∎

11institutetext: CNRS, Institut de Recherche en Astrophysique et Planétologie,
9 Avenue du colonel Roche, BP 44346, F-31028 Toulouse Cedex 4, France 11email:
<EMAIL_ADDRESS>
# Estimating, monitoring and minimizing the travel footprint associated with
the development of the Athena X-ray Integral Field Unit

An on-line travel footprint calculator released to the science community

Didier Barret

(Accepted for publication on April 8th, 2020. Published in June 2020 in
Experimental Astronomy, Volume 49, Issue 3, p.183-216,
https://ui.adsabs.harvard.edu/abs/2020ExA....49..183B/abstract)

###### Abstract

Global warming imposes us to reflect on the way we carry research, embarking
on the obligation to minimize the environmental impact of our research
programs, with the reduction of our travel footprint being one of the easiest
actions to implement, thanks to the advance of digital technology. The X-ray
Integral Field Unit (X-IFU), the cryogenic spectrometer of the Athena space
X-ray observatory of the European Space Agency will be developed by a large
international consortium, currently involving $\sim 240$ members, split over
13 countries, 11 in Europe, Japan and the United States. The travel footprint
associated with the development of the X-IFU is to be minimized. For that
purpose, a travel footprint calculator has been developed and released to the
X-IFU consortium members. The calculator uses seven different emission factors
and methods leading to estimates that differ by up to a factor of $\sim 5$ for
the same flying distance. These differences illustrate the lack of standards
and regulations for computing the footprint of flight travels and are
explained primarily, though partly, by different accounting of non-CO2
effects. When accounting for non-CO2 effects, the flight emission is estimated
as a multiple of the direct CO2 emission from burning fuel, expressed in
CO2-equivalent (CO2eq), with a multiplication factor ranging from 2 to 3.
Considering or ignoring this multiplication factor is key when comparing
alternative modes of transportation to flying. The calculator enables us to
compute the travel footprint of a large set of travels and can help identify a
meeting place that minimizes the overall travel footprint for a large set of
possible city hosts, e.g. cities with large airports. The calculator also
includes the option for a minimum distance above which flying is considered
the most suitable transport option ; below that chosen distance, the emission
of train journeys are considered. To demonstrate its full capabilities, the
calculator is first run on one of the largest scientific meetings; the fall
meeting of the American Geoscience Union (AGU) gathering some $\sim 24000$
participants and the four meetings of the lead authors of the working group I
of the Intergovernmental Panel on Climate Change (IPCC) preparing its sixth
assessment report. In both examples, the calculator is used to compute the
location of the meetings that would minimize the travel footprint. Then, the
travel footprint of a representative set of X-IFU related meetings is
estimated to be $\sim 500$ tons of CO2eq per year (to place this number in
perspective, it is equivalent to $\sim 2$ million kilometers driven by an
average passenger vehicle). Of this amount, each annual consortium meeting
accounts for $\sim 100$ tons, being located at a site of minimum emission and
for a minimum distance for flying of 700 km. Actions to reduce the X-IFU
travel footprint are being implemented, e.g., the number of large consortium
meetings has been reduced to one per year and face-to-face working meetings
are progressively replaced by video conferences. As the on-line travel
footprint calculator may be used to all scientific collaborations and
meetings, the calculator and its methodology described in this paper are made
freely available to the science community111https://travel-footprint-
calculator.irap.omp.eu.

##  1 Introduction

Global warming poses a threat for the habitability of our planet, calling for
rapid actions from all sectors to reduce CO2 emissions, as well as the net
effect of non-CO2 emissions. More specifically, in order to keep global
warning below $1.5^{\circ}$C, CO2 emission must be reduced globally by 45% by
2030 (from the 2010 values) with the need to reach net zero by 2050
ipccreport2019 . In 2018, the total CO2 emissions from all aviation services
was 918 million metric tons. That was 2.4% of global CO2 emissions from fossil
fuel use, with a 32% increase over the last five years graver . Aviation
contributes to $\sim 10$% of all Greenhouse Gas (GHG) emissions from all
transportation sources sims . The demand for flying is expected to increase
following the growth and development of the world economy, the development of
trade and commerce and cultural exchanges among peoples and nations, thus
leading to an increase of the aircraft emission. At the current pace, aircraft
emissions of carbon dioxide may reach $\sim 2400$ million metric tons by 2050,
with an annual traffic growth rate of 4.6% and a 2% efficiency saving, e.g.
terrenoire ; tabuchi  and references therein.

In addition, it is generally agreed that aviation contributes to climate
change more than just with the emission of CO2 from burning fuels, by
releasing gases and particles directly into the upper troposphere and lower
stratosphere where they have an impact on atmospheric composition. Atmospheric
changes from aircraft result from three types of processes: direct emission of
radiatively active substances (e.g., CO2 or water vapor); emission of chemical
species that produce or destroy radiatively active substances (e.g., NOx,
which modifies O3, CH4 concentrations); and emission of substances that
trigger the generation of aerosol particles or lead to changes in natural
clouds (e.g., contrails) ipcc99 ; lee2009 ; shine . While the radiative
forcing due to changes in CO2 is as well characterized as those from any other
sources due to human activity, estimating the forcings of non-CO2 agents
acting on shorter timescales than CO2 is much more challenging and still
subject to some uncertainties shine , see also terrenoire . Because of that,
in the tools commonly used to compute the emissions of flights, either non-CO2
effects are not accounted for, or simply modeled by a multiplier of the direct
CO2 emission. The CO2-multiplier varies between 1.3 to 2.0, based on the
estimates of the Global Warming Potential222Global Warming Potential (GWP): An
index, based on radiative properties of greenhouse gases, measuring the
radiative forcing following a pulse emission of a unit mass of a given
greenhouse gas in the present-day atmosphere integrated over a chosen time
horizon, relative to that of carbon dioxide. The GWP represents the combined
effect of the differing times these gases remain in the atmosphere and their
relative effectiveness in causing radiative forcing. The Kyoto Protocol is
based on GWPs from pulse emissions over a 100-year time frame. of aviation
emissions integrated over a 100 year timescale shine .

In response to the criticality of the situation, the International Civil
Aviation Organization (ICAO), as a specialized United Nations agency to
address all matters related to international civil aviation, including
environmental protection, and its member states have committed together to the
so-called Carbon neutral growth from 2020 onwards. They define what they call
as aspirational goal of keeping the global net CO2 emissions from
international aviation from 2020 at the same level, through technological and
operational improvements, the use of sustainable aviation fuels and the Carbon
Offsetting and Reduction Scheme for International Aviation
(CORSIA)333https://www.icao.int/environmental-
protection/CORSIA/Documents/CORSIA_FAQs_October%202019_final.pdf. The above
approach has however received a fair amount of criticisms and its overall
effectiveness would need to be demonstrated, e.g. the controversial biofuels,
especially those from palm oil that would result in more emissions even than
the fossil fuels they replace and are a cause of biodiversity loss,
deforestation and human rights abuses, or because offsetting diverts the focus
from reducing emissions to trading on emissions444Criticisms on the CORSIA
process are briefly listed at the URL
https://en.wikipedia.org/wiki/Carbon_Offsetting_and_Reduction_Scheme_for_International_Aviation
and references therein., see also warnecke  showing that the CORSIA scheme
will only compensate for the emissions increase if robust criteria for the
eligibility of offset credits are adopted.. This has been re-emphasized in the
IPCC 2019 report on climate change and land (SRCCL) which flagged the limited
potential to use additional land for afforestation and bioenergy production,
in a context of growing concerns for increasing pressures on land,
conservation of terrestrial biodiversity and food security skula .

Because carrying research is generally associated with traveling all across
the world (especially since the 80s), our GHG emission is for a vast majority
of scientists, most notably for the most senior ones, e.g. epfl , dominated by
travels. ETH Zurich has reported that more than half of their total GHG
emission was due to business travel. Of these, 94.3% are caused by flights,
only 4.6% by car journeys and 1.1% by rail travel ethzurich . Similarly EPFL
estimated that one third of their CO2 emissions was due to air business
travels, with 87% due to travels done by plane epfl . Interestingly enough, a
study of travelers at the University of British Columbia revealed that
Academic air travel had a limited influence on professional success
WYNES2019959 .

Scientists and in particular astronomers and astrophysicists, who are often
exposed to the public and who can also talk about the rather unique place of
the Earth in the Universe and its long evolutionary sequence, must lead by
example and take actions to reduce their environmental impact. This issue is
now actively discussed within the science community and ways of doing
astronomy in a low Carbon future are now being proposed for immediate
implementation williamson2019arxiv ; matzner2019arxiv ; stevens2019arxiv . The
mobilisation goes obviously well beyond the astronomical community, and there
are a large number of initiatives in academia, questioning the way to continue
research, while minimizing its environmental impact, e.g epfl ; ethzurich ;
lequere2015 ; Flying2019 ; flyinglesinacademia ; Labos1point5  to list a few
resources and initiatives.

In this context, it is worth looking at the travel footprint associated with
the development of a large astronomy project, such as the X-ray Integral Field
Unit (X-IFU): the cryogenic spectrometer of the flagship Athena Space X-ray
observatory of the European Space Agency barret2018spie . The X-IFU consortium
currently involves 13 countries, 11 in Europe plus Japan and the United
States. It can thus be anticipated that the X-IFU travel footprint is large,
and actions to reduce the project footprint must be put in place and have
their impact quantified. Those actions must be well thought as not to impact
the development of the project. Face-to-face meetings are often necessary to
solve technical issues, to build the instrument in sequence, but also because
the so-called _social engineering_ enables people to know each other better
and feel part of a larger team which is critical for the success of long term
endeavor that often represents space projects.

In order to raise awareness on the need to change our attitude towards flying,
but also for computing and monitoring the travel footprint associated with the
development of the X-IFU, a dedicated X-IFU travel footprint calculator has
been developed. Along the development of the calculator, it was realized that
there is not a single commonly accepted method to estimate flight related
emissions, with estimates that may differ by up to a factor of 5 or more, from
one method to another. Hence, the X-IFU calculator provides estimates from
seven different methods, including some of the most widely used, from either
national governmental agencies or Carbon offsetting companies. In interacting
with members of the community, the need for a transparent calculator became
obvious, and the calculator was automized and developed as an easy-to-use web
application.

The paper is organized as follows. In section §2, I describe the main features
of the X-IFU travel footprint calculator. In section §3, I describe two
illustrative applications of the calculator: one for the Fall meeting of the
AGU and another one for the four lead author meetings of the WGI of the IPCC.
In sections §4 and §5, from a representative set of meetings and travels, I
compute the overall travel footprint related to the X-IFU project. In section
§6, I discuss the ways to reduce the travel footprint of the X-IFU. The
conclusions are listed in §7. Appendices A in §8 and B in §9 provide more
information on the calculator methodology and the data and methods used in
this paper.

##  2 The X-IFU travel footprint calculator

###  2.1 Overview

The tool computes the travel footprint associated with round trip flights,
according to the data and methodology of several publicly available
calculators. It does so for a set of trips from a given city of origin to a
set of destinations. If multiple destinations are provided, the tool ranks the
destinations according to the associated carbon footprint. Similarly, the tool
allows us to compute the travel footprint of a large set of trips, e.g.,
corresponding to a conference, a meeting…. For this, the city of departure for
each participant to the event has first to be provided. The tool ranks the
cities of departure according to their associated footprint (see Fig. 11 for
the graphical representation). If multiple destination cities are provided,
the tool ranks the cities of destination according to the associated travel
footprint, as to identify the host city associated with the minimum footprint,
hereafter referred to as the site of minimum emission.

While most online calculators enable to compute the footprint of a limited
number of trips, this tool enables us to compute the footprint of a larger
number of trips in an automated way. It is therefore suited to estimate the
travel footprint associated with the development of large projects, involving
a lot of traveling.

The originality of the X-IFU calculator lies in that it provides an estimate
based on seven different methods, whose results are found to differ
significantly. If more than one method is selected by the user, the tool
returns the mean of the estimates of all selected methods. A travel footprint
calculator with similar functionalities as the X-IFU one was developed by
lhackel , considering only the DEFRA emission coefficients (see below). In
addition, the X-IFU calculator enables us to compute the travel footprint
associated with train journeys, assuming a predefined set of minimum distances
for flying, ranging from 100 km to 1000 km. This enables us to evaluate the
benefits of traveling by train in a direct way.

###  2.2 Which methods are used?

The tool incorporates data from seven different sources. Those have been
selected somewhat arbitrarily, as being some of the most frequently used,
referred to or commented on. For some of them, the methodology used is rather
well documented, e.g. atmosfair, DEFRA, ICAO, myclimate. An introduction to
the general principles of the methodology used by those calculators is
presented in e.g., jardine . In alphabetic order, the data considered are
from:

  * •

ADEME: the French Environment & Energy Management Agency with the mean
emission coefficients taken from the so-called Carbon database ademe

  * •

The French Ministry of Ecology and Inclusive Transition ministryecology

  * •

atmosfair: a German Carbon offsetting non-profit organisation atmosfair

  * •

DEFRA: UK Department for Environment, Food & Rural Affairs: the UK government
department responsible for safeguarding the natural environment in the UK
defra . As an example, the climatecare carbon offsetting company uses the
DEFRA emission coefficients.

  * •

ICAO icao . This is a widely used on-line calculator. China Airlines for
instance adopts the ICAO methodology.

  * •

KLM Carbon compensation service data klm

  * •

myclimate: a Carbon offsetting non-profit organisation, used in particular by
Lufthansa myclimate

As stated above, the list is obviously not exhaustive but represents a variety
of estimates from low to high values. Yet the web application has been
designed in such a way that it enables new methods to be added.

Figure 1: Emission factors (CO2 in dashed lines, or CO2eq in continuous lines,
per passenger in kg) as a function of the distance flown in one continuous leg
(km) as derived from the seven data sources used by the X-IFU calculator. The
emission is computed assuming economy seating. Jumps in the functions are
related to boundaries of interval distances over which the fit is applied or
the mean emission factors defined.

ADEME, DEFRA and the French Ministry of Ecology provide mean emission factors,
as a function of flight distance. myclimate provides an analytical formula.
For ICAO and atmosfair the on-line calculators have been run for a wide range
of flights of varying distances ($\sim 100$ flights spanning from 300 km to
12000 km) and the estimates have been fitted with linear functions, covering
adjacent distance intervals. For its Carbon compensation service, KLM provides
on its website a table of emissions for a large range of KLM flights. The KLM
data have also been fitted with linear functions. Deviations by up to a few
tens of percent may be found between the data and the linearly interpolated
values, but those deviations are much smaller than the differences in the
estimates of the various methods considered (see Appendix B in §9 for further
details on the different methods). The emission functions for the seven
methods are shown in Figure 1.

###  2.3 Accounting for non-CO2 effects

Emission factors or travel footprint calculators account for or ignore non-CO2
effects. The ICAO calculator does not account for any non-CO2 effects awaiting
for the scientific community to settle on the
issue555https://www.icao.int/environmental-
protection/CarbonOffset/Pages/FAQCarbonCalculator.aspx. The same appears to be
the case for the emission factors of the French Ministry of Ecology and
Inclusive Transition and the KLM data, as the estimates provided are close,
although a little higher than the ones of ICAO (see §2.4 for a discussion on
parameters entering the emission factors). The methods which do not account
for non-CO2 effects are plotted with dashed lines in Figure 1. DEFRA provides
emission factors with and without a multiplier. Here we use the DEFRA emission
factors, which include a multiplication factor, referred to as a radiative
forcing index, of 1.9 to account for non-CO2 effects, noting however that
DEFRA raises a warning on the uncertainty associated with the latter value
defra . A 2.0 multiplier is considered by myclimate, ADEME, and atmosfair
considers a multiplier of 3 for all emissions above 9 km, accounting for the
profile of the flight (for long distance flights, this means a multiplier of
$\sim 2.8$, see Figure 13). The methods applying a multiplier are shown with
continuous lines in Figure 1.

Accounting for a multiplier, the multiplied direct flight emission is
expressed in units of kg of CO2eq.

###  2.4 Other parameters entering the emission factors

As shown in Figure 1, differences by up to a factor of $\sim 5$ are found
between the different data providers for flights of 10000 km legs. It is
beyond the scope of this paper to discuss each method in details, but it is
clear that, besides the multiplier assumed by the data providers, there are
other reasons why the emission factors differ from one method to another. All
estimates are computed assuming economy seating, so that cannot explain the
observed differences. Parameters entering the emission factors are:

  * •

the actual fuel consumption per aircraft kilometer (which depends on the plane
and engine type, age, the way the plane is operated in flight, the take off
and landing sequences, including taxying…),

  * •

the fleet considered,

  * •

the correction for deviations from the great circle distance (due to holding
patterns, avoidance of bad weather conditions…),

  * •

the assumed seating configuration of the plane and the weighting by seating
class,

  * •

the passenger load factor,

  * •

the fraction of the fuel burnt allocated to the freight,

  * •

the addition or not of the emission related to the pre-production (refinery)
and transport of the fuel used by the aircraft666myclimate assumes that the
emission factor for combustion of jet fuel (kerosene) to 3.15 kg CO2eq/kg jet
fuel and the factor for pre- production used here is 0.538 kg CO2eq/kg jet
fuel.,

  * •

the addition or not of the emission related to the fabrication, maintenance
and disposal of the aircraft,

  * •

and finally the emission related to the airport infrastructure itself.

Let us take the illustrative example of the seating configuration as a key
parameter entering the emission factors. On an Airbus A380, the seating
capacity ranges from 525 passengers split between the first, business and
economy classes to more than 800 in a single-class economy class layout.
Depending on the way the plane is assumed to be filled the CO2 emission factor
per passenger.km could differ by about a factor of 1.5 (that may explain in
part why the ICAO estimates are lower than the others in Figure 1, because it
makes the assumption that all aircrafts are entirely configured with economic
seats). The parameters entering the emission factors are not always available
for the methods used by the calculator, while one would naively expect that
they are somewhat standardized and known to some accuracy. However, this is
clearly not the case and the way they are considered by the data providers
must explain the remaining factor of 2-2.5 difference, on top of the different
assumptions they consider for accounting for non-CO2 effects. In addition to
the need to settle on the issue of the accounting for non-CO2 effects, this
clearly calls for a neutral organization to define a commonly accepted
methodology for computing aircraft emissions, even more so because the overall
demand for flying is predicted to increase significantly over the next
decades, with little hope to make it more energy efficient.

###  2.5 Accounting for train emission

The minimum distance considered for flying (one leg of the round trip computed
as the great circle distance between the origin and destination cities) is an
input to be selected by the user (it is set to 500 km by default). Below the
minimum distance for flying, it is assumed that the journey is done by train.
The tool then computes the travel footprint associated with train journeys.
The train travel distance between city pairs is computed from the great circle
distance multiplied by a factor of 1.35. This factor was derived from the
comparison between road distances, assumed to be a proxy of the train travel
distances, and great circle distances (see section §8.3).

For the train emission factors (covering operations only), the calculator
assumes the mean of the emission factors of national and international rails,
as provided by DEFRA (i.e. 23.1 grams of CO2eq per passenger.km) defra  (more
about the assumptions used by DEFRAcan be found in §9.7). It should be
stressed however that factors for the train emission will depend strongly on
the way the train is powered. For instance, the French emission factors
provided by ADEME are 3.4 and 5.1 grams of CO2eq per passenger.km for high
speed trains and normal trains respectively. This rather low value is
explained by the fact that electricity production is mainly associated with
low carbon emission (nuclear, hydropower, renewable, with only a small fossil
fuel fraction). On the other hand for a train powered by Diesel, the emission
may approach $\sim 80$ grams of CO2eq per passenger km777A train footprint
calculator for four different ways of powering trains from low CO2 electricity
(hydroelectric, sun, wind…) to Diesel is available at
https://www.engineeringtoolbox.com/CO2-emissions-transport-car-plane-train-
bus-d_2000.html. The calculator assumes a mean emission of $\sim 22$ grams of
CO2eq per passenger.km when the energy source is an even blend of non-emission
electric power and power from power plants fired with hydrocarbons (gas, oil
or coal)..

Adopting the mean DEFRA value for train and the DEFRA emission factor for
short-haul flights, a round trip Amsterdam-Munich ($\sim 1300$ km in total)
will generate $\sim 40$ kg and $\sim 220$ kg of CO2eq by train and plane
respectively.

##  3 First applications of the X-IFU travel footprint calculator

We now consider two applications of the travel footprint calculator to
illustrate its capabilities.

###  3.1 The case for a large international conference: the annual meeting of
the American Geoscience Union (AGU)

The annual meeting of the AGU is one of the largest scientific conferences,
gathering in San Francisco more than 24000 scientists coming from over one
hundred countries. The footprint of the 2019 fall meeting was carefully
estimated to be $\sim 69000$ tons of CO2eq by klover , who also evaluated the
benefits of virtual participation from the most distant attendees. It was
found that emissions could be reduced by 76% provided that the 36% highest
emitting attendees (from almost every country outside of North America) would
participate virtually. Similarly, holding the conference in 3 hubs (Chicago,
Seoul, Paris), and no virtual participation would result in a saving of $\sim
70$% klover .

As a sanity check, we run the X-IFU footprint calculator from the list of
cities of departure provided by klover . Considering the atmosfair method,
consistent with the high emission factors used by klover , and assuming a
minimum distance for flying of 300 km, we estimated the total emission of the
meeting to be $\sim 68300$ tons of CO2eq, i.e. a value consistent with the one
of klover . Considering the ICAO method, the total emission would reduce to
$\sim 14800$ tons of CO2. Running the X-IFU footprint calculator with the
average of the ADEME, myclimate, and DEFRA estimates, and assuming train
journeys for great circle distances less than 700 km, the total emission from
the meeting, amounts to $\sim 50500$ tons of CO2eq. This clearly shows how
sensitive the estimate travel footprint is to the assumptions used by the
calculator, with a factor of 5 difference between the low and high estimates.

We have also run the calculator to search for the site of minimum emission in
the United States, requiring potential city hosts to have at least one large
international airport. With the above assumptions, the city of minimum
emission would be Detroit, with a total footprint of $\sim 45500$ tons of
CO2eq. The footprint associated with Chicago and Minneapolis are within $\sim
0.5$% of Detroit (and Chicago would be the city of minimum emission for an
audience restricted to the $\sim 14$ thousands US participants). This means
that the travel footprint associated with San Francisco is $\sim 11$% higher
than the one of Detroit (see Figure 2). The next four AGU December meetings
will be held in San Francisco (2020), New Orleans (2021), Chicago (2022) and
San Francisco again in 2023. If attended by an audience similar to the 2019
meeting, one can already anticipate that New Orleans would increase the
footprint by $\sim 13$%, compared to Detroit or Chicago, and $\sim 2$%, more
than San Francisco (about 1000 tons of CO2eq more, see Figure 2).

Figure 2: Percentage increase of travel footprint with respect to the city
corresponding to the minimum footprint (Detroit), for cities hosting a large
international airport in the United States. The X-IFU footprint calculator was
run with the average of the ADEME, myclimate, and DEFRA estimates, assuming
train journeys for great circle distances less than 700 km. With these
assumptions, for the AGU 2019 Fall meeting, the city minimizing the overall
footprint would have been Detroit (while the meeting was held in San
Francisco). The total footprint associated with San Francisco was $\sim 5000$
tons of CO2eq higher (11%). The four upcoming meetings are San Francisco
(2020), New Orleans (2021), Chicago (2022) and San Francisco again in 2023.
They are highlighted by green and red bars. Assuming the same audience as the
2019 Fall meeting, the travel footprint associated with New Orleans for the
2021 meeting would be 13% higher than a similar meeting held in Detroit.

Let us now get some feeling about what these large numbers actually mean. As a
global progress report on climate action, the 2019 Emissions Gap report
emgr2019  issued by the United Nations Environment programme stated that in
2018, the total greenhouse gas emissions reached a peak of 51.8 GtCO2eq
(excluding emissions from land-use change), with emission growing steadily at
a rate of 1.5% over the last decade. For a world population of 7.5 billions,
this translates to an average emission of $\sim 7$ tons of CO2eq per capita.
With this number, the total footprint of the 2019 Fall meeting of the AGU
($\sim 50500$ tons of CO2eq) is equivalent to the annual footprint of about
7200 world citizens, but released within a week. Alternatively, on average,
each of the 24000 participants increased her/his own footprint by $\sim 2.1$
tons of CO2eq by attending the meeting.

###  3.2 IPCC Working Group I AR6 Lead Author meetings

The IPCC Working Group I (WGI) aims at assessing the physical scientific basis
of the climate system and climate change. It is currently in its Sixth
Assessment cycle, leading to the sixth Assessment Report (AR6). This
assessment is done by authors from various regions of the world, and supported
by bureau members and a Technical Support Unit (partly based in Paris Saclay,
France, and Beijing, China, where co-chairs are located). As part of the
preparation of the AR6, four Lead Author Meetings (LAM) are needed: one took
place in Guangzhou (China, 215 participants), another one in Vancouver
(Canada, 222 participants), another one in Toulouse (France, 248 participants)
and the last one is planned in Santiago (Chile, 304 participants). In an
inter-governmental organization such as the IPCC, there is a systematic search
for balance between regions of the world where meetings are hosted. The
location of Lead Author meetings results from proposals from governments,
cities, and universities or research centers. Here we are interested to
compare the travel footprint of the four meeting locations, with respect to
their attendance. The participant list to each of the four LAM was provided by
the Working Group I Technical Support Unit. For illustrative purposes, the
travel footprint calculator was first run, considering the ICAO, DEFRA, and
atmosfair estimates, i.e. from the lowest to the highest estimates, and
considering a minimum distance for flying of 700 km, equivalent to accepting
train journeys up to $\sim 10$ hours or so. Summing the footprint of the 4
meetings, the estimates of ICAO, DEFRA, and atmosfair are $\sim 870$ tons of
CO2 and $\sim 2700,4940$ tons of CO2eq respectively, meaning a factor of $\sim
5.5$ difference in the computed emissions between the low (ICAO) and high
(atmosfair) estimates. This range simply reflects the different emission
functions reported in Figure 1.

Figure 3: Left) The travel footprint associated with the 4 IPCC WGI Lead
Author Meetings, based on the DEFRA emission factors and assuming a minimum
distance for flying of 700 km. The footprint is computed at the city of
minimum emission (bottom bar of each sub-panel), for an intermediate city host
(middle bar of each sub-panel), and finally for the host city of the meeting
(top bar of each sub-panel). Right) The total travel distance to reach the
various city hosts is plotted in millions of km. Barcelona refers to the city
of Venezuela.

The four LAM meetings ought to be located in four different continents, namely
Asia, North America, Europe and South America. The calculator was run to
identify the city of each continent that would be associated with the minimum
emission given the audience of each meeting. For this purpose, a list of
cities hosting large international airports in each continent, was given as an
input to the calculator as the list of potential city hosts of the LAM. The
calculator was run assuming the DEFRA emission factors and considering a
minimum distance for flying of 700 km. The results are shown in Figure 3.
Because for all four LAM, there is a relatively large number of participants
from Western Europe, reflecting a geographical imbalance in the authors of the
AR6 WGI Report linked to an existing imbalance in ongoing world climate
science research activities, the best meeting locations is expected to be on
the western part of Asia and the eastern part of South and North America. This
is obviously what shows Figure 3. For example, for the meeting in Asia,
Istanbul would have reduced the emission from 584 tons to 400 tons of CO2eq
($\sim 30$% reduction). Constraining the meeting to be in China, Beijing would
have reduced the emission to 530 tons of CO2eq ($\sim 10$% reduction).
Similarly, for the LAM4, having it located at Barcelona (Venezuela) would have
saved $\sim 20$% of the emission compared to Santiago. For the other two
meetings (LAM2 and 3), the city selected was within $\sim 10$% of the city of
minimum emission. For indication, the total travel distance of the 4 LAM is
also shown in Figure 3. It varies from $\sim 3$ (Toulouse) to $\sim 7$
(Santiago) millions of km.

Translating again the above numbers to meaningful values, if one assuming a
total footprint of the four LAM of 2700 tons of CO2eq (i.e. the intermediate
DEFRA estimate), spread over four weeks, this is equivalent to the annual
footprint of about 400 people (considering a mean annual emission per capita
of $\sim 7$ tons of CO2eq emgr2019 ). Alternatively, for each meeting, on
average, each participant increased its own footprint by $\sim 2.7$ tons of
CO2eq.

The above example shows the power of having such a travel footprint calculator
for optimizing the meeting locations of recurring events of similar
attendance. The calculator can obviously be run for optimizing the location of
events worldwide, across continents, geographical regions…. Obviously in large
international inter-governmental organizations, such as the IPCC, political,
economical, ethical, visibility arguments (including the need to support
developing countries) must necessarily enter into consideration when selecting
meeting locations. The calculator presented here may still help to guide the
optimum choice within such boundary conditions.

##  4 Application to X-IFU related travels

As stated earlier, the X-IFU travel footprint calculator was first developed
to provide X-IFU consortium members with a tool to easily access the footprint
associated with their X-IFU related travels. It ought to be used for those
organizing meetings related to X-IFU to find the optimum location, being the
site of minimum emission. It was developed under the assumption that facing
real numbers would be more efficient to convince people to change their habits
than just talking about the mandatory need to do so. The tool is now provided
as part of the registration form to consortium meetings, where each member can
also easily see the benefit of traveling by train. It is now used to monitor
the overall footprint of all X-IFU related travels.

###  4.1 X-IFU Consortium meeting with 120 attendees

Let us now first compute the travel footprint of a Consortium meeting, similar
to the last one that was held in Toulouse. It was attended by 120 people
originating from 26 cities, apart from Toulouse. The next one is planned to be
in Liège (April 2020), with the clear recommendation to travel by train
whenever possible888After the submission of the paper, because of the
covid-19, it was decided to turn the Consortium Meeting #11 into a full
virtual meeting. Ajustements were made to the program to maximize remote
participation.. Every other consortium meeting will be located in France. For
the September 2019 Consortium meeting, in total 85 people traveled to Toulouse
(the remaining 35 attendees are either from CNES or IRAP). Let us define the
objective of a minimum distance for flying of 700 km, meaning that
participants from e.g. Milan, Paris, Saclay, Grenoble, Marseille reached
Toulouse by train. The X-IFU travel footprint calculator was run, assuming the
mean of the ADEME, myclimate, and DEFRA estimates, and considering such a
minimum distance for flying. The footprint associated with such a consortium
meeting is shown in Figure 4. The total emission of the meeting would be about
$\sim 62$ tons of CO2eq (it would be 70 tons of CO2eq, under the assumption
that no one came by train). As can be seen, 4 attendees from Japan would then
represent 25% of the travel emission, more than twice the travel emission from
12 attendees from SRON Utrecht. Considering only the atmosfair and ICAO
estimates, one would get a travel footprint of $\sim 72$ tons of CO2eq and
$\sim 22$ tons of CO2 respectively.

Figure 4: Top) The travel footprint of an X-IFU consortium meeting held in
Toulouse. The carbon dioxide equivalent emission is provided for each city of
origin. Identical trips (i.e. identical origins) are summed. The number of
trips per city of origin is indicated at the top of each bar. The minimum
distance for flying is 700 km. The travel emission associated with train
journeys is indicated by red bars (see also the inset). The footprint is
computed as the mean of the ADEME, myclimate, and DEFRA estimates. Bottom) The
split of the total travel emission as a function of the city of origin for the
travelers. The number of attendees per city of origin is shown after the name
of the city, before the percentage of the emission.

###  4.2 Extended X-IFU Consortium meeting with 240 attendees - computing the
site of minimum emission

The X-IFU Consortium includes 240 active members as of January 2020. This
number is expected to grow towards the delivery of the flight model of the
instrument, when there will be a significant ramping up of the hardware
activities, including integration and testing. The X-IFU consortium is
currently spread over 39 cities as shown in Figure 5.

Figure 5: The location of the cities making up the X-IFU consortium are
indicated by a blue symbol. The great circle path from Toulouse is marked with
a red line. The X-IFU consortium is spread over 39 cities, in Europe, the
United states and Japan.

We now wish to estimate the site of minimum emission, assuming that such
extended consortium meetings are held in a city of a participating institute
to the X-IFU consortium. The X-IFU travel footprint calculator was run,
assuming the mean of the ADEME, myclimate, and DEFRA estimates, and
considering 700 km as a minimum distance for flying.

The results are shown in Figure 6. As can be seen, all consortium meetings
held in Western Europe, which corresponds to the geographic barycenter of the
X-IFU Consortium, have a total CO2eq emission around 100 tons. Having a
meeting on the US east coast would multiply that amount by 5-6 and having a
meeting in Japan or on the West coast of the US would have an associated CO2eq
emission 8-9 times higher, reaching about 900 tons of CO2eq per meeting. Such
a large value would exclude the possibility to hold a meeting in the latter
sites with such a large attendance. Accepting 100 tons as a maximum amount of
CO2eq emission for an extended consortium meeting would imply that the
attendance of such a meeting in Japan be reduced by a factor of $\sim 10$,
assuming everyone flies in economy seats.

Figure 6: The travel footprint of consortium meetings attended by 240
participants as a function of the host city for the meeting. The X-IFU travel
footprint calculator was run, assuming the mean of the ADEME, myclimate, and
DEFRA estimates, and considering 700 km as a minimum distance for flying. The
number of travels by plane is indicated at the top of each bar (with the
exception of the locals, all other attendees are traveling by train). The
carbon dioxide equivalent emission is summed over all cities of origin and
provided for each city of destination.

The calculator offers the option to select the minimum distance for flying
from 100 km to 1000 km, which then enables us to visualize easily the savings
associated with train travels. We have repeated the computation above but
varying the minimum distance for flying. In the worse case scenario in which
nobody travels by train, the site of minimum emission would correspond to a
travel footprint of 147 tons of CO2eq. This amount would reduce by about 42%
if the minimum distance for flying is set to 1000 km. This is illustrated in
Figure 7 where we show the savings, against the case where no one travels by
train for the meeting described above. Obviously, this stands for the
geographical configuration of the X-IFU Consortium, but this still shows that
significant savings (several tens of %) can be achieved by considering
traveling by train instead of traveling by plane.

Figure 7: The saving in footprint as a function of the minimum distance for
flying in km. The saving is against the case in which everyone flies.

###  4.3 Scaling to smaller X-IFU related meetings

Assuming that the travels to consortium meetings are representative of the
travels within the X-IFU consortium, from the above numbers, meaning accepting
a minimum distance for flying of 700 km, one obtains a mean footprint per
travel of $\sim 120/240=0.5,600/240=2.5,850/240=3.5$ tons of CO2eq for a
meeting in western Europe, United States and in Japan, respectively.

Two types of smaller face-to-face physical meetings are foreseen for the
project. The first one is the so-called project manager meeting, held at CNES
in Toulouse, and attended by up to 30 traveling participants twice a year. The
project manager meetings would amount to $2\times 30\times 0.5=30$ tons of
CO2eq.

The second type of face-to-face meetings foreseen are topical meetings on a
specific topic (e.g. instrument sub-system interfaces). Assuming 10 such
topical meetings, 8 in Europe, 1 in the US and 1 in Japan, all attended by at
most 10 traveling participants, the overall emission is $8\times 10\times
0.5+1\times 10\times 2.5+1\times 10\times 3.5=35+25+35=95$ tons of CO2eq.

In addition to the above meetings, one may consider that 10 key people of the
X-IFU consortium, e.g. the Principal Investigator, the Project Manager, 2 Co-
Principal Investigators, Chairs of the X-IFU science advisory team, Chair of
the X-IFU calibration team/instrument scientist, instrument manager,
performance manager …will travel 10 times per year across the consortium,
including once in the United States and once in Japan. This is another
$10\times(8\times 0.5+2.5+3.5)=100$ tons per year.

###  4.4 X-IFU presence to conferences

The X-IFU and its subsystems must be presented at international conferences,
e.g. to promote its scientific capabilities and keep informed the community of
the progresses of the project. Within the CNES and IRAP teams (about 60
members at the end of 2019, including engineers, scientists, support staff), a
coordination effort is already in place to limit the list of attendees to the
bare minimum, most particularly to conferences in very distant locations (e.g.
the upcoming SPIE Astronomical Telescopes and Instrumentation meeting in
Yokohama, Japan). Based on current numbers, one can estimate 30 participations
to conferences in total per year for the CNES/IRAP teams (24 in Europe, 3 in
Japan and 3 in United States). This translates to an annual travel footprint
to conferences of $\sim 30$ tons of CO2eq per year, equivalent to an average
of $\sim 0.5$ tons of CO2eq per year per member of the team.

With the $\sim 60$ institutes and institutions involved in the X-IFU
consortium and its 240 active members, it is hard to estimate precisely the
associated travel footprint to conferences. It is up to each participating
institute in the X-IFU consortium to define its own policy with respect to
attending conferences, but there will be a strong incentive to optimize and
coordinate whenever possible their presence to conferences. Extrapolating the
CNES/IRAP number per personal (0.5 ton of CO2eq per year) to the whole
consortium, one would obtain a total footprint of 120 tons CO2eq per year for
X-IFU representation to conferences. Again, this number should be considered
uncertain, but should also be seen as a maximum value being computed prior to
any optimizations of the X-IFU presence to conferences (see discussion below).

###  4.5 Traveling for downstream activities: integration and testing

In the current baseline, the X-IFU instrument will be integrated in Toulouse.
The X-IFU today consists of about 25 independent sub-systems to be delivered
by the team responsible of the procurement. There will be two instrument
models before the flight model, to be integrated in Toulouse (engineering and
qualification models). The staffing required for the integration sequence of
these instrument models is not known yet, but it appears plausible that about
100 engineers and scientists from the Consortium will have to travel to
Toulouse several times for each model. Note that each sub-system may also have
its own integration sequence involving additional travels. These travels are
absolutely mandatory but their footprint will also be monitored. Assuming 3
models and 100 travels per model and 3 travels to Toulouse per model
integration, this would amount for an CO2eq emission of $3\times 100\times
3\times 0.5=450$ tons (again considering that a minimum distance for flying is
700 km). Although this number should be considered with great caution and very
likely on the low side (e.g. as not accounting for trouble shooting
activities, travels to support calibration…), it is comparable to the one year
CO2eq emission budget of all other X-IFU related meetings (see Table 1).

Table 1: Summary of CO2eq emissions for a representative set of X-IFU meetings: Consortium meetings, project manager meetings, face-to-face topical meetings, key personnel travels, participation to conferences and finally travels by consortium partners to Toulouse for integrating the X-IFU models to CNES (Toulouse). This covers the period onwards up the delivery of the flight model of the X-IFU. These numbers should be considered as maximum values, against which reduction measures are to be applied.  Meeting type | CO2eq (tons)
---|---
Consortium meeting (120 attendees, current, one per year, up to 2023) | 60
Consortium meeting (240 attendees, max, one per year, 2024 up to 2029) | 120
Project manager meetings at CNES (30 attendees, 2 per year, up to 2029) | 30
Topical face-to-face meetings (10 people, 10 per year, up to 2029) | 95
Additional travels by 10 key consortium members (10 per year, up to 2029) | 100
Participation to international conferences (up to 2029) | 120
Total travel emission per year | $\sim 405-465$
Travels to CNES for the integration of X-IFU instrument models | $\sim 450$
Integrated travel footprint (2015-2029) | $\sim 7000$

##  5 Integrated X-IFU travel footprint and what it means

The travel footprint of the X-IFU is summarized in table 1. The list of X-IFU
related meetings is built upon more than five years of the existence of the
consortium, hence the travel footprint budget derived, although approximative,
can be considered representative. Integrated from 2015 until the year of the
delivery of the flight model of the X-IFU, the total travel footprint
associated with the development of the X-IFU is rounded to $\sim 7000$ tons of
CO2eq. It is interesting to note that this is less than the saving of 10000
tons that would have been achieved by moving the 2019 Fall meeting of the AGU
from San Francisco to Detroit/Chicago. This being said, the numbers provided
in table 1 are rather large, and should be seen as maximum values, against
which efficient reduction measures are going to be implemented.

So let us first consider the average annual travel footprint of X-IFU to be
$\sim 500$ tons of CO2eq and relate this rather abstract number to quantities
that can be better apprehended. According to the U.S. Environmental Protection
Agency Greenhouse Gas Equivalencies
Calculator999https://www.epa.gov/sites/production/files/widgets/ghg-
calc/calculator.html, 500 tons of CO2eq correspond to $\sim 106$ passenger
vehicles driven for one year, 85 home electricity use for one year, could be
avoided by recycling $\sim 170$ tons of waste or would be sequestered by $\sim
8300$ tree seedlings grown for 10 years.

Assuming a mean worldwide emission of $\sim 7$ tons of CO2eq per capita
emgr2019 , the annual footprint of the X-IFU project contributes equally to
the greenhouse gas emission of $\sim 70$ persons. Alternatively, the X-IFU
travel related emission of each consortium member accounts already for about
30% of the current average level of emission worldwide. As stated in the
Emission gap report emgr2019 , by 2030 (which is the horizon for the
development of the X-IFU), GHG emissions would need to be reduced by 25% (=4.9
tons of CO2eq per capita) or 55% (=2.9 tons of CO2eq per capita for a
projected world population of 8.5 billions) from the 2018 values to limit the
global warming to below $2^{\circ}$C and $1.5^{\circ}$C, respectively. This
means that in 2030, the annual travel footprint of each X-IFU Consortium
member if unchanged (about 2 tons of CO2eq) would amount for $\sim 40$% and
$\sim 70$% of a sustainable annual carbon budget.

In Europe, by 2050, in the context of necessary reductions by developed
countries as a group, according to the IPCC, the European Union objective is
to reduce GHG emissions by 80-95% below 1990 levels. With current measures,
the target calculations including emissions from international aviation and
excluding emissions and removals from the land sector set the ultimate goal of
GHG emission between $\sim 0.5$ and $\sim 2$ tons per capita at the 2050
horizon (for an assumed projected population of $\sim 523$ millions people).
This gives clear markers against which the mean annual X-IFU related travel
footprint of $\sim 2$ tons of CO2eq should be compared to, given that on
average flight related travel emission is at most a few percents of total
greenhouse gas emission per capita, the latter being dominated by
contributions from other means of transportation, housing, food, goods and
services.

##  6 Reducing the X-IFU travel footprint

Based on the numbers presented in table 1, the X-IFU Consortium board has
already agreed to take actions to monitor and reduce the travel footprint of
the X-IFU. The first decision taken was to reduce the number of Consortium
meetings to one per year instead of two. The numbers presented in table 1 are
the one to work against, and to which reduction measures will be proposed for
implementation within the X-IFU Consortium on the shortest possible term.
Those measures have already been discussed in the context of reducing the
environmental impact of scientific activities epfl ; ethzurich ;
williamson2019arxiv ; matzner2019arxiv ; stevens2019arxiv ; lequere2015 ;
Flying2019 ; flyinglesinacademia ; Labos1point5 , such as conferences, but
they now have to be put in place along the development of a large
infrastructure, such as the X-IFU. The actions foreseen for X-IFU are listed
below:

  * •

Favor video conferences whenever possible, even for meetings in which critical
matters are discussed and face-to-face discussions may be preferred. As an
example, analyzing the total cost of videoconferencing, including operating
costs of the network and videoconferencing equipment, lifecycle assessment of
equipment costs, although the footprint of virtual meetings depend on many
factors such as distance travelled, meeting duration, and the technologies
used, ong  found that videoconferencing takes at most 7% of the energy/carbon
of an in-person meeting. A video-conferencing system (e.g. Zoom) has been set-
up for both the CNES and IRAP teams, and provides satisfaction, even for
meetings attended by more than 40 people. It is thus likely that some topical
meetings will be held by videoconference. This may have the further benefit of
increasing attendance and increasing efficiency due to the time saved by not
traveling.

  * •

Optimize the travels of all participants to the one week long Consortium
meetings by organizing topical meetings, next to the plenary sessions, as to
cover a full week. On a project like X-IFU, we do need physical interactions
within and outside work. This also means that special attention should be
brought to the social events linked to consortium meetings.

  * •

Avoid short duration single goal meetings, and make the best use of each
travel, extending the stay to participate to nearby events (schools,
seminars,…), to network, especially for the youngest members of the
Consortium.

  * •

Keep the CO2eq emission of all consortium meetings around 100 tons, and
restrict the attendance accordingly, but sweep the locations across the whole
consortium, i.e., not always meeting in western Europe. This is required not
to impose the most distant members of the X-IFU consortium to fly every time,
hence being always exposed to jet lags, fatigue, losing time on travels…. This
is also required to ensure that each contributing country receives proper
visibility.

  * •

Investigate the possibility to hold multi-node distributed consortium
meetings: in Europe, Japan, and United States for instance. This scheme is now
broadly discussed for large scientific events. Assuming a minimum distance for
flying of 700 km (and the mean of the ADEME, myclimate, and DEFRA estimates),
and assuming one hub in Europe, one in Japan and one in the United States, if
each hub is located at the site associated with the minimum emission, the 100
tons could be reduced to $\sim 36$ tons of CO2eq by meeting in Geneva,
Sagamihara, and Greenbelt, meaning a reduction by a factor of 3. This shall be
considered as a maximum saving, as each hub meeting may receive attendees
originally associated with another hub (e.g. youngest members of the
Consortium, Consortium board members…).

  * •

Locate always X-IFU related meetings at the site of minimum emission for the
given audience, reachable by train within less than a day, close to
international hubs which can be reached directly (to avoid connecting
flights), with the list of attendees restricted to key persons, whose presence
is mandatory. Changes in meeting organization may be needed to enable people
to reach the meeting place by train, without impacting their private life,
i.e. not having to travel over the weekends.

  * •

Coordinate the presence of X-IFU to international conferences, restricting the
number of attendees to the bare minimum, sending representatives of the
project residing close to the conference sites, and privilege those
conferences where there is a clear policy towards minimizing the travel
footprint and/or which promote alternative ways of attendance (e.g., virtual
participation). Give priority to the youngest consortium members to attend
conferences, e.g., for presenting and promoting their work and contribution to
the project but also for networking…. Within X-IFU, one should accompany and
support the transition to evaluating differently the evolution of academic
careers, as to ensure that those who fly less are not penalized.

  * •

Compensate: X-IFU is funded by public money. At the level of the funding
agencies, there is no global compensation mechanism yet implemented.
Compensation occurs only at personal level. Shall compensation be made
possible in the future, its efficiency should however be demonstrated. In all
cases, compensating should not be seen as the way to continue as before, but
should come together with the undertaking of all efforts in reducing our
emission in the first place.

  * •

Organize scientific conferences related to climate changes and its societal
impacts as part of the plenary sessions of each consortium meetings. This
should help in triggering a more global reflection on the way we keep our
research programs going along the reduction path of our footprint, while at
the same time supporting the development of similar projects in emerging
countries through international cooperation.

  * •

Dedicate personnel to implement all the above actions in a coordinated way
across the Consortium.

Adopting a greener approach, whenever possible, will also be pursued, e.g.,
reducing waste, using re-usable items during X-IFU related events. Along the
development of the X-IFU, many scientists and engineers will be exposed to the
grand public. Each of us should engage with the public about the threat
imposed by climate change and the impact of aviation, and should promote
alternatives to flying to work collectively on a large scale project.

This paper focuses entirely on the travel footprint of the project. Estimating
the global footprint of the project is a humungous task which goes beyond the
scope of this paper. Nevertheless, it will be computed and presented
elsewhere.

##  7 Conclusions

A travel footprint calculator has been developed to monitor and reduce the
travel footprint associated with the development of the Athena X-ray Integral
Field Unit. The calculator uses seven different emission factors and methods
leading to estimates that differ by up to a factor of $\sim 5$ for the same
flying distance, calling for the need of standardization and regulation for
estimating the aircraft emission and its associate impact on global warming.
Those estimates should be based on a transparent, rigorous, reliable and
indisputable methodology, worked out by an independent body. Amongst others,
this is required for the science community to properly evaluate the impact of
its activities, to question its contribution towards the goal of mitigating
climate change in the best possible way, while reflecting globally on how to
carry research in the decades to come, taking full benefits of the rapidly
improving technologies of communication.

Running the calculator on a set of X-IFU related meetings, the annual travel
footprint of the project is about 500 tons. Actions are being implemented to
lower this number, with strong incentive to use video conferences, whenever
traveling is not absolutely required. Traveling will not be banned but each
travel will be optimized. Reducing the travel footprint of the project will
require the involvement of each consortium member and strong implication and
exemplarity by the consortium leads, starting with the X-IFU PI. The
initiative has received strong support throughout the X-IFU consortium, it now
remains to convert this into daily acts.

The full capabilities of the calculator have been illustrated by running it on
the Fall meeting of the AGU and the lead author meetings of Working Group 1 of
the IPCC. It has also been tested and used for some other applications (the
X-ray Astronomy 2019 conference, the Cerenkov Telescope Array project, the
EWASS 2019 conference,…). It is released for use by the science community at
the following URL https://travel-footprint-calculator.irap.omp.eu.

###### Acknowledgements.

The author wishes to thank Vincent Albouys, the CNES X-IFU project manager,
for his immediate support to the initiative of reducing the travel carbon
footprint of the X-IFU project. I am grateful to Isabelle Dangeard (Institut
Universitaire de Technologie de Quimper (IUT de Quimper)) for her help and
guidance in understanding the various methods (and their shortcomings) in
estimating carbon budgets and footprints, and for extensive comments to the
manuscript. Very special thanks to Valérie Masson Delmotte, Co-Chair of IPCC
Working Group I for discussions and for detailed and constructive comments all
along the preparation of the paper. Thanks also to Jan Fuglestvedt, Vice Chair
of IPCC WGI for insightful comments on the accounting of non-CO2 effects.
Thanks to Elisabeth Lonnoy for providing the list of participants of the 4
Lead Author Meetings of the IPCC Working Group I. Thanks to all the colleagues
who have provided inputs along the development of the calculator, the website
application and the comments on the paper: Leonard Burtscher, Deborah
Costanzo, Felix Fuerst, Pierre-Louis Frison, Catherine Jeandel, Milan Kloewer,
Jurgen Knoedelseder, Luigi Tibaldo, Maeva Voltz and Stéphane Paltani. I would
to thank Antoine Goutenoir for his enthusiasm, his dedication and skills in
developing the web application of the calculator, and thank you to IRAP for
hosting the web application of the calculator. Finally, the author is grateful
to an anonymous referee for a very careful reading of the paper.

## References

  * (1) IPCC.  Global warming of $1.5^{\circ}$ c: An ipcc special report on the impacts of global warming of $1.5^{\circ}$ c above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. 2019 intergovernmental panel on climate change. isbn 978-92-9169-153-1 (2019).  URL https://www.ipcc.ch/site/assets/uploads/sites/2/2019/06/SR15_Summary_Volume_Low_Res.pdf
  * (2) B. Graver, K. Zhang, D. Rutherford, https://theicct.org/publications/co2-emissions-commercial-aviation-2018 (2019)
  * (3) R. Sims, R. Schaeffer, F. Creutzig, X. Cruz-Nunez, M. D’Agosto, D. Dimitriu, M. Figueroa Meza, L. Fulton, S. Kobayashi, O. Lah, A. McKinnon, P. Newman, M. Ouyang, J. Schauer, D. Sperling, G. Tiwari, Atmospheric Environment,Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. (2014)
  * (4) E. Terrenoire, D.A. Hauglustaine, T. Gasser, O. Penanhoat, Environmental Research Letters 14(8), 084019 (2019).  DOI 10.1088/1748-9326/ab3086
  * (5) H. Tabuchi, https://www.nytimes.com/2019/09/19/climate/air-travel-emissions.html (2019)
  * (6) IPCC.  Ipcc special report aviation and the global atmosphere, summary for policymakers aviation and the global atmosphere, a special report of ipcc working groups i and iii in collaboration with the scientific assessment panel to the montreal protocol on substances that deplete the ozone layer, published for the intergovernmental panel on climate change, 1999, intergovernmental panel on climate change (1999).  URL https://www.ipcc.ch/site/assets/uploads/2018/03/av-en-1.pdf
  * (7) D.S. Lee, D.W. Fahey, P.M. Forster, P.J. Newton, R.C.N. Wit, L.L. Lim, B. Owen, R. Sausen, Atmospheric Environment 43(22), 3520 (2009).  DOI 10.1016/j.atmosenv.2009.04.024
  * (8) K. Shine, Encyclopedia of Aerospace Engineering. Edited by Richard Blockley and Wei Shyy, 2010 John Wiley & Sons, Ltd. ISBN: 978-0-470-68665-2 (2010)
  * (9) C. Warnecke, L. Schneider, T. Day, S. La Hoz Theuer, H. Fearnehough, Nature Climate Change 9(3), 218 (2019).  DOI 10.1038/s41558-019-0415-y
  * (10) P. Shukla, J. Skea, R. Slade, R. van Diemen, E. Haughey, J. Malley, M. Pathak, J. Portugal Pereira, https://www.ipcc.ch/site/assets/uploads/sites/4/2019/11/03_Technical-Summary-TS.pdf (2019)
  * (11) C. Joachim, M. Aleksandra, B. Leonardo, Z. Dominic, T. Daniel, O. Giel, (2014)
  * (12) ETH Zurich Stay grounded, keep connected, (2019).  URL https://ethz.ch/services/en/organisation/executive-board/vice-president-human-resources-and-infrastructure/mobilitaetsplattform/air-travel.html
  * (13) S. Wynes, S.D. Donner, S. Tannason, N. Nabors, Journal of Cleaner Production 226, 959 (2019).  DOI https://doi.org/10.1016/j.jclepro.2019.04.109.  URL http://www.sciencedirect.com/science/article/pii/S0959652619311862
  * (14) K. Williamson, T.A. Rector, J. Lowenthal, arXiv e-prints arXiv:1907.08043 (2019)
  * (15) C.D. Matzner, N.B. Cowan, R. Doyon, V. Hénault-Brunet, D. Lafrenère, M. Lokken, P.G. Martin, S. Morsink, M. Normandeau, N. Ouellette, M. Rahman, J. Roediger, J. Taylor, R. Thacker, M. van Kerkwijk, arXiv e-prints arXiv:1910.01272 (2019)
  * (16) A.R.H. Stevens, S. Bellstedt, P.J. Elahi, M.T. Murphy, arXiv e-prints arXiv:1912.05834 (2019)
  * (17) C. Le Quéré, S. Capstick, A. Corner, D. Cutting, M. Johnson, A. Minns, H. Schroeder, K. Walker-Springett, L. Whitmarsh, R. Wood, https://tyndall.ac.uk/sites/default/files/publications/twp161.pdf (2015)
  * (18) Flying Academic.  Flying less: Reducing academia’s carbon footprint (2019).  URL https://academicflyingblog.wordpress.com/
  * (19) Flying Less in Academia: A Resource Guide, http://flyinglessresourceguide.info (2020)
  * (20) Labos 1point5, https://labos1point5.org/ (2020)
  * (21) D. Barret, T. Lam Trong, J.W. den Herder, L. Piro, M. Cappi, J. Houvelin, R. Kelley, J.M. Mas-Hesse, K. Mitsuda, S. Paltani, G. Rauw, A. Rozanska, J. Wilms, S. Bandler, M. Barbera, X. Barcons, E. Bozzo, M.T. Ceballos, I. Charles, E. Costantini, A. Decourchelle, R. den Hartog, L. Duband, J.M. Duval, F. Fiore, F. Gatti, A. Goldwurm, B. Jackson, P. Jonker, C. Kilbourne, C. Macculi, M. Mendez, S. Molendi, P. Orleanski, F. Pajot, E. Pointecouteau, F. Porter, G.W. Pratt, D. Prêle, L. Ravera, K. Sato, J. Schaye, K. Shinozaki, T. Thibert, L. Valenziano, V. Valette, J. Vink, N. Webb, M. Wise, N. Yamasaki, F. Douchin, J.M. Mesnager, B. Pontet, A. Pradines, G. Branduardi-Raymont, E. Bulbul, M. Dadina, S. Ettori, A. Finoguenov, Y. Fukazawa, A. Janiuk, J. Kaastra, P. Mazzotta, J. Miller, G. Miniutti, Y. Naze, F. Nicastro, S. Scioritino, A. Simonescu, J.M. Torrejon, B. Frezouls, H. Geoffray, P. Peille, C. Aicardi, J. André, C. Daniel, A. Clénet, C. Etcheverry, E. Gloaguen, G. Hervet, A. Jolly, A. Ledot, I. Paillet, R. Schmisser, B. Vella, J.C. Damery, K. Boyce, M. Dipirro, S. Lotti, D. Schwander, S. Smith, B.J. Van Leeuwen, H. van Weers, N. Clerc, B. Cobo, T. Dauser, C. Kirsch, E. Cucchetti, M. Eckart, P. Ferrando, L. Natalucci, in _SPIE_ , _Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series_ , vol. 10699 (2018), _Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series_ , vol. 10699, p. 106991G.  DOI 10.1117/12.2312409
  * (22) L. Hackel, https://lhackel.shinyapps.io/travel_footprint/ (2019)
  * (23) C. Jardine, https://www.eci.ox.ac.uk/research/energy/downloads/jardine09-carboninflights.pdf (2019)
  * (24) Carbon database, http://www.bilans-ges.ademe.fr/ (2019)
  * (25) GHG information for transport services, (2019).  URL https://www.ecologique-solidaire.gouv.fr/sites/default/files/Information_GES%20-%202019.pdf
  * (26) atmosfair Flight Emissions Calculator, Documentation of the Method and Data, (2016).  URL https://www.atmosfair.de/wp-content/uploads/atmosfair-flight-emissions-calculator-englisch-1.pdf
  * (27) DEFRA, 2019 Government greenhouse gas conversion factors for company reporting: Methodology paper, (2019).  URL https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/823744/2019-ghg-cf-methodology-paper.pdf
  * (28) ICAO Carbon Emissions Calculator Methodology, (2017).  URL https://www.icao.int/environmental-protection/CarbonOffset/Documents/Methodology%20ICAO%20Carbon%20Calculator_v10-2017.pdf
  * (29) KLM, data, (2019).  URL https://www.klm.com/travel/nl_en/prepare_for_travel/fly_co2_neutral/all_about_sustainable_travel/index.htm
  * (30) The myclimate Flight Emission Calculator, (2019).  URL https://www.myclimate.org/fileadmin/user_upload/myclimate_-_home/01_Information/01_About_myclimate/09_Calculation_principles/Documents/myclimate-flight-calculator-documentation_EN.pdf
  * (31) M. Klover, https://github.com/milankl/CarbonFootprintAGU (2020)
  * (32) United Nations Environment Programme, https://wedocs.unep.org/bitstream/handle/20.500.11822/30797/EGR2019.pdf (2019)
  * (33) D. Ong, T. Moors, V. Sivaraman, Computer Communications (Elsevier), 50:86-94, Sep 2014. DOI: (2014)

##  8 Appendix A: Methodology used for the travel footprint calculator

###  8.1 How does the calculator work?

A round trip is defined by a pair of cities. The two cities are geolocated and
from their longitude and latitude the great circle distance is computed. This
is the shortest path a plane can follow. Some methods thus consider uplift
correction factors, to account for deviations from the shortest paths (due to
holding patterns, avoidance of bad weather conditions…) in computing the
emission of a flight (e.g. DEFRA uses a multiplying factor of 8% to the
emission coefficient, myclimate uses instead a detour constant of 95 km
independently of the distance flown, see §9.5). In addition, two cities may
not be connected by a direct flight. Connecting flights are associated with
larger emissions because they increase the travel distances, adding
takeoffs/landings/…which have a larger fuel consumption than during the
cruise. This is accounted for by the calculator by increasing all great circle
distances by 5% (lhackel  considered 2.7%, based on US airline statistics).
Each method translates to a function giving the flight emission in kg as a
function of the flight distance in km, see Figure 1. Thus from the increased
great circle distance, the flight emission of a flight associated with a trip
between a city pair is computed and multiplied by two to account for a round
trip.

###  8.2 Seating category

The tool assumes economy seats for computing the flight emission. The emission
factors for different seat classes relate to the area occupied by the seat in
the plane. Figure 8 shows the DEFRA, the atmosfair, and the myclimate
multiplying factors inferred from their methodology. As can be seen, there are
some differences between the different methods, but on average it can be
considered that the footprint can be multiplied by $\sim 1.5$, $\sim 2$ and
$\sim 3$ for flying in Premium Economy, Business and First class. Note that
the above factors should also depend in principle on the occupancy rate of the
seats in each category.

Figure 8: Multiplication factor of the emission factors with respect to
economy seats as considered by the DEFRA, atmosfair, and myclimate methods.

###  8.3 Approximating train travel distances from great circle distances

The footprint calculator enables to chose a train journey between city pairs,
instead of a flight. To compute the train travel distance, one assumes that
the shortest road distance between two cities is a proxy of the train travel
distance. We have computed road distances from Toulouse and Paris to a set of
cities across Europe, within $\sim 1300$ km. We plot road distances versus
great circle distances in Figure 9. As can be seen, under the above
assumption, with some scattering, road distances are found on average 35%
larger than great circle distances. Since the calculator computes the great
circle distances between city pairs, to compute the emission of the train
journey, a 1.35 multiplication factor is applied to the emission associated
with the measured great circle distance.

Figure 9: Road distances computed from googlemaps versus great circle
distances computed from the X-IFU footprint calculator for a set of city
pairs, with either Toulouse or Paris as the city of origin. The road distance
is assumed to be a proxy of the train travel distance. Hence the train travel
distance is computed from the great circle distance multiplied by 1.35.

Some may select train traveling instead of flying based on the duration of the
trip. It is thus worth relating the minimum distance for flying to a travel
time duration by train. In Europe, from a shallow survey of trip durations
between major cities, it appears that the average speed of trains is about 100
km/hour (there are cases however for which excellent connections enables to
reach speed significantly larger, up to 200 km/h). This means that on average
it takes about 8 hours to travel by train between two cities with a great
circle distance of 500 km (including connection, deviations from the shortest
paths…). 100 km/hour may be on the low side by a couple of 10 km/hour in
countries where high speed trains are common, and on the high side by the same
amount in countries where high speed trains are less available or not
available at all.

###  8.4 Input and output data for the calculator

The web application of the X-IFU calculator is based on a form with very
limited inputs requested. The inputs are provided in US English for the city
and country names, without diacritics. On each line, the city and country
names must be separated by a comma. Pasting a csv file in the form is
possible, provided that a comma separates the city and country names. The
calculator provides comma-separated-values (csv) and for convenience Excel
(xlsx) template files, which can then be filled in and later uploaded. The
first row of the files must be labeled with City and Country.

A round trip is defined by a city pair. If the user enters cityA as the origin
city, and twice cityB as destinations, the tool returns the cumulative
emission and distance from two round trips involving cityA and cityB, and
indicates that 2 round trips were involved. The same happens if the user
enters twice cityA as the city of origin and cityB as the sole destination.

The calculator can be run under three configurations:

  * •

A single city of origin and multiple destinations: this would be the case for
an individual or an organization for which the city of departure is always the
same. After grouping the city pairs, the calculator ranks the cities of
destination according to the associated travel footprint.

  * •

Multiple cities of origin and a single destination: this would be the case for
computing the footprint of a conference/meeting…. After grouping the city
pairs, the calculator ranks the cities of origin according to the associated
travel footprint.

  * •

Multiple cities of origin and multiple destinations: this would be the case
for finding the city corresponding to the minimum travel footprint or
comparing the travel footprint associated with different hosts of an event.
The site of minimum emission is obviously associated with the smallest summed
travel distance when only flights are considered, but may differ slightly when
train journeys are allowed. After grouping the city pairs, the calculator
ranks the city of destination according to the associated travel footprint
summed over all cities of origin.

The user can select any combination of the seven methods implemented by the
calculator. If more than one method is selected, the calculator returns the
mean of the estimates of the selected methods.

The above cases can be run with the assumptions that all travels are by plane,
or by considering a minimum distance for flying chosen between 100, 300, 500,
700 and 1000 km (500 km being the default option).

The result page provides a summary plot which can be downloaded in svg and png
formats (see Figure 11 for a plot example), as well as a csv and raw yaml
file, which can be used for further processing. The csv file lists the name of
the city as in the form, the address to which it was geolocated, the CO2eq
emission (in kg), the cumulative distance traveled, the number of trips
possible by train (i.e. when the distance is less than the minimum flying
distance, e.g. 500 km) and the number of trips by plane. The plot and the csv
file always rank the cities against their associated footprint.

###  8.5 Trouble shooting and caveats

The estimation relies on the quality of the inputs provided. It can go wrong
if a city is not properly geolocated. This may happen because the name of the
city is wrongly spelled or the geolocator (OpenStreetMap, OSM) is confused. An
error should be listed at the end of the result page. Do not be surprised, if
the name recovered by the geolocator is not exactly the one you had expected
(e.g. a city is located at the address of an embassy). An error may also occur
if the input file submitted does not comply with the requested format (e.g.
the first row is not properly labeled), including font encoding. If nothing
happens during a request, it is most likely caused by the geolocator being
unavailable. In this case, try again a few minutes later. The calculator has
been run on some large input files, such as for the AGU meeting (24000 cities
of origin and 20 possible destinations). The larger the input data set, the
longer it takes and the higher the chances are that the geolocator gets stuck
in the process.

The numbers provided by the tool do not come with uncertainties, which must be
pretty large as shown by the range of models considered in the calculator. In
all cases however, the numbers can be used for relative comparisons, e.g. when
comparing two cities for hosting a conference.

###  8.6 The X-IFU travel footprint calculator input form

Inputs to the X-IFU travel footprint calculator are to be provided through the
form shown below (see Figure 10). The data provided through the form will
remain confidential, as will be the results.

Figure 10: The form for entering the inputs to the calculator.

###  8.7 Graphical output of the travel footprint calculator

The X-ray Astronomy 2019 conference was held in Bologna, gathering about 340
participants (data courtesy of D. Costanzo). The graphical output of the
footprint calculator is shown in Figure 11.

Figure 11: The graphical output of the online X-IFU travel footprint
calculator run for the case of the X-ray Astronomy 2019. The X-IFU calculator
ranks the cities according to their associated travel footprint. The minimum
distance for flying is assumed to be 500 km. Travels from cities at the top of
the plot above Geneva (included) are assumed to be done by train. The CO2eq
emission (summed over all the participants traveling from the given city)
labels the green symbols on the plot (the number of travelers is not shown and
the difference of emissions between two nearby cities is related to a
different number travelers). The emission is computed from the mean of the
ADEME, atmosfair, myclimate, and DEFRA estimates, assuming all passengers flew
in economy class. The total emission from the meeting was $\sim 465$ tons of
CO2eq, and for the audience of the meeting Bologna was close to the site of
minimum emission. At the bottom of the figure, Cambridge refers to the city in
the United States.

##  9 Appendix B: Emission factors from the different methods

###  9.1 Derived emission factors from atmosfair, ICAO, and KLM data

The on-line calculators have been run for a range of flight distances for
atmosfair, and ICAO atmosfair ; icao . The KLM data have been extracted from
their Carbon compensation service website. The data have then been fitted by
linear functions. The data points and the best fits are shown in Figure 12.
Deviations of up to a few tens of % can be found between the data points and
the best fits. ICAO does not include a multiplier for non-CO2 effects in its
estimates. No information is provided by KLM on that issue, but given the
proximity of the estimates with the ICAO ones it seems very likely that KLM
also ignores radiative forcing in the estimates provided on their website.

Figure 12: Two on-line calculators (atmosfair and ICAO) for which no
analytical functions exist have been run for a wide range of distances and the
provided estimates fitted by linear functions, within adjacent distance
intervals. Data from the KLM compensation service web site have been retrieved
and fitted the same way. The differences between the atmosfair and the other
two methods are explained only in part by the fact that ICAO and KLM do not
consider a multiplication factor for non-CO2 effects, while atmosfair
considers a multiplication factor of 3 for altitudes larger than 9 km.

The direct CO2 emission multiplier used by atmosfair is plotted in figure 13
as a function of the great circle distance. Accounting for the profile of the
flight, the multiplier reaches around $\sim 2.95$ for long distance flights.

Figure 13: The atmosfair carbon emission calculator provides two estimates for
the flight emission: one value which corresponds to the estimate of the fuel
burnt and one value which provides the other part of the climate impact
(contrails, ozone formation and other effects). This plots shows the ratio
between the latter and the former. A multiplier coefficient of 3 is applied
for all emissions above 9 km, which translates to a multiplication factor of
2.95 for long distance flights.

###  9.2 Mean emission factors from the French ADEME Carbon database

ADEME provides mean emission factors as a function of leg distance and seat
capacity of the plane ademe . For the X-IFU calculator, for each leg, we have
assumed the emission factors as the average over all seat capacities of the
plane, as listed in the last column of Table 2. It is worth noting that ADEME
provides emission factors for planes of low seat capacity, and that those are
significantly larger than the ones associated with bigger planes. Such small
high emitting planes may not be part of the fleet considered by the other
calculators. The same applies to the data provided by the French Ministry for
the Ecological and Inclusive Transition (§9.4).

Distance | $<50$ | 50-100 | 100-180 | 180-250 | $>250$ | Average
---|---|---|---|---|---|---
(km) | seats | seats | seats | seats | seats |
0-1000 | 0.683 | 0.453 | 0.314 | 0.293 | … | 0.4358
1000-2000 | 0.906 | 0.314 | 0.258 | 0.216 | … | 0.4235
2000-3000 | 1.2 | 0.209 | 0.237 | 0.209 | … | 0.4638
3000-4000 | … | … | 0.230 | 0.230 | 0.251 | 0.2370
4000-5000 | … | … | 0.293 | 0.307 | 0.258 | 0.2860
5000-6000 | … | … | 0.286 | 0.230 | 0.223 | 0.2463
6000-7000 | … | … | … | 0.223 | 0.209 | 0.2160
7000-8000 | … | … | … | 0.202 | 0.209 | 0.2055
8000-9000 | … | … | … | 0.223 | 0.23 | 0.2265
9000-10000 | … | … | … | 0.216 | 0.223 | 0.2195
10000-11000 | … | … | … | … | 0.216 | 0.2160
$>$11000 km | … | … | … | … | 0.223 | 0.2230
Table 2: ADEME Carbon database emission coefficient for flights in kg of CO2eq
per km.passenger ademe . The data can be retrieved from the URL:
http://www.bilans-ges.ademe.fr, but requires identification.

###  9.3 DEFRA

DEFRA provides mean emission factors for different flight types (see Table 3).
The values include a 1.9 multiplier to account for non-CO2 effects. The
multiplier is referred to as a Radiative Forcing Index by DEFRA. Short haul
are for flights below 3700 km and long-haul above. For computing the DEFRA
emission of long haul flights, the mean of the emission coefficients of
flights from/to the UK and international flights has been assumed.

Haul | kg CO2eq
---|---
Short-haul, to/from UK | 0.156
Long-haul, to/from UK | 0.150
International, to/from non-UK | 0.138
Table 3: The DEFRA emission factors taken from defra  in kg of CO2 per
passenger.km. A 1.9 multiplier is included in the emission factors.

###  9.4 Mean emission factors from the French Ministry for the Ecological and
Inclusive Transition.

The French Ministry for the Ecological and Inclusive Transition provides mean
emission factors as a function of leg distance and seat capacity of the plane,
in a similar way as ADEME ministryecology . For the calculator, we have
averaged the emission factors over all seat capacities of the plane, as listed
in the last column of Table 4. No radiative forcing is included in these
emission coefficients. Similar to ADEME the ministry provides high emission
factors for small capacity planes.

Table 4: Mean emission coefficients as derived from green house gas information for transport services from the Ministry for the Ecological and Inclusive Transition. These are the emission coefficients for flights in kg of CO2 per passenger.km. Reference: GHG information for transport services. Application of Article L. 1431-3 of the French transport code. Methodological guide. Updated version resulting 67 article of the law n∘ 2015-992. Version of June 2019. Taken from Table 5 and 21 bis from ministryecology . Distance | $<50$ | 50-100 | 101-180 | 181-250 | $>250$ | Average
---|---|---|---|---|---|---
(km) | seats | seats | seats | seats | seats | …
0-1000 | 0.223 | 0.187 | 0.141 | 0.117 | … | 0.167
1000-2000 | 0.254 | 0.161 | 0.117 | 0.095 | 0.123 | 0.150
2000-3000 | … | … | 0.109 | 0.091 | 0.101 | 0.100
3000-4000 | … | … | 0.105 | 0.099 | 0.099 | 0.101
4000-5000 | … | … | 0.153 | 0.126 | 0.090 | 0.123
5000-6000 | … | … | 0.150 | 0.098 | 0.088 | 0.112
6000-7000 | … | … | … | 0.100 | 0.082 | 0.091
7000-8000 | … | … | … | 0.091 | 0.087 | 0.089
8000-9000 | … | … | … | 0.095 | 0.087 | 0.091
9000-10000 | … | … | … | 0.073 | 0.083 | 0.078
10000-11000 | … | … | … | … | 0.095 | 0.095
$>$11000 km | … | … | … | … | 0.094 | 0.094

###  9.5 myclimate

myclimate provides an analytical formula as given below myclimate , whose
parameters are listed in Table 5.:

$E=\frac{ax^{2}+bx+c}{S\times PLF}\times(1-CF)\times CW\times(EF\times
M+P)+AF\times x+A$ (1)

in which

  * •

E: CO2-eq emissions per passenger [kg]

  * •

x: Flight Distance [km] which is defined as the sum of GCD, the great circle
distance, and DC, a distance correction for detours and holding patterns, and
inefficiencies in the air traffic control systems [km]

  * •

S: Average number of seats (total across all cabin classes)

  * •

PLF: Passenger load factor

  * •

CF: Cargo factor

  * •

CW: Cabin class weighting factor

  * •

EF: CO2 emission factor for jet fuel combustion (kerosene)

  * •

M: Multiplier accounting for potential non-CO2 effects

  * •

P: CO2eq emission factor for preproduction jet fuel, kerosene

  * •

AF: Aircraft factor

  * •

A: Airport infrastructure emissions

  * •

The part $ax^{2}+bx+c$ is a nonlinear approximation of f(x) + LTO

  * •

LTO: Fuel consumption during landing and takeoff cycle including taxying [kg]

  * •

Short-haul is defined as $x<1500$ km and long-haul as $x>2500$ km. In between,
a linear interpolation is used.

Aircraft type | Generic short-haul | Generic long-haul
---|---|---
Average seat number (S) | 153.51 | 280.21
Passenger load factor (PLF) | 0.82 | 0.82
Detour constant (DC) | 95 | 95
1 - Cargo factor (1 - CF) | 0.93 | 0.74
Economy class (CW) | 0.96 | 0.80
Business class weight (CW) | 1.26 | 1.54
First class weight (CW) | 2.40 | 2.40
Emission factor (EF) | 3.15 | 3.15
Preproduction (P) | 0.54 | 0.54
Multiplier (M) | 2 | 2
Aircraft factor (AF) | 0.00038 | 0.00038
Airport/Infrastructure (A) | 11.68 | 11.68
a | 0. | 0.0001
b | 2.714 | 7.104
c | 1166.52 | 5044.93
Table 5: myclimate model parameters as taken from myclimate .

###  9.6 Normalization of the emission coefficients

It is interesting to compare the emission coefficients of the different
methods without any multiplier, accounting for non-CO2 effects. Figure 14
shows the range of the emission coefficients derived from the seven
estimators. As can be seen, for short flying distances, a factor of $\sim 3$
is observed between the minimum and maximum of the emission factors. The
factor goes to $\sim 2$ for large flying distances. For those distances, the
mean emission factor levels off around $\sim 80\pm 30$ g of CO2 per
passenger.km. Although not apparent in the figure, the emission coefficients
should reflect the fact that aircrafts are generally most efficient at
intermediate distances, as for short distances the energy-intensive take-off
and climb sections represent a large fraction of the fuel consumed, while for
long distance flights the fuel to be carried at take-off and climb-out
requires extra energy.

Figure 14: Range of emission factors (CO2 per passenger.km in g) as a function
of the distance flown in one continuous leg (km). The lower and upper
boundaries of the colored region are given by the minimum and maximum emission
factors of the seven methods used by the calculator. The mean values of
emission factors of the seven methods are shown with filled black circles. No
multiplication factor, accounting for non-CO2 effect is applied, enabling the
methods to be compared. The emission is computed assuming economy seating.

###  9.7 DEFRA train emission factors

For DEFRA the international rail factor ($\sim 6$ g of CO2 per passenger.km)
is based on a passenger-km weighted average of the emission factors for
Eurostars routes, e.g., London-Brussels, London-Paris. The methodology applied
in calculating the Eurostar emission factors accounts for the total
electricity use by Eurostar trains on the UK and France/Belgium track
sections, the total passenger numbers (and therefore calculated passenger km)
on all Eurostar services, the emission factors for electricity (in kgCO2 per
kWh) for the UK and France/Belgium journey sections. On the other hand, the
national rail factor refers to an average emission per passenger km for diesel
and electric trains in 2017-18 ($\sim 40$ g of CO2 per passenger.km). For both
international and national rail factors, the CH4 and N2O emission factors have
been estimated from the corresponding emission factors for electricity
generation, proportional to the CO2 emission factors. The sum of CO2 CH4 and
N2O emissions is expressed in CO2eq (see defra  for more details).