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Abstract

This study estimates factors of importance for the carbon dioxide equivalent (CO2e) emissions generated by travellers flying for different reasons based on representative Austrian micro data for the period 2014–2016. The annual average number of flights taken by adults vary between 0.1 (visiting friends) and 0.8 (going on holiday), and the amount of CO2e emissions generated by each return flight is approximately 1100 kg. This leads to a total of 6 million tonnes CO2e emissions per year. Results of the Pseudo Poisson Maximum Likelihood estimations reveal that the amount of CO2e emissions created is related to socio-demographic, locational and seasonal factors, although mainly for the largest group of travellers: the holiday makers. In this group, individuals with university degrees, young persons (16–24 years) and capital city residents generate the largest amounts of emissions, as opposed to persons with children and large households. Residents of the capital region each quarter cause 64 kg more CO2e emissions than inhabitants of rural areas, persons with university degrees create 74 kg larger emissions than those without degrees and young adults instigate 90 kg more emissions than middle-aged persons. CO2e emissions of holiday flights are highest in the first quarter of the year. The importance of education is also pronounced for CO2e emissions related to business travel, as is gender.
RESEARCH ARTICLE
Determinants of CO
2
emissions generated by air travel vary
across reasons for the trip
Martin Thomas Falk
1
&Eva Hagsten
2
Received: 10 July 2020 /Accepted: 23 December 2020
#The Author(s) 2021
Abstract
This study estimates factors of importance for the carbon dioxide equivalent (CO
2
e) emissions generated by travellers flying for
different reasons based on representative Austrian micro data for the period 20142016. The annual average number of flights
taken by adults vary between 0.1 (visiting friends) and 0.8 (going on holiday), and the amount of CO
2
e emissions generated by
each return flight is approximately 1100 kg. This leads to a total of 6 million tonnes CO
2
e emissions per year. Results of the
Pseudo Poisson Maximum Likelihood estimations reveal that the amount of CO
2
e emissions created is related to socio-demo-
graphic, locational and seasonal factors, although mainly for the largest group of travellers: the holiday makers. In this group,
individuals with university degrees, young persons (1624 years) and capital city residents generate the largest amounts of
emissions, as opposed to persons with children and large households. Residents of the capital region each quarter cause 64 kg
more CO
2
e emissions than inhabitants of rural areas, persons with university degrees create 74 kg larger emissions than those
without degrees and young adults instigate 90 kg more emissions than middle-aged persons. CO
2
e emissions of holiday flights
are highest in the first quarter of the year. The importance of education is also pronounced for CO
2
e emissionsrelated to business
travel, as is gender.
Keywords Air travel CO
2
emissions .Tourist air travel .Business air travel .Count data models
Introduction
Services related to tourism, including air transportation,
are increasingly questioned because of their presumptive
negative impact on global carbon emissions. Air travel is
considered to be the most environmentally damaging form
of transportation with respect to climate change (Gössling
and Upham 2009; Gössling and Humpe 2020)and
emissions from aviation are more harmful than those from
ground traffic (Lee et al. 2009). Findings based on re-
search undertaken before the Covid-19 pandemic ground-
ed most aircraft fleets in early 2020 point to the fact that a
small group of individuals contributes to a large amount
of the Co
2
emissions (Gössling et al. 2009;Brandand
Preston 2010;GösslingandHumpe2020). Despite this,
the characteristics of those individuals who generate the
largest amount of flight-related emissions by reason for
travel (holiday, visiting friends and relatives or business)
are presently unknown.
The aim of this study is to gain more insights into aspects of
importance for the CO
2
emissions generated by air travellers
with different reasons for their trips. Socio-demographic, lo-
cational and seasonal factors are employed to explain the
amount of emissions at the individual level. The analysis uses
a representative micro data set of 17,400 observations on
Austrian residents that travel at least once per quarter for rea-
sons of businesses, holidaying or visiting friends and relatives
during the period 20142016. The Pseudo Poisson Maximum
Likelihood estimator (PPML) is employed to estimate the
relationships.
Responsible editor: Philippe Garrigues
*Martin Thomas Falk
martin.falk@usn.no
Eva Hagsten
evamarie@hi.is
1
School of Business, Department of Business and IT, University of
South-Eastern Norway, Campus Bø; Gullbringvegen 36,
3800 Bø, Norway
2
School of Social Sciences, University of Iceland, Reykjavík, Iceland
https://doi.org/10.1007/s11356-020-12219-4
/ Published online: 12 January 2021
Environmental Science and Pollution Research (2021) 28:22969–22980
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Until 2020, long-haul air travel is the fastest growing
segment of passenger mobility (Airbus 2018)andICAO
(2009) estimates a 300% increase in emissions from air
travel by 2050. Total aviation emissions are considered to
account for 20% of the global tourism carbon footprints
(Lenzen et al. 2018), while aviation itself represents be-
tween 2.0 and 2.5% of total annual CO
2
emissions (Lee
et al. 2009, Graver, Zhang and Rutherford, 2019). Current
discussions encompass not only the sustainability of fre-
quent flying, primarily by businesstravellers(Young
et al. 2014), but increasingly also unnecessaryleisure
and holiday travel (Becken 2002; Holden and Norland
2005; Graham and Metz 2017).
With the deregulation of the European and other aviation
markets and the subsequent emergence of low-cost airlines,
the share of holiday air travel in total number of passengers is
increasing (OConnell and Williams 2005;Tsui2017;
Álvarez-Díaz et al. 2019). Recently, new groups of environ-
mental activists have appeared that emphasise the emissions
caused by flying, introducing the Swedish term flygskam
(flight shame) (Gössling et al. 2019; Gössling 2019; Gössling
et al. 2020).
1
These groupsadvocate alternative transportation
modes such as train, even if the travel time is ten or twenty
folded.
Air travel for purposes of business, migration and ed-
ucation as well as to visit friends and relatives may be
difficult to avoid. Many firms, institutions and organisa-
tions are active on the international arena and long-
distance relationships are not uncommon. There are also
national or European parliamentarians, for instance, who
are expected to have a close relationship with their con-
stituencies even if the distances are far. Yet, holiday travel
by air could be prevented to a certain extent, as environ-
mentally friendly means of transportation are available for
short- or medium-long distances. In general, the distribu-
tion of emissions caused by air transportation is highly
uneven, with few people accounting for the largest pro-
portion(BrandandPreston2010).
Hardly any studies examine the link between aviation
emissions and socio-demographic characteristics. An excep-
tion is Bruderer Enzler (2017), who uses a two-part model to
investigate the determinants of air traffic emissions based on
the Swiss environmental survey. Other studiesfocus on green-
house gas emissions of all individual travellers, independent
of characteristics (e.g. Brand and Preston 2010), long-distance
travellers (Reichert et al. 2016) or travel emissions generated
by the urban population (Czepkiewicz et al. 2018a,2018b;
Czepkiewicz et al. 2019).
Another strand of the literature examines flying behaviour
in general, independently of the amount of CO
2
emissions
caused. Examples of this include the behaviour of (a) the
urban population in Iceland (Czepkiewicz et al. 2020), (b)
international celebrities (Gössling 2019), (c) German holiday
makers (Gössling et al. 2017) and (d) Swiss inhabitants and
distances of their flights (Schubert et al. 2020). Dargay and
Clark (2012) explore the determinants of travel for five differ-
ent purposes (business, commuting, leisure, holidays and
visits from friends and relatives), but without accounting for
the emissions generated.
This study contributes results on a far more detailedlevel of
air travellers and their carbon footprints than hitherto available
based on a regularly re-occurring representative official sur-
vey. In addition, the analysis takes into account how the im-
portance of socio-demographic, locational and seasonal fac-
tors varies across reasons for travel.
The paper is structured as follows: the Conceptual back-
groundsection introduces the theoretical background and
provides the main hypothesis, the Empirical modelsection
presents the empirical approach while the Data and descrip-
tive statisticssection describes the dataset. The empirical
results are revealed in the Empirical resultssection and the
Conclusionsection concludes.
Conceptual background
Few studies investigate the characteristics of the group of
air travellers that generate the largest amounts of CO
2
emissions. A review of 27 studies examining the behav-
iour of long-haul travellers only encompasses three stud-
ies relating to CO
2
emissions (Czepkiewicz et al. 2018a).
Graham and Metz (2017) propose a distinction between
discretionaryleisure travel (including holiday travel)
and non-discretionarybusiness travel where air travels
motivated by visiting friends and relatives are in principle
voluntary but often indispensable.
Socio-demographic characteristics as well as location
may have an important influence on CO
2
emissions of indi-
viduals and households in general (Qu et al. 2016;Bülbül
et al. 2020). Analyses of CO
2
emissions associated with air
travel reveal that socio-demographic characteristics and lo-
cation of individuals are equally important (Reichert et al.
2016;BrudererEnzler2017;Czepkiewiczetal.2018a;
Czepkiewicz et al. 2018a,2018b). Common features con-
sidered in these cases are age, gender, household type, ed-
ucation, occupation and income. Persons living in urban
regions with airports close by are not only more likely to
go by plane but are also using this opportunity regularly and
subsequently generate more CO
2
emissions (Czepkiewicz
et al. 2018a). One reason behind this pattern is the so-called
escape travel or compensation hypothesis (Heinonen et al.
1
See for instance article in Time Magazine, May 16, 2019 Now I Am
Speaking to the Whole World.How Teen Climate Activist Greta Thunberg
Got Everyone to Listen(https://time.com/collection-post/5584902/greta-
thunberg-next-generation-leaders/).
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2013;Reichertetal.2016) postulating that high urban den-
sity limits the quality of life and thus creates demand for
frequent weekend trips and other short breaks.
Czepkiewicz et al. (2018a) mention that the positive rela-
tionship between urban density and long-distance travel be-
haviour is still significant when demographic and socio-
economic variables are controlled for.
Research that explicitly models the CO
2
emissions
demonstrates that greenhouse gas emissions by urban res-
idents are five times higher than those generated by indi-
viduals living in rural areas (Heinonen et al. 2013).
Additionally, there are a number of studies that point to
the importance of education and income as drivers of air
travel emissions (e.g. Bruderer Enzler 2017). Likewise,
the phase of life appears to be important for the emissions
created. Based on the Swiss environmental survey,
Bruderer Enzler (2017) finds that household characteris-
tics and family size are important, while the role of gender
is less obvious. Brand and Preston (2010), for instance,
suggest that gender is not significantly related to overall
emissions from private, non-business travel while Brand
andBoardman(2008) show that single-person households
produce the highest average travel emissions per person,
mainly caused by air travel.
Unfortunately, recent literature is difficult to compare
because of variations in sample sizes, definitions and calcu-
lations of CO
2
emissions from air travel (total air travel
emissions or by purpose) as well as estimation methods
used (multivariate or bivariate). There are, however, a few
common denominators indicating that socio-demographic
factors are of importance, although possible differences be-
tween leisure (holiday and visiting friends or relatives) and
business travellers are largely neglected. Influenced by the
determinants highlighted in the literature, and the gaps re-
vealed, the emissions generated are analysed for the travel
purpose (holiday, business, visiting friends or relatives) to-
gether with socio-demographic, location and seasonal char-
acteristics based on a representative sample of trips and
travellers. Data available on destination country makes it
possible to calculate the amount of emissions as carbon
dioxide equivalents (CO
2
e) caused by the flights. This leads
to the main hypothesis (H1):
H1: The determinants of CO
2
e emissions generated by air
travel vary across reasons for the trip. Implicitly, the hypoth-
esis rests on the assumption that there is a relationship be-
tween emissions generated and socio-demographic, locational
and seasonal factors.
Empirical model
There are numerous studies on the choice to travel by air
(Czepkiewicz et al. 2018a). This study follows Bruderer
Enzler (2017) and Reichert et al. (2016), who model the
amount of air travelrelated CO
2
e emissions, CO2e
itp
,asa
function of several socio-demographic factors including loca-
tion, departure quarter and departure year:
CO2eitp ¼β0pþX
5
A¼1
βjpAAGECAT A
it þX
2
E¼1
βjpEEDUE
it þβjpW WOMENit
þβjpCCH I LDREN it þX
3
S¼1
βjpLLABOU RST AT U SL
it þX
5
H¼1
βjpH HHSIZEH
it
þX
8
F¼1
βjp F FEDSTATEF
it þX
2
Y¼1
βjpY YEAR
Y
it þX
3
Q¼1
βjpQQU ARTERQ
it þεipt;
where iis the individual, tdenotes quarterly data (2014:1
to 2016:4) and pis reason for travel (holiday, visit friends
and relatives or business). The explanatory variables en-
compass AGECAT denoting age class, EDU reflecting the
level of education with no degree as the reference catego-
ry and WOMEN if the traveller is female. CHILDREN is a
dummy variable for travelling at least once with children,
HHSIZE is a set of dummy variables measuring the
household size with one reflecting the reference category
and LABOURSTATUS is a group of dummy variables for
the labour market position (employed, unemployed, stu-
dent or retired, with unemployed as reference category).
Variable FEDSTATE relates to the region where the trav-
eller resides with the province Lower Austria as the ref-
erence category. Macroeconomic factors such as price ef-
fects and fluctuations of the business cycle are captured
by annual year dummy variables YEAR, QUARTER con-
trols for calendar effects within a single year with the first
quarter (January to March) as the reference category and
ε
ipt
is the error term.
The Pseudo Poisson Maximum Likelihood estimator can
be used to assess the determinants of the CO
2
e emissions
generated by different groups of travellers. Santos Silva and
Tenreyro (2006) argue that this estimator is suitable for
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dependent variables that contain a large proportion of zero
values as in this case, where 82% of the leisure travellers
and 95% of the business travellers did not fly at all in a given
quarter and thus generate no CO
2
e emissions. Another advan-
tage is that the PPML estimator is consistent in the presence of
heteroscedasticity. The CO
2
e emission equation can be writ-
ten in its exponential form (subscript tomitted):
CO2ei¼exp X ißðÞþϵi;
where Xis the vector of explanatory variables mentioned
above (all in form of dummy variables) and ßcontains the
parameters to be estimated.
Data and descriptive statistics
Data for this analysis originate from the official Austrian trav-
el survey (Statistics Austria 2017). This is a quarterly repre-
sentative survey on holiday and business travels with at least
one overnight stay, undertaken by persons living in Austria
aged 15 years or older. The survey is stratified by federal state,
age and gender. Each quarter, around 3500 randomly selected
persons are interviewed by telephone. Participation in the sur-
vey is voluntary. The dataset encompasses information on
actual domestic as well as international (outbound) trips by
destination and purpose, length of stay, accommodation type,
departure month, transportation mode and expenditures. A
wide range of socio-demographic factors accompany the data
such as educational attainment, gender, age class, labour mar-
ket status, travel company size and federal state where the
traveller resides. Although information is available from
2012 onwards, methodological changes of the travel survey
restrict the estimation sample to the period 20142016.
Data on destination country makes it possible to calculate
the amount of CO
2
emissions (expressed in carbon dioxide
equivalents) caused by the flights. The largest destination air-
port in each country is used for this exercise. There are two
different emission calculators available (https://co2.
myclimate.org/en/flight_calculators/new and https://www.
icao.int/environmental-protection/Carbonoffset/Pages/
default.aspx)(Table4, Appendix) although the one from the
ICAO has a limited coverage of airports and is thus not used
here. The myclimate flight calculator determines the amount
of CO
2
emissions that an aircraft generates per passenger for a
given flightroute using the real distance. Nitrogen compounds
and aerosols are also taken into account and converted into
CO
2
. Business flights are associated with 30% more CO
2
emissions for short- and medium-haul flights. Since informa-
tion is not available on the number of flights in business class,
the calculation method for economy flights is used for all
flights as in Reichert et al. (2018). CO
2
e emissions generated
by each quarterly trip are aggregated to the individual level.
Although Baumeister (2017) concludes that almost no single
flight generates similar emissions to another, depending on the
number of stops and the vintage of the plane, a more detailed
calculation of the emissions cannot be made here because
information about the travel itself is not available.
Descriptive statistics also reveal that 1% of the holiday
travellers and 1% of the business air travellers account for
one-fifth and almost two-thirds of CO
2
e emissions during
the sample period in their respective groups (Table 1).
Emissions generated by flights to friends and relatives are
negligible in this context.
Based on the average number of holiday flights per person
and year (0.8), the amount of CO
2
e emissions can be calcu-
lated. The emissions are then scaled up to the total adult pop-
ulation of 7.6 million in Austria, of which 60% goes on hol-
iday (European Comission 2016). With the corresponding
CO
2
e emissions of 1100 kg per person and flight, this results
in a total amount of CO
2
e emission per year of approximately
6 million tonnes from air travel (and 4.0 million tonnes from
the holiday travel). Based on a representative travel survey for
Sweden, Åkerman (2012) calculates 4.2 million tonnes of
CO
2
equivalent emissions from international air travel for
the period 20152016 (1 year, including all types of flights).
In addition, descriptive statistics show that the CO
2
e emis-
sions per person with at least one flight vary markedly over
quarters, where the highest appears in the first quarter,
reflecting the longer flight distances during this time of year
(Table 2).
The CO
2
e emissions generated by each holiday traveller
(with at least one flight per quarter) are larger for highly
skilled individuals (tertiary degrees), those living in the capital
city of Vienna and for young people (Fig. 1).
Table 1 Amount of CO
2
e emissions generated by different groups of
air travellers (kg)
Holiday Visit friends or relatives Business
Total
2014 1,040,046 131,970 382,938
2015 1,075,104 141,004 327,740
2016 1,086,714 206,876 340,358
Upper one percentile of emitters
2014 199,718 89,174 250,866
2015 226,692 101,876 208,306
2016 218,786 155,320 214,724
Contribution of the upper one percentile, per cent
2014 19.2 67.6 65.5
2015 21.1 72.3 63.6
2016 20.1 75.1 63.1
Source: Austrian travel survey and https://co2.myclimate.org/en/flight_
calculators/new
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Among business air travellers, those with higher education
account for the majority of emissions (Table 5,Appendix).
CO
2
e emissions created are higher in the older age classes of
holiday travellers.
Empirical results
The Pseudo Poisson Maximum Likelihood estimations
show that the amount of COe
2
emissions generated by
air travellers residing in Austria relates to socio-demo-
graphic, locational and seasonal factors, although mainly
for the largest group of travellers: the holiday makers
(Table 3). In this group, young adults, those with tertiary
degrees, residents of the capital city and men leave larger
traces of CO
2
e emissions. Individuals travelling with chil-
dren and those in large household generate far less emis-
sions. There is also a strong seasonal pattern, where the
lowest CO
2
e emissions can be observed for the second
and fourth quarters of the year.
The labour market status is not related to the amount of
CO
2
e emissionscreated by holiday travellers. CO
2
e emissions
resulting from flights to friends and relatives show that the
main aspects of importance are the educational level and the
capital region, while the remaining factors are of less or no
importance. Since the capitals attract highly educated individ-
uals, it can be expected that there is also a larger amount of
residents with families elsewhere. Emissions related to
Table 2 Average number of
flights per quarter and CO
2
e
emissions per flight in 20142016
Number of flights
Holiday Visit friends or relatives Business
2014 Q1 0.16 0.03 0.09
2014 Q2 0.21 0.03 0.09
2014 Q3 0.27 0.02 0.06
2014 Q4 0.17 0.03 0.09
2015 Q1 0.20 0.04 0.08
2015 Q2 0.21 0.03 0.09
2015 Q3 0.23 0.02 0.05
2015 Q4 0.14 0.04 0.08
2016 Q1 0.14 0.03 0.07
2016 Q2 0.21 0.03 0.09
2016 Q3 0.23 0.03 0.05
2016 Q4 0.15 0.05 0.08
Sum 2014 Q1Q4 0.81 0.12 0.34
Sum 2015 Q1Q4 0.78 0.12 0.30
Sum 2016 Q1Q4 0.74 0.15 0.28
CO
2
e emissions per person in kg (persons with at least one flight in any of the categories)
Holiday Visit friends or relatives Business
2014 Q1 1534 831 1251
2014 Q2 820 851 1228
2014 Q3 817 758 1276
2014 Q4 1091 991 1241
2015 Q1 1364 890 1205
2015 Q2 914 816 1096
2015 Q3 835 1007 992
2015 Q4 1236 1111 1275
2016 Q1 1670 1059 1233
2016 Q2 742 1105 859
2016 Q3 876 876 1185
2016 Q4 1257 1027 1654
Mean 2014 Q1Q4 1065 858 1249
Mean 2015 Q1Q4 1087 956 1142
Mean 2016 Q1Q4 1136 1017 1233
Source: Austrian travel survey. Average Co2 emissions per flight in kg are calculated based on a return flight
using https://co2.myclimate.org/en/flight_calculators/new
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business travels are crucially related to the educational level,
labour market status, gender and location. Season is far less
important, but there is a reduction in the summer quarter (July
to September).
The marginal effects (dy/dx) of the PPML estimations di-
rectly indicate the strength of the associations and reveal that
young holiday travellers aged 1524 years produce the highest
amount of CO
2
e emissions per quarter compared to the refer-
ence category 3544 years (90 kg more). Persons with higher
education generate 74 kg and 76 kg more emissions for holi-
day and business travels, respectively, than those without de-
grees. Inhabitants of the capital region are responsible for an
addition of 64 kg emissions for holiday flights than people
living in the rural provinces. Strong associations can also be
observed for holiday travellers with children (66 kg) and
persons living in large households (52 kg and 72 kg in
households with 5 and 6 or more persons, respectively).
Emissions from holiday flights are lowest in the spring and
autumn months (65 kg each). In terms of emissions from
business flights, women relate to a reduced amount of emis-
sions with 68 kg. Overall, the results mean that the hypothesis
formulated cannot be rejected.
In general, the results do not deviate from the recent
but fragmented literature, although the analysis performed
here goes beyond earlier research both with respect to the
large representative dataset and the calculation and
modelling of flight-related CO
2
e emissions by travel pur-
pose. Estimates based on total emissions mask the hetero-
geneity of the air travel behaviour among different
groups, where emissions created by those travelling to
visit friends and relatives are less related to socio-
demographic factor than for the holiday makers. This ap-
proach also allows a ranking of the importance of the
explanatory variables, where young persons, those with
higher degrees or residents of the capital city generate
more CO
2
e emissions for their holiday flights and highly
educated persons and men for those of business flights.
Several robustness checks have been conducted. First, oth-
er CO
2
e calculation methods are used. The findings are not
sensitive to the choice of the CO
2
e calculator (results are
available upon request). Second, the central variables age,
education and place of residence are interacted to investigate
possible moderating effects. The results for holiday flights
show that persons with tertiary education living in the capital
city cause the highest amount of emissions (unreported results
are available upon request).
Given that the emissions generated clearly vary across
kind of travellers, with the holiday makers being
Fig. 1 Box plots of individual CO
2
e emissions per group of travellers.
Source: Austrian travel survey
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responsible for the largest amount, possible policy inter-
ventions need to be customised. Literature suggests both
voluntary initiatives and soft measures as well as hard
actions (flight taxes, emissions taxes, carbon budget) to
reduce the emissions of holiday flights (Becken 2007;
Higham et al. 2016;Shaarietal.2020), with hard mea-
sures being considered the most effective ones (Higham
et al. 2016). Gössling et al. (2020) show that a two-thirds
majority of survey respondents are in favour of market-
based measures that increase the cost of flying, policies
that force airlines to reduce their emissions and legislation
to abolish subsidies. Using a willingness-to-pay approach,
Seetaram et al. (2018) demonstrate that travellers are will-
ing to pay a higher departure tax for business class and
long-haul travel. Another policy option is to replace short-
haul and domestic flights with train connections
(Dällenbach 2020). According to Baumeister (2019), air-
planes have no advantage over trains for distances under
400 km and the emission reduction potential would be
particularly pronounced if the trains were run with renew-
able energy, shifting the responsibility of cleaner
aviation to the supply side, where new technologies might
Table 3 PPML estimations of the amount of CO
2
e emissions generated by air travel 20142016
(i) (ii) (iii)
Holiday Visit friends and relatives Business
dy/dx z-stat dy/dx z-stat dy/dx z-stat
Age 1524 (ref. cat. 3544) 89.66 *** 4.05 14.45 * 1.97 15.81 0.95
Age 2534 55.53 *** 3.05 5.71 0.85 6.37 0.65
Age 4554 48.31 *** 2.97 5.38 0.78 4.02 0.43
Age 5564 23.41 1.25 2.46 0.32 3.19 0.26
Age 65+ 29.53 1.22 5.21 0.50 9.72 0.47
Education medium (ref. low) 22.23 1.53 1.39 0.21 5.87 0.46
Education tertiary level 74.00 *** 4.42 15.95 ** 2.29 76.05 *** 5.57
Women 19.91 ** 2.24 6.45 * 1.72 68.50 *** 8.31
Travellers with children 65.62 *** 4.43 4.48 0.84
Employed (ref. unemployed) 7.80 0.26 5.73 0.56 52.00 *** 2.65
Student 2.97 0.09 19.01 1.63 42.18 * 1.89
Pensioners/out of labour force 0.95 0.03 18.26 1.48 42.52 * 1.65
Burgenland (ref. Lower Austria) 22.74 0.76 8.10 0.50 35.96 1.40
Vienna 63.77 *** 4.79 24.37 *** 4.22 24.16 ** 2.33
Carinthia 61.06 ** 2.49 6.42 0.71 3.05 0.17
Styria 30.07 * 1.80 10.73 1.37 5.33 0.46
Upper Austria 25.74 * 1.78 1.52 0.20 11.76 1.08
Salzburg 3.42 0.17 16.61 ** 1.96 7.92 0.46
Tyrol 6.59 0.34 10.92 1.35 36.68 ** 2.11
Vorarlberg 0.48 0.02 16.66 * 1.90 31.69 ** 2.10
Household size =2 (ref. =1) 27.19 * 1.85 11.66 ** 2.12 19.64 * 1.82
Household size =3 17.92 1.07 7.59 1.25 25.27 ** 2.29
Household size =4 41.45 ** 2.30 14.00 ** 2.03 22.74 ** 2.05
Household size =5 52.28 ** 2.30 14.68 * 1.74 11.51 0.95
Household size =6 71.55 ** 2.47 2.30 0.25 21.50 1.00
Year 2015 (ref. year 2014) 6.45 0.60 0.07 0.01 14.57 * 1.82
Year 2016 14.75 1.37 9.18 ** 2.06 14.93 * 1.85
Quarter 2 (ref. quarter 1) 65.25 *** 4.94 1.79 0.34 2.19 0.25
Quarter 3 30.81 ** 2.56 7.74 1.48 19.25 ** 2.11
Quarter 4 65.40 *** 4.46 8.21 1.55 8.59 0.85
Number of observations 17,374 17,374 17,374
Log pseudolikelihood 6,390,698 1,774,704 3,087,037
Notes: Asterisks ***, ** and * denote significance at the 1, 5 and 10% levels. dy/dx denotes the marginal effects. Estimated by the Poisson pseudo-
maximum likelihood estimator. Source: Austrian travel survey, Statistics Austria
22975Environ Sci Pollut Res (2021) 28:22969–22980
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
be used as alternative solutions. Several airlines experiment
with biofuels, for instance, with mixed evidence (Filimonau
et al. 2018;Lu2018; Efthymiou and Papatheodorou 2019).
Other options suggested are electric airplanes, but this is not
for the near future (Baumeister et al. 2020).
Conclusions
This study provides novel empirical evidence on aspects
of importance for the carbon dioxide equivalent (CO
2
e)
emissions caused by different groups of air travellers,
based on a large representative dataset on travel behaviour
by Austrian residents for the period 20142016. Poisson
Pseudo-Maximum Likelihood estimations show that the
amount of CO
2
e emissions generated by different groups
of travellers depend on socio-demographic, locational and
seasonal factors, although mainly so for the largest group
of travellers: the holiday makers. Education, location of
residence, age and season are aspects most relevant for
CO
2
e emissions generated by this group of travellers
while education and gender (men) are driving CO
2
eemis-
sions by business travellers. Socio-demographic, location-
al and seasonal factors are of less or no importance for
emissions related to visiting friends and relatives.
The results imply that presumptive policy measures to re-
duce travel by air need to be customised. Given that the largest
amount of emissions are produced by persons with higher
degrees, supposedly not sensitive to air fares, additional mea-
sures targeting the demand side such as flight taxes might not
be effective in reducing emissions. Instead, focus might need
to shift to the supply side and to new technologies.
Travel surveys from the national statistical office are com-
mendable sources for the analysis of the CO
2
e emissions and
are generally available in a large group of countries. Future
studies should be based on comparable data for a larger group
of countries.
Authorscontributions Martin Falk contributed 50% to conceptualisa-
tion, literature review, data work, estimation, writing of the paper and
revision. Eva Hagsten contributed 50% to conceptualisation, literature
review, data work, estimation, writing of the paper and revision.
Funding Open Access funding provided by University Of South-Eastern
Norway.
Data availability Official data underlying the study can be ordered from
Statistics Austria: http://www.statistik.at/web_de/services/mikrodaten_
fuer_forschung_und_lehre/datenangebot/standardisierte_datensaetze_
sds/index.html#index18
Compliance with ethical standards
Competing interests The authors declare that they have no conflicts of
interest.
Ethical approval Not applicable.
Consent to participate Yes.
Consent to publish Yes.
22976 Environ Sci Pollut Res (2021) 28:22969–22980
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 4 List of airports and CO
2
equivalent emissions for return flights starting in Vienna
Country Airport code Passenger CO
2
e/pax/return (KG)
IACO co2.myclimate.org
Belgium BRU 220.6 408
Denmark CPH 197.0 396
Germany FRA 153.4 328
Finland HEL 281.2 550
France CDG 223.2 438
Greece ATH 247.4 502
United Kingdom LHR 251.0 500
Ireland DUB 310.0 628
Italy FCO 195.2 370
Luxembourg LUX 185.6 368
Netherlands AMS 209.0 416
Portugal LIS 392.2 832
Sweden ARN 289.4 504
Spain PMI 277.2 548
Iceland KEF 454.4 1020
Norway OSL 286.8 530
Switzerland ZRH 156.0 322
Baltic States (Estonia, Latvia, Lithuania) RIX 226.4 454
Croatia SPU 127.8 298
Malta MLA 263.4 526
Poland WAW 166.8 308
Romania OTP 200.4 382
Slovakia KSC n.a. 256
Slovenia LJU n.a. 232
Turkey AYT 301.0 628
Czech Republic PRG 107.8 236
Hungary BUD 89.0 220
Cyprus LCA 344.2 740
Bosnia and Herzegovina SJJ 144.4 294
Serbia BEG 122.0 286
Bulgaria SOF 201.0 376
Russia VKO 225.2 606
Rest of Europe KBP 221.2 446
Egypt CAI 372.6 852
Tunisia TUN 277.6 522
Rest of Africa CPT 1154.4 3000
USA EWR 780.8 2200
Canada YYZ 640.6 2200
Central and South America GIG n.a. 3200
China PEK 659.6 2400
Rest of Asia BKK 741.4 2800
Australia, New Zealand and islands north-east of them in the Indian Ocean SYD n.a. 5600
Note: Carbon dioxide equivalent (CO
2
e) emissions refer to a return flight in the economy class
Source: https://co2.myclimate.org/en/flight_calculators/new and https://www.icao.int/environmental-protection/Carbonoffset/Pages/default.aspx
Appendix
22977Environ Sci Pollut Res (2021) 28:22969–22980
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 5 CO
2
equivalent
emissions by travel purpose and
characteristics (20142016 per
year on average)
Holiday Visit friends and relatives Business
Age class
Age 1524 149,987 31,637 42,061
Age 2534 147,586 23,303 69,492
Age 3544 114,967 19,657 71,300
Age 4554 232,321 21,889 93,893
Age 5564 209,535 32,777 54,158
Age 65+ 212,515 30,688 19,442
Education
No degree 140,241 25,908 28,804
Education medium (ref. low) 588,495 73,613 125,485
Education tertiary level (university) 334,310 60,220 194,853
Children
Travellers without children 953,099 139,703
Travellers with children 113,812 20,247
Gender
Men 479,351 65,081 258,291
Women 587,560 94,869 92,054
Labour market status
Employed 625,065 79,477 294,857
Unemployed 7131 436 865
Student 104,954 26,215 30,503
Pensioners/out of labour force 306,820 50,603 19,589
Federal state
Burgenland 24,463 3921 4349
Lower Austria 214,381 22,467 63,390
Vienna 298,087 61,015 118,511
Carinthia 43,749 5049 16,561
Styria 117,865 10,399 37,699
Upper Austria 176,731 20,924 52,360
Salzburg 68,081 13,519 17,789
Tyrol 76,701 13,214 13,829
Vorarlberg 46,853 9441 25,857
Household size
Household size =1 131,148 29,400 57,703
Household size =2 449,902 52,675 92,257
Household size =3 192,613 30,444 67,095
Household size =4 186,356 27,054 79,995
Household size =5 70,164 11,089 37,861
Household size =6 36,728 9287 15,434
Note: CO
2
equivalent emissions. Source: Austrian travel survey
22978 Environ Sci Pollut Res (2021) 28:22969–22980
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