1
Measuring air connectivity between China and Australia
Abstract: This paper assesses air connectivity between China and Australia for the period
2005–16 using a Connectivity Utility Model. Our direct connectivity measure shows that as a
gateway city, Sydney continues to play a key role in facilitating the movements of people and
goods between China and Australia. Guangzhou has become the city best connected with
Australia since 2011 as measured by direct connectivity. When indirect connections are
considered, the largest increases in overall connectivity from 2005 to 2016 can be observed
among Australia’s major capital cities, particularly Sydney, Melbourne and Brisbane.
Chinese carriers are the key drivers behind the increases. There have been rises and falls for
airports serving as a hub between China and Australia. Guangzhou has forged its strong
status as a transfer hub between Australia and China thanks to the quick expansion of China
Southern. The gaps between Guangzhou and other transfer hubs measured by hub
connectivity have widened since 2010.
Key words: Direct connectivity; indirect connectivity; hub connectivity; air transport; China;
Australia
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1. Introduction
Air transport between Australia and China has experienced a phenomenal growth during the
past decade. In 2005 only Beijing, Shanghai and Guangzhou in China had direct flights to
Sydney and Melbourne in Australia. At that time, many Chinese travellers used Hong Kong,
Singapore and even Seoul, Korea as a transfer point to Australia. However, in 2016, there
were seven Chinese airlines providing direct flight services between China and Australia –
from ten cities in China to Australia’s major capital cities, although Sydney and Melbourne
remained the most interested destinations.
In December 2016 an open skies arrangement was concluded between China and Australia. It
removed all capacity restrictions for each country’s airlines. This arrangement arose through
an intention to cater for the increasing number of Chinese visitors to Australia. Since 2010
Chinese tourists have been Australia’s biggest spenders, and the China market is expected to
be the most valuable inbound market for the next decade (Zhang and Peng, 2014).
Geographers have identified the expansion of air transport as one of the key drivers of
globalisation (Adey et al., 2007). Air transport has not only facilitated tourism between China
and Australia, but it has also provided access to new markets for businesses in both countries.
China has been Australia’s largest trading partner since 2009. Although China and Australia
concluded a free trade agreement in 2015, it is understood that trade barriers due to
infrastructure and transport services, especially air transport services, cannot be resolved
easily in a free trade agreement because most free trade agreements do not address the
liberalisation of air transport services. Improving connectivity is increasingly a topic at the
top of international trade and transport policy agenda (Calatayud et al., 2016).
Scholarly research into the air transport connections between the two nations is rare. Gao and
Koo (2014) might be the only academic paper to discuss the emergence of the ‘Canton
Route’. A comprehensive assessment of the air connectivity between the two countries is
lacking. Huang and Wang (2017) investigated the spatial patterns of indirect connections of
the top hub airports in China for 2012 and 2015, but they did not specifically address the
connectivity between China and Australia. Considering the strong economic ties between
China and Australia, it is necessary to assess the air connectivity between the two nations
given its strong relationship to economic development and growth (Basile et al., 2006; Zhang,
2012; Banno and Redondi, 2014; Li and Qi, 2016). It is particularly important for a city or a
3
region to understand the level of its air connectivity in the air transport network in order to
attract tourists, business investment and human capital.
In considering both the direct and indirect connections for Chinese and Australian cities for
the period 2005–16, we make two contributions. First, this paper extends the Connectivity
Utility Model (ConnUM) developed in Zhu et al. (2018) and considers all of the flight
schedule information in calculating the air connectivity at the airport/city and national levels,
which is of great value to governments at all levels for identifying gaps in air service
provisions, detecting weak points in the existing transport network and seeking ways to
improve the reliability and accessibility of the network to reduce travel time and costs (Hadas
et al., 2017). This research also examines the hub connectivity of the key airports that have
served as transfer hubs between China and Australia, which will provide a strong basis for
airport managements to develop strategies to boost their hub status. Second, in the airline
economics literature there are numerous studies examining the relationship between air
service provision and economic activities (see, e.g., Button and Taylor, 2000; Matsumoto,
2004, 2007, Matsumoto et al., 2016). However, few of these studies use a comprehensive and
systematic measure of air transport service provision that captures consumers’ utility. The
comprehensive and consistent measure for air connectivity developed in this research will
provide a better proxy of air service provision and can be used in future studies examining the
effects of air transport on economic activities.
The next section will review the relevant literature, followed by a description of the ConnUM
for measuring air connectivity. Section 4 reports the results and interpretations. The last
section provides a summary and a conclusion.
2. Literature review
A safe and well-connected transport network is vital for the efficient functioning of a
country’s economy. Numerous literature has examined the intertwined relationship between
air transport and economic development (see, e.g., Cristea 2011; Banno and Redondi, 2014;
Van De Vijver et al., 2014; Matsumoto et al., 2016). Baker et al. (2015) reveal significant bi-
directional relationship between regional economic growth and regional air transport services
in Australia. Blonigen and Cristea (2015) provide strong evidence that a 50% increase in an
average city’s air traffic growth could result in an additional 7.4% increase in real GDP in the
US. Liu et al. (2013) show that cities with a higher level of air connectivity are appealing to
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globalised business service firms, which in turn can stimulate the development of aviation
connections. However, Ng et al. (2018) argue that transport infrastructures have a strong
influence over the patterns of regional and national development, but such influence is greater
on the more established cities ad areas. In particular, O'Connor (2010) and O'Connor et al.
(2016) pointed out that a large part of the world’s sea and air cargoes are handled at a small
number of cities and places, which play a significant role in the location decisions of the
largest logistics service companies. Therefore, the aviation gateway cities have significant
implications to a country’s economy (O’Connor and Fuellhart, 2015).
There has been much literature evaluating air services at cities (see, e.g., Derudder and
Witlox, 2014). For a long time, Tokyo, Hong Kong and Singapore were the most important
international aviation hubs in East Asia (Matsumoto, 2004, 2007). However, Matsumoto et
al. (2016) found that Seoul, Guangzhou, Ho Chi Minh City and Hanoi, have been rising
rapidly in terms of their aviation hub status since 2000. De Wit et al (2009) reveal that while
Tokyo has the best network performance and hub competitive position in the Asia Pacific
rim, Chinese airports are experiencing the most striking growth of network development.
They also found that the network performance deteriorates at Oceanian airports. O’Connor et
al. (2018) examined the distribution of air services among Chinese cities between 2005 and
2015 with a consideration of the number of routes, departures, and the number of seats
available. They found that the competitive position of Chinese cities in the nations’ air
transport market did not changed much during the studied period. Although a small number
of regional cities have strengthened their role in the air transport network, the ranks of the
seven leading cities remain unchanged. In particular, cities in the three mega-metropolitan
regions, Beijing–Tianjin, Shanghai, Hong Kong–Shenzhen–Guangzhou, play a dominant role
in China’s air transport systems (Zheng et al. 2009, Wu and Dong, 2015).
Air transport deregulation is one of the key factors that drive the changes in airlines’
behaviour and the provisions of air services at national, regional and local levels (Goetz and
Sutton, 1997; Burghouwt and Hakfoort, 2001; Wei, 2007; Williams, 2009). Wang et al.
(2014) analyse the evolution process of the air transport network of China from 1930 to 2012
and report a three-stage evolvement since the 1980s when the deregulation process began:
hub formation, a complex network structure and emerging multi-airport systems. Over the
last 20 years, Chinese major carriers have developed multiple hubs (bases) through mergers
and acquisitions (Zhang and Round, 2008). Such expansion was supported by the local
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governments--many secondary cities in China provide subsidies to attract airlines to build
bases and operate international services out of their local airports (Zhang and Lu, 2013). In
the case of Australia, O’Connor (1998) observed a shift of the country’s international airline
linkages in favour of the Asian countries from 1985 to 1996, reflecting the shifts in trade and
immigration. Such shifts strengthened the role of Cairns and Brisbane as gateways for the
tourism industry, but the dominance role of the nation’s aviation gateways, Sydney and
Melbourne, was not weakened, which is still the case today (Fuellhart and O’Connor, 2013;
O’Connor and Fuellhart, 2015; O’Connor, 2018).
When assessing the air services at cities and regions, researchers have used different
definitions for air connectivity. Traditional approaches to measuring air connectivity include
the number of destinations or the number of direct flights from/to an airport. A good review
of recently developed connectivity models can be found in Burghouwt and Redondi (2013),
Suau-Sanchez et al. (2015), Calatayud et al. (2016) and Zeigler et al. (2017). Calatayud et al.
(2016) show that some transport economics literature defines connectivity based on
infrastructure availability and capacity (see, e.g., Moreno and Lopez, 2007; Wilmsmeier and
Hoffmann, 2008). Márquez-Ramos et al. (2011) referred to this as a narrow concept of
connectivity because this definition only focuses on the physical properties of a network. A
broader definition for connectivity emphasises the importance of the availability and the
capacity of transport services within the framework of complex systems theory (see, e.g.,
Alderighi et al., 2007; Malighetti et al., 2008; Redondi et al., 2011). Zeigler et al. (2017) note
that one stream of air connectivity measures is constructed based on flight schedule date such
as the NetScan (Veldhius, 1997) and the accessibility index (Redondi et al., 2013) models.
Another stream of measures is demand-based studies which require the use of passenger
traffic or booking data (e.g., Wang et al., 2011). The connectivity measure used in this
research is the single transport mode version of the model developed in Zhu et al. (2018) that
has its origin in the NetScan model. The ConnUM incorporates multiple quality-adjustment
factors such as capacity and velocity penalties so as to correct/adjust for the quality of a
connection. It can be used to measure the direct and indirect, single- and multi-modal
connections of a city, region or country. By revealing the level of air connections of cities in
China and Australia using such a comprehensive measure, this research will add knowledge
to the understanding of the hierarchy and the specific roles of cities in the two countries.
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3. Methodology
2.1 The Connectivity Utility Model
The ConnUM is modified to consider the air transport mode only. By incorporating multiple
connection quality factors such as travel time, speed, comfort and convenience, the
connectivity calculated in this study captures air passengers’ travel utility and reflects both
the quantity and quality levels of air transport services provided to passengers. The
construction of a modified ConnUM measuring air connectivity is briefly described below.
1
Previous literature such as Vowles (2001), Bowen (2010), and O’Connor and Fuellhart
(2012) has shown that airline type, aircraft used and low cost carrier (LCC) operations are
significant factors that can shape the air service at a city. In fact, for most travellers the
availability of seats (capacity), trip duration (velocity) and the quality of transfer (for indirect
connections) are among the most important quality factors. These are considered in our
model. Considering that dissatisfaction with any one of those three preferences would lead to
0 utility (Keeney, 1974), a multiplicative utility function is adopted. We use equal weights for
all preferences.
2
For convenience, the utility scores of capacity, velocity and transfer quality
are named as capacity discount, velocity discount and transfer discount, respectively. The
connectivity of flight k from airport i to airport j is:
3
Connectivity
=
×
×
(1)
where
denotes the capacity discount for connection k between airports i and
j.
represents the velocity discount and
is the transfer discount.
4
As noted in Zhang et al. (2017), capacity is a key indicator measuring connection quality.
Larger aircraft are usually preferred by passengers as they tend to carry more passengers, and
provide more seats and space (Coldren et al., 2003). A concave function in the form of a
1
Readers can refer to Zhu et al. (2018) for the full methodology for constructing the multi-modal connectivity
measure based on ConnUM.
2
We tried different weights and the results were quite consistent.
3
k represents a unique connection (direct or indirect) between origin airport i and destination airport j. Every
connection on a route between airport i and airport j is treated as a different connection, even for the
connection with the same flight number on a different date, as the connection might use a different type of
aircraft and thus represents a unique level of service quality. This implies that the frequency for every
connection is always 1. We understand that in reality, many people would regard a flight as a direct flight if it
has an intermediate stop, but there is no change in the flight number.
4
Transfer discount is always 1 for direct flights.
7
squared root is used for measuring capacity discount. This is because the marginal benefit of
having more seats in a plane diminishes after a certain point, and the extra benefit of having
an additional 100-seat flight is greater than that of changing a 100-seat aircraft to a larger
aircraft with a capacity of 200 seats for a connection. This reflects the fact that passengers
place more value on frequency (Wei and Hansen, 2005). We choose the capacity of the
largest aircraft in use in 2005, 434 seats, as a benchmark to calculate the capacity discount,
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which can be expressed as:
=
Seat
(2)
where
represents the number of seats on connection k. For an indirect connection it is
the number of seats of the flight segment with a smaller seat capacity. Seat
is the number of
seats of the benchmark aircraft, which is 434 in this study.
Velocity is another important indicator for connection quality. If the passenger takes an
indirect connection, the time he/she spends at the transit stop would be more uncomfortable
or stressful than the in-flight time. Therefore, both the extra time needed at the airports and
the penalty for transfer should be reflected in the velocity discount. The velocity discount is
calculated based on the following system of equations:
=
+ p
×
+
(3)
Velocity
=
(4)
D
=
Velocity
Velocity
(5)
where
is the adjusted time length (duration) of connection
from
to
. The scheduled in-flight time between two airports is the difference
between the scheduled arrival time and scheduled departure time,
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represented by
. Extra time at the airports is represented by
. For indirect
connections the additional penalty for spending time at the transfer airport is a penalty factor
5
In effect, we can choose any type of aircraft as our benchmark because it will only affect the scale of the
capacity discount and, subsequently, the connectivity scores without hampering the purpose of comparing
these scores across airports or over years.
6
Transfer time is included in indirect connections. We understand that the actual flying time might be shorter
than the published estimated flying time as these days longer taxiing time at the busy airports is required and
the estimated flying time will have to consider this as well as other infrastructure constraints.
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p
times
. The velocity (Velocity
) is calculated by dividing the great circle
distance between airport i and airport j by the adjusted time duration. The velocity discount is
calculated by comparing the velocity of a connection against the benchmark velocity,
Velocity
, which is assumed to be 850 km/h.
7
In this study we assume that the extra time
needed at the airports (both departure and arrival airports) is 100 minutes for domestic flights
and 180 minutes for international flights. Also, following previous literature such as De Wit
et al. (2009), we assume the extra penalty for transfer time to be 50%.
The quality of indirect connections is largely dependent on the quality of transfer. Two
aspects of the transfer quality are considered here: time and service. The time quality for
transfer measures the quality of transfer time. When the transfer time is too short, passengers
will have to rush to the boarding gate for the next flight, but still have a high chance of
missing the connection. Thus, the transfer time quality is very low. However, if the transfer
time is too long, the long wait will result in lower transfer quality. The transfer time quality
function is thus built by mapping the difference between the transfer time and the minimum
connection time (MCT) at an airport,
, for each indirect connection.
8
We set the time
quality as 0.2 when the transfer time is the same as the MCT, 1 when the transfer time is 30
minutes longer than the MCT and 0.7 when the transfer time is 3 hours or more than the
MCT.
9
The time quality function is as follows:
=
0,
< 10
+ 10× 0.02, 10
< 0
30
× 0.8 + 0.2, 0
< 30
1
30
500
, 30
180
0.7,
> 180
(6)
7
Statista (2018) suggests that major commercial jet aircraft cruise at about 420–500 knots or 778–926 km/h.
Therefore, we assume that the average speed is 850 km/h.
8
The MCT standards used here are those published by China Eastern Airline at Shanghai Pudong International
Airport and Shanghai Hongqiao International Airport. That is, the MCT is 120 minutes if the transfer is within
the same terminal and 160 minutes if the transfer occurs between terminals.
9
We acknowledge that the assignment of the value of 0.2 is arbitrary and the connectivity values will depend on
the values assigned to the time and service qualities. One can always try different values for these parameters,
but this would not change the ranking order of the airports considered. However, future studies can consider
using survey data to elicit more accurate values for these parameters.
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where
represents the time quality for using indirect connection k from airport i to airport
j;
represents the difference between the transfer time and the MCT at the transfer airport
for indirect connection k.
The service quality for transfer measures the quality of transfer services such as walking
distance, waiting lounge comfortableness and the availability of a flexible arrangement when
a connection is missed. Service quality is different for different indirect connections. In most
instances the service quality is mainly decided by the relationship between the airlines
operating the two flight segments. When both flights are operated by the same airline or by
airlines in the same alliance, the transfer service quality is generally better than the situation
where the two segments are operated by two separate airlines without any cooperation
agreement. In the case where one flight is operated by a LCC, the service quality would be
relatively less desirable. Therefore, in this research the service quality for a transfer is
assumed to be 1 when both flights are operated by the same airline and there is no LCC
involved. A value of 0.9 is assigned for the service quality when the two segments are served
by two airlines from the same alliance group. The value is 0.3 when two full-service airlines
from different alliance groups are involved.
10
If the whole journey is served by the same
LCC, the service quality is also assumed to be 0.3. We give a value of 0.1 to service quality
for all other situations.
11
The transfer discount can be expressed as:
=
×
(7)
where
represents the time quality for the transfer of indirect connection k from airport i
to airport j, and
is the service quality of transfer for indirect connection k from airport i to
airport j.
10
It should be noted that this study does not distinguish the experiences of different travel cabins.
11
The transfer service quality may also be affected by the transfer airport’s facilities and services, which may
cause an overestimation of indirect connectivity if the transfer airport’s facilities and services are less
desirable to passengers.
10
For simplicity, this research only considers one transfer for an indirect connection.
12
This is a
realistic simplification as the vast majority of passengers travelling between Australia and
China use direct flights or indirect flights with only one transfer point. Two types of
connections, direct and indirect, are shown in Figure 1. De Wit et al. (2009) also identified a
third type of connection: hub connection. In the indirect connection shown in Figure 1,
airport x acts as a transfer hub, enabling the cooperation between connection k
1
and
connection k
2
. This type of hub connectivity is known as centrality (Burghouwt and Redondi,
2013). The centrality of airport x is the total connectivity of indirect connections with a
transfer at airport x. The centrality of airport x can be defined as:
centrality
= connectivity
,,
(8)
Figure 1: Two types of connections: direct and indirect.
The directional connectivity from airport i to airport j is the connectivity of route ij, which
is the aggregate connectivity for all connections (direct and indirect) on the route:
connectivity
= connectivity
(9)
The connectivity of airport i is the aggregate of the connectivity for all routes starting or
ending at airport i, which can be expressed as:
connectivity
= connectivity
+ connectivity
(10)
2.2 The data
Given the restriction of data availability, we consider a period from 2005 to 2016. For each
year, two weeks’ flight information was collected: 10–16 April and 10–16 November. All the
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We understand that we may underestimate the connectivity when passengers use two or more transfers.
However, in 2016, only 1.7% of the passengers travelling from China to Australia involved two or more
transfers according the AirportIS database.
Airport i
Airport j
Connection K
Airport
j
Airport x
Airport i
Connection K1
Connection K2
11
direct flight information to and from China and Australia was extracted from IATA AirportIS
database.
13
The flight data includes the flight number,
14
the number of seats, the origin
airport, the destination airport, and the take-off and landing times. The time zone is then
matched to every airport and the airline block time is calculated in minutes. As the
connection dataset is a full set of all available air services to and from Chinese and Australian
airports, the overall connectivity and centrality are produced for all airports involved.
15
Indirect connections are generated with the direct connection data for all Chinese and
Australian airports, following the enumeration methods with programs coded in R language.
The produced indirect connection dataset is then filtered with loose constraints in travel
distance and transfer time: when taking indirect connections, the total distance of the
connection is constrained to be smaller than twice the direct distance between the origin and
destination airports; the transfer time at hub airports is limited to between 30 minutes and 24
hours. The distance and transfer time constraint can be expressed as:
+
2 ×
(11)
30
1440
(12)
where
denotes the great circle distance
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between origin airport i and transfer
airport x;
denotes the great circle distance between transfer airport x and
destination airport j;
denotes the great circle distance between origin airport i and
destination airport j;
denotes the transfer time at the transfer airport for indirect
connection k from airport i to airport j. Another obvious constraint for indirect flights
connecting China and Australia is that the original airport and the destination airport must be
in either China or Australia.
4. Results and Analysis
3.1. Direct connectivity
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The AirportIS database is constructed based on the reported information from IATA’s Billing and Settlement
Plan (BSP). The BSP reported information does not include data from low cost carriers, direct airline sales,
scheduled charter or other carriers not using agencies. In areas where the ticket sales are not supported by BSP,
there would be no ticket sales information.
14
For code-sharing flights, only the operating flights are retained.
15
Flights from mainland China to Hong Kong and Macau are treated as international flights.
16
All great circle distances in this research are calculated with the Python package ‘Geographiclib’ using tGPS
coordinates of OD.
12
Figures 2 and 3 show the direct connectivity between Australia and China of selected airports
of the two countries for the period 2005–16, respectively. It can be seen that before 2010 only
Sydney and Melbourne had direct connections to mainland China and only Shanghai,
Guangzhou and Beijing were connected to Australia with direct services. Through China
Southern, direct air services became available for Brisbane and Perth from 2010 and 2011,
respectively. The direct connectivity score of Sydney almost tripled from 52 in 2005 to 148 in
2016. During the same period, the direct connectivity score for Melbourne increased from 14
to 101. The connectivity difference between Sydney and Melbourne can be considered
substantial, given that in 2016 the connectivity sores for Brisbane and Perth were only 19 and
14, respectively. This is consistent with the findings reported in O’Connor (2018) that
Sydney accounted for about 50% of passengers on direct services between China and
Australia in 2016 and this number was about 30% for Melbourne. It is apparent that Sydney
was in a leading position throughout the period in terms of the direct connectivity with China.
The 2009 global financial crisis had a strong and negative impact on air connectivity and the
direct connectivity almost halved for Sydney in 2009 but it rose quickly in 2010 and
continued to grow in the following years. As a gateway and global city (O’Connor, 2018),
Sydney continues to play a key role in facilitating the movements of people and goods
between China and Australia.
Of the Chinese cities, Shanghai Pudong had the lead in direct connectivity before 2011,
followed by Guangzhou and Beijing. However, Guangzhou surpassed Shanghai from 2011
and became the city best connected with Australia. Chengdu, Nanjing and Chongqing
established their direct connection with Australia from 2013 and more Chinese secondary
cities launched direct services to Australia in 2016, including Hangzhou, Qingdao, Fuzhou,
Xiamen and so on. The ongoing deregulation in China’s domestic market and the more
liberal arrangement between China and Australia is key to this outcome.
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Figure 2: Direct connectivity (vertical axis) between Australia and China at Australian
airports, 2005–16.
Figure 3: Direct connectivity (vertical axis) between Australia and China at Chinese airports,
2005–16.
0
20
40
60
80
100
120
140
160
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Sydney Melbourne Brisbane Perth Cairns Gold Coast
0
20
40
60
80
100
120
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Guangzhou Shanghai Pudong Beijing Chengdu Xiamen Nanjing
14
As shown in Figure 4, in 2016 China Southern was the largest contributor to the direct
connectivity between China and Australia (38%), followed by China Eastern’s 21.8% and Air
China’s 18.6%. Qantas made a contribution of only 6.2%.
Figure 4: Individual carriers’ contribution to direct air connectivity between China and
Australia in 2016.
3.2 Overall connectivity (direct and indirect)
The overall connectivity scores of Chinese and Australian airports considering both direct
and indirect connections are reported in Appendices 1 and 2. The airports are ranked based on
their overall connectivity in 2016. Note that only indirect connections with one transfer point
are considered in constructing the overall connectivity indices. Thanks to the contribution of
indirect connections, we can see that the values of overall connectivity of the major cities in
the two countries are 10–20 times higher than those of direct connectivity, implying that
indirect connections give people living in these cities more choices for travelling between
China and Australia.
Due to the small number of large cities, in general, Australia’s main airports have much
higher overall connectivity than their Chinese counterparts as these cities have good
connections with major Asian hubs, which gives them a higher level of indirect connectivity.
However, for its smaller cities their overall connectivity (purely due to indirect connections)
China
Southern,
108.21, 38%
China Eastern,
59.09, 21%
Air China,
52.73, 19%
Qantas, 17.69,
6%
Xiamen, 17.21,
6%
Sichuan,
12.95, 5%
Hainan, 9.91,
3%
Others, 6.54,
2%
China Southern China Eastern Air China Qantas
Xiamen Sichuan Hainan Others
15
is rather small in value, suggesting limited air services between Australia’s regional and
remote areas and its capital cities.
Figures 5 and 6 present a comparison of the connectivity between 2005 and 2016 for
Australian and Chinese airports, respectively. A substantial increase in overall connectivity
can be observed for Sydney, Melbourne, Brisbane and Perth airports in the former. The
increases for major Chinese airports were also impressive but were not comparable with the
large increases in Sydney and Melbourne. The trend can be seen more clearly in Figures 7
and 8, which show that the growth rate is much higher for Australia’s major cities,
particularly Sydney and Melbourne. Shanghai remained the best connected city in China
throughout the period. However, the connectivity of Beijing and Guangzhou had caught up to
it quickly by the end of 2016. Beijing surpassed Guangzhou to be the second best connected
city with Australia, and was very close to Shanghai. The connectivity scores of many
secondary cities did not differ much and they experienced modest growth (Figure 7).
Figure 5: Comparison of overall connectivity for Australian cities (the size of the circle
represents the level of connectivity in 2016).
16
Figure 6: Comparison of overall connectivity for Chinese cities (the size of the circle
represents the level of connectivity in 2016).
Figure 7: Overall connectivity (vertical axis) evolution trend at major Australian airports,
2005–16.
0
500
1000
1500
2000
2500
3000
3500
4000
4500
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Sydney Melbourne Brisbane Perth Cairns Adelaide
17
Figure 8: Overall connectivity (vertical axis) evolution trend at major Chinese airports,
2005–16.
We have calculated the loss of the overall connectivity between China and Australia if a
carrier is excluded from the network in 2016. The results are reported in Table 1. If China
Southern dropped out in 2016 the overall air connectivity between the two countries would
suffer a loss of 39.1%. The exit of China Eastern would result in a reduction in connectivity
by 20%, followed by Air China’s 17.7%, Qantas’ 14% and Cathay Pacific’s 12.6%.
Table 1: The loss of overall connectivity if an airline ceased operation in 2016.
Airline
Loss of air connectivity
China Southern
39.1%
China Eastern
20.0%
Air China
17.7%
Qantas
14.0%
Cathay Pacific
12.6%
Cathay Dragon
11.3%
Singapore Airlines
6.1%
Xiamen Airlines
4.9%
Shenzhen Airlines
3.0%
Thai Airways
2.8%
Korean Air
2.1%
Virgin Australia
1.8%
0
100
200
300
400
500
600
700
800
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Shanghai Beijing Guangzhou Chengdu Xiamen Nanjing
18
3.3 Hub connectivity
The level of centrality at the transfer airport represents the service level of the airport as a
transfer hub. Table 2 presents the top ten airports that had the highest hub connectivity in
selected years from 2005 to 2012. Figures 9 and 10 show the map of centrality for the major
cities in 2005 and 2016, respectively. In 2005 the top ten were Guangzhou, Sydney, Hong
Kong, Kuala Lumpur, Tokyo Narita, Singapore, Seoul Incheon, Bangkok, Shanghai Pudong
and Brisbane while in 2016 they were Guangzhou, Beijing, Shanghai Pudong, Hong Kong,
Seoul Incheon. Sydney, Taipei, Singapore, Bangkok and Chengdu. Throughout the period,
Guangzhou remained the best connected city except in the 2008–09 period when Guangzhou
was hit severely by the global financial crisis: Hong Kong took the first place then. In 2005
the connectivity of top ten airports varied from 142.45 to 489.18 while the range was between
269.81 and 2045.01 in 2016. China’s three gateway cities experienced the largest increases.
Table 2: The top ten transfer hubs between China and Australia from 2005 to 2016.
2005
Centrality
2008
Centrality
2011
Centrality
2014
Centrality
2016
Centrality
Guangzhou
489.18
Hong Kong
718.80
Guangzhou
1382.01
Guangzhou
1782.26
Guangzhou
2045.01
Sydney 267.14 Guangzhou 431.85 Hong Kong 825.85
Shanghai
Pudong
990.43 Beijing 1279.97
Hong Kong 250.64
Tokyo
Narita
362.72 Beijing 694.32 Hong Kong 855.37
Shanghai
Pudong
1099.44
Kuala
Lumpur
232.69
Seoul
Incheon
353.38
Shanghai
Pudong
655.87 Beijing 739.68 Hong Kong 860.96
Tokyo
Narita
220.84
Shanghai
Pudong
334.16
Seoul
Incheon
436.66
Seoul
Incheon
490.78
Seoul
Incheon
560.29
Seoul
Incheon
207.51 Beijing 333.31 Sydney 358.25 Bangkok 381.89 Sydney 530.06
Singapore
202.54
Sydney
318.39
Singapore
244.66
Sydney
380.82
Taipei
455.85
Bangkok
182.43
Singapore
258.68
Bangkok
208.39
Singapore
305.43
Singapore
404.68
Shanghai
Pudong
171.80
Kuala
Lumpur
216.31
Kuala
Lumpur
196.29
Kuala
Lumpur
304.84 Bangkok 382.28
Brisbane
142.45
Bangkok
178.47
Melbourne
173.57
Taipei
288.05
Chengdu
269.81
19
Figure 9: Centrality map of major transfer hubs between Australia and China in 2005.
20
Figure 10: Centrality map of major transfer hubs between Australia and China in 2016
21
Figure 11 presents the evolution of the centrality of the major transfer hubs between Australia
and China for the period 2005–16. It can be seen that Guangzhou secured its strong status as
a transfer hub through China Southern’s services. The gaps between Guangzhou and the other
transfer hubs widened after 2010. China Southern began an aggressive marketing campaign
in 2009 and increased its flight routes to all major Australian capital cities, including
Adelaide, Brisbane and Perth. In 2012 China Southern signed a strategic cooperation
agreement with Tourism Australia to build the ‘Canton Route’ – the route connecting Europe,
Asia and Australia. From 2012 to 2016 the number of transit passengers using the ‘through
check-in’ service increased from 458,000 to 1.74 million and the ‘through checked’ bags
increased from 481,000 to 2.02 million. All these actions stimulated the demand for China
Southern’s services to Australia and secured its leading role in the China–Australia market,
thereby strengthening Guangzhou’s transfer hub status (China Southern, 2018).
Both Hong Kong and Guangzhou are located in the south of China. This strategic location
has made them ideal transfer points for passengers travelling between China and Australia. It
can be seen that Hong Kong, a traditional transit hub for Chinese travelling to Australia, has
maintained a quite stable level of centrality since 2007. It is still an ideal transfer hub,
considering its convenient airport facilities and Cathay Pacific’s extensive network (Tsui et
al., 2017). In 2016, about 13% of the passengers travelling from China to Australia made a
transfer at Hong Kong. Compared with China’s main airports, including Beijing, Shanghai
and Guangzhou, the transfer procedures and signs at Hong Kong Airport are much clearer
and easier to follow. Cathay Pacific and its wholly owned subsidiary, Cathay Dragon, operate
flights to and from many first and second tier cities in China and transport the passengers to
other parts of the world via Hong Kong. Air China and Cathay Pacific have a cross
shareholding structure: Air China owns 30% of Cathay Pacific and Cathay Pacific has 18% of
Air China. Cathay Pacific can take advantage of its alliance with Air China to continue to
strengthen Hong Kong’s hub status.
22
Figure 11: Evolution of the hub connectivity (vertical axis) of major transfer hubs between
Australia and China, 2005–16.
After 2010 Shanghai Pudong Airport was in the second or third place as a transfer hub.
However, given that the calculation of connectivity for Shanghai Pudong does not include
Shanghai Hongqiao Airport, the actual level of centrality of Shanghai Pudong should be
higher than that presented in Table 2. Shanghai-based China Eastern and Qantas have had a
codesharing agreement for a long time. In 2014 the Australian antitrust body approved the
two carriers setting up a joint venture that allows them to coordinate schedules and pricing as
well as the use of airport facilities such as lounges. The strong partnership between China
Eastern and Qantas has strengthened Shanghai Pudong’s hub status as it offers greater
convenience for passengers travelling to their final destinations via Shanghai. However, the
lack of fast and reliable connections between Shanghai Hongiqao and Shanghai Pudong
Airports may impede the further development of Shanghai Pudong into a transfer hub as most
domestic flights depart from and arrive in Shanghai Hongqiao. One solution, which is being
considered, is to construct a high-speed railway between the two airports.
Singapore is a natural choice as a transfer point because of its geographic location and close
economic links with China and Australia. As a result, it remained in the top ten transfer hubs
during the study period. In 2010 the ASEAN-China Air Transport Agreement was signed to
establish an unlimited air service arrangement (passengers and cargo) between China and
0
500
1000
1500
2000
2500
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Guangzhou Beijing Shanghai Pudong Hong Kong Seoul Incheon Sydney
23
members of the Association of Southeast Asian Nations (ASEAN), which has helped boost
the air services between China and Singapore. It is expected that Singapore will remain an
important transfer hub with the open skies arrangement.
It is worth noting that Korean airlines seized the opportunity in the last decade or so to
operate flights to many Chinese medium-sized cities and to transport Chinese passengers to
international destinations, including Australia, via Seoul. Korean airlines offer very
competitive prices, which have attracted passengers from north and northeast China. It is
expected that Seoul will continue to be a significant transfer hub for Chinese passengers in
the immediate future, given the close economic and cultural ties between the two countries
and especially considering that Korea has been actively seeking an open skies agreement with
China to strengthen Seoul’s hub status.
Any Chinese airline wanting to launch a service from a China to Australia would consider
Sydney as the first destination. It is, therefore, not surprising to see that Sydney has been
consistently ranked as a key transfer hub. The China–Australia open skies agreement is the
catalyst for the continual growth in air passenger traffic between the two countries. Sydney
Airport has some constraints that restrict its transfer hub status. A flight curfew is one
example: it means an effective closure of the airport between 11 p.m. and 6 a.m. daily. The
airport is also restricted to a maximum of 80 aircraft movements per hour (i.e., 20 every 15
minutes) between 6 a.m. and 11 p.m.: again, this limits traffic growth.
5. Conclusion and implications
This paper has adopted a connectivity model to measure direct and indirect air connectivity
between China and Australia, taking into consideration factors such as capacity, velocity and
the quality of transfer. Compared with other connectivity measures, the proposed ConnUM
incorporated more service quality dimensions and, therefore, can better reflect the quality and
quantity of the air service provisions of a city, pairs of cities and intercity networks.
In the case of China and Australia, our direct connectivity measure shows that Sydney was in
a leading position throughout the study period. This is not surprising as many Chinese
immigrants and students choose to live and study in Sydney. Visiting friends and relations
was significant part of air travel between Australia and China (O’Connor and Fuellhart,
2014). For Chinese cities, Shanghai Pudong led in direct connectivity prior to 2011, followed
24
by Guangzhou and Beijing. However, Guangzhou surpassed Shanghai Pudong from 2011 and
became the best connected Chinese city with Australia.
For overall connectivity, Australia’s gateway cities’ (Sydney and Melbourne) have much
higher connectivity than their Chinese counterparts (Beijing, Shanghai and Guangzhou). In
China, the connectivity scores of gateway cities were on average two to three times higher
than those of most secondary cities in 2016. In contrast, Sydney’s overall connectivity was
four to fourteen times as high as that of some capital cities such as Perth and Adelaide. This
may suggest that air transport in China has become more dispersed than in Australia. In fact,
from 2005 to 2016, the dominant role of Sydney and Melbourne in terms of their ability to
attract new air services substantially strengthened, suggesting a certain degree of inertia in the
overall geography of Australia’s air transport. O’Connor et al. (2018) attribute such
geographic inertia to airlines’ preference to basing their organisations and operations in large
cities. As a result, LEK (2017) found that there is limited Chinees dispersion of visitors
beyond Australian east coast cities, due in part to the lack of iconic attractions and Mandarin
translation services.
The concentration and geographic inertia of air services may limit the the policy to change
the spatial distribution of income and opportunity from large cities to regional areas
(O’Connor et al., 2018). For example, the overcrowding problem in Sydney and Melbourne
has elicited much debate as to how to move people from large cities to regional areas. One
proposal suggests that new migrants should stay in the regions for five years before they can
move to big cities. However, any proposals of this kind need to consider the role of air
services. This research finds that Australia’s small cities have a quite low air connectivity
with China, suggesting that they do not have a sufficient number of flights to the major hubs.
Wang et al. (2019) claim that economic activities in regional areas is more sensitive to the
development of the airline industry. Therefore, the effective policy implementation of
shifting people from Sydney and Melbourne should consider support measures in increasing
regional airports’ connectivity with the outside world.
This is the first research that examines the connectivity of the major hub airports for
passenger travel between China and Australia. The results of this research have significant
policy implications for aviation authorities and airport managements in terms of developing
and defending an airport’s hub status of. Guangzhou has forged its status as an emerging
25
transfer hub thanks to the quick expansion of China Southern, suggesting that an airport’s
home airline is the key driver in growing an airport’s connectivity. Hong Kong’s hub status
can be further strengthened by cooperation between Cathay Pacific and its Chinese strategic
partners. When a city has two airports, constructing a high-speed transport link and
coordinating flight schedules between them will enhance the city’s overall connectivity.
Shanghai’s air connectivity would be much higher if a high-speed railway could link its two
airports. When Beijing’s second airport opens in 2019, the same issue will arise. Finally,
open skies arrangements for Singapore, South Korea and Malaysia with China and Australia
could help retain the transfer hub status of Singapore, Seoul and Kuala Lumpur, respectively,
with those two countries. Similar strategies could be used by other airports in this region to
build their hub status.
Acknowledgement
The authors wish to thank Professor Kevin O’Connor, and the anonymous reviewers for their
insightful comments and constructive suggestions.
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Appendix 1: Overall air connectivity (direct and indirect connectivity) of Australian airports.
Airport
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Sydney
929.92
1090.10
1195.62
1413.57
835.86
1887.04
2135.96
2431.86
2593.29
2922.04
3011.50
3905.72
Melbourne
410.14 428.10 501.19 750.63 530.42 1086.57 1437.21 1542.76 1667.23 1795.22 2134.56 2531.19
Brisbane
253.35
278.05
409.24
433.94
200.18
586.14
863.41
990.76
1086.61
1142.59
1296.97
1456.86
Perth
171.49 180.43 221.95 282.13 257.14 329.82 457.36 621.03 663.56 720.07 861.46 887.87
Cairns
96.87
96.95
160.08
166.82
40.61
134.19
119.09
191.30
215.51
156.61
177.13
333.52
Adelaide
85.46 98.68 148.87 197.45 44.88 186.41 211.46 224.55 253.92 251.99 267.43 272.25
Gold Coast
20.74
26.57
24.57
33.42
27.96
59.10
58.30
70.02
85.45
84.42
91.77
146.78
Canberra
16.17 26.05 24.84 20.14 11.93 24.92 24.49 31.65 32.42 36.55 33.91 70.70
Darwin
11.83
12.21
13.39
16.45
10.25
17.11
21.47
31.92
47.79
53.14
48.54
56.46
Hobart
11.62 12.94 14.31 17.76 10.13 22.86 25.67 22.67 25.37 23.88 24.34 39.90
Launceston
4.65
5.46
5.77
7.21
5.52
14.65
15.54
14.33
17.47
22.25
20.48
28.41
Tamworth
6.07 10.75 11.33 8.50 4.75 15.41 12.47 16.03 14.27 14.37 14.74 24.02
Wagga Wagga
7.23
11.74
13.15
10.15
5.80
12.05
12.74
14.42
12.74
18.04
17.28
22.86
Albury
7.72 13.25 12.90 11.74 4.71 13.16 14.27 14.66 14.42 16.63 13.77 22.24
Dubbo
6.25
10.46
11.46
7.69
4.62
11.44
10.33
10.74
13.06
14.28
13.25
21.24
Armidale
4.33 7.31 9.52 8.30 4.95 9.47 8.88 9.23 11.16 13.97 12.45 17.10
Newcastle
3.40
4.03
4.76
4.47
2.51
6.22
9.02
9.45
11.63
10.47
12.49
16.76
Townsville
1.93 2.26 1.90 2.61 1.21 3.83 6.26 7.99 8.02 7.33 9.88 15.50
Toowoomba
7.81
14.88
Melbourne Avalon
4.92 6.52 7.78 9.22 3.48 6.87 7.61 5.89 6.30 8.55 8.18 13.24
Mildura
1.22
1.14
1.81
3.04
1.99
5.69
6.38
6.10
7.40
8.62
10.05
11.63
Ballina
3.48 4.37 3.62 4.69 2.22 4.70 5.29 5.68 5.97 7.35 7.38 11.47
Devonport
0.91
0.62
1.33
2.70
1.81
5.70
6.07
6.20
6.75
7.82
9.53
10.21
Moree
0.62 1.44 1.61 1.77 1.44 3.77 4.69 4.25 1.63 5.80 6.33 7.78
Mackay
0.50
0.45
0.96
1.49
0.78
3.04
5.00
5.24
8.38
7.11
7.69
7.38
32
Appendix 2: Overall air connectivity (direct and indirect connectivity) of Chinese airports.
Airport
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Shanghai Pudong
378.16
378.57
466.57
454.08
302.23
542.18
530.97
611.40
601.44
636.13
627.60
672.32
Beijing
225.78
338.42
320.25
339.64
223.47
380.74
450.53
420.08
431.60
467.50
537.06
655.43
Guangzhou
237.49
272.68
277.82
265.95
174.04
378.42
445.41
501.25
524.19
549.78
591.20
617.96
Chengdu
69.38
70.18
79.78
138.47
78.38
187.84
195.15
203.49
239.66
268.93
262.83
350.81
Xiamen
86.46
91.85
115.28
136.70
104.46
154.84
163.07
160.17
185.90
190.89
201.98
284.27
Nanjing
51.58
56.44
70.95
92.54
52.50
107.19
140.15
148.83
199.53
199.97
213.56
258.47
Xi’an
37.43
54.43
53.24
64.55
31.56
80.26
99.95
140.23
147.26
161.57
196.97
250.97
Qingdao
64.43
67.49
128.06
150.07
63.19
125.22
137.13
143.28
163.33
172.14
183.14
235.63
Changsha
9.23
29.15
55.57
74.16
42.94
88.85
128.12
134.25
141.24
164.37
205.20
233.27
Shenzhen
38.54
35.02
35.34
46.16
22.25
58.34
95.12
84.32
111.06
138.25
161.60
224.70
Chongqing
40.07
45.81
49.60
76.89
42.98
96.43
103.78
108.39
110.35
152.45
177.87
221.60
Fuzhou
29.36
25.77
41.15
59.49
46.69
81.40
102.24
109.46
133.55
143.39
158.60
219.87
Kunming
68.28
73.35
87.52
106.98
50.88
110.85
141.31
174.02
179.99
190.62
189.87
210.54
Hangzhou
58.52
51.56
96.33
129.74
59.91
112.54
122.36
118.18
126.97
140.13
157.42
204.83
Wuhan
23.30
29.08
50.65
67.94
39.75
87.43
100.75
124.65
146.61
150.61
167.26
203.73
Dalian
60.56
68.85
131.22
145.66
63.29
146.80
146.21
160.63
157.01
163.67
153.53
184.51
Zhengzhou
21.80
21.08
28.26
36.99
25.19
68.33
86.84
96.68
121.25
132.69
150.04
183.52
Ningbo
26.46
23.05
37.47
52.60
37.03
79.71
98.07
121.79
98.46
99.23
122.51
156.80
Shenyang
51.18
58.63
66.92
72.19
40.31
96.90
99.71
110.62
111.23
120.38
122.43
156.78
Tianjin
33.12
35.09
48.85
54.70
31.35
81.70
87.24
103.94
90.59
99.81
119.00
145.61
Haikou
29.85
41.21
50.82
70.12
36.65
62.34
72.93
96.40
93.37
101.37
107.58
139.23
Shanghai Hongqiao
16.11
15.97
17.33
21.35
9.19
49.40
72.27
73.46
74.05
77.90
88.66
138.13
Nanning
18.96
23.62
28.85
35.71
19.85
55.41
73.61
67.51
83.26
94.50
94.87
129.28
Hefei
7.11
10.14
10.64
14.73
12.96
46.93
61.72
68.12
74.28
74.56
98.88
124.75
Changchun
13.00
15.97
25.84
42.45
29.52
70.72
78.28
87.88
95.70
97.69
108.36
122.30
Taiyuan
13.29
15.50
17.95
18.74
9.05
30.15
54.60
77.77
78.75
85.68
95.01
114.58
Shantou
20.55
20.97
27.73
38.27
23.36
59.62
71.73
76.00
68.62
76.06
100.15
112.21
Sanya
25.96
25.44
24.73
39.63
21.99
60.24
75.64
87.87
100.60
103.46
100.60
111.00
Guiyang
31.03
31.57
34.48
45.78
30.96
58.56
70.16
69.55
73.22
83.11
88.45
110.01
Jinan
18.51
20.42
18.34
25.41
9.23
36.95
45.82
51.79
65.74
75.22
87.30
107.33
