# The carbon footprint of IRAP

Pierrick Martin, Sylvie Brau-Nogué, Mickael Coriat,
Philippe Garnier, Annie Hughes, Jürgen Knödlseder, Luigi Tibaldo
IRAP, Université de Toulouse, CNRS, CNES, F-31028 Toulouse, France

(25 April 2022)

##  1 Summary

We present an assessment of the greenhouse gases (GHG) emissions of the
Institute for Research in Astrophysics and Planetology (IRAP), located in
Toulouse (France). It was performed following the established “Bilan Carbone”
methodology, over a large scope compared to similar previous studies,
including in particular the contribution from the purchase of goods and
services as well as IRAP’s use of external research infrastructures, such as
ground-based observatories and space-borne facilities. The carbon footprint of
the institute for the reference year 2019 is $7400\pm 900$ t
CO${}_{2}\textrm{e}$. If we exclude the contribution from external research
infrastructures to focus on a restricted perimeter over which the institute
has some operational control, IRAP’s emissions in 2019 amounted to $3300\pm
400$ t CO${}_{2}\textrm{e}$.

Over the restricted perimeter, the contribution from purchasing goods and
services is dominant, about 40% of the total, slightly exceeding the
contribution from professional travel including hotel stays, which accounts
for 38%. Local infrastructures make a smaller contribution to IRAP’s carbon
footprint, about 25% over the restricted perimeter. We note that this
repartition may be specific to IRAP, since the energy used to produce the
electricity and heating has a relatively low carbon footprint. Over the full
perimeter, the large share from the use of ground-based observatories and
space-borne facilities and the fact that the majority of IRAP purchases are
related to instrument development indicate that research infrastructures
represent the most significant challenge for reducing the carbon footprint of
research at our institute.

With $\sim 260$ staff members employed at our institute, our results imply
that performing research in astronomy and astrophysics at IRAP according to
the standards of 2019 produces average GHG emissions of 28 t
CO${}_{2}\textrm{e}$/yr per person involved in that activity. This figure lies
well above the target global average budget of 2 t CO${}_{2}\textrm{e}$/yr per
capita by 2050. However, the footprint is spread across a variety of social
and economic sectors, and so are the benefits of the research activity. As a
consequence, the emission reduction to be achieved by scientific research
should be made on open and democratic grounds, in a debate reaching beyond
scientific communities and informed by facts and figures such as those
presented here.

Regardless of the exact reduction goals for our community, the magnitude of
the challenge and the necessity to quickly engage into an effective transition
calls for acting on all aspects of the problem: lowering the carbon intensity
of our activities, reducing their pace, and shifting our work practices
towards less emission-intensive options. At the level of our own institute, we
show that emissions in the restricted perimeter can be reduced by up to 30%,
by changing our traveling habits and adopting different practices for
commuting. Revising the criteria for the purchase of goods and services could
provide an additional significant reduction. At the community level, the most
urgent objective should be to lower the carbon footprint of research
infrastructures and avoid that it keeps growing with the deployment of new
facilities.

###### Contents

  1. 1 Summary
  2. 2 Introduction
  3. 3 Methodology and scope
    1. 3.1 Local and national context
    2. 3.2 Methodology and tools
    3. 3.3 Cartography of activities and flows
    4. 3.4 Scope and boundaries
  4. 4 Data collection and emissions assessment
    1. 4.1 User survey
    2. 4.2 Electricity and heating
    3. 4.3 Waste production
    4. 4.4 Water consumption
    5. 4.5 Air conditioning
    6. 4.6 Meals
    7. 4.7 Commuting
    8. 4.8 Professional travel and hotel stays
    9. 4.9 Purchase of good and services
    10. 4.10 Computer equipment
    11. 4.11 External computing, storage, and data flow
    12. 4.12 Research infrastructures
    13. 4.13 Emission sources excluded from our scope
  5. 5 Synthesis
    1. 5.1 The carbon footprint of IRAP
    2. 5.2 Comparison and discussion
  6. 6 The way forward
    1. 6.1 Facilitating carbon accounting
    2. 6.2 Possible avenues for reduction
  7. 7 Acknowledgements

##  2 Introduction

In a 2018 Special Report ‘Global warming of 1.5 ∘C’ [25], the
Intergovernmental Panel on Climate Change (IPCC) presented evidence that
anthropogenic emissions, such as greenhouse gases (GHG) and aerosols and their
precursors, have increased the global mean surface temperature by
approximately +1.0 ∘C (with a likely range of 0.8-1.2 ∘C) above 1850-1900 pre-
industrial levels. The increase in carbon dioxide content of the atmosphere is
unprecedented over geological times. From past and ongoing emissions, the
expected increase rate is 0.1-0.3 ∘C/decade, which would lead to +1.5 ∘C
sometime during 2030-2050 if no mitigation measures are taken. Subsequent
releases of IPCC reports have confirmed this conclusion, and strengthened the
attribution of climate change to human influence [24]. The phenomenon seems to
be accelerating, and each of the last four decades has been successively
warmer than any decade that preceded it since 1850.

The IPCC reports that the impacts of anthropogenic climate change are already
perceptible in the intensity and frequency of climate and weather extremes,
such as floods, heat waves and droughts. These changes affect entire human
societies, and concerns are progressively shifting to adaptation to climate
change, especially in developing countries [16]. Natural ecosystems are also
strongly affected, with an unprecedented collapse in biodiversity. While land
use and sea exploitation are the dominant drivers of biodiversity loss,
climate change is having a growing impact on biodiversity, exacerbating other
drivers. The global assessment report on biodiversity and ecosystem services
of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem
Services (IPBES) states that the rate of species extinction across the globe
is tens to hundreds of times higher than the average rate over the past 10
million years, and is accelerating [9].

The main messages from the IPCC report can be summarized as follows: (i) every
fraction of a degree counts, and there are significant differences between a
+1.5 ∘C and +2.0 ∘C global warming scenario in terms of adverse consequences;
(ii) our choices over the next two decades are crucial to secure emission
pathways remaining below or only slightly overshooting +1.5 ∘C; (iii) such
pathways require far-reaching and systemic transitions and transformation in
nearly all sectors of human activity. The latter is emphasized in the IPBES
report, which states that sustainability in our use of nature for 2030 and
beyond may only be achieved through “fundamental, system-wide reorganization
across technological, economic and social factors, including paradigms, goals
and values”.

The +1.5 ∘C scenario with no or limited overshoot requires anthropogenic CO2
emissions to drop by 40-60% by 2030 relative to the 2010 levels [25]. This
implies an average reduction rate of 7.6% every year over the next decade
[35]. We recently went through an unexpected episode that illustrates the
magnitude of the necessary changes. In 2020, as a result of the drastic global
response to the COVID-19 pandemic, CO2 emissions fell by 6.4% [and
subsequently bounced back; see 33]. This is about the level of reduction we
need to achieve every year, sustained over more than a decade.

Over the past decade, a growing fraction of the scientific community has
recognized the urgency of these results, mirroring the increasing concern
within civil society [e.g. 1, 20, 12]. Complementing the legitimate and
necessary calls to stand up as scientists to warn citizens and policymakers of
the major threat posed by climate change [31], a growing number of individuals
and institutions are now willing to assess the environmental impact of
scientific research as it is currently practiced.

In the field of astronomy and astrophysics, several quantitative assessments
and discussions about our impact on the environment – mostly focussing on GHG
emissions – have recently been published. These studies have addressed several
different facets of the community and its activities: a single research
institute [18], a national community [32, 37], existing or planned research
infrastructures [14, 6], high-performance computing [29], large astronomy
meetings [10], and possible mitigating solutions [26, 11]. Despite their
differences, these studies all find that significant efforts must be made to
align our research practices with IPCC-recommended trajectories for the next
decades.

In this paper, our goal is to contribute to this collective reflection by
presenting a carbon footprint assessment for the Institut de Recherche en
Astrophysique et Planétologie (IRAP) located in Toulouse, France. Our
reporting uses 2019 as the reference year and includes a large number of
emission sources not considered by earlier studies, e.g. the emissions from
purchased goods and services, and an estimate for the impact of using large-
scale research equipments such as ground-based observatories and space-borne
telescopes and probes. The result suggests that previous efforts were most
likely underestimating the carbon footprint of research in astronomy by a
large factor, which puts us even further away from the assigned goals for
sustainability of human activities. Our work strengthens the evidence that
massive GHG emissions are at the very heart of our research activities and
highlights the urgent need for an ambitious community-wide plan for the
future, challenging the deep cultural roots of astrophysics as we exercise it
today.

##  3 Methodology and scope

###  3.1 Local and national context

In France, the practice of GHG emission assessment in the public and private
sectors was formalized by legislation passed in 2010 [21], extended and
complemented by further legislation in 2015 [22]. The objectives and
requirements of these GHG emission assessments are summarized in a practical
guide by Ref. [27]. Together, these documents identify the private and public
actors that are legally bound to conduct an assessment of their GHG emissions,
and define the minimal standards for conducting, publishing, and updating such
assessments.

A research laboratory, as it is legally structured in France, is not subject
to this legal obligation, which applies to higher-level institutions such as
universities and national research organizations like the “Centre National de
la Recherche Scientifique (CNRS)”. The latter, however, are often very large
entities, gathering up to a few tens of thousands employees involved in very
different fields of activity, and spread over a large number of geographical
sites. Conversely, laboratories seem the relevant scale for initiating such an
assessment: they employ a few tens to a few hundreds of people located at a
small number of physical sites, are relatively homogeneous in terms of their
activity, and offer a more direct and efficient access to the administration,
which is key to collecting the relevant data.

The idea of conducting a carbon footprint assessment at IRAP was promoted by a
group of a dozen persons organized in an official commission of the institute
since 2018. The commission has representatives on the institute council and
its role is to assist the direction of the institute in all efforts related to
reducing the environmental impact of institute activities, e.g. raising staff
awareness, waste management, promoting environment-friendly options for
travel, etc. The formal decision to conduct a carbon footprint assessment was
taken jointly with the institute’s direction, and the work started in late
2019. Similar developments occurred in six local institutes that together with
IRAP form the “Observatoire Midi-Pyrénées” (OMP) research federation. Although
these institutes specialise in different scientific domains (oceanography,
geophysics, biosphere, ecology, climatalogy), it was relevant to join forces
because office space, services and facilities are largely shared among OMP
institutes.

###  3.2 Methodology and tools

As a first step, 24 OMP staff members, including eight from IRAP, followed a
40 h training course about how to conduct a carbon footprint assessment with
the Bilan Carbone (BC) methodology. This training was funded by the
participants’ institutes and the CNRS. The training was delivered by the
“Institut Formation Carbone”111https://www.if-carbone.com/IFC_WEB. BC is a
carbon accounting methodology and set of tools that have been developed and
used in France for more than 20 years. It has been applied to private
companies, industries, and structures of all kinds and sizes including
universities and research institutes. The method is compatible with other
reporting methods such as the GHG Protocol [38] or ISO 14064-1 [17]. The
method is based on emission factors taken from the “Base Carbone”
database222http://www.basecarbone.fr/fr/accueil/ of the French “Agence pour le
Développement et la Maîtrise de l’Energie (ADEME)”.

Our work complements community efforts emerging in France to systematically
track the carbon footprint of research institutes [23] by providing an in-
depth and more complete assessment of our institute. This is important
because, as we show later, our results suggest that the perimeter of the
assessment needs to be widened as much as possible in order not to miss
potentially dominant emission sources.

As recommended in the BC methodology, intermediate results were reported to
the institute in July 2021, after approximately six months of data collection.
The purpose of this mid-term restitution is to inform colleagues, validate the
data obtained so far, and agree on final steps. The present document
constitutes the final report, which will serve as a basis for the definition
of a long-term reduction plan.

###  3.3 Cartography of activities and flows

Carbon accounting following the BC method starts with a cartography of the
institute that aims to capture all its activities and the input and output
flows that they generate. The main guiding principles of this exercise are
relevance and completeness. Such a cartography is meant to provide a strategic
view of the institute, allowing a census of everything that the institute
critically depends upon, as well as a perspective on everything that the
institute produces and delivers to external partners333This exercise goes
beyond carbon emissions. It provides a critical examination of how an
organization’s activities depend on fossil fuels and other primary resources,
thereby revealing the vulnerability of an organization to future evolutions in
the supply and pricing of commodities..

The overall philosophy of the BC method is to identify the organization’s most
powerful lever arms to achieve large GHG emission reductions globally, rather
than compiling a list of the emissions that an organization is directly or
solely responsible for. An exhaustive approach is key to developing a
perennial and effective reduction plan since it takes into consideration the
deep changes that may be required to achieve significant permanent reductions.
Carbon accounting over a highly restricted scope risks excluding an
organization’s dominant sources of emissions and hiding the reasons behind
certain sources of carbon emissions. As such, the reduction measures based on
a restricted carbon footprint assessment may have limited effectiveness in the
long term. As an example, there has been a strong focus within the scientific
community on the impact of air travel, and several reports have identified it
as the major contributor to their carbon footprint [e.g. 18]. In the case of
IRAP – but the result is likely to apply to many other astronomy institutes –
we show below that air travel is not the dominant source of emissions. As
such, flights should not be the only target of reduction measures if IRAP is
to achieve a reduction trajectory compatible with IPCC recommendations.

In this cartography, the IRAP is described as a structure where the following
core activities and duties are performed:

  1. 1.

Instrument development including hardware and software

  2. 2.

Observations, laboratory experiments, and data analysis

  3. 3.

Analytical and numerical modelling of natural phenomena

  4. 4.

Teaching, training, and public outreach

  5. 5.

Animation and participation in the scientific community

The fifth category includes a broad range of activities, such as participation
in national and international conferences, expert panels, selection committees
and time allocation committees at the national and international level. The
fourth category extends significantly beyond the perimeter of the institute.
We decided to mostly exclude it from our reporting since most of the impact of
teaching and training, including the commuting of students, is more
appropriately assessed at the level of universities and schools. What remains
inside our scope from the fourth category are expenses connected to students
directly using our resources and facilities (e.g. electricity used during
their internships in the institute), the commuting of students affiliated to
IRAP for the whole of 2019 (mostly PhDs), and regular transfers of the staff
between the institute facilities and teaching sites.

The above activities are associated with flows that can be inputs, outputs, or
internal in nature. These can be further organized into the following
categories: (i) people, e.g. commuting of the staff, scientific visitors,
professional travel, conference attendees, etc ; (ii) material, e.g.
electronic components, chemical products, computer equipments, etc ; (iii)
services, e.g. subcontracting, numerical resources, observational data, etc ;
(iv) internal support, e.g. heating, electricity, administration, canteen,
etc.

A major input to our activity is the large amounts of observational data from
research infrastructures like ground-based observatories or space-borne
telescopes. This is counted as external service, that we generally do not own,
control or pay for, but nevertheless has an significantly impact on our
activities. Similarly, some crucial numerical resources such as
supercomputing, cloud storage, astronomical databases and various internet
facilities, are counted as external services because their operations and
related impacts are external to the institute (computing and data storage
performed on site at IRAP is accounted for in the purchase of computer
equipment and consumption of electricity however). We note that emissions in
the same flow category are not necessarily treated in the same way. In
practice, the accounting is driven by the format and availability of the
related information.

###  3.4 Scope and boundaries

Based on the above cartography of our activities and associated flows, we
define the temporal, organizational, and operational scope of our carbon
footprint assessment as follows:

Reference year: We adopted 2019 as reference year. As this was before the
COVID-19 pandemic, it presents a snapshot of our recent activities as they
were before lockdowns and travel restrictions. Whether the carbon footprint
will be affected in the long term by changes in activity or attitude patterns
following COVID-19 remains to be seen. Moreover, the actual distribution of
our emissions, as presented below, suggests that that COVID-related
restrictions in 2020 and 2021 had only a minor impact on our carbon footprint.

Organisation: The assessment concerns IRAP, a research institute spread over
three sites (two in Toulouse and one in Tarbes), two of which are shared with
other institutes (the Belin site in Toulouse and the Tarbes site). These three
buildings and their related support services are areas over which IRAP has
significant effective control, and where it can rapidly implement mitigation
measures. Our footprint goes beyond these sites, and the numerous facilities
that are necessary to our daily work, such as external computing centers and
other research infrastructures, are included as external service providers in
our assessment. Teaching and training activities, which concerns a non-
negligible fraction of IRAP staff, is essentially left out of the scope as
explained above.

People: We consider all persons that were employed for the whole of 2019,
whether their status was permanent or not. This excludes most undergraduate
students, short-term interns, and PhDs and postdocs that left the institute
during 2019. In total, we identified 116 researchers, 28 postdocs, 78
engineers, technicians and administrative staff, and 41 PhD students that were
employed at IRAP over the full year of 2019.

Operations: According to the ISO14069 nomenclature, we include emissions for
the following categories of activity (usually termed scope): direct emissions
from owned or controlled sources (scope 1), indirect emissions from the
generation of purchased energy (scope 2), and all other indirect emissions
(scope 3). In our case, the latter category is by far the dominant one.

Source | Activity data | Emission data ( t CO${}_{2}\textrm{e}$)
---|---|---
Electricity | Roche: 1863 MWh | $113\pm 11$
| Belin: 398 $\pm$ 80 MWh | $24\pm 5$
| Tarbes: 15 $\pm$ 3 MWh | $1\pm 0.2$
| Total: 2276 $\pm$ 80 MWh | $138\pm 12$
Heating | Roche: 857 MWh | $59\pm 18$
| Belin: 177 $\pm$ 35 MWh | $40\pm 8$
| Tarbes: 38 $\pm$ 8 MWh | $9\pm 2$
| Total: 1072 $\pm$ 36 MWh | $108\pm 20$
Water | Roche: 4271 m3 | $1.7\pm 0.3$
| Belin: 428 $\pm$ 86 m3 | $0.2\pm 0.04$
| Tarbes: 45 $\pm$ 9 m3 | $0.01\pm 0.002$
| Total: 4744 $\pm$ 86 m3 | $2\pm 0.3$
Air conditioning | Roche: 11.1 kg of R410A | $21\pm 6$
| Tarbes: 1.59 kg of R410A | $3.1\pm 0.9$
|  0.92 kg of R22 | $1.6\pm 0.5$
|  0.19 kg of R32 | $0.13\pm 0.04$
| Total | $26\pm 6$
Waste |  $155\pm 41$ tons | $55\pm 20$
Food |  $44500\pm 15000$ meals | $85\pm 50$
Commuting |  $(1.6\pm 0.3)\times 10^{6}$ km | $174\pm 67$
Internal commuting |  $(1.0\pm 0.2)\times 10^{5}$ km | $10\pm 4$
Professional travel | flight : $(5.9\pm 0.1)\times 10^{6}$ km | $1126\pm 48$
| train: $(2.5\pm 0.03)\times 10^{5}$ km | $1.2\pm 0.1$
| car/cab: $(1.8\pm 0.06)\times 10^{5}$ km | $42\pm 6.0$
Hotel |  $3996\pm 59$ nights | $75\pm 6$
Computer equipment | 139 (139-153) units | $81\pm 40$
Goods and services | 3.657 M€ | $1335\pm 342$
External computing |  $7.0\pm 3.5$ MhCPU | $33\pm 26$
External storage |  $293\pm 129$ TB | 26 (4-63)
Data flow |  $293\pm 129$ TB | 1.5 (0.3-3.2)
Observational data | space: 46 missions | $2800\pm 600$
| ground: 39 observatories | $1300\pm 500$
Total (restricted perimeter) |  $3258$$\pm 359$
Total (full perimeter) |  $7418$$\pm 860$
Table 1: Summary of emission sources. Uncertainties are quoted as a range of
values when they are strongly asymmetric. The uncertainties for the emission
data include relative uncertainties on activity data and emission factors
added in quadrature. For some categories (meals, commuting, internal
commuting, external storage), activity data are presented in an aggregate form
and uncertainties are propagated from individual items quadratically. The
total is given over the full perimeter of the assessment, and over a
restricted perimeter that excludes the contribution of external research
infrastructures including external computing and storage.

##  4 Data collection and emissions assessment

###  4.1 User survey

Most of the data used in the assessment were obtained from our administration
in the form of listings of our consumption, purchases, and travel. For the
data that were not available to the administration (commuting practices,
meals, and use of computing/data storage resources external to our institute),
we conducted an online user survey. The survey form was based on a protocol
provided in [23], but was expanded and adapted to suit our needs.

The response rate was 59% among faculty and staff researchers, 51% among
engineering, technical, and administrative personnel, and 29% among PhD
students and postdocs. The lower response rate among PhD students and postdocs
is due to the fact that many of those that were at our institute in 2019 had
left by the time we conducted the survey. We extrapolated the data collected
to the entire population by assuming that the sample available is
representative within each of the three personnel subcategories listed above.
Following the BC methodological guide we assigned to these data a nominal
uncertainty of 50%.

###  4.2 Electricity and heating

Source | Incoming | Delivered | Combustion only | Full LCA |
---|---|---|---|---|---
| MWh PCI | MWh | $\frac{\rm tCO2e}{\rm MWh\,PCI}$ |  t CO${}_{2}\textrm{e}$ | $\frac{\rm tCO2e}{\rm MWh\,PCI}$ |  t CO${}_{2}\textrm{e}$ |
Wood | 45927 |  | 0.0132 | 606 | 0.0244 | 1121 |
Natural gas | 10585 |  | 0.187 | 1979 | 0.227 | 2403 |
Total | 56512 | 51136 |  | 2585 |  | 3524 |
EF ($\frac{\rm kgCO2e}{\rm kWh}$) |  |  |  | 0.0506 |  | 0.0689 |
Table 2: Calculation of the emission factor (EF) of the university boiler
house in 2019, based on the total wood and gas consumption during the year.
The final emission factor is obtained by dividing the total CO2e emissions
obtained from life cycle analysis (LCA) by the total energy delivered by the
boiler house in 2019.

Figure 1: Electricity and heating consumption at IRAP, and the corresponding
GHG emissions. Data for the three sites of the institute are shown separately.

As described in Sect. 3.4, IRAP is spread over three different sites: the
Roche and Belin buildings in Toulouse, and another building in the city of
Tarbes. The Roche building is entirely occupied by IRAP while the Belin and
Tarbes premises are shared with other laboratories. The Roche site is
connected to the heating network of the university, while the heating on the
Belin and Tarbes sites is provided by natural gas boilers. Since the
electricity and gas consumption is measured at the building level, we
estimated our share at the Belin and Tarbes sites based on the relative
surface occupancy of the different laboratories. Emission factors for the
electricity and gas were taken from ADEME.

The university heating network is supplied by a boiler house fuelled by both
wood and natural gas. The main fuel in 2019 was wood. The preliminary emission
factor for the 2019 heat production provided by the boiler house services did
not include the upstream and methane emissions for the use of wood as a fuel.
We therefore calculated our own emission factor based on a life cycle analysis
(LCA) for wood and gas fuels and the heat production data of the boiler house
in 2019, and obtained a final emission factor of 0.0689 kg CO2e/kWh. Table 2
provides details on the calculation of this emission factor. Note that wood
combustion is considered to emit biogenic CO2 not fossil CO2. Whatever its
origin, biogenic or fossil, CO2 gas increases the greenhouse effect in the
same way. However, unlike fossil fuels, biomass can be renewed on a human
timescale, although the exact length of the cycle varies considerably (e.g.
annual crops vs. forests). For biomass of forest origin, biogenic carbon
emissions are not counted if this biomass comes from a country where
harvesting remains below the biological growth of the forest, which is the
case in France. Therefore, the emission factor for wood combustion listed in
Table 2 only takes into account methane emissions.

For the natural gas used in the heating of the Belin and Tarbes sites, we use
an emission factor of 0.227 kg CO2e/kWh corresponding to the French gas mix in
2015 (more recent estimates were not available). For the electricity, we use
an emission factor of 0.0607 kg CO2e/kWh corresponding to the French
electricity mix in 2019.

Electricity and heating consumption, together with their corresponding GHG
emissions, are listed in Table 3 and presented in Fig. 1. We note that there
are no uncertainties associated with the electricity and heat consumption data
for the Roche site as it is accurately metered. For the Belin and Tarbes
sites, we added a conservative $20\%$ uncertainty related to the calculation
of IRAP’s share based on surface occupancy. In addition, we used uncertainties
of $5\%$ and $10\%$ for the natural gas and electricity emission factors,
respectively, as provided in the ADEME database. For the university heating
network, we used the generic $30\%$ applied to heating networks in the ADEME
database. In total, electricity and heating consumptions emit $246\pm 23$ t
CO${}_{2}\textrm{e}$.

###  4.3 Waste production

Waste production is associated with a carbon footprint that was estimated from
the information provided by the logistical services of the institute. The
weight of IRAP’s waste is not systematically calculated, so our estimates are
based on the number of containers for each type of waste, the frequency of
collection, and the volume of containers. Assuming 41 weeks with full
containers (a time period corresponding to the university’s operations
excluding vacations), we estimated a total volume of waste of $508$ m3 per
year, separated into several types: paper ($9\%$ of total volume), cardboard
($17\%$), dry recyclable waste ($16\%$), household waste ($58\%$). Assuming a
density of 0.3 kg/l for dry waste, we deduce a yearly collected weight of
$155$ tons. In the absence of a better estimate, we assumed an uncertainty of
50% on the collected mass of each type of waste for each of the three sites of
the institute.

Appropriate emission factors for each type of waste, which depends on their
end of life, i.e. burning or recycling, were taken from the ADEME database
with a recommended uncertainty of 30%. This leads to a total emission from
waste production of $55$ t CO${}_{2}\textrm{e}$, with a final uncertainty of
20 t CO${}_{2}\textrm{e}$ resulting from quadratic combination of relative
uncertainties for each type of waste and quadratic sum over all types of
waste.

IRAP’s waste-related carbon footprint is essentially due to household waste,
$58\%$ of total emissions, that is burnt, and to the recyclable waste, $39\%$
of emissions, that is only partially recycled. Increasing the share of waste
that is recycled, and most importantly limiting the amount of waste produced,
are necessary to reduce the carbon footprint of IRAP’s waste production.

###  4.4 Water consumption

Site | Electricity | Heating | Water
---|---|---|---
| MWh |  t CO${}_{2}\textrm{e}$ | MWh |  t CO${}_{2}\textrm{e}$ | m3 |  t CO${}_{2}\textrm{e}$
Roche | 1863 | 113 $\pm$ 11 | 857 | 59 $\pm$ 18 | 4271 | 1.7 $\pm$ 0.3
Belin | 398 $\pm$ 80 | 24 $\pm$ 5 | 177 $\pm$ 35 | 40 $\pm$ 8 | $428\pm 86$ | 0.2 $\pm$ 0.04
Tarbes | 15 $\pm$ 3 | 1 $\pm$ 0.2 | 38 $\pm$ 8 | 9 $\pm$ 2 | $45\pm 9$ | 0.01 $\pm$ 0.002
Total | 2276 $\pm$ 80 | 138 $\pm$ 12 | 1072 $\pm$ 36 | 108 $\pm$ 20 | $4744\pm 86$ | 2 $\pm$ 0.3
Table 3: Electricity, heating and water consumption per site and their
corresponding GHG emissions.

Water consumption is measured at the building level. As for electricity and
heating, we estimated our share of the water consumption at the Belin and
Tarbes sites based on the relative surface occupancy of the laboratories. We
associated a conservative $20\%$ uncertainty on these estimates, except for
the Roche site where water consumption is metered and should have a negligible
uncertainty.

The related GHG emissions were estimated from the ADEME emission factors for
water distribution $0.262\pm 0.052$ kg CO2e/m3 and wastewater treatment
$0.132\pm 0.015$ kg CO2e/m3. Water consumption is detailed in Table 3 and
contributes to a total of $2\pm 0.3$ t CO${}_{2}\textrm{e}$.

###  4.5 Air conditioning

In addition to the electricity consumption already taken into account in Sect.
4.2, air conditioning also contributes to GHG emissions due to refrigerant gas
leakage.

For the Roche site, the leakage was estimated based on the amount of gas
refilled during the year, totalling 11.1 kg of R410A gas. Using a global
warming potential (GWP) of $1928$ over 100 yr, with a $30\%$ uncertainty as
recommended by ADEME, this corresponds to $21\pm 6$ t CO${}_{2}\textrm{e}$.
The uncertainty is here related to the GWP only since the amount of gas
refilled is precisely monitored and billed by the maintenance company.

For the Tarbes site, the IRAP share of refrigerant gas leakages are 0.92 kg of
R22, 0.19 kg of R32, and 1.59 kg of R410A. Using global warming potentials of
1760 and 677 for R22 and R32 respectively, we obtain equivalent emissions of
$4.8\pm 1.0$ t CO${}_{2}\textrm{e}$ assuming $30\%$ uncertainty on the GWP.

Unfortunately, we could not obtain the refrigerant gas consumptions for the
air conditioning system on the Belin site. However, air conditioning is mainly
used in technical and common rooms in this building, unlike the Roche site
where air conditioning is provided in each office, suggesting that GHG
emissions due to refrigerant gas leakage on the Belin is significantly below
the result for the Roche site. The final IRAP carbon footprint should not be
significantly impacted by this omission. Overall, refrigerant gas leakage
contributes a total of $26\pm 6$ t CO${}_{2}\textrm{e}$.

###  4.6 Meals

Meals taken at the workplace are necessary to the personnel to carry out their
work, and therefore must be included in a GHG assessment. Following the ADEME
recommendations for entities that are not part of the food and agriculture
business, we take a simplified approach based on the number of meals [4]. The
main factor that determines GHG emissions from a meal is its content in animal
products444See, e.g., https://ourworldindata.org/environmental-impacts-of-
food..

From our user survey, we estimated the number of meals in three categories:
standard, flexitarian (reduced amount of animal products), and vegetarian. As
for all activity data extracted from the user survey, we assume these numbers
have an uncertainty of 50%. We apply the emission factors proposed by ADEME to
these three meal categories under the hypothesis that the meat content in non-
vegetarian meals is composed of 25% of high-carbon meats (namely beef) and for
75% of lower-carbon meats (namely chicken) or fish. This yields 2.585 kg
CO${}_{2}\textrm{e}$/meal for standard meals, 1.103 kg
CO${}_{2}\textrm{e}$/meal for flexitarian meals, and 0.510 kg
CO${}_{2}\textrm{e}$/meal for vegetarian meals, with uncertainties of 50%.

The results for the number of meals and GHG emissions are shown in Fig. 2.
Meals contribute a total of $85\pm 50$ t CO${}_{2}\textrm{e}$ to IRAP’s carbon
footprint. We note that 75% of these meals are provided by a canteen, and,
specifically, 73% by the CNRS canteen.

Figure 2:  Number of meals and their corresponding GHG emissions.

###  4.7 Commuting

Figure 3:  Distances and corresponding GHG emissions from home-to-work
commuting and commuting between workplaces. Distances and GHG emissions are
shown according to the mode of transportation. We used several subcategories
and accounted for ride-sharing to estimate the GHG emissions, see text for
details. When aggregating distances for several subcategories, uncertainties
are propagated quadratically.

Emissions from home-to-work commuting are included in IRAP’s GHG assessment
according to French legislation and the BC method. We also consider here
commuting between workplaces (e.g., between the institute and a teaching
site). These trips are not covered in our assessment of professional travel
since they are undertaken by IRAP personnel via private means. We estimated
commuting distances using the survey (uncertainties are assumed to be 50%) and
converted them to GHG emissions using BC tools. The modes of transportation
that we consider, along with their emission factors, are listed in Table 4.
Walking and cycling are assumed to generate zero emissions. We also took into
account ride-sharing for cars and motorcycles. The results are summarized in
Fig. 3.

Transportation mean | Fabrication | Upstream | Combustion
---|---|---|---
Petrol car | 0 | 37 g CO${}_{2}\textrm{e}$/km $\pm 60\%$ | 164 g CO${}_{2}\textrm{e}$/km $\pm 60\%$
Diesel car | 0 | 39 g CO${}_{2}\textrm{e}$/km $\pm 60\%$ | 151 g CO${}_{2}\textrm{e}$/km $\pm 60\%$
Electric car | 84 g CO${}_{2}\textrm{e}$/km $\pm 70\%$ | 20 g CO${}_{2}\textrm{e}$/km $\pm 70\%$ | 0
Hybrid car | 48 g CO${}_{2}\textrm{e}$/km $\pm 70\%$ | 29 g CO${}_{2}\textrm{e}$/km $\pm 70\%$ | 106 g CO${}_{2}\textrm{e}$/km $\pm 70\%$
Motorcycle | 0 | 12 g CO${}_{2}\textrm{e}$/km $\pm 60\%$ | 52 g CO${}_{2}\textrm{e}$/km $\pm 60\%$
Bus | 0 | 0 | 137 g CO${}_{2}\textrm{e}$/km/pass. $\pm 60\%$
Subway/Tram | 0 | 3.0 g CO${}_{2}\textrm{e}$/km/pass. $\pm 60\%$ | 0
Train | 0 | 2.5 g CO${}_{2}\textrm{e}$/km/pass. $\pm 60\%$ | 0
Electric bike/scooter | 0 | 11 g CO${}_{2}\textrm{e}$/km $\pm 50\%$ | 0

Table 4:  Individual modes of transportation considered in our estimate of GHG
emissions from commuting with their emission factors and uncertainties.
Emission factors are decomposed into fabrication of batteries (electric and
hybrid cars only), upstream emissions related to the production of
fuel/energy, and combustion. For public transportation, emission factors are
given per passenger.

The total estimated GHG emissions are $174\pm 67$ t CO${}_{2}\textrm{e}$ from
home-to-work commuting and $10\pm 4$ t CO${}_{2}\textrm{e}$ from commuting
between workplaces. In both cases, conventional cars represent the principal
source of emissions. We note that for commuting between workplaces the median
distance travelled by car is 2.5 km.

We also evaluated the impact of working remotely on GHG emissions. In 2019
(i.e. before the COVID-19 pandemic), working remotely was a relatively rare
practice at our institute. Through the survey we estimated that remote working
was practised by staff members for a grand total of about 100 days per week,
i.e., less than 10% of the total working time. This permitted to avoid 31 t
CO${}_{2}\textrm{e}$ from commuting. However, working remotely is also
associated with rebound emissions that we estimated based on Ref. [2]. The
rebound effects taken into account are those due to new transport usage, the
usage of the private residence as workplace (increased heating and energy
consumption etc.), and increased use of video communications. This yields
$\sim$9 t CO${}_{2}\textrm{e}$ of rebound emissions, and therefore a net
balance of 22 t CO${}_{2}\textrm{e}$ that were avoided in 2019 thanks to
remote working. We emphasise that this figure should not to be subtracted from
our final total. We evaluate them here simply to provide an order of magnitude
estimate for the reductions that would be made possible by remote working.

###  4.8 Professional travel and hotel stays

Figure 4: Distances and GHG emissions from professional travel, shown
according to the mode of transportation, with car and taxi grouped into a
single category.

The emissions related to professional travel, transport and hotel nights, were
estimated based on travel listings containing the following information:
transport mode (flight, train, car…), departure and destination locations, and
dates. For each trip, online tools were used to estimate the distance from
departure to destination555https://labos1point5.org/ges-1point5. We assumed an
uncertainty of $20\%$ on travelled distances, except for flights outside
France with an increased uncertainty of $50\%$ to include the non-geodesic
distances induced by possible flight connections.

GHG emissions were computed for each trip based on transport mode and
distance, and the appropriate emission factor retrieved from the ADEME
database. Regarding air travel emission factors, three different emission
factors are provided depending on travelled distance, and the effect of
contrails is taken into account by multiplying the final CO2 equivalent
emissions by a factor two, as recommended by ADEME. Uncertainties on emission
factors were taken from the ADEME database, with in particular a 45%
uncertainty for flights, and we combined them quadratically with the relative
uncertainty on travelled distances. The total distances travelled by IRAP
personnel per transport mode in 2019 (see also Fig. 4), and the resulting
CO${}_{2}\textrm{e}$ emissions are presented in Table 5.

Transport mode | Distance (km) | Emissions ( t CO${}_{2}\textrm{e}$)
---|---|---
Flight | 5942809 ($93.3\%$) | 1126.2 ($96.3\%$)
Train | 247203 ($3.9\%$) | 1.2 ($0.1\%$)
Car | 179794 ($2.8\%$) | 41.9 ($3.6\%$)
Cab | 728 ($0.01\%$) | 0.2 ($0.01\%$)
Table 5: Total distances and associated emissions from professional trips by
IRAP staff members for the different modes of transportation. Their relative
contribution is shown in parentheses. Uncertainties on the figures can be
found in Table 1.

The professional travel considered here includes trips by people who are not
employed at IRAP (e.g. visitors for short-term collaboration, seminar
speakers,…). These are included because they are part of the normal working of
the institute, and a necessary contribution to our research activities. They
typically consist in one return trip per person, and represent about 15% of
the carbon emissions due to professional travel at IRAP.

The total distance travelled in France is $1.05\times 10^{6}$ km, about the
same distance in Europe, and $4.27\times 10^{6}$ km outside of Europe. The
average distance travelled per return trips are 1000, 2100, and 15000 km,
respectively. The average distance travelled per person per year is about
25000 km, or 34000 km if administrative and technical staff are excluded from
the calculation (i.e. assuming that trips are only undertaken by research
staff). This corresponds to $\sim 4.6$ t CO${}_{2}\textrm{e}$ and $\sim 6.3$ t
CO${}_{2}\textrm{e}$ per person per year, respectively. Train represents
$3.8\%$ of the travelled distance, and $58\%$ of ground transport, but only
$0.1\%$ of emissions due to its very low emission factor compared to aircrafts
and cars.

The professional travel practices and the associated emissions are very
unevenly distributed within the institute, as illustrated in Fig. 5. About 20%
of IRAP’s professional travel emissions are due to only a dozen staff members,
and 50% of the emissions originates from fewer than 20% of the travellers.
Roughly $10$% of IRAP staff members did not travel at all in 2019. In
principle, this concentrated emission profile should make it easier to engage
efficient reduction measures, and reasonable limits on the number of allowed
trips per year would translate into significant emission reductions. This is
addressed more quantitatively in Sect. 6.2.

Figure 5: Top panel: Distribution of the number of trips per person per year
as a function of destination (France, Europe, out of Europe). Bottom panel:
Cumulative distribution of GHG emissions over travellers (including IRAP staff
and visitors).

The duration and destination of each trip yields the number of nights away
from home, assumed to be spent in hotels, with a $20\%$ uncertainty on that
number to cover possible inaccuracies in the listings or alternative
accommodation (e.g. with friends or family). Emission factors for hotel nights
in each country were taken from the full set of conversion factors proposed by
the United Kingdom Department for Business, Energy and Industrial
Strategy666https://www.gov.uk/government/publications/greenhouse-gas-
reporting-conversion-factors-2019, with associated uncertainties of $80\%$.
The total number of hotel nights was $3996$ in 2019, leading to $75.2$ t
CO${}_{2}\textrm{e}$ emissions with an uncertainty of $6.4$ t
CO${}_{2}\textrm{e}$ resulting from quadratic combination of relative
uncertainties for each travel and quadratic sum over all missions.

In summary, professional travel emissions contributed $1245$ t
CO${}_{2}\textrm{e}$ of IRAP’s GHG emissions in 2019, with 94% of that amount
arising from transport, mostly air travel, and a non-negligible 6% arising
from hotel accommodation.

###  4.9 Purchase of good and services

ID | Category | Expense | Emission | Share
---|---|---|---|---
|  | (k€) | ( t CO${}_{2}\textrm{e}$) | (%)
E | Consulting/Insurance/Human Resources | 493 | 122 | 9.17
I | Computing/Telecommunications | 668 | 117 | 8.77
O | Optical | 518 | 174 | 13.01
T | Electronics | 673 | 262 | 19.64
R | Mechanics/Automation | 354 | 144 | 10.78
C | Communication/Documentation | 159 | 44 | 3.30
P | Nuclear/Particle Physics | 144 | 134 | 10.02
A | General supplies | 139 | 59 | 4.43
F | Freight/Transport | 43 | 28 | 2.16
V | Vacuum | 82 | 55 | 4.10
B | Buildings/Infrastructure | 128 | 53 | 3.96
G | Cryogenics/Laboratory gases | 28 | 28 | 2.10
N | Chemistry/Biology | 47 | 54 | 4.05
Table 6: Distribution of the main sources of GHG emissions in the purchase of
goods and services, listing only those with an individual share $>2$% (which
together account for more than 95% of the total). The first column lists the
identifier in the French NACRES nomenclature. Figure 6: Average emission
factors for the purchase of goods and services, organized by the main
categories of expenditure, as a function of the cumulative expenditure. The
size of the rectangles is proportional to the GHG emissions. The last gray
block aggregates all shares that individually contribute less than 2% of the
total GHG emissions. The fractional contribution to the GHG emissions for each
category is shown in parentheses.

As mentioned in Sect. 3.3, our activities rely on important input flows of
material and services, e.g. the purchase of electronic equipment, and
subcontracting the design of certain instrument components. Not included in
this category are the acquisition of computers, use of external numerical
resources, personal travel services, and use of observational data since we
deal with these sources using specific approaches in other sections below.

Ideally, the environmental impact of goods and services purchased in the
context of our activities would be assessed and communicated by the suppliers.
This so-called supplier-specific information, however, rarely exists and it is
beyond the scope of our work to calculate it ab initio since it would require
conducting complete GHG accounting for a wide diversity of products, based on
the physical flows needed to produce them. Instead, we used a cost-based
approach that converts the economic value of the purchased goods and services
into an estimate for the associated GHG emissions using economic sector-
average emission factors (typically equating a k€ or M€ of expenditure on a
given family of products, e.g. electronic equipment or software development,
to kg CO${}_{2}\textrm{e}$ or t CO${}_{2}\textrm{e}$).

In practice, our activity data for this category are IRAP’s annual expenditure
through the Paul Sabatier University or CNRS, sorted into about 2000
categories that are labelled according to a specific code (NACRES, standing
for Nomenclature Achats Recherche Enseignement Supérieur). We associated these
expenditure categories with emission factors obtained from ADEME for about 35
broad activity classes [3]. We tested an alternative set of $\sim 400$ factors
from the United States Environmental Protection Agency (EPA) [36], with a much
finer description of industrial and economic activities. The difference
between the final results calculated using the ADEME and EPA factors was at
the 25% level, and there was overall good agreement in the distribution of
emissions over the different expenses777The EPA set of emission factors for
the different GHG gases was converted into CO${}_{2}\textrm{e}$ using global
warming potential values for a 100-year time horizon from the GHG Protocol
[30] and the spendings in 2018 US dollars were translated into 2019 euros
using a currency exchange rate of 0.911.. We therefore kept the ADEME set of
emission factors since it is more relevant to France than the EPA set. The
expenditure-based approach is admittedly the least precise approach in GHG
accounting, but it offers a way forward when other GHG accounting techniques
cannot be achieved within a reasonable amount of time.

The expenditure on goods and services at IRAP for 2019 amounts to 3657 k€,
excluding all expenses that are dealt with in a more specific way in other
sections of this paper (e.g. energy, computers, travel etc). The uncertainty
on total expenses is assumed to be zero, and the final uncertainty on GHG
emissions is computed as the quadratic sum of uncertainties over all broad
purchase categories, assuming for the latter a relative uncertainty of 80% as
recommended by ADEME and the BC method. Such an approach in computing the
uncertainty is justified by the large number of different purchases, with a
mix of tailor-made products and mass-consumption items.

The emissions from the purchase of goods and services amount to $1335\pm 342$
t CO${}_{2}\textrm{e}$. These GHG emissions divided by the expenditure
correspond to an average emission factor of 365 kg CO${}_{2}\textrm{e}$/k€,
which is of the same order as the emission factor for electronic, optical, and
computer equipment in the ADEME database ($\sim 400$ kg
CO${}_{2}\textrm{e}$/k€). The distribution of the emissions into broad
purchase categories is presented in Table 6, and the corresponding average
emission factors are illustrated in Fig. 6. The bulk of the emissions is due
to the purchase of technical equipment, most likely in connection to the
numerous instrument development projects based at IRAP. The share related to
the local infrastructure amounts to $\lesssim 10-15$% (obtained by summing
categories A, B, and a fraction of E).

###  4.10 Computer equipment

We treat the purchase of computer equipment separately from the purchase of
goods because it allows a finer and more reliable assessment of the associated
GHG emissions. The formal approach to this contribution is to estimate the
total equipment in use at the institute during our reference year 2019, and to
add up the GHG emissions associated with each machine for an amortization
period of typically 4-5 years (which means that machines older than that are
not counted, while others have a yearly contribution corresponding to the
total GHG emissions due to their production divided by the amortization
period). In practice, it was impossible to implement such a calculation owing
to the difficulty that we encountered to obtain a robust census of all our
computer equipment. Instead, we summed the number of computers and other main
peripherals purchased over the period 2018-2020, and computed yearly GHG
emissions per type of equipment assuming an amortization period of three
years. Such an extraction was made by an automated scanning of purchase
listings, but may not be highly accurate because of the lack of uniform, clear
labels in the purchase orders. From a visual inspection of the labels and
expenses of the different purchases, we consider that the volume of equipment
eventually counted in our report is a lower limit, which probably
underestimates the true value by at most 10%.

The emission factors for different computer equipment and peripherals were
taken from the ECODIAG tool provided by the ECOINFO dedicated working group of
the CNRS [15]. They correspond to cradle-to-gate emissions, and do not include
end-of-life impacts, while power usage during the equipment lifetime is
counted in our electricity consumption (except for the small part used when
the equipment is taken out of the institute). ECODIAG offers emission factors
for a variety of models for each type of equipment and we selected those
labeled as default, which seem to correspond to the typical equipments mainly
in use in the French astrophysics research community. Nevertheless, we also
extracted a range of emission factors that characterise the diversity of
equipment in use at the institute, to illustrate the possible uncertainty
range due to the imprecise identification of each item. Emission factor
variations depending of the equipment model are typically of the order of
20-40%, or higher in the case of PC/workstations. In addition, ECOINFO warns
that they can increase up to threefold depending on the set of options chosen
for the equipment. We eventually retain a typical uncertainty on emission
factors of 50%, following the recommendation by ADEME.

The results are presented in Table 7. We purchase every year about 120
computers (at least over 2018-2020), for a staff of about 260 people, which
suggests a typical lifetime of about 2-3 years per computer and/or a
progressive inflation of the average number of computer equipment per person
if the actual lifetime is longer. This leads to GHG emissions of $81\pm 40$ t
CO${}_{2}\textrm{e}$.

Equipment | Number | Factor | Range | Emission
---|---|---|---|---
|  | ( kg CO${}_{2}\textrm{e}$/unit) | ( kg CO${}_{2}\textrm{e}$/unit) | ( t CO${}_{2}\textrm{e}$)
Laptop | 71 | 300 | 250-370 | 21.3
PC/Workstation | 45 | 600 | 170-650 | 27.0
Server | 6 | 1300 | - | 7.8
Standalone screen | 10 | 430 | 350-590 | 4.3
Tablet | 7 | 150 | - | 1.0
Total |  |  |  | 80.8
Table 7: Distribution of GHG emissions associated with different types of
computer equipment and peripherals. The numbers of items correspond to the
average over the period 2018-2020. Total emissions include one screen per
workstation, on top of the standalone ones. No emission factor range is given
for servers and tablets on the ECODIAG tool.

###  4.11 External computing, storage, and data flow

We estimated the usage of external resources for computing and data storage
via our online survey (Sect. 4.1). For computing, we estimate a total usage of
7 MhCPU. We convert this into GHG emissions using the emission factor of 4.68
g CO${}_{2}\textrm{e}$/hCPU estimated by [8] for a computing center in
Grenoble with an uncertainty of $80\%$. It is appropriate to use an emission
factor based on a French computing center (low carbon impact of electricity)
because $>99\%$ of the computing occurs in centers located in France. This
yields $\sim$33 t CO${}_{2}\textrm{e}$ of GHG emissions.

We took a similar approach for external data storage. In this case, we
estimate a total of $\sim$39 TB of data stored in France and $\sim$254 TB of
data stored in other countries. The carbon footprint of data storage is highly
uncertain and depends on the type of storage (short-term or long-term), the
occupancy rate of the storage centers, and the carbon intensity of the
electricity. We adopt the overall emission factors estimated in Ref. [7]. For
France we have an interval of 7 to 40 g CO${}_{2}\textrm{e}$/GB/yr and we
adopt a representative value of 25 g CO${}_{2}\textrm{e}$/GB/yr. Based on
their study of the impact of the electricity carbon intensity, we assign to
data storage in countries other than France (mostly U.S.A.) a carbon footprint
which is four times larger, namely an interval of 28 to 160 g
CO${}_{2}\textrm{e}$/GB/yr with a representative value of 100 g
CO${}_{2}\textrm{e}$/GB/yr. This yields a representative value of 26 t
CO${}_{2}\textrm{e}$ for the associated GHG emissions, within an interval of 4
to 63 t CO${}_{2}\textrm{e}$.

Last, we estimated the impact of data transfer over the global network,
generated by a variety of day-to-day use of numerical services such as
exchanging emails, videoconferencing, or downloading large astronomical data
sets. Monitoring of the ingoing and outgoing data flows at IRAP is not
performed with a sufficient accuracy, so we had to assume a typical amount of
data transferred over the network over a year. As a representative number, we
used the total amount of data stored externally, 293 TB ($\pm 50\%$), because
these data had to be transferred at least once and such a typical volume of
about 1TB/capita/year encompasses the data flows generated by the regular use
of the network by one individual over a year: thousands of emails (hundreds of
which with MB-sized attachments), hundreds of hours of audioconferencing or
videoconferencing (with data transfer rate of 30-100 MB/h and 500-2400 MB/h,
respectively), dozens of hours of web browsing (with data transfer rate of
50-300 MB/h), and downloads of all kinds (papers, software installations or
updates, astronomical datasets). There is most likely a large, order-of-
magnitude, scatter in data transfer from one individual to another depending
on staff category (e.g. in time spent in videoconferencing or amount of data
downloaded) and usage profile (e.g. audioconferencing versus high-definition
videoconferencing). Nevertheless, our purpose here is to estimate the typical
weight that data transfer represents in our final footprint, and the results
can easily be rescaled to an actual use case. Ultimately we find that data
transfer represents only a very small contribution to our carbon footprint.

The amount of GHG emissions is obtained by associating the volume of data
transferred to an electricity consumption, and then with an average carbon
intensity for electricity. We considered an emission factor combining both and
obtained in Ref. [13], about 1-2 g CO${}_{2}\textrm{e}$/GB, for the transfer
of data between Orsay and Montpellier in France via the RENATER network. The
estimate includes the impact of power consumption, manufacturing and
installation of the equipments, and network supervision activities. The cost
of data transfer increases strongly with distance/number of nodes between
emitter and receiver, and carbon intensity of electricity. Conversely, it
decreases with increasing load of the network and increasing lifetime of the
equipments. To be more representative of international data transfer, over
long distances and consuming electricity that has on average a much higher
carbon intensity than in France, we considered a typical range of 2-10 g
CO${}_{2}\textrm{e}$/GB as emission factor, with a baseline value of 5 g
CO${}_{2}\textrm{e}$/GB. The latter value is consistent with that extrapolated
for 2019 in Ref. [5]. Eventually, this yields yearly GHG emissions of about
1.5 t CO${}_{2}\textrm{e}$ for all IRAP staff, with a likely range 0.3-3.2 t
CO${}_{2}\textrm{e}$.

###  4.12 Research infrastructures

Scientists from our institute use a long list of research infrastructures,
such as space telescopes, space probes and ground-based observatories.
According to the BC method, the carbon footprint associated with the use of
these facilities needs to be included in the institute’s carbon footprint
estimate. Details of the carbon footprint estimation method for research
infrastructures are presented separately in Ref. [19]. In short, we identified
the facilities that were used from all refereed publications co-authored by
IRAP scientists that were published in 2019, resulting in a list of 46 space
missions and 39 ground-based observatories. We estimated the carbon footprint
of each facility using monetary ratios, and consolidated the results for space
missions with an alternative estimate based on the payload wet mass. Dedicated
emission factors for this analysis were derived from published carbon
footprint assessment reports. For space missions, we derived an emission
factor of 140 t CO${}_{2}\textrm{e}$/M€ mission cost and 50 t
CO${}_{2\textrm{eq}}$/kg payload wet mass, for ground-based observatories we
derived 240 t CO${}_{2}\textrm{e}$/M€ construction cost and 250 t
CO${}_{2}\textrm{e}$/M€ operating costs. We note that these emission factors
are on the low side of the sector-based estimates provided by ADEME, hence it
seems unlikely that our adopted values are significantly overestimated.

As activity data, we gathered full mission cost and payload launch mass
estimates for space missions and construction, and yearly operating costs for
ground-based observatories from publicly available documents. These data were
complemented by information provided directly by some of the facilities, and a
parametric cost model for some of the 1–2 metre class telescopes in our list.
All activity data collected are provided in the supplementary information of
Ref. [19]. Based on these activity data and the aforementioned emission
factors, we computed lifecycle and annual carbon footprint estimates.

To determine which fraction of the research infrastructure carbon footprint
should be attributed to our laboratory, we use two methods. In the first
method, we multiply the annual carbon footprint of an infrastructure with the
fraction of peer-reviewed papers in 2019 that have authors affiliated to IRAP.
We determined this fraction from the Astrophysics Data System (ADS) using a
full text search for the year 2019. For this purpose, we constructed a
dedicated query string for each infrastructure with the aim to cover as many
infrastructure-related publications as possible while keeping the false
positives at a minimum. This results in a footprint of $20\pm 3$ kt
CO${}_{2}\textrm{e}$ for IRAP in 2019. Attributing this footprint equally to
the 144 astronomers with PhD degree that worked at IRAP in 2019 results in
$139\pm 23$ t CO${}_{2}\textrm{e}$ per astronomer. If we instead divide the
annual research infrastructure carbon footprint equally by the total number of
staff that worked at IRAP in 2019 (263 people), we obtain $76\pm 12$ t
CO${}_{2}\textrm{e}$ per IRAP staff member.

We note that this attribution method does not provide IRAP’s share of the
total carbon footprint of research infrastructures among all existing
astronomical institutes in the world. Since scientific articles are often
signed by authors from multiple institutes, each of these institutes will get
the same attribution, implying that the sum of all attributions will exceed
the total carbon footprint of all research infrastructures. The share can
however be estimated by replacing the number of peer-reviewed papers by the
number of unique authors, i.e. multiply the annual carbon footprint of an
infrastructure with the fraction of unique authors of peer-reviewed papers in
2019 that are affiliated to IRAP. This results in a carbon footprint of
$4.0\pm 0.7$ kt CO${}_{2}\textrm{e}$ for IRAP in 2019. For each of the IRAP
astronomers with PhD degree this corresponds to a footprint of $27.4\pm 4.8$ t
CO${}_{2}\textrm{e}$, for each person working at IRAP in 2019, the footprint
is $15.0\pm 2.6$ t CO${}_{2}\textrm{e}$. We note that some double-counting may
still occur using this approach since an individual may be affiliated to
multiple institutes. But for a given individual, the computation of the share
should be accurate.

###  4.13 Emission sources excluded from our scope

Some sources of GHG emissions were not included in our assessment, or only
partially, or not in the way they should have been according to the principles
of the BC method. The main reason in most cases was the unavailability of, or
difficulty in obtaining access to, the relevant data. We list these sources
here to provide a complete picture of what should be the full scope of the
assessment, and remind that the final carbon footprint we report here is
formally a lower limit. All the items listed below constitute possible avenues
for improvement of future assessments.

People: This should include people attending events organized by the
institute, such as workshops or conferences. Doing so would likely increase
the institute’s GHG emission, but it would allow the institute to identify a
potentially powerful lever arm. IRAP, as the sole or primary organizer of an
event, could opt for relocating it to a location that minimizes the total
travelled distance, replacing it by a virtual gathering, or, for a recurrent
event, reducing its frequency. All such measures would have a very efficient
overall return. In practice, however, no major conference or workshop was
included in the present assessment because: (i) IRAP was, to our knowledge,
rarely the sole or primary organizer of such an event in 2019; (ii) collecting
the corresponding travel data for all participants was unfeasible. Ultimately,
the only cases we took into account were trips by invited researchers (e.g.
seminar speakers) because their travel and accommodation was paid for by the
institute.

Material: According to the BC methodology, delivery and freight are supposed
to be handled as a distinct category, to separate the impact of producing
goods from that of transporting them. The expected quantity here is typically
the amount of mass transported over a total distance, split into
transportation means (rail, road, air, sea). In practice, it was impossible to
get such detailed information for all incoming material (e.g. electronic or
optical components) and outgoing equipment (e.g. delivery of instruments or
parts of instruments). A part of the freight is indirectly included via the
purchase of transportation services, but with a monetary approach, rather than
a physical one.

Services: It is recommended to include the GHG emissions associated with the
use of services provided by IRAP to the outside world, e.g. open databases and
public software developed and/or maintained at IRAP. The rationale for
including this contribution is the same as for conference organization :
reducing the subsequent emissions by optimization at the source as much as
possible. For numerical services, we do not keep an exhaustive census of the
software and databases provided by IRAP, with homogenous statistics about
their use. Partial information exists in the case of some community services
supported by the institution (the Services Nationaux d’Observations).
Assessing the impact of the data storage and transfer generated by IRAP’s SNO
would be an interesting avenue to explore in future assessments.

Internal support: IRAP relies on support activities, such as administration,
maintenance, and financial and insurance services provided by higher-level
institutions (mainly the Paul Sabatier University and the CNRS). Not all of
these activities are local and easily integrated into our assessment. The BC
methodology also requires to include the carbon impact of the construction of
buildings, with a specific treatment for amortization over typically 10-20
years. According to this rule, most of our buildings would already be
amortized, but some recent installations should have been taken into account
but were not. We can however provide an order of magnitude for the most recent
building construction in our institute, the “Plateforme d’Ingénierie et
d’Instrumentation Spatiale (P2IS)”, inaugurated in 2015. It has a floor
surface of 368 m2 and consists of a mix of offices and technical rooms.
According to the ADEME database, emissions from construction range from 650 kg
CO2e/m2 for office buildings to 825 kg CO2e/m2 for industrial buildings. This
implies total GHG emissions in the range $239-304$ t CO${}_{2}\textrm{e}$ for
the construction of P2IS, or about $12-15$ t CO${}_{2}\textrm{e}$/yr assuming
an amortization period of 20 years, which is a relatively minor contribution
in our total footprint. This assessment should however be performed for all
extraordinary operation or acquisition, e.g. the purchase of a large equipment
for instrumental development. In the absence of a well-established inventory
and an easy way to associate these to GHG emissions, we excluded them from the
scope of this assessment.

##  5 Synthesis

###  5.1 The carbon footprint of IRAP

Figure 7: Distribution of IRAP’s GHG emissions in 2019, for the restricted and
full perimeter, without and with the use of external research infrastructures,
respectively (top and bottom). Only wedges with a relative contribution above
1% are labelled.

The breakdown of all IRAP’s GHG emissions per source is summarized in Table 1
and presented in Fig. 7. The final estimated quantity for 2019 over the full
perimeter of the assessment is about 7400 t CO${}_{2}\textrm{e}$, or an
average of 28 t CO${}_{2}\textrm{e}$/yr per staff member. We also defined a
restricted perimeter, by leaving out the contribution of external research
infrastructures including external computing and storage. Over this restricted
perimeter, our emissions amount to 3300 t CO${}_{2}\textrm{e}$ or an average
of 13 t CO${}_{2}\textrm{e}$/yr per staff member. The rationale for this
distinction is that IRAP has significant operational control over the
restricted perimeter, but a more limited one over external research
infrastructures, so such a separation is relevant in view of a local reduction
plan. This practical distinction is however relevant only to the means and
strategies for implementing reduction measures, and should not elide that
IRAP’s total 2019 footprint is 7400 t CO${}_{2}\textrm{e}$.

The operation of the local infrastructure – heating, electricity, commuting,
food, waste, and $\sim 10-15$% of the purchase of goods and services – makes a
relatively small contribution to IRAP’s carbon footprint, about 800 t
CO${}_{2}\textrm{e}$/yr. Thus, while it remains relevant to reduce IRAP’s
consumption of electricity and gas consumption and to switch away from
conventional cars for local transport, initiatives focussed on the institute’s
local infrastructure will not be sufficient to achieve emission reductions
that are compatible with France’s 2050 targets (i.e. reducing emissions by a
factor of 5). We note that the relatively small contribution of local
infrastructure may be specific to IRAP, and that other astronomy institutes
may have quite different emission profiles. The energy used to produce the
institute’s electricity and heating has a relatively low carbon footprint,
with electricity being predominantly of nuclear origin in France and heating
of our largest building arising from biomass burning888For more information
about the use of biomass as an energy source, and its actual carbon footprint
and possible role in reduction strategies, see
https://easac.eu/publications/details/key-messages-from-european-science-
academies-for-unfccc-cop26-and-cbd-cop15/, corresponding to emission factors
of 0.06-0.07 kg CO2e/kWh. Assuming a worst-case carbon intensity of $\sim 0.8$
kg CO2e/kWh instead, typical of Australia, the related sources would amount to
about 2700 t CO${}_{2}\textrm{e}$/yr, comparable to the sum of professional
travels and purchase of goods and services at IRAP. A higher carbon intensity
of electricity would also affect other sources such as external computing, and
to some extent the purchase of goods and services. If IRAP were situated in a
country that relies significantly on fossil fuels for electricity production
and heating, we estimate that our total 2019 footprint would be at least 10000
t CO${}_{2}\textrm{e}$, or 38 t CO${}_{2}\textrm{e}$/yr per staff member.

The most significant sources of GHG emissions at IRAP, ordered by decreasing
contribution, are: the use of astronomical research infrastructures, the
purchase of goods and services, and professional trips, with a predominant
share of air travel in the latter. As mentioned above, the bulk of the
emissions from the purchase of goods and services is attributable to the
numerous instrument development projects performed at IRAP. Together with the
preponderant share from the use of observational data, this clearly points to
the main driver of our large carbon footprint: astronomical research
infrastructures. More precisely, it is the current practices related to
conceiving, designing and developing astronomical research infrastructures,
and the scale and cadence at which we deploy them. Moreover, it is likely that
a significant fraction of professional trips at IRAP are also connected to
instrument development projects, which makes the conclusion even stronger
(although we could not check this point owing to the lack of the relevant
information).

Whether such a trend can be considered as generic in the community remains to
be confirmed by performing exhaustive carbon footprint assessments at other
institutes. IRAP has a long history in instrument development and
observational data analysis, which certainly weighs in the present result.
Institutes that specialise in theory and numerical simulations may have a
smaller footprints, although this will depend on the amount of supercomputing
performed and the carbon intensity of the electricity powering the computing
clusters. Ultimately, however, such a distinction may be irrelevant since
research in astronomy and astrophysics requires all of these activities
(theory, simulations, observations). What is needed is a community-scale
strategy for our use of all resources and facilities.

###  5.2 Comparison and discussion

IRAP’s total emissions in 2019 ($\sim$7400 t CO${}_{2}\textrm{e}$) correspond
to 28 t CO${}_{2}\textrm{e}$/yr per staff member, on average, from
professional activities only. This can be compared to the average footprint of
a French resident (combining private and professional emission sources) of 11
t CO${}_{2}\textrm{e}$/yr, although that comparison is not directly meaningful
as we will discuss below.

More relevant is a comparison with other institutes. Ref. [18] estimated that
the annual emissions of the Max Planck Institute for Astrophysics (MPIA)
located in Heidelberg, Germany, amounts to 18 t
CO${}_{2}\textrm{e}$/yr/researcher, and $\sim 9$ t
CO${}_{2}\textrm{e}$/yr/staff member if the load is shared over all employees,
including all support staff. Therefore, work-related emissions only are 60%
higher than the average for German residents, 12 t CO${}_{2}\textrm{e}$/yr,
which includes the impact of both professional and private activities. Nearly
half of the MPIA emissions arise from air travel, and a quarter from the local
electricity consumption of supercomputing [18]. The latter point is an
interesting difference between MPIA and IRAP: the emissions from
supercomputing at IRAP amount to 0.2 t CO${}_{2}\textrm{e}$/yr per researcher,
on average, and this covers the impact from electricity consumption,
equipment, and operations of the computing centers; at MPIA, supercomputing
generates an average 4.6 t CO${}_{2}\textrm{e}$/yr per researcher, from
electricity consumption only. Only a small part of the difference can be
explained by the carbon intensity of electricity, which is a factor $\sim 4$
higher at MPIA with respect to IRAP.

The situation of Australian astronomers is more alarming, with emissions
amounting to 42 t CO${}_{2}\textrm{e}$/yr/researcher, or twice the average
impact of an Australian resident, 21 t CO${}_{2}\textrm{e}$/yr [32], which
already ranks among the highest on the planet. We emphasise, however, that the
perimeter adopted in the assessments by [32] and [18] is much more restricted
than the one used here.

To put these numbers in a global perspective, the world average rate of GHG
emissions today is $\sim 5-6$ t CO${}_{2}\textrm{e}$/yr per capita. To achieve
carbon neutrality, i.e. for anthropogenic emissions to be absorbed by natural
carbon sinks from land and oceans, the global average must be reduced to 2 t
CO${}_{2}\textrm{e}$/yr per capita by 2050. The average emission rates
compatible with carbon neutrality will be even lower if the planet’s capacity
to capture carbon dioxide has been strongly degraded by 2050 and/or if the
world population has inflated strongly beyond 10 billion inhabitants [34].

There is ongoing debate in the community about the exact meaning of these
emission rates per capita, and the way that the burden of GHG emission
reductions should be shared. A common response from those interested in
maintaining the status quo is to argue that the GHG emissions from astronomy
research should be attributed across a much larger population base than
researchers and the staff of astronomical research institutes, i.e. over the
full production chain including the suppliers of goods and services, or over
the general population since the benefits of astronomy research are enjoyed by
society at large. However, we stress that in carbon accounting, the objective
is not to uniquely identify emissions with a given group of individuals, but
rather to quantify all emissions that an activity depends upon to exist and
generates while it is in operation. The ultimate goal is to identify all
possible avenues for GHG emission reduction.

Our assessment shows that performing research in astronomy and astrophysics at
IRAP in 2019 stimulated GHG emissions equivalent to 28 t
CO${}_{2}\textrm{e}$/yr per person involved in that activity, on average. That
these emissions are spread across a variety of social and economic sectors is
obvious, and should not be used as an argument to lower our impact by
distributing it over a larger population base. One way of viewing the global
average target of 2 t CO${}_{2}\textrm{e}$/yr per capita by 2050 is as an
average budget that should not be exceeded, and within which societies should
fit what they deem necessary to human life. In a democratic debate about how
this target should be achieved and which activity sectors should be afforded
permission to exceed the average value, the place of research should be
discussed (alongside all other sectors) with a quantitative estimate of their
costs and benefits. Here we estimate that the carbon cost of astronomy
research is 28 t CO${}_{2}\textrm{e}$/yr per person employed in that activity
when it is performed according to IRAP’s 2019 standards. Obviously, not all
activity sectors in society (including research) can be accorded a footprint
that exceeds the average allocated budget, otherwise the latter will rapidly
be exceeded. Shifting our professional practices to bring the average carbon
footprint of astronomy research closer to the target would thus seem an
important step to guarantee the future of our field.

##  6 The way forward

###  6.1 Facilitating carbon accounting

Regardless of the strategies that are adopted to reduce our emissions, regular
assessments of the institute’s carbon footprint to take stock of the
situation, monitor progress, and adjust the emissions reduction plan will be
required. During our work to estimate the carbon footprint of IRAP, it became
clear that a key challenge lies in the availability of activity data. The
problems we faced ranged from information being totally absent (e.g. fine-
grained measurements of the numerical data flows generated by our activity),
to data being incomplete (e.g. professional travel data missing the
departure/arrival locations or the reasons for travelling), or tedious to
collect (e.g. constructing a list of computer equipment purchases by hand). In
this section, we present some recommendations for how to improve this
situation and move towards seamless – or at least less painful – carbon
accounting. These recommendations may not be directly relevant for astronomy
institutes based outside of France.

Preparing the information in a complete and relevant format at the source is
an urgent short term goal. National suppliers responsible for providing
services for travel and hotel reservations, computer equipment and catering
are ideally positioned to amalgamate global information that can be extracted
for later use at different levels, from small entities such as institutes, to
research federations such as the “observatoires des sciences de l’univers”,
and umbrella institutions like the CNRS. Such a requirement should be rapidly
negotiated at the national level.

At the level of campuses, information pertaining to infrastructure such as
electricity, heating, water and waste should also be collected centrally and
shared among all users of the same site. When a specific mode is used to
supply heating, the relevant emission factor should be updated according to
the actual operation of the facility (e.g. actual mix of gas and wood in the
case of our university).

At the institute level, it is desirable to have activity data organized in the
most appropriate way to raise awareness and trigger action. In a laboratory
like IRAP, knowing the carbon footprint of different IRAP-based projects would
help staff members appreciate their actual contribution to the institute’s
emissions, as well as the required effort to be made. Some development of the
software used by our administration is needed to easily associate activity
data such as travels and purchases to the relevant department or project
within the institute, all this while preserving anonymity of the individuals.

Facilitating carbon accounting requires information that has not previously
been collected, or revising the format of the collected information. At least
initially, this will imply an increased administrative burden. Care should be
taken to not overload our colleagues and ensure that enough time is allocated
for them to contribute to this high-priority duty.

The source of GHG emissions at IRAP with the largest uncertainties, both in
terms of activity data and emission factors, was activity related to digital
technologies. Our estimates of the use of external storage and data transfer
are highly uncertain, as are the estimates for their emission factors, with
order-of-magnitude scatter frequently encountered in the literature. Although
these activities made a relatively small contribution to our footprint (in
part because of the low carbon intensity of electricity in France), the
rapidly evolving nature of this field, and in particular the strong and
continuous increase in many practices, e.g. the volume of data stored in
clouds, massive computing, data flows, requires a careful assessment of its
impact, to make sure that it remains under control in terms of environmental
sustainability. On the side of activity data, more measurements by the
Information & Technology services are clearly needed for us to have a better
understanding of the actual situation. An assessment of the ingoing and
outgoing flows of data, split into usage, is clearly missing today to guide us
towards a better practice. On the side of emission factors, expert work is
required to fully appraise the relevant emission factors appropriate at a
given time, location, and for a given usage. In France, such an effort is
conducted by the ECOINFO research group, and some of their pioneering studies
were critical to our estimate. We recommend strong and lasting support from
our institutions to this group and, going beyond carbon accounting, regular
staff training about the environmental impact of digital technologies.

Finally, as already emphasised and recommended in Ref. [19], there is a
crucial lack of reliable information about the carbon footprint of ground-
based observatories and space-borne instruments. The first estimates provided
in that paper and performed in the context of the present assessment for IRAP
suggest that it is a large, if not dominant, share for most institutes. We
therefore encourage the entities running these research infrastructures to
rapidly assess their total footprint, including construction and operation,
and to publicly share them so that all institutes can include the information
in a uniform way in their carbon footprint assessments.

We emphasise that widespread carbon accounting by all actors involved in
astronomy research is just a step towards solutions, and not the solution
itself. There is a growing risk that carbon footprint assessment becomes
essentially a communication measure, while the hard decisions that must be
made to reduce emissions are left aside. Transitioning from fair and relevant
carbon accounting to far-reaching reduction strategies requires institutional
mechanisms that are yet to be envisaged or implemented. In that respect, the
situation in France is enlightening: carbon accounting has been mandatory for
more than a decade and companies and institutions of all kinds have published
their carbon footprint, but this requirement appears to have had zero impact
on France’s GHG emission curves. The reasons are manifold, but the conclusion
is inevitable : carbon assessments without emission reduction strategies are
ineffective.

###  6.2 Possible avenues for reduction

Scenario | Emissions | Gain
---|---|---
| ( t CO${}_{2}\textrm{e}$) | Rel. Restr. Full
Professional travels
(1169 t CO${}_{2}\textrm{e}$, 35% restricted total, 16% full total)
1 - Train in FR | 952 | -19% / -6.7% / -2.9%
2 - Plane 2 non-EU | 977 | -16% / -5.9% / -2.6%
3 - Train in FR, Plane 4 EU+2 non-EU | 719 | -38% / -14% / -6.1%
4 - Train in FR, Plane 2 EU+1 non-EU | 508 | -57% / -20% / -8.9%
Commuting
(184 t CO${}_{2}\textrm{e}$, 5.7% restricted total, 2.5% full total)
1 - $<$2.5km bike/foot, for the rest -20% car | 155 | -16% / -0.9% / -0.4%
2 - $<$5km bike/foot, for the rest -40% car | 125 | -32% / -1.8% / -0.8%
3 - same as 2 with 50% electric/hybrid cars | 103 | -44% / -2.5% / -1.1%
4 - 3 days remote working | 152 | -17% / -1.0% / -0.4%
Meals
(85 t CO${}_{2}\textrm{e}$, 2.6% restricted total, 1.2% full total))
1 - 50% standard meals $\mapsto$ flexitarian | 65 | -24% / -0.6% / -0.3%
2 - standard meals $\mapsto$ flexitarian/vegetarian | 37 | -56% / -1.5% / -0.7%
3 - 100% vegetarian meals | 22 | -74% / -1.9% / -0.9%
Computer equipment
(81 t CO${}_{2}\textrm{e}$, 2.5% restricted total, 1.1% full total))
1 - 4-year lifetime for computers | 59 | -27% / -0.7% / -0.3%
2 - 6-year lifetime for computers | 50 | -38% / -1.0% / -0.4%
Heating and electricity
(246 t CO${}_{2}\textrm{e}$, 7.6% restricted total, 3.3% full total)
1 - SNBC national strategy | 125 | -49% / -3.7% / -1.6%
Table 8: Benefits from various reduction scenarios over a selected set of GHG
sources. The gain column lists the gain for the source of emission under
consideration, then the gain relative to the restricted perimeter, and lastly
the gain relative to the full perimeter.

At the moment of writing, there is no clear target that has been defined for
the reduction of emissions from scientific research, or by astronomy in
particular. To gauge the magnitude of the necessary reductions, we can
consider France’s national reduction targets of 50% by 2030 and 80% by 2050.
However, these figures do not account for the fact that some activity sectors
of our society will likely be allowed to continue producing a higher level of
GHG emissions, and that other activity sectors will be required to achieve
even larger reductions in order to compensate those sectors. The appropriate
reduction goal for research is a political question that needs to be addressed
by a wide-ranging democratic decision process that that weighs the GHG
emissions from research activities against its societal benefits. Evidently,
this discussion should not be restricted to the research community alone.

The distribution of our GHG emissions suggests the contours of a reduction
plan. The dominant contribution of astronomical research infrastructures
clearly sets the long-term goal of a decarbonization strategy, in an effort
going well beyond IRAP and challenging our culture of research. Locally, the
quantitative carbon accounting presented above suggests some non-neligigible
emission reductions that could be achieved in the short term via measures that
would not be technically difficult to implement. As outlined in Ref. [28], a
successful emissions reduction plan should combine both aspects: initial steps
that quickly achieve visible emission reductions to set the organization in
motion, and a longer term strategy that attacks the bulk of the emissions
footprint.

A local reduction plan should be carefully co-constructed by the institute’s
direction and staff. We thus refrain from making strong recommendations or
listing priorities here. Instead, we illustrate the magnitude of the emissions
reduction that could be expected from some actions that could be implemented
immediately with limited impact on our daily research activities. Table 8
summarizes these for a series of possible reduction scenarios:

  1. 1.

Professional travel: limiting the number of trips per year and per individual
in and out of Europe and/or imposing train travel for all domestic travel; the
estimated reduction does not include the cost of hotel accommodation.

  2. 2.

Commuting: changing our transport habits by shifting to less carbon-intensive
means for short distances, like walking or cycling, and reducing the remaining
mileage done by car by diverting a fraction of it towards carpooling and
public transportation (which could be even more effective if an increased
fraction of personal cars become electric or hybrid as anticipated in France
in national reduction strategies), or by extending the practice of remote
working (accounting for rebound emissions under the hypothesis of no
adjustments in the lab space usage).

  3. 3.

Meals: reducing the meat content in our food by progressive shifting to
flexitarian, flexitarian and vegetarian, or vegetarian only meals.

  4. 4.

Computer equipment: using laptops and other personal computers or workstations
over longer times, shifting from an average value of about 2 years in 2019 to
4 and 6 years; this does not apply to servers or tablets or standalone
screens.

  5. 5.

Heating and electricity: reducing our consumption following the objectives set
for the buildings sector in the “Stratégie Nationale Bas Carbone (SNBC)”; the
exact scenario for achieving this is not specified but would certainly include
a better thermal insulation and some optimization of the power distribution.

The solutions considered in Table 8 outline a possible path towards a 20-30%
reduction of our footprint over the restricted perimeter. Unsurprisingly, the
major step would come from the evolution of our traveling habits. Such
changes, however, are not so drastic since emissions from professional travel
are very unevenly distributed at IRAP, with 20% (50%) of the total amount
being due to 12 (45) people only. Such a concentration should in principle
make it easier to efficiently tackle the problem. On top of this, an ensemble
of smaller measures can provide a total reduction of about 5%, by acting on
the lifetime of computer equipment or adopting different practices for
commuting and meals, while an ambitious renovation plan for our buildings
would lead to a 4% benefit if we achieve the national objectives of the SNBC.

Even when we ignore the contribution of astronomical research infrastructures
and focus on the emissions associated with IRAP’s restricted perimeter, these
measures would not achieve the target of a 50% reduction in GHG emissions by
2030. This confirms that meeting the ambitious goal recommended by IPCC for
the coming decades will necessarily imply deep changes in our research
culture. Over the restricted perimeter most closely controlled by IRAP, i.e.
the $3258$ t CO${}_{2}\textrm{e}$, achieving such reduction target will
require reducing the emissions associated with the purchase of goods and
services that supply IRAP’s instrument development projects. This presents an
unavoidable challenge to our current relation to that facet of our activity.
We cannot act on the offer of goods and services, but we can modify our demand
by: (i) introducing clauses in purchase rules to disfavor/exclude suppliers
not meeting certain environmental standards; (ii) restructuring our activities
so as to favor less carbon-intensive purchases. These solutions go beyond IRAP
and involve a progressive shift of standards that should be promoted and
supported at the institutional level.

In France, the SNBC describes strategies to achieve a reduction of GHG
emissions from the industrial sector by 35% by 2030, from a reference level in
2015. While this does not meet the 40-60% goal set by IPCC, it would be step
in the right direction, and would contribute to reducing the emissions
associated with the purchase of goods and services at IRAP. The impact of the
SNBC at IRAP, however, depends on the fraction of purchases coming from French
suppliers. We note, moreover, that SNBC has failed to meet its objectives over
2015-2018999https://www.ecologie.gouv.fr/suivi-strategie-nationale-bas-
carbone, with an overall decrease of emissions by 1% per year whereas more
than 2% were predicted, which mechanically raises the effort to be made in
subsequent years. It is increasingly clear that we cannot rely on the
decarbonization of our suppliers and partners from other economic sectors to
achieve a 50% reduction in GHG emissions at IRAP by 2050\.

Formally at least, the predicament at IRAP is simple and is in no way specific
to scientific research. As illustrated above, our carbon footprint is the
product of our activity data and the carbon intensity of those activities.
Reducing IRAP’s carbon footprint can be achieved in three different ways:
lowering the carbon intensiveness of our existing activities, reducing the
pace of our activities, and changing our activities towards low-carbon
alternatives. We believe we should make use of all possible levers because:
(i) the magnitude of the reduction to be achieved by society within a decade
requires us to act on all possible aspects of the problem; (ii) reducing
and/or shifting the activity is directly under our control; and (iii)
modifying our activities can have quick and direct effects, as opposed to the
uncertain decarbonization trajectories of suppliers and partner organizations.
These alternatives become especially acute when we consider the full scope of
IRAP’s GHG emissions, rather than the restricted perimeter.

We emphasize that the carbon intensity of the construction and operation of
large research infrastructures is already relatively low : 140-250 t
CO${}_{2}\textrm{e}$/M€ (see Sect. 4.12 and [19] for more detail). This is at
the low end of sector-based emission factors publised by ADEME for a broad
range of activities, from $\sim$100 t CO${}_{2}\textrm{e}$/M€ for tertiary
activities with little material or technical input, to $\sim$2000 t
CO${}_{2}\textrm{e}$/M€ for heavy industries [3]. In other words, instrument
development and the operation of telescopes already has a relatively low
carbon intensity. Our massive footprint instead comes from the large and
growing number of facilities we have at our disposal.

Our recommendation for a community-based reduction strategy would therefore be
to divert a growing fraction of our budgets to fund the decarbonization of
existing operational infrastructures, to pursue research and development of
low-carbon technologies on which future projects will be based, and to reduce
the cadence and scale of the deployment of new research infrastructures. The
latter point cannot be left out of the equation, otherwise any benefit in
decarbonizing existing facilities will promptly be annihilated by an increase
in the number of facilities. An effective, long-term emissions reduction plan
for IRAP (and astronomy research more widely) requires difficult decisions
that must be made today, including for projects that are already under study.
The timescales involved in the development of astronomical research
infrastructures lock in our emissions for the next decades, and the problem
will only be exacerbated if we continue to postpone the implementation of a
far-reaching emissions reduction strategy.

##  7 Acknowledgements

We acknowledge the many colleagues who participated in this effort. We are
grateful to the director of the institute, Philippe Louarn, for supporting
this initiative from the very start. We warmly thank our colleagues from the
administration for helping us to collect the necessary data (by alphabetical
order): Marie-Pierre Arberet, Teddy Baï, Stéphanie Baylac, Pauline Bénard,
Sélim Benguesmia, Jean-François Botte, Sylvie Cieutat, Marjorie Cloup,
Christelle Feugeade, Josette Garcia, Jean-Louis Lefort, Danièle Millet. We
also acknowledge financial support from IRAP and CNRS for the training to the
“Bilan Carbone” methodology.

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