 seats. In the Hong Kong/Sanya airspace, aircraft less than 100 seats were rarely used and 75% of its aircraft were with passenger capacity more than 150 seats, 24% were having more than 300 seats. The disparity of aircraft capacity is explained not only by the higher passenger demand but also the longer flight distance. Small aircraft are mainly for short-range flights; and most short-range aircraft have less than 150 seats; whereas most longrange aircraft have 150 seats or above (Lee, 2000). Most flights in the Hong Kong/Sanya airspace used HKIA, which is an international hub and has more long-haul international flights than the airports in the Greek airspace. Another difference is that there are turboprops in the Greek airspace operating on air flights (about 13% of total flights in high season), such as AN26, AT45, BE20, DH8A, GLF3, and JS41, while in the Hong Kong/Sanya airspace, all flights are operated by jets (Table 4). Table 5 makes an international comparison of the aircraft size and CO2 emission efficiency within the airspace and of the entire flights at the Greek and Hong Kong/Sanya airspaces. For the total average CO2 emission per passenger-km within the respective airspace, it was lower in the Greek FIR (0.10 in both the low and high seasons) than the Hong Kong/Sanya FIRs (0.17). Generally, there is no clear relationship between the aircraft size and the levels of CO2 emission per passenger-km within an airspace. Once again, when the entire flights are considered, the picture changes. At the flight level, the total average CO2 emission was 0.11 tonnes per passenger-1000 km in the Hong Kong/Sanya airspace. In Greek, it was 0.15 for the low season and 0.11 for the high season. During the high season, the share of larger aircraft (over 200 seats) in the Greek FIR increased noticeably (from 3% in the low season to 10.9% in the high season). This was probably associated with longer distances flown by international tourists during its high season. When the entire atmosphere (both within and outside the respective airspaces) is considered, there is an inverse relationship between the aircraft size and the levels of CO2 emission per passenger-km. Smaller aircraft tend to be less carbon friendly when both the passenger loads and the flight distance are taken into consideration. So, we further explore the impact of flight distance on CO2 emission in the following part. 4.2. Emission efficiency, aircraft size and range of flights
(Department of Environment, 2008). Fig. 6 shows the passenger capacity distribution of all aircraft flying in the two airspaces during the study period. The Greek airspace tends to have smaller aircraft and its average passenger capacity was 133 in the low season and 154 in the high season. In comparison, the Hong Kong/Sanya airspace tends to have larger aircraft more frequently and its average passenger capacity was 217. In the high season, about 42% of the aircraft operating in the Greek airspace were with a capacity
To analyze the relationship between emission efficiency and flight distance, aircraft are classified into groups. According to RAMS Plus default allocations of aircraft size, there are five categories of aircraft: Heavy (e.g. B747/777 and A330/340), Light medium (e.g. A319/320, B727/737, and Fokker70/100), Light (e.g. Beech, Cessna or Embraer aircraft, and LJ45), Small (e.g. Dash 8, Embraer 145, and Fokker50), and Ultra Medium (e.g. B757 and DC8) (Pejovic et al., 2008). Aircraft in the two airspaces are grouped into five
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B.P.Y. Loo et al. / Journal of Transport Geography 34 (2014) 185–193 Table 4 Aircraft size and aircraft types operated in the Greek and Hong Kong/Sanya FIRs. Aircraft size
Greek FIR (low season)
Greek FIR (high season)
Hong Kong/Sanya FIRs
0–50
AT45, BE20, CL60, CN35, CRJ2, DH8A, DHC6, E135, E145, GLF3, JS41, SB20 AT72, CRJ9, DH8D, E170, E190 A319, A320, B463, B733, B734, B735, B736, B737, E145, E190, MD82, MD87, RJ1H A319, A320, A321, B737, B738, B752
ERJ
A30B, A321, B762, B763
AN26, AT45, BE20, CL60, CRJ1, CRJ2, DH8A, E135, E145, GLF3, GLF4, JS41, SB20, SF34 AT72, B462, CRJ9, DH8D, E170, E190, F100, F70 A319, A320, B463, B703, B732, B733, B734, B735, B736, B737, B738, B752, E190, MD82, MD83, MD87, RJ1H, YK42 A319, A320, A321, B734, B737, B738, B739, B752, B762, MD81, MD82, MD83, MD90 A30B, A310, A321, B752, B762, B763, B764
A332
A332, A333, B753, B763, B772, DC10, IL96
A346
A332, A333, B772
–
A306
– A319, A320, B733, B734, B737, B738, E190 A310, A320, A321, B738, B757, B777, MD90 A310, A330, A332, A343, B738, B763, B767, B777 A330, A332, A333, A340, A343, B772, B744, B747 A320, A330, A333, A340, A346, B744, B772, B773, B777, B744, DC10 B744, B747, B773
–
A333, B744
–
B742
B743, B744
A388, B747
51–100 101– 150 151– 200 201– 250 251– 300 301– 350 351– 400 401– 450 451– 500
Note: Some aircraft types may belong to more than one size group, e.g. A319 and A320, because of different configurations chosen by different airlines.
Table 5 Aircraft size and CO2 emission efficiency in the Greek and Hong Kong/Sanya FIRs. Aircraft size
Percentage of air flights (%) Greek FIR
HK/Sanya FIRs
Low season
High season
0–50 51–100 101–150 151–200 201–250 251–300 301–350 351–400 401–450 451–500
10.7 13.1 25.2 47.9 2.0 0.7 0.2 0.0 0.0 0.1
6.0 9.3 26.6 47.2 7.8 1.8 0.4 0.4 0.2 0.3
Total
100.0
100
Average CO2 emission per dist pax within airspace (tn/pax/km)
Average CO2 emission per pax km (tn/pax/1000 km)
Greek FIR
Greek FIR
HK/Sanya FIRs
Low season
High season
0.2 0.0 24.4 35.4 3.9 12.3 13.2 10.3 0.0 0.4
0.18 0.10 0.11 0.09 0.10 0.11 0.10 – – 0.11
0.24 0.10 0.11 0.08 0.10 0.12 0.11 0.14 0.08 0.14
100.0
0.10
0.10
HK/Sanya FIRs
Low season
High season
0.31 – 0.14 0.14 0.20 0.26 0.19 0.23 – 0.22
0.29 0.17 0.15 0.12 0.11 0.13 0.11 – – 0.13
0.30 0.13 0.12 0.09 0.07 0.10 0.09 0.07 0.08 0.10
0.22 – 0.11 0.10 0.10 0.12 0.10 0.11 – 0.08
0.17
0.15
0.11
0.11
Fig. 7. Range of flights and CO2 emission per passenger-km in Greek FIR (high season). Fig. 8. Range of flights and CO2 emission per passenger-km in Hong Kong/Sanya FIRs.
categories based on size: Heavy (more than 200 seats), Ultra Medium (151–200 seats), Light Medium (101–150 seats), Light (51–100 seats) and Small (no more than 50 seats). Figs. 7 and 8 show the range of flights and CO2 emission per passenger-km in Greek FIR (high season) and Hong Kong/Sanya FIRs respectively. In accordance with previous studies, CO2 emission per passenger-km tend
to decrease as flight distance gets longer. So, the most efficient flights are the long distance flights operated by heavy aircraft and the very short distance flights are the most inefficient ones. In this part of the analysis, the focus is put on the two hub airports within the airspaces. From the bar chart of aircraft size
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AIA (low season)
AIA (high season)
60 50 40 30 20 10 0 0-50
51-100
101-150 151-200 201-250 251-300 301-350 351-400 401-450 451-500
Aircraft passenger capacity Fig. 9. Distribution of aircraft passenger capacities in AIA and HKIA.
Change in distance [%]
15% 10%
Distance Range: 250 nm-500 nm
5% 0% -5% -10% -15%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
Change in cruise CO2 emissions [%] B737-100
Change in distance [%]
15% 10%
B737-400
A320
Distance Range: 500 nm-750 nm
5. Discussion and conclusions
5% 0% -5% -10% -15% -20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
15%
20%
Change in cruise CO2 emissions [%] B737-100
Change in distance [%]
15% 10%
B737-400
A320
Distance Range: 1000 nm-1500 nm
5% 0% -5% -10% -15% -20%
-15%
-10%
The emission estimates derived in this paper rely on two simplifying approximations, that is, (1) the use of representative aircraft/ engine types when fuel burn data for the actual flight are missing; and (2) the use of the flight plan distance as a proxy for the actual flight distance. These two figures may differ due to airspace and weather conditions. To study the effect of these assumptions on the emission estimates, a sensitivity analysis is conducted using several combinations of aircraft types and distance profiles. Based on the approximations, a variety of commonly employed aircraft types in the two FIR airspaces are considered. These include A320, B737-400 and B737-100 which respectively serve 27%, 19% and 6% of air traffic in the Hong Kong–Sanya FIR. In Greece the above aircraft types serve 43%, 22% and 6% of flights in high season and 56%, 5% and 1% in low season respectively. Fig. 10 shows the sensitivity results for the chosen aircraft types and three distance profiles: (i) 250–500 nm, (ii) 500–750 nm, and (iii) 1000– 1500 nm. Case (i) includes the longer intra-zonal flights and all intra-zonal in Greece. It also covers 24% of inter-zonal flights in the Hong Kong–Sanya FIR. Fig. 10 shows that a 15% distance deviation maintains CO2 emission values within a 15% interval for all selected aircraft types. Similar conclusions are valid for case (ii) which includes 12% of inter-zonal flights from/to Greece (in high season) and 21% of inter-zonal flights from/to Hong Kong–Sanya. Case (iii) includes 51% of inter-zonal flights from/to Greece (high season) and 21% of inter-zonal flights from/to Hong Kong–Sanya. In this case, an increase of 15% in distance results in more than 15% increase in CO2 emissions only for B737-400 type. In summary, CO2 estimates are robust to changes in the assumptions made on flight distance profiles and engine type.
-5%
0%
5%
10%
Change in cruise CO2 emissions [%] B737-100
B737-400
A320
Fig. 10. Percentage changes of aircraft CO2 emissions due to variations in flight distance and engine type.
in AIA and HKIA (Fig. 9), the former tends to use smaller aircraft and the latter uses larger aircraft more, which is mainly due to their different hub roles. The mean aircraft size in Athens and Hong Kong was 136 and 260, respectively. The use of the larger aircraft does mean that people living within the airspace of the larger international hub airports would have to tolerate higher CO2 emissions, though the overall CO2 emission per passenger-1000 km over the entire atmosphere is lower.
This paper is concerned with the assessment of aircraft CO2 emissions at three levels by using selected air traffic data: airport level, airspace level and entire flight level. At the airport level, aircraft emissions at the LTO cycle are systematically estimated. At the airspace level, flights are distinguished as intra-zonal flights which depart from and arrive at airports of the same airspace, and inter-zonal flights which extend outside the study airspaces. When analyzing CO2 emissions of inter-zonal flights, boundary exit and entry waypoints are incorporated in order to define the segment of flight attributed to the airspace. At the flight level, both LTO and cruise cycles of the entire flight are considered. Fuel burn rates are taken from the EMEP CORINAIR and ICAO Engine Exhaust Emissions databanks. Detailed fuel burn and carbon emissions are calculated for any time interval. Case studies were conducted using 2010 traffic data for the Greek airspace and the Hong Kong/Sanya airspace. The differences in carbon emissions were mainly attributable to the aircraft fleet composition, passenger load factors, flight distance and different taxi times. Based on the estimation of CO2 emissions at different levels, the paper sheds light on the CO2 emission of air transport activities at two hub airports: AIA and HKIA. Nowadays, hubbing has become a global trend in air transport because of the enormous economic savings (Graham, 1995; Nero, 1999). The environmental impact, especially on CO2 emissions, has been much less understood. This paper clearly shows that CO2 emissions associated with hubbing activities are not evenly distributed over space. This international comparison of AIA and HKIA provides preliminary evidence to suggest that the overall CO2 emission per passenger-1000 km over the entire atmosphere is actually lower with hubbing activities. However, due to the larger aircraft used, especially at international hubs with long flights, the CO2 emissions are highly concentrated at the hub airports and their respective airspaces. As a result, the CO2 emission efficiency is also lower around the airport and its
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airspace, especially for international airport. People living within the airspace of international hub airports have to tolerate higher CO2 emissions. Therefore, more environmental measures should be introduced at international hub airports to address the issues. For instance, HKIA has introduced a fixed ground power and preconditioned air systems for aircraft during park time (Hong Kong Government, 2011). Nonetheless, these measures basically only reduce the CO2 emissions of air traffic beyond the LTO cycle for aircraft parked at gates. Moreover, air traffic congestion at international hub airports should be considered more seriously because it would have more adverse environmental implications on CO2 emission for residents living within the airspace of the international hub airports. Capacity constraints at hub airports need to be addressed in a timely manner. The CO2 emissions associated with longer wait/taxi time for departing aircraft and circling time for arriving aircraft will occur either at the airport or within the airspace, where people are already suffering from higher CO2 emissions associated with the international growth of air transport. References Athens International Airport, 2012. Traffic Statistics.. Chapman, L., 2007. Transport and climate change: a review. J. Transp. Geogr. 15, 354–367. Civil Aviation Department Hong Kong, 2002–2005. Annual Report. Civil Aviation Department Hong Kong, Hong Kong. Cristea, A., Hummels, D., Puzzello, L., Avetisyan, M., 2013. Trade and the greenhouse gas emissions from international freight transport. J. Environ. Econom. Manage. 65, 153–173. Department of Environment, Food and Rural Affairs (DEFRA), 2008. 2008 Guidelines to DEFRA’s GHG Conversion Factors: Methodology Paper for Transport Emissions Factors. British Government, London. EUROCONTROL, 2011. Coverage of 2010 European Air Traffic for the Base of Aircraft Data (BADA)-Revision 3.9. EUROCONTROL Experimental Centre. European Environment Agency, 2009. EMEP/EEA Air Pollutant Emission Inventory Guidebook, Part B: Civil and Military Aviation. European Environment Agency, Copenhagen. Givoni, M., Rietveld, P., 2010. The environmental implications of airlines’ choice of aircraft size. J. Air Transp. Manage. 16, 159–167. Graham, B., 1995. Geography and Air Transport. John Wiley & Sons Ltd., Chichester. Hellenic Civil Aviation Authority, 2011. Air Traffic Statistics. . Hong Kong Government, 2011. LCQ11: Reduce Aircraft’s Air Pollutant Emissions. . Hong Kong International Airport, 2010. Air Traffic Statistics. . Hong Kong International Airport, 2012. Facts & Figures. . International Civil Aviation Organization, 2012. ICAO Aircraft Engine Emissions Databank. . Jardine, C.N., 2005. Part 1: Calculating the Environmental Impact of Aviation Emissions. Environmental Change Institute, Oxford University Center for the Environment, Oxford.
193
Kalivoda, M.T., Kudrna, M., 1997. Methodologies for Estimating Emissions from Air Traffic. MEET Project Report. Kesgin, U., 2006. Aircraft emissions at Turkish airports. Energy 31, 372–384. Lee, J.J., 2000. Historical and Future Trends in Aircraft Performance, Cost, and Emissions. Massachusetts Institute of Technology. Macau International Airport, 2011. Traffic Statistics.. Matsumoto, H., 2004. International urban systems and air passenger and cargo flows: some calculations. J. Air Transp. Manage. 10 (4), 239–247. Meilan International Airport, 2010. Operation Statistics 2010. . Morrell, P., Lu, C., 2007. The environmental cost implication of hub–hub versus hub by-pass flight networks. Transport. Res. Part D: Transp. Environ. 12, 143–157. Nero, G., 1999. A note on the competitive advantage of large hub-and-spoke networks. Transport. Res. Part E: Log. Transport. Rev. 35, 225–239. Nero, G., Black, J.A., 1998. Hub-and-spoke networks and the inclusion of environmental costs on airport pricing. Transport. Res. Part D: Transp. Environ. 3, 275–296. O’Kelly, M.E., 1998. A geographer’s analysis of hub-and-spoke networks. J. Transp. Geogr. 6, 171–186. O’Kelly, M.E., 2012. Fuel burn and environmental implications of airline hub networks. Transport. Res. Part D: Transp. Environ. 17, 555–567. Pagoni, I., Psaraki, V., 2010a. Emissions at Greek airports and the environmental impact of air tourism. In: 89th Transportation Research Board Annual Meeting, Washington DC, U.S.A. Pagoni, I., Psaraki, V., 2010b. A method for the calculation of aircraft emissions. In: 14th World Conference of Air Transport Research Society (ATRS), Porto, Portugal. Pagoni, I., Psaraki, V., 2013. A tool for calculating aircraft emissions and its application to Greek airspace. Transportation Planning and Technology. doi:10.1080/03081060.2013.851510. Papatheodorou, A., Arvanitis, P., 2009. Spatial evolution of airport traffic and air transport liberalisation: the case of Greece. J. Transp. Geogr. 17 (5), 402–412. Pejovic, T., Noland, R.B., Williams, V., Toumi, R., 2008. Estimates of UK CO2 emissions from aviation using air traffic data. Climatic Change 88, 367–384. Penner, J.E., Lister, D., Griggs, D.J., Dokken, D.J., McFarland, M., 1999. Aviation and the Global Atmosphere: A Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. Psaraki, V., Pagoni, I., Loo, B.P.Y., Li, L., 2012. Estimation of Airspace-based carbon emissions: preliminary analysis of Greek and South China airspaces. In: 16th World Conference of Air Transport Research Society (ATRS), Tainan, Taiwan. Schürmann, G., Schäfer, K., Jahn, C., Hoffmann, H., Bauerfeind, M., Fleuti, E., Rappenglück, B., 2007. The impact of NOx, CO and VOC emissions on the air quality of Zurich airport. Atmos. Environ. 41, 103–118. Schipper, Y., Rietveld, P., 1997. Economic and Environmental Effects of Airline Deregulation. Tinbergen Institute, Amsterdam, Rotterdam. Symeonidis, P., Ziomas, I., Proyou, A., 2004. Development of an Emission Inventory System from Transport in Greece. Environ. Model. Softw. 19, 413–421. Turgut, E.T., Rosen, M.A., 2010. Assessment of emissions at busy airports. Int. J. Energy Res. 34, 800–814. Williams, V., Noland, R.B., 2006. Comparing the CO2 emissions and contrail formation from short and long haul air traffic routes from London Heathrow. Environ. Sci. Policy 9, 487–495. Wit, R.C.N., Dings, J.M.W., Leon, P.M.d., Thwaites, L., Peeters, P., Greenwood, D., Doganis., R., 2002. Economic Incentives to Mitigate Greenhouse Gas Emissions from Air Transport in Europe. CE, Delft. Woodmansey, B.G., Patterson, J.G., 1994. New methodology for modeling annualaircraft emissions at airports. J. Transport. Eng. 120, 339–357.
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Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo
CO2 emissions associated with hubbing activities in air transport: an international comparison Becky P.Y. Loo a,⇑, Linna Li a, Voula Psaraki b, Ioanna Pagoni b a b
Department of Geography, The University of Hong Kong, Pokfulam, Hong Kong Faculty of Civil Engineering, National Technical University of Athens, 5 Iroon Polytechniou Street, Zografou Campus, 15773 Athens, Greece
a r t i c l e Keywords: CO2 emissions Air transport Airspace Hubbing Greece Hong Kong
i n f o
a b s t r a c t Hubbing is an important operational practice in air transport. Many studies have been conducted to examine the benefits and impacts of hubbing from an economic perspective. However, its impact on CO2 emissions, especially across different air spaces, is not well understood. This paper explores the impact of hubbing activities in air transport from an environmental perspective. With a detailed methodology and data from the Greek and Hong Kong/Sanya flight information regions (FIRs), three levels of CO2 emissions are estimated: airport-based, airspace-based and flight-based. After contrasting the CO2 emission efficiencies of Athens International Airport (AIA) and the Hong Kong International Airport (HKIA), aircraft type and flight distance are examined to explain their emission efficiency differences. It is found that HKIA is associated with poorer CO2 emission efficiency at the airport and airspace levels because of the larger aircraft and longer flight distance. However, when CO2 emission efficiency at the flight level is considered, HKIA, with a higher passenger load factor, performs better. Major international hub airports should implement additional environmental measures to minimize the impact of hubbing activities on CO2 emissions at the airport and airspace levels. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Hubs are special nodes in a network to facilitate connectivity between interacting places (O’Kelly, 1998). Hubbing is a practice of airlines to accommodate passengers or goods going through intermediate centers, with a purpose to maximize the number of potential connections available to the airlines (Graham, 1995). As a consequence of airline and airport deregulation and privatization, it has become a global trend in air transport (Nero and Black, 1998). Most studies about hubbing activities in air transport focused on the economic dimension. Despite a few doubts about the effect of lower unit costs (Morrell and Lu, 2007), it is believed that airline hubbing can alter airport operations and lead to economies of density, and particularly, economies of scope (Graham, 1995; Nero, 1999). Through the consolidation of passengers from multiple spoke cities across an airspace, larger aircraft can be employed to fly routes more frequently at higher load factor, thus reducing the cost per passenger. Meanwhile, aircraft arrivals and departures as well as passengers at the hub airports can increase accordingly. However, the impact of hubbing on the environment, especially CO2 emissions, has received little attention. This paper ⇑ Corresponding author. Address: Room 1034, Department of Geography, The Jockey Club Tower, The University of Hong Kong, Pokfulam, Hong Kong. Tel.: +852 3917 7024; fax: +852 2559 8994. E-mail address: [email protected] (B.P.Y. Loo). 0966-6923/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jtrangeo.2013.12.006
aims to explore the relationship between CO2 emissions and air traffic activities by using two illustrative cases of hub airports with different mixes of connecting and local traffic and different scales. In particular, it shows that the environmental impact of airport hubs needs to be understood geographically at different levels. The CO2 emission efficiency or environmental impact of airport hubs will differ depending on the geographical scale that one is looking at. Currently, aviation is the second largest contributor of CO2 emissions in the transport sector, accounting for about 2% of the global anthropogenic CO2 emissions (Penner et al., 1999). Considering the rapid growth of air traffic, which is the highest amongst all transport modes (Chapman, 2007), CO2 emissions of the air industry is projected to increase greatly in the future. Most studies about reducing air transport CO2 emissions focus on the aircraft and engine technology, alternative fuels, air traffic management, and regulatory and economic measures (Penner et al., 1999; Lee, 2000). Nonetheless, hubbing, as an important operational practice in air transport, can also have significant impact on CO2 emissions and warrants more research. On the one hand, hubbing can lead to higher CO2 emissions at airport hubs by increasing the volume of air transport activities. On the other hand, it may influence CO2 emission efficiency by changing load factors, aircraft size utilized and flight distance. Several studies have discussed the relationship between CO2 emissions and aircraft size at different flight
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distances (Wit et al., 2002; Jardine, 2005; Williams and Noland, 2006; Department of Environment, 2008; Givoni and Rietveld, 2010). Usually, larger aircraft types have higher CO2 per flightkm but smaller aircraft types with lower emissions per flight-km cannot operate long-haul routes (Williams and Noland, 2006). However, airlines usually choose to use small aircraft types and increase flight frequency, the large aircraft types thereby are limited on long haul routes (Givoni and Rietveld, 2010). Methodologically, load factor should be considered as a factor; and CO2 emission per passenger-km is an important indicator of CO2 emission efficiency (Williams and Noland, 2006; Department of Environment, 2008). Research also found that with the same aircraft type, short-haul air flights tend to have higher CO2 emission per seat-kilometer than medium or long-haul flights (Department of Environment, 2008). It can be explained by the larger component of landing and take-off cycle, which requires more fuel burn for average distance than cruise cycle, in short flights than medium or long flights. When the distance gets longer than about 5000 km, the weight of extra fuel that must be carried may slightly increase the emission factor (Jardine, 2005). However, studies which directly examine CO2 emissions associated with hubbing activities in air transport are still limited. One study found that CO2 emission per passenger of hub and spoke networks is significantly higher than the pointto-point networks in all cases, because of larger aircraft types used at many major hubs due to scarce slots (Morrell and Lu, 2007). Another study, however, shows that the use of larger aircraft with higher load factors by the hub-and-spoke system may be beneficial for reducing the total CO2 emission but deteriorate the environment situation of the hubs (Schipper and Rietveld, 1997). It is also found that hub location could also influence fuel cost and CO2 emission. O’Kelly (2012) shows that a high degree of connectivity could reduce fuel cost while a pure single assignment hub network could increase fuel cost. In this well developed literature context, there is now a need for some comparative analysis, showing empirical evidence on emissions at different scale and type of airport hub. Therefore, two hub airports with different mixes of connecting and local traffic of very different scale are chosen to study the relationship between CO2 emissions and air traffic activities. Comprehensive analysis of this issue calls for an enormous array of data, including aircraft types, flight paths and flight distances, so that the choice of airports in this study was constrained by data availability. It was also recognized that an understanding of local airport operations, and access to supplementary data on flight details would be valuable. Hence, the case studies are the authors’ respective home airports: Hong Kong International Airport (HKIA), as an international hub, and Athens International Airport (AIA), as a national hub. HKIA is commonly recognized as an international hub airport and has over 100 airlines operating flights to about 170 locations worldwide, including 49 destinations in mainland China. Its position as an international airport hub has been established (Matsumoto, 2004). In 2010, its total passenger number reached 50.9 million and total cargo was 4.1 million tonnes. Its total air traffic movement was 30.7 million, of which 24.8 million was for passenger, 5.1 million was for cargo (Hong Kong International Airport, 2012). The percentage of transit passenger in arrivals was about 3% in the period of 2001–2005 (Civil Aviation Department Hong Kong, 2002–2005). For AIA, its total number of passengers in 2010 was 15.4 million, of which 5.6 million was domestic and 9.8 million was international. Its total aircraft movement was 19.2 million, of which 16.7 million was for passenger and 0.9 million was for cargo (Athens International Airport, 2012). The passengers of AIA accounted for 40% of the total passengers in all airports in Greece in 2010 (Hellenic Civil Aviation Authority, 2011). It is both a domestic hub and tourist airport (Papatheodorou and Arvanitis, 2009). Transit passengers
accounted for 0.6% of its total passengers (Hellenic Civil Aviation Authority, 2011). Recognizing the differences between these two airports, this study attempts to quantify and systematically compare the impact of an international hub and a national hub on CO2 emissions at the three geographical levels of the airport site, the airspace region, and the entire atmosphere. The following section introduces an improved methodology, based on Pagoni and Psaraki (2010a, 2010b, 2013) and Psaraki et al. (2012), for the estimation of CO2 emissions at three different levels. Then, the estimation results are presented and contrasted. After that, emission efficiencies of different aircraft types and flight distances are analyzed to understand the environmental impact of the airport hubs better. The paper ends with some discussion and concluding remarks.
2. Methodology When estimating the CO2 emissions, researchers typically only considered the emissions generated by aircraft during the Landing/Take-off (LTO) cycle (Kesgin, 2006; Schürmann et al., 2007; Turgut and Rosen, 2010). This study adopts a three-level approach to estimate CO2 emissions of airport hubs: airport-based, airspacebased and flight-based. CO2 emissions for the entire flights departing from or arriving at the study airspaces are computed using Eq. (1) below. Subsequently, emissions for each of the three levels are determined. Airport-based CO2 emissions include those generated during the LTO cycle. Therefore, each flight is divided in two main parts: the LTO cycle, which includes operations below 3000 feet and is, therefore, related to airport emissions, and the climb-cruise-descent cycle (called cruise thereafter), which includes operations above 3000 feet. The LTO cycle is further divided in five phases: taxi-out, take-off, climb-out, approach landing and taxi-in. The rate of fuel consumption (and the resulting CO2 emissions) is proportional to the drag and has to be balanced by the thrust of the engine. The take-off phase requires full engine thrust, and thus more fuel (European Environment Agency, 2009). As the aircraft ascends to higher altitudes, drag decreases, so does the rate of fuel burn. CO2 emissions within a given airspace are next computed. Two airspaces are studied, the Flight Information Regions (FIR) of Greece and the FIR of Hong Kong/Sanya as defined by ICAO. Airspace-based emissions include those generated during the flight segments contained in these airspaces. For this reason, flights are divided into two categories: intra-zonal flights that depart from and arrive at airports of the same airspace, and inter-zonal flights whose origin and destination airports are not in the same airspace. In the case of the Greek airspace, the distinction coincides with domestic and international flights respectively. This is not true in the Hong Kong/Sanya airspace which is part of the larger China’s airspace. Intra-zonal and inter-zonal flights are illustrated in Fig. 1. Flights from airport 1 to airport 2 are considered as intra-zonal, since both airports belong to the same airspace. In contrast, flights from airport 1 to airport 3 are inter-zonal flights, since airport 3 is not located in the same airspace under study. For the inter-zonal flight departing from Airport 1 in Fig. 1, the emissions include those emitted during take-off (at the airport 1), climb out and the part of the cruise segment that extends until the exit waypoint. Similar considerations apply to inter-zonal flights arriving at the study FIR. At the third level, computed emissions are related to the actual flight distance of the entire flight, whether intra-zonal or inter-zonal. Obviously, for intra-zonal flights, airspace-based and flight-based emissions are the same. Air traffic data were obtained from the Civil Aviation Authorities in Greece and China. The data include flight code, origin and
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Fig. 1. Definition of intra-zonal and inter-zonal flights.
destination airports, aircraft types, distances flown and taxi times in the study airports. Data describing the Flight Information Region (FIR) boundaries (i.e. exit and entry waypoints) were used to define the part of flight within the study FIR. The following formulas are applied to obtain CO2 emissions for any time interval.
ECO2 ¼
! 8 n n X 4 X X > > > FC d;a;cruise þ ne;a FC e;mj tmj for jets > EICO2 > < i¼1 i¼1 j¼1 n X 5 > X > > > FC d;a;mj EI > CO 2 :
187
the flight plan is not available, the flight distance is approximated by the Great Circle Distance (GCD), which is the shortest distance between two points on the surface of a sphere. A factor depending on the GCD and the airspace characteristics is then applied to account for delays, airspace constraints, detours around weather and estimates of the actual distance flown. Finally, time spent in each LTO sub-phase is treated in the following way: taxi times (taxi-in and taxi-out times) are defined for each specific airport within the study areas, while times for climb-out, approach landing and take-off are specified using the typical LTO cycle defined by ICAO (European Environment Agency, 2009). Thus, take-off lasts 0.7 min, climb-out 2.2 min and approach landing 4 min. Once fuel burn and CO2 emissions at the specific airports, airspaces and the entire flights are determined, emission efficiencies are calculated by dividing the CO2 emissions by the product of passenger carried and flight distance. Comparisons of emission efficiencies are made and explained by taking into account the different aircraft types used. It is hoped that a better understanding of the environmental impact of hubbing activities at different levels can be gained. 3. CO2 emissions based on airports, airspaces and entire flights
3.1. Background information of Greek and Hong Kong/Sanya airspaces
for turboprops
i¼1 j¼1
ð1Þ ECO2 is the mass of CO2 emissions for n flights, EICO2 is the emission index of CO2 (which is 3.157 kg CO2/kg fuel), FCd,a,cruise is the fuel consumption during cruise of aircraft type a for flight distance d,ne,a denotes the number of engines for aircraft type a, FCe,mj is the fuel burn rate of engine type e for mj mode of LTO, tmj is the time spent in mode mj of LTO cycle and FCd,a,mj is the fuel consumption of aircraft type a for flight distance d during mj flight mode (j = 1, . . . , 5). In this study, the fuel burn rate for the LTO cycle (FCe,mj) is obtained from the ICAO Engine Exhaust Emissions databank (International Civil Aviation Organization, 2012) and the fuel burn rate during the cruise phase (FCd,a,cruise) is taken from the EMEP CORINAIR database (European Environment Agency, 2009). The ICAO databank does not provide fuel burn rates for turboprop aircraft. Thus, the EMEP CORINAIR database is considered both for the cruise and the LTO cycle to estimate FCd,a,mj for turboprops. Both databases provide aircraft fuel consumption data based on several flight characteristics and have been widely used for emissions modeling (Cristea et al., 2013; Symeonidis et al., 2004). To calculate CO2 emissions during LTO using the ICAO databank, the engine type used has to be determined for each aircraft. The model incorporates a sub-model that maps the actual aircraft type to the engine type used. Both databanks provide the fuel burn rates for specific aircraft types. Thus, a classification of aircraft types to their equivalent ones based on aircraft technical specifications from aircraft manufacturers and other sources was necessary for turboprops (European Environment Agency, 2009; EUROCONTROL, 2011). In a few cases where information on equivalent aircraft types is not available, the aircraft is substituted with an aircraft type of similar characteristics, that is, maximum take-off weight, size, cruise speed and range. Flight distance is based on the route operated per flight defined by the flight plan, which is obtained from the Civil Aviation Authorities. For inter-zonal flights, the route for each flight within an airspace is determined using the boundary waypoints (exit and entry) of the respective FIR. When
In this section, the computational model described above is applied to the Greek airspace and the Hong Kong/Sanya airspace in 2010. The Greek airport system consists of 38 active civilian airports, the majority of which serve both intra-zonal and inter-zonal flights. Fig. 2 shows an overview of the Greek airspace along with the boundary waypoints (red triangles1) and the main airports (red circles). Many Greek airports are located on islands. The Greek air traffic exhibits seasonal demand volatility. During the low season, many airports cease to operate as coordinated airports. They handle almost exclusively domestic flights and accommodate much lower traffic levels than those they have been planned for. During the high season, especially from June to August, tourist flows, mainly interzonal ones, reach their peak and most airports situated on Greek islands are being used. Air traffic in the high season accounts for 60% of the total annual traffic, as indicated in Fig. 3. The Hong Kong/Sanya airspace consists of two FIRs: Hong Kong FIR and Sanya FIR. In contrast to the Greek airspace, most of the area covered by the airspace consists of water body rather than landmass. There are four civilian airports in the Hong Kong/Sanya region: Hong Kong International Airport, Macau International Airport, Haikou Meilan International Airport and Sanya Phoenix International Airport. All these four airports are located on islands and serve both intra-zonal and inter-zonal flights, most of which are inter-zonal flights. Fig. 4 presents the Hong Kong/Sanya airspace along with the boundary waypoints (red triangles) and the airports (red circles). As shown in Fig. 5, Hong Kong/Sanya airspace shows no distinct difference in aircraft movement by month and features no strong seasonal patterns. Air traffic statistics are from the statistics of HKIA (2010), Macau International Airport (2011) and Meilan International Airport (2010). The daily traffic samples in the Hong Kong/Sanya and Greek airspaces form the basis for the calculation of fuel burn and CO2 emissions. To reflect seasonal variations in air traffic volumes, the Greek air traffic data were recorded both for the low and high seasons by selecting two typical days per season. The air traffic in Hong Kong/ Sanya (Fig. 5) features no strong seasonal patterns. Thus, one 1 For interpretation of color in Figs. 2 and 4, the reader is referred to the web version of this article.
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Aircraft Movements (Year 2010)
Fig. 2. The Greek FIR with its boundary waypoints and airports.
Greek airspace 60,000
Low season 22 % of total traffic
High season 60% of total traffic
Low season 18 % of total traffic
50,000 40,000 30,000 20,000 10,000 0 Jan
Feb
Mar
Apr
May
Intra-zone
Jun
Jul
Inter-zone
Aug
Sep
Oct
Nov
Dec
Total
Fig. 3. Seasonal and intra/inter-zonal breakdowns of aircraft movements in the Greek FIR (based on HCAA, 2010).
typical day was taken as representative for the annual air traffic operations. The characteristics of these traffic samples are shown in Table 1. Interpolation factors are applied to determine annual
indexes for 2010. It is assumed that aircraft fleet composition and route lengths on the sample days are typical and representative over 2010. In the estimation, light aircraft (with a seating capacity of up to 10 passengers) primarily used for personal and business flying were omitted. Table 1 shows that intra-zonal flights in the Hong Kong/Sanya airspace, i.e. flights between the four airports within the territory, accounted for only 1% of the total traffic and inter-zonal flights dominated the total air traffic, due to the fact that most of the airspace is above the South China Sea with a few large tourist attractions only. On the other hand, the annual intra-zonal flights over the Greek airspace accounted for 30% of the total traffic, due to the more dispersed human settlements and tourist attractions over the airspace. 3.2. Estimation results of CO2 emissions Fuel burn and CO2 emissions are computed for the two main flight cycles: cruise and LTO. Emission characteristics are reported
Fig. 4. The Hong Kong/Sanya airspace with its boundary waypoints and airports.
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times as in AIA and it carried 3.1 times passengers as that of Athens. The load factor was higher in Hong Kong (0.78) than in Athens (0.72). At the airport level, the fuel burn and CO2 emissions of Hong Kong were 3.29 and 3.52 times higher than those of Athens. Considering the CO2 emissions within the airspaces, the absolute volume of CO2 emissions generated by HKIA was 2.49 times as that of AIA. Finally, the total volume of CO2 emissions of entire flights generated by HKIA was 7.56 times as that of AIA. The aircraft fleet mix was certainly a major factor. Due to its international hub status, flights from and to HKIA are mainly served by large aircraft, as opposed to smaller ones in AIA. Another factor that influences aircraft emissions around the airports is the time spent in LTO sub-modes. For climb out, approach landing and take-off modes, ICAO typical times-in-mode were assumed for many previous studies (Woodmansey and Patterson, 1994; Kalivoda and Kudrna, 1997; Kesgin, 2006; Turgut and Rosen, 2010). In this study, taxiing times incorporated into the calculation procedure are airport-specific. The taxi time was 21 min for HKIA and 12 min for AIA in 2010. This difference accounts for much of the different emission levels between the two airports. Generally, the taxi time depends a lot on the airport size and the volume of air passenger and cargo that it handles. Taking into consideration passenger load factors, the average CO2 emission per passenger carried (tn/pax) was 2.26 times higher at HKIA than that of AIA. However, based on the passengers carried and the total CO2 emitted to the entire atmosphere (both within and outside the respective airspace), the picture changes. The average emission efficiency of HKIA was better than that of AIA. The average CO2 emission per passenger-km was 0.11 and 0.14 for HKIA and AIA respectively.
Aircraft Movements (Year 2010)
40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 Jan
Feb
Mar
Apr May
Hong Kong
Jun
Jul
Macau
Aug
Sep
Haikou
Oct
Nov
Dec
Total
Fig. 5. Seasonal breakdowns of aircraft movements in the Hong Kong/Sanya airspace (Sanya airport data not available).
within the study airspace and their airports as well as the entire flights. Emissions are calculated using Eq. (1) given the aircraft or engine type, the distance of the entire flight, the time-in-mode (during LTO), etc. Especially for aircraft emissions within a given airspace more analytical process is required. For inter-zonal flights, we first calculate the emissions for the entire flight and then further computations are done to determine emissions only for the segment of flight within the study FIR (see Fig. 1). For intra-zonal flight, the distance of the entire flight is used. Emissions based on airports are computed by summing the LTO cycles. Emissions of the entire flights are computed by adding both cruise and LTO cycles of the whole flight distance. Our estimates on fuel burn and CO2 emissions are presented in Table 2. The total fuel burn and CO2 emissions were much higher in the Hong Kong/Sanya airspace in comparison to the Greek airspace. The number of flights in the Hong Kong/Sanya airspace was 1.36 times as in the Greek airspace. CO2 emissions in the Hong Kong/Sanya airspace were 1.27, 1.68 and 3.95 times higher than in the Greek case at the airport, airspace and flight levels. The total distance (of the entire flights) associated with all flights at the Hong Kong/Sanya airspace was 2.06 times as that of Greek flights, but the emissions of the former was 3.95 times higher. In order to examine the CO2 emissions associated with hubbing activities in air transport more closely, AIA and HKIA are considered separately in Table 3. The number of flights in HKIA was 1.5
4. CO2 emissions and hubbing activities in air transport
4.1. Emission efficiency and aircraft size In passenger transport, the economics of using larger aircraft has been well established (Graham, 1995; Nero, 1999). However, for CO2 emissions, the effects of using larger aircraft are not as clearly established because larger aircraft may be less efficient in CO2 emissions in terms of CO2 emission per passenger-km (Williams and Noland, 2006). Notably, it may be influenced by the aircraft technology and the load factor of the flights
Table 1 Characteristics of the Greek and Hong Kong/Sanya airspaces. Characteristics
Greece
Hong Kong/Sanya 2
163,270 nm2 4
Study Area Number of civil airports within study airspace
159,920 nm 38
Study year Annual passenger flights (year 2010) – Total – Intra-zonal – Inter-zonal
2010
2010
306,814 90,617 (30%) 216,197 (70%)
416,100 4380 (1%) 411,720 (99%)
Day of air traffic sample Daily passenger flights in traffic samplea
Two typical days per season Low-density
High-density
430 190 240
1410 329 1081
Low-density
High-density
172,496 74,077 98,418
624,291 119,007 505,284
– Total – Intra-zonal – Inter-zonal Daily distances in traffic sample within study airspacea – Total [km] – Intra-zonal [km] – Inter-zonal [km]
One typical day 1140 12 1128
287,981 7088 280,893
a Greece: Daily flights and distances during each season are given as averages of the two typical days of the same season. The low-density season is from January to April and from October to December (153 days). The high-density season is from May to September (212 days).
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Table 2 Annual aircraft fuel burn and CO2 emissions of the Greek and Hong Kong/Sanya airspaces (flight-based). Study airspace (2010) Greece
Hong Kong/ Sanya
306,814
416,100
Air traffic (number of flights) Fuel burn Airport level: At airports within airspace [Mt] – Total – Intra-zonal – Inter-zonal
0.25 0.06 0.19
0.31 0.00 0.31
Airspace level: Within study airspace [Mt] – Total – Intra-zonal – Inter-zonal
0.52 0.12 0.40
0.87 0.01 0.86
Flight level: Over entire flights [Mt] – Total – Intra-zonal – Inter-zonal
1.76 0.12 1.63
6.95 0.01 6.94
CO2 emissions Airport level: At airports within airspace [Mt] – Total – Intra-zonal – Inter-zonal
0.78 0.18 0.60
0.99 0.01 0.98
Airspace level: Within study airspace [Mt] – Total – Intra-zonal – Inter-zonal
1.64 0.39 1.25
2.76 0.04 2.72
Flight level: Over entire flights [Mt] – Total – Intra-zonal – Inter-zonal
5.55 0.39 5.16
21.95 0.04 21.91
Distance flown within study airspace [km] Distance flown of entire flights [km]
132,085,537 485,839,372
105,112,939 1,001,892,547
Greek FIR (high season)
Greek FIR (low season)
50 40 30 20 10 0 0-50
Table 3 Air traffic activities and CO2 emissions in Athens and Hong Kong (passenger-based). Study airspace (2010) Athens (AIA)
Hong Kong (HKIA)
Air traffic (number of LTOs) Passenger number Average load factor
160,304 15,733,114 0.72
236,520 48,954,165 0.78
Fuel burn – Airport level [Mt] – Airspace level [Mt] – Flight level [Mt]
0.07 0.27 0.79
0.23 0.66 5.98
0.21 0.84 2.50 0.14
0.74 2.09 18.89 0.33
0.14
0.11
CO2 emissions – Airport level [Mt] – Airspace level [Mt] – Flight level [Mt] Average CO2 emission per passenger carried (tn/pax) Average CO2 emission per pax km (tn/ pax/1000 km)
Hong Kong/Sanya FIRs 60
51-100 101-150 151-200 201-250 251-300 301-350 351-400 401-450 451-500
Aircraft passenger capacity Fig. 6. Distribution of aircraft passenger capacities in the Greek and Hong Kong/ Sanya FIRs.
less than 150 seats and 89% were less than 200 seats; in the low season, about 49% of its aircraft were less than 150 seats and 97% were less than 200 seats. In the Hong Kong/Sanya airspace, aircraft less than 100 seats were rarely used and 75% of its aircraft were with passenger capacity more than 150 seats, 24% were having more than 300 seats. The disparity of aircraft capacity is explained not only by the higher passenger demand but also the longer flight distance. Small aircraft are mainly for short-range flights; and most short-range aircraft have less than 150 seats; whereas most longrange aircraft have 150 seats or above (Lee, 2000). Most flights in the Hong Kong/Sanya airspace used HKIA, which is an international hub and has more long-haul international flights than the airports in the Greek airspace. Another difference is that there are turboprops in the Greek airspace operating on air flights (about 13% of total flights in high season), such as AN26, AT45, BE20, DH8A, GLF3, and JS41, while in the Hong Kong/Sanya airspace, all flights are operated by jets (Table 4). Table 5 makes an international comparison of the aircraft size and CO2 emission efficiency within the airspace and of the entire flights at the Greek and Hong Kong/Sanya airspaces. For the total average CO2 emission per passenger-km within the respective airspace, it was lower in the Greek FIR (0.10 in both the low and high seasons) than the Hong Kong/Sanya FIRs (0.17). Generally, there is no clear relationship between the aircraft size and the levels of CO2 emission per passenger-km within an airspace. Once again, when the entire flights are considered, the picture changes. At the flight level, the total average CO2 emission was 0.11 tonnes per passenger-1000 km in the Hong Kong/Sanya airspace. In Greek, it was 0.15 for the low season and 0.11 for the high season. During the high season, the share of larger aircraft (over 200 seats) in the Greek FIR increased noticeably (from 3% in the low season to 10.9% in the high season). This was probably associated with longer distances flown by international tourists during its high season. When the entire atmosphere (both within and outside the respective airspaces) is considered, there is an inverse relationship between the aircraft size and the levels of CO2 emission per passenger-km. Smaller aircraft tend to be less carbon friendly when both the passenger loads and the flight distance are taken into consideration. So, we further explore the impact of flight distance on CO2 emission in the following part. 4.2. Emission efficiency, aircraft size and range of flights
(Department of Environment, 2008). Fig. 6 shows the passenger capacity distribution of all aircraft flying in the two airspaces during the study period. The Greek airspace tends to have smaller aircraft and its average passenger capacity was 133 in the low season and 154 in the high season. In comparison, the Hong Kong/Sanya airspace tends to have larger aircraft more frequently and its average passenger capacity was 217. In the high season, about 42% of the aircraft operating in the Greek airspace were with a capacity
To analyze the relationship between emission efficiency and flight distance, aircraft are classified into groups. According to RAMS Plus default allocations of aircraft size, there are five categories of aircraft: Heavy (e.g. B747/777 and A330/340), Light medium (e.g. A319/320, B727/737, and Fokker70/100), Light (e.g. Beech, Cessna or Embraer aircraft, and LJ45), Small (e.g. Dash 8, Embraer 145, and Fokker50), and Ultra Medium (e.g. B757 and DC8) (Pejovic et al., 2008). Aircraft in the two airspaces are grouped into five
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B.P.Y. Loo et al. / Journal of Transport Geography 34 (2014) 185–193 Table 4 Aircraft size and aircraft types operated in the Greek and Hong Kong/Sanya FIRs. Aircraft size
Greek FIR (low season)
Greek FIR (high season)
Hong Kong/Sanya FIRs
0–50
AT45, BE20, CL60, CN35, CRJ2, DH8A, DHC6, E135, E145, GLF3, JS41, SB20 AT72, CRJ9, DH8D, E170, E190 A319, A320, B463, B733, B734, B735, B736, B737, E145, E190, MD82, MD87, RJ1H A319, A320, A321, B737, B738, B752
ERJ
A30B, A321, B762, B763
AN26, AT45, BE20, CL60, CRJ1, CRJ2, DH8A, E135, E145, GLF3, GLF4, JS41, SB20, SF34 AT72, B462, CRJ9, DH8D, E170, E190, F100, F70 A319, A320, B463, B703, B732, B733, B734, B735, B736, B737, B738, B752, E190, MD82, MD83, MD87, RJ1H, YK42 A319, A320, A321, B734, B737, B738, B739, B752, B762, MD81, MD82, MD83, MD90 A30B, A310, A321, B752, B762, B763, B764
A332
A332, A333, B753, B763, B772, DC10, IL96
A346
A332, A333, B772
–
A306
– A319, A320, B733, B734, B737, B738, E190 A310, A320, A321, B738, B757, B777, MD90 A310, A330, A332, A343, B738, B763, B767, B777 A330, A332, A333, A340, A343, B772, B744, B747 A320, A330, A333, A340, A346, B744, B772, B773, B777, B744, DC10 B744, B747, B773
–
A333, B744
–
B742
B743, B744
A388, B747
51–100 101– 150 151– 200 201– 250 251– 300 301– 350 351– 400 401– 450 451– 500
Note: Some aircraft types may belong to more than one size group, e.g. A319 and A320, because of different configurations chosen by different airlines.
Table 5 Aircraft size and CO2 emission efficiency in the Greek and Hong Kong/Sanya FIRs. Aircraft size
Percentage of air flights (%) Greek FIR
HK/Sanya FIRs
Low season
High season
0–50 51–100 101–150 151–200 201–250 251–300 301–350 351–400 401–450 451–500
10.7 13.1 25.2 47.9 2.0 0.7 0.2 0.0 0.0 0.1
6.0 9.3 26.6 47.2 7.8 1.8 0.4 0.4 0.2 0.3
Total
100.0
100
Average CO2 emission per dist pax within airspace (tn/pax/km)
Average CO2 emission per pax km (tn/pax/1000 km)
Greek FIR
Greek FIR
HK/Sanya FIRs
Low season
High season
0.2 0.0 24.4 35.4 3.9 12.3 13.2 10.3 0.0 0.4
0.18 0.10 0.11 0.09 0.10 0.11 0.10 – – 0.11
0.24 0.10 0.11 0.08 0.10 0.12 0.11 0.14 0.08 0.14
100.0
0.10
0.10
HK/Sanya FIRs
Low season
High season
0.31 – 0.14 0.14 0.20 0.26 0.19 0.23 – 0.22
0.29 0.17 0.15 0.12 0.11 0.13 0.11 – – 0.13
0.30 0.13 0.12 0.09 0.07 0.10 0.09 0.07 0.08 0.10
0.22 – 0.11 0.10 0.10 0.12 0.10 0.11 – 0.08
0.17
0.15
0.11
0.11
Fig. 7. Range of flights and CO2 emission per passenger-km in Greek FIR (high season). Fig. 8. Range of flights and CO2 emission per passenger-km in Hong Kong/Sanya FIRs.
categories based on size: Heavy (more than 200 seats), Ultra Medium (151–200 seats), Light Medium (101–150 seats), Light (51–100 seats) and Small (no more than 50 seats). Figs. 7 and 8 show the range of flights and CO2 emission per passenger-km in Greek FIR (high season) and Hong Kong/Sanya FIRs respectively. In accordance with previous studies, CO2 emission per passenger-km tend
to decrease as flight distance gets longer. So, the most efficient flights are the long distance flights operated by heavy aircraft and the very short distance flights are the most inefficient ones. In this part of the analysis, the focus is put on the two hub airports within the airspaces. From the bar chart of aircraft size
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B.P.Y. Loo et al. / Journal of Transport Geography 34 (2014) 185–193 HKIA
AIA (low season)
AIA (high season)
60 50 40 30 20 10 0 0-50
51-100
101-150 151-200 201-250 251-300 301-350 351-400 401-450 451-500
Aircraft passenger capacity Fig. 9. Distribution of aircraft passenger capacities in AIA and HKIA.
Change in distance [%]
15% 10%
Distance Range: 250 nm-500 nm
5% 0% -5% -10% -15%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
Change in cruise CO2 emissions [%] B737-100
Change in distance [%]
15% 10%
B737-400
A320
Distance Range: 500 nm-750 nm
5. Discussion and conclusions
5% 0% -5% -10% -15% -20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
15%
20%
Change in cruise CO2 emissions [%] B737-100
Change in distance [%]
15% 10%
B737-400
A320
Distance Range: 1000 nm-1500 nm
5% 0% -5% -10% -15% -20%
-15%
-10%
The emission estimates derived in this paper rely on two simplifying approximations, that is, (1) the use of representative aircraft/ engine types when fuel burn data for the actual flight are missing; and (2) the use of the flight plan distance as a proxy for the actual flight distance. These two figures may differ due to airspace and weather conditions. To study the effect of these assumptions on the emission estimates, a sensitivity analysis is conducted using several combinations of aircraft types and distance profiles. Based on the approximations, a variety of commonly employed aircraft types in the two FIR airspaces are considered. These include A320, B737-400 and B737-100 which respectively serve 27%, 19% and 6% of air traffic in the Hong Kong–Sanya FIR. In Greece the above aircraft types serve 43%, 22% and 6% of flights in high season and 56%, 5% and 1% in low season respectively. Fig. 10 shows the sensitivity results for the chosen aircraft types and three distance profiles: (i) 250–500 nm, (ii) 500–750 nm, and (iii) 1000– 1500 nm. Case (i) includes the longer intra-zonal flights and all intra-zonal in Greece. It also covers 24% of inter-zonal flights in the Hong Kong–Sanya FIR. Fig. 10 shows that a 15% distance deviation maintains CO2 emission values within a 15% interval for all selected aircraft types. Similar conclusions are valid for case (ii) which includes 12% of inter-zonal flights from/to Greece (in high season) and 21% of inter-zonal flights from/to Hong Kong–Sanya. Case (iii) includes 51% of inter-zonal flights from/to Greece (high season) and 21% of inter-zonal flights from/to Hong Kong–Sanya. In this case, an increase of 15% in distance results in more than 15% increase in CO2 emissions only for B737-400 type. In summary, CO2 estimates are robust to changes in the assumptions made on flight distance profiles and engine type.
-5%
0%
5%
10%
Change in cruise CO2 emissions [%] B737-100
B737-400
A320
Fig. 10. Percentage changes of aircraft CO2 emissions due to variations in flight distance and engine type.
in AIA and HKIA (Fig. 9), the former tends to use smaller aircraft and the latter uses larger aircraft more, which is mainly due to their different hub roles. The mean aircraft size in Athens and Hong Kong was 136 and 260, respectively. The use of the larger aircraft does mean that people living within the airspace of the larger international hub airports would have to tolerate higher CO2 emissions, though the overall CO2 emission per passenger-1000 km over the entire atmosphere is lower.
This paper is concerned with the assessment of aircraft CO2 emissions at three levels by using selected air traffic data: airport level, airspace level and entire flight level. At the airport level, aircraft emissions at the LTO cycle are systematically estimated. At the airspace level, flights are distinguished as intra-zonal flights which depart from and arrive at airports of the same airspace, and inter-zonal flights which extend outside the study airspaces. When analyzing CO2 emissions of inter-zonal flights, boundary exit and entry waypoints are incorporated in order to define the segment of flight attributed to the airspace. At the flight level, both LTO and cruise cycles of the entire flight are considered. Fuel burn rates are taken from the EMEP CORINAIR and ICAO Engine Exhaust Emissions databanks. Detailed fuel burn and carbon emissions are calculated for any time interval. Case studies were conducted using 2010 traffic data for the Greek airspace and the Hong Kong/Sanya airspace. The differences in carbon emissions were mainly attributable to the aircraft fleet composition, passenger load factors, flight distance and different taxi times. Based on the estimation of CO2 emissions at different levels, the paper sheds light on the CO2 emission of air transport activities at two hub airports: AIA and HKIA. Nowadays, hubbing has become a global trend in air transport because of the enormous economic savings (Graham, 1995; Nero, 1999). The environmental impact, especially on CO2 emissions, has been much less understood. This paper clearly shows that CO2 emissions associated with hubbing activities are not evenly distributed over space. This international comparison of AIA and HKIA provides preliminary evidence to suggest that the overall CO2 emission per passenger-1000 km over the entire atmosphere is actually lower with hubbing activities. However, due to the larger aircraft used, especially at international hubs with long flights, the CO2 emissions are highly concentrated at the hub airports and their respective airspaces. As a result, the CO2 emission efficiency is also lower around the airport and its
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airspace, especially for international airport. People living within the airspace of international hub airports have to tolerate higher CO2 emissions. Therefore, more environmental measures should be introduced at international hub airports to address the issues. For instance, HKIA has introduced a fixed ground power and preconditioned air systems for aircraft during park time (Hong Kong Government, 2011). Nonetheless, these measures basically only reduce the CO2 emissions of air traffic beyond the LTO cycle for aircraft parked at gates. Moreover, air traffic congestion at international hub airports should be considered more seriously because it would have more adverse environmental implications on CO2 emission for residents living within the airspace of the international hub airports. Capacity constraints at hub airports need to be addressed in a timely manner. The CO2 emissions associated with longer wait/taxi time for departing aircraft and circling time for arriving aircraft will occur either at the airport or within the airspace, where people are already suffering from higher CO2 emissions associated with the international growth of air transport. References Athens International Airport, 2012. Traffic Statistics.
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Kalivoda, M.T., Kudrna, M., 1997. Methodologies for Estimating Emissions from Air Traffic. MEET Project Report. Kesgin, U., 2006. Aircraft emissions at Turkish airports. Energy 31, 372–384. Lee, J.J., 2000. Historical and Future Trends in Aircraft Performance, Cost, and Emissions. Massachusetts Institute of Technology. Macau International Airport, 2011. Traffic Statistics.