# Know your footprint - Evaluation of the professional carbon footprint for
individual researchers in high energy physics and related fields

Valerie Lang111Email<EMAIL_ADDRESS>Naman Kumar Bhalla
Simran Gurdasani  Pardis Niknejadi

###### Abstract

Understanding the environmental impact of professional activities is becoming
paramount in current times, especially within sectors that historically have
had significant resource utilisation, such as High Energy Physics (HEP) and
related fields. The _young High Energy Physicists_ (yHEP) association launched
the _Know your footprint_ (Kyf) campaign to evaluate the
$\mathrm{CO}_{2}$-equivalent emissions generated by HEP-related research. This
study delves into the carbon footprints associated with four distinct
categories: _Experiments_ , tied to extensive collaborations with substantial
infrastructure; _Institutional_ , representing the resource consumption of
research institutes and universities; _Computing_ , focusing on simulations
and data analysis; and _Travel_ , covering professional trips such as to
conferences, meetings, and workshops. The findings in this assessment are
integrated into a tool for self-evaluation, the _Know-your-footprint_ (Kyf)
calculator, which allows colleagues to assess their personal and professional
footprint, and optionally share their data with the yHEP association. The aim
of the Kyf campaign is to heighten awareness, foster sustainability, and
inspire the community to adopt more environmentally-responsible research
methodologies.

###### keywords:

High Energy Physics , CO_2  $emissions,Knowyourfootprint,yHEP$

††journal: arXiv

[first]organization=Albert-Ludwigs-Universität, city=Freiburg, country=Germany
[second]organization=Deutsches Elektronensynchrotron (DESY), city=Hamburg,
country=Germany

##  1 Introduction

The impact of the atmospheric presence of $\mathrm{CO}_{2}$ on the ground
temperature of the Earth was first predicted in 1896 [1], indicating
temperature variations as a function of the amount of $\mathrm{CO}_{2}$ (at
the time denoted as carbonic acid), the time of the year and the latitude. For
Europe, the prediction for doubling the atmospheric $\mathrm{CO}_{2}$ content
amounted to an average temperature increase of around
$6\text{\,}{}^{\circ}\mathrm{C}$.

In 2023, the measured $\mathrm{CO}_{2}$ content in the atmosphere has reached
nearly $425\text{\,}\mathrm{p}\mathrm{p}\mathrm{m}$ [2], compared to a maximum
of about $300\text{\,}\mathrm{p}\mathrm{p}\mathrm{m}$ over the last
$800\,000\text{\,}\mathrm{y}$ears before industrial age. The $\mathrm{CO}_{2}$
content in the atmosphere has thus increased by a factor of about 1.4 compared
to the maximum, and around 1.9 compared to the mean over the last
$800\,000\text{\,}\mathrm{y}$ears [3]. The average temperature in Europe in
2023 has climbed up by $1.90\text{\,}{}^{\circ}\mathrm{C}$ compared to pre-
industrial levels (reference years: 1850-1900) [4], which is also used as
reference for temperature differences in the following. The year 2023 is in
direct competition with the year 2020 which was the warmest year on record in
Europe with a $2.07\text{\,}{}^{\circ}\mathrm{C}$ increase. Globally, 2023
surpassed the previously warmest year 2016
($1.32\text{\,}{}^{\circ}\mathrm{C}$ increase), reaching an increase of
$1.48\text{\,}{}^{\circ}\mathrm{C}$.

Scientific research has thus been drawing a consistent picture for more than
100 years, with continuously refined measurements and scenario predictions.
Decisive actions counteracting the changes to the atmosphere and the planetary
ecosystem, however, have been and are still lacking.

According to the Sixth Assessment Report (AR6) by the Intergovernmental Panel
on Climate Change (IPCC), Summary for Policymakers (SPM) [5], _“[i]t is
unequivocal that human influence has warmed the atmosphere, ocean and land”_ ,
leading to changes across the climate system that are _“unprecedented over
many centuries to many thousands of years”_.

Relevant for changes of the climate are the cumulative $\mathrm{CO}_{2}$
emissions. Part of the $\mathrm{CO}_{2}$ emissions are re-absorbed by carbon
sinks on land and ocean, but the higher the cumulative $\mathrm{CO}_{2}$
emissions are, the lower is the fraction of absorption by land and ocean
carbon sinks. This results in higher contributions for both the fraction and
the absolute amount that remain in the atmosphere.

The IPCC AR6 SPM estimates historical cumulative anthropogenic
$\mathrm{CO}_{2}$ emissions from 1850 to 2019 to $2390\pm 240\text{\,}$
Gigatonnes of $\mathrm{CO}_{2}$ (Gt$\mathrm{CO}_{2}$). In order to achieve a
likelihood of $67\text{\,}\%$ to limit human-induced global warming to a
maximum increase of $1.5\text{\,}{}^{\circ}\mathrm{C}$
($2.0\text{\,}{}^{\circ}\mathrm{C}$), which are the two thresholds explicitly
mentioned in the Paris Agreement 2015 [6] to reduce risks and impacts of
climate change, only () remain to be emitted. This remaining carbon budget is
defined, starting from the beginning of 2020 until global net zero
$\mathrm{CO}_{2}$ emissions are reached.

Using the most naive assumption of equal yearly emissions until 2050, i.e. for
30 years, and a static population of 8 billion people, which was reached on 15
November 2022 [7], the remaining carbon budget per person per year accumulates
to () for a maximum temperature increase of $1.5\text{\,}{}^{\circ}\mathrm{C}$
($2.0\text{\,}{}^{\circ}\mathrm{C}$).

In Germany, the average $\mathrm{CO}_{2}$ emissions accumulate to around
$10\text{\,}$– per person per year [8, 9], i.e. more than a factor of two
above what is required for the scenario limiting global warming to an increase
of $2.0\text{\,}{}^{\circ}\mathrm{C}$. This, however, only summarises
emissions from private life. For researchers in the area of high energy
physics (HEP), emissions resulting from research activities need to be added
on top.

The first step for reducing emissions is to quantify current emissions and
raise awareness. The _Know your footprint_ (Kyf) campaign by the _young High
Energy Physicists_ (yHEP) association [10] targets these aspects for the
professional footprint of researchers in the area of HEP and related fields.
While scientific research and development activities contribute only a
fraction of the carbon footprint compared to industrial activities, it is
crucial for the scientific community to understand and mitigate the
environmental impact of their research. Knowledge of the own carbon footprint
will allow to identify areas requiring most urgent action and indicate
possible starting points for action to reduce the footprint. This paper
discusses the data sources and calculations conducted to evaluate the
$\mathrm{CO}_{2}$-equivalent emissions for researchers in HEP for yHEP’s Kyf
campaign and Kyf calculator.

##  2 General setup

The Kyf campaign by the yHEP association evaluates the carbon footprint of
researchers in HEP, mostly in, or associated to, Germany. For emissions of
private life in Germany, we refer to the _Carbon Calculator_ by the German
Federal Environment Agency (_Umweltbundesamt_ \- UBA) [8, 9, 11].

For the professional footprint, we target four distinct categories:
_Experiments_ , _Institutions_ , _Computing_ and _Travel_.

The _experimental_ footprint comprises the emissions from (large) research
infrastructures, collaborations or research projects, i.e. especially in
experimental physics, the experimental setup. The _institutional_ footprint
summarises the emissions from the home university or research centre of the
individual researcher, which should be understood as the institution where the
researcher spends the larger part of her/his working time. _Computing_ focuses
on the individual researcher’s footprint of running simulations and data
analysis, optionally considering data storage impacts as well. _Travel_ covers
the individual’s professional trips such as to conferences, meetings, or
workshops.

The footprints in each category are broken down to the level of the individual
researcher so that they can be placed in relation to each other as well as to
the individual’s emissions for private life. Several options in each category
allow adjusting to the researcher’s individual situation, although some
simplifications are needed to allow for sufficient user-friendliness and
manageable scope of the first version of the Kyf calculator by the yHEP
association. Refinements are considered possible in the future. The Kyf
calculator is published on the webpage of the yHEP association [10].

The calculations that form the basis of the Kyf calculator are discussed in
detail in Sections 3 to 6. An example footprint is given in Section 7. The Kyf
calculator provides feedback to individual researchers regarding their own
professional carbon footprint, and optionally allows to share the data
anonymously with the yHEP association. This data will be used by the yHEP
association to obtain and later publish an overview of the professional
footprint of researchers in HEP and related fields in, and associated to,
Germany. The focus on Germany implies that numbers for the German electricity
grid, German institutions, etc. will be used in the calculations, where
applicable. Other institutions or interest groups are encouraged to transfer
the considerations by yHEP’s Kyf campaign to different countries, citing this
publication as basis for their considerations. In case of questions, contact
can be established with us as authors directly, or with the yHEP association
through the webpage [10].

##  3 Experiment, collaboration or project footprint

The experiment, collaboration or project footprint strongly depends on the
corresponding experiment, collaboration or project. In order to provide a
rough estimate, we use the experiments at the Large Hadron Collider (LHC) as
benchmark for large HEP experiments, DESY electricity consumption (excluding
the European XFEL) as benchmark for smaller HEP experiments and the European
Southern Observatory (ESO) as benchmark for experimental research in
astronomy.

To evaluate the per-researcher environmental footprint, two numbers are
needed: an estimate of the total amount of emissions by the experiment,
collaboration or project in tonne-$\mathrm{CO}_{2}$-equivalent
($\mathrm{tCO}_{2}\mathrm{e}$), and the number of people that the footprint
should be distributed over. The ratio of these two numbers provides the per-
person benchmark footprint.

The design of experimental setups in HEP has traditionally been driven,
mainly, by the scientific question that is to be answered, without systematic
consideration for minimising its carbon footprint. The scientific output is
credited to the authors of the scientific publications. In the area of HEP,
authorship on the publications typically requires membership in the
experimental collaboration. We therefore assign the carbon footprint of the
experiment to the members of the experimental collaboration, or the users (and
operators) of the experimental facility, where corresponding numbers are
available.

More indirect beneficiaries of the knowledge gained in the experiment,
collaboration, or project, such as _the industry_ or _the public_ , are not
considered here. These categories are too vague to provide a good reference
for the carbon footprint of an experimental collaboration, and do not allow
for well-defined boundaries of the organisation, i.e. the experiment,
collaboration or project. In contrast to a company, where the footprint could
alternatively be assigned to the “product” of the company and could thus be
attributed to the “consumer”, _knowledge_ as a “product” of scientific
experiments is not easily quantifiable and thus not distributable to
“consumers”. In order to maintain the responsibility for the emissions with
those who can impact their reduction, we estimate the per-researcher footprint
without consideration of such indirect benefits. This approach is not intended
to scrutinise scientists for the environmental impact of their research, but
rather aims to highlight key areas where the scientific community can focus
its sustainability efforts, ensuring a more environmentally responsible
approach to advancing knowledge.

###  3.1 LHC experiments

The LHC [12] is the largest particle accelerator housed at CERN [13] colliding
proton beams at the location of four major particle detectors: ATLAS [14], CMS
[15], ALICE [16] and LHCb [17]. While ATLAS and CMS are large general-purpose
detectors, ALICE and LHCb are specialised for specific investigations. Due to
different sizes of the detectors, it is expected that the larger experiments
also have higher annual $\mathrm{CO}_{2}$ emissions compared to the two
smaller experiments. Hence, the Kyf calculator aims at estimating separate
values for annual emissions per person for the larger (ATLAS and CMS) and
smaller (ALICE and LHCb) LHC experiments.

Since 2017, CERN publishes a biennial environmental report listing its
$\mathrm{CO}_{2}$ emissions. In order to estimate the annual footprints for
this benchmark scenario, the environmental reports for 2017–18 [18], 2019–20
[19] and 2021–22 [20] were used to get an estimate of years with, as well as
without, the LHC in operation. In addition, the latest technical design report
from the LHCb collaboration, which includes a section on environment
protection and safety, was used to estimate the difference in
$\mathrm{CO}_{2}$ emissions between the larger and smaller LHC experiments
[21]. In this section, the annual emissions are distributed into two phases:
Run phase (2017, 2018 and 2022) and Shutdown phase (2019–21).

CERN categorises its $\mathrm{CO}_{2}$ emissions into three scopes: scope 1
for direct emissions from sources such as detector operations and heating,
scope 2 for indirect emissions primarily arising from electricity consumption,
and scope 3 for emissions from other indirect sources such as travel, commute,
waste, catering and procurement. For the estimation of the footprint for
affiliation to the LHC experiments, only scopes 1 and 2 are relevant with the
following corrections applied:

  * 1.

Scope 1: CERN includes the emissions from heating, non-LHC experiments and
others into scope 1. Since this estimate is only for LHC experiments, the
corrected scope 1 value, corresponding to LHC experiments (particle detection
and cooling) are extracted using PlotDigitizer [22] (see A) from the figure:
_CERN scope 1 emissions for 2017–2022 by category_ , on page 18 in CERN’s
environmental report 2021–2022 [20]. These total and corrected scope 1 values
for all years are listed in Table 1.

  * 2.

Scope 2: The scope 2 contribution of CERN’s $\mathrm{CO}_{2}$ emissions
include the electricity consumption of the Meyrin and Prevessin sites, which
do not correspond to the consumption for powering the LHC and should be
corrected for. CERN’s environmental report 2021–2022 mentions that powering
the CERN campus corresponds to 5% of the total energy consumption which was
used as the correction factor for the scope 2 values [20]. Since, the scope 2
emissions for 2017–20 were recalculated for the 2021–22 environment report,
but given only in a figure without the final values, they were extracted using
PlotDigitizer [22] from the figure: _CERN scope 2 emissions for 2017–2022_ ,
on page 19 in CERN’s environmental report 2021–2022 [20]. The values for 2021
and 2022 were directly listed in the same report. The total and corrected
scope 2 values are listed in Table 2.

| Year | Total | Corrected
---|---|---|---
Run | 2017 | $193\,600\text{\,}$ | $168\,293\text{\,}$
2018 | $192\,100\text{\,}$ | $162\,718\text{\,}$
2022 | $184\,173\text{\,}$ | $152\,444\text{\,}$
Mean | - | $161\,151\text{\,}$
Shutdown | 2019 | $78\,169\text{\,}$ | $56\,446\text{\,}$
2020 | $98\,997\text{\,}$ | $75\,958\text{\,}$
2021 | $123\,174\text{\,}$ | $89\,547\text{\,}$
Mean | - | $73\,984\text{\,}$
Table 1: Total and corrected scope 1 contribution to CERN’s total emissions for the various years in Run and Shutdown phases [18, 19, 20]. The corrected values were extracted using PlotDigitizer [22] from the figure: _CERN scope 1 emissions for 2017–2022 by category_ , on page 18 in CERN’s environmental report 2021–2022 [20]. All emission values are provided in . | Year | Total | Corrected
---|---|---|---
Run | 2017 | $66\,667\text{\,}$ | $63\,333\text{\,}$
2018 | $74\,884\text{\,}$ | $71\,140\text{\,}$
2022 | $63\,161\text{\,}$ | $60\,003\text{\,}$
Mean | - | $64\,825\text{\,}$
Shutdown | 2019 | $28\,527\text{\,}$ | $27\,101\text{\,}$
2020 | $26\,202\text{\,}$ | $24\,891\text{\,}$
2021 | $56\,382\text{\,}$ | $53\,563\text{\,}$
Mean | - | $35\,185\text{\,}$
Table 2: Total and corrected scope 2 contribution to CERN’s total emissions
for the various years in Run and Shutdown phases [20]. Values for 2017–20 were
not given and were extracted using PlotDigitizer [22] from the figure: _CERN
scope 2 emissions for 2017–2022_ , on page 19 in CERN’s environmental report
2021–2022 [20]. The corrected value is 95% of the total value. All emission
values are provided in .

The technical design report of the LHCb experiment lists their total emission
for 2022 to be of which 51% is assigned to scope 1 [21], i.e.

$\displaystyle\text{S1}_{\text{Small}}^{\text{Run}}$
$\displaystyle=0.51\times$4400\text{\,}\mathrm{t}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$$
$\displaystyle=$2244\text{\,}\mathrm{t}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

Since the LHC was operating in 2022, $\text{S1}_{\text{Small}}^{\text{Run}}$
is defined as the scope 1 contribution for the smaller experiments in the Run
phase. In order to estimate the scope 1 contribution for the smaller
experiments in the Shutdown phase ($\text{S1}_{\text{Small}}^{\text{SD}}$), it
is assumed that the emissions for larger and smaller experiments scale by the
same factor between the two phases. This factor ($\text{S1}^{\text{Run/SD}}$)
can be calculated from the mean corrected scope 1 contribution for the two
phases from Table 1 as

$\displaystyle\text{S1}^{\text{Run/SD}}$
$\displaystyle=\frac{$161\,151\text{\,}$}{$73\,984\text{\,}$}$
$\displaystyle=2.18\,.$

This results in $\text{S1}_{\text{Small}}^{\text{SD}}$ being estimated as

$\displaystyle\text{S1}_{\text{Small}}^{\text{SD}}$
$\displaystyle=\frac{\text{S1}_{\text{Small}}^{\text{Run}}}{\text{S1}^{\text{Run/SD}}}$
$\displaystyle=\frac{$2244\text{\,}\mathrm{t}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$}{2.18}$
$\displaystyle=$1030\text{\,}\mathrm{t}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

The scope 1 contribution for the larger experiments
($\text{S1}_{\text{Large}}$) for each phase can be estimated using the mean
corrected scope 1 contribution for all LHC experiments
($\text{S1}_{\text{All}}$) from Table 1 and $\text{S1}_{\text{Small}}$. This
is done by assuming an equal contribution from both the smaller experiments
and an equal contribution also from the two larger experiments and calculated
as

$\displaystyle\text{S1}_{\text{All}}$
$\displaystyle=2\times\text{S1}_{\text{Small}}+2\times\text{S1}_{\text{Large}}$
(1) $\displaystyle\text{S1}_{\text{Large}}$
$\displaystyle=\frac{\text{S1}_{\text{All}}-2\times\text{S1}_{\text{Small}}}{2}\,.$
(2)

The resulting values for the larger experiments are

$\displaystyle\text{S1}_{\text{Large}}^{\text{Run}}$
$\displaystyle=$78\,332\text{\,}\mathrm{t}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\
\text{and}$ $\displaystyle\text{S1}_{\text{Large}}^{\text{SD}}$
$\displaystyle=$35\,962\text{\,}\mathrm{t}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

To estimate the scope 2 contribution of individual experiments, it is assumed
that all four experiments share an equal contribution to the mean corrected
scope 2 emissions of all experiments ($\text{S2}_{\text{All}}$) given in Table
2. This assumption is based on the premise that the dominant scope 2 emissions
come from the electricity consumption for running the entire accelerator
complex, up to and including the LHC, which is equally necessary for all four
experiments. In reality, the electricity consumed by the four experiments
might vary and could be refined in the future if more data becomes available.
The scope 2 contributions for the smaller ($\text{S2}_{\text{Small}}$) and
larger ($\text{S2}_{\text{Larger}}$) experiments, calculated individually for
the Run and Shutdown phases, are given as

$\displaystyle\text{S2}_{\text{Small}}=\text{S2}_{\text{Large}}=\frac{\text{S2}_{\text{All}}}{4}\,.$
(3)

Using this relation, the scope 2 contributions for the Run and Shutdown phases
can be calculated as

$\displaystyle\text{S2}_{\text{Small}}^{\text{Run}}=\text{S2}_{\text{Large}}^{\text{Run}}$
$\displaystyle=\frac{$64\,825\text{\,}\mathrm{t}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$}{4}$
$\displaystyle=$16\,206\text{\,}\mathrm{t}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,,$

and

$\displaystyle\text{S2}_{\text{Small}}^{\text{SD}}=\text{S2}_{\text{Large}}^{\text{SD}}$
$\displaystyle=\frac{$35\,185\text{\,}\mathrm{t}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$}{4}$
$\displaystyle=$8796\text{\,}\mathrm{t}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

After summing the contributions from scopes 1 and 2 to obtain the total
footprint of smaller and larger experiments in the Run and Shutdown phases, a
weighted mean of the two phases is taken assuming a Run phase of
$4\text{\,}\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{s}$ and Shutdown
phase of $3\text{\,}\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{s}$,

$\text{Overall annual
emission}=\frac{4\times\text{Total}^{\text{Run}}+3\times\text{Total}^{\text{SD}}}{7}\,.$
(4)

The values for the individual scopes in the Run and Shutdown phases for
smaller and larger experiments are summarised in Table 3 along with the total
values for each phase and the overall values calculated using Equation 4.

| Phase | Scope 1 | Scope 2 | Total
---|---|---|---|---
Small | Run | $2244\text{\,}$ | $16\,206\text{\,}$ | $18\,450\text{\,}$
SD | $1030\text{\,}$ | $8796\text{\,}$ | $9826\text{\,}$
Overall | - | - | $14\,754\text{\,}$
Large | Run | $78\,332\text{\,}$ | $16\,206\text{\,}$ | $94\,538\text{\,}$
SD | $35\,962\text{\,}$ | $8796\text{\,}$ | $44\,758\text{\,}$
Overall | - | - | $73\,204\text{\,}$
Table 3: Contribution from individual scopes in the Run and Shutdown phases
for smaller and larger experiments along with the total values for each phase,
calculated as $\text{Total}=\text{Scope\leavevmode\nobreak\
1}+\text{Scope\leavevmode\nobreak\ 2}$, and the overall values calculated
using Equation 4.

Finally, to calculate the per person annual footprint for someone affiliated
with one of the smaller (larger) LHC experiments, the overall contribution
from Table 3 is divided by the mean of total members listed on the public
webpages of ALICE [23] and LHCb [24] (CMS [25] and ATLAS [26]) experiments.
These values are listed in Table 4. The overlap of members between the four
experiments is assumed to be negligible. The annual emission per person for
smaller (larger) LHC experiments are estimated to be ().

| Experiment | Members | Mean | Emissions
---|---|---|---|---
Small | ALICE |  $1968\text{\,}$ [23] | $1684\text{\,}$ |
LHCb |  $1400\text{\,}$ [24]
Large | CMS |  $6288\text{\,}$ [25] | $6144\text{\,}$ |
ATLAS |  $6000\text{\,}$ [26]
Table 4: Total members affiliated to the four LHC experiments. The values in
the _Mean_ column is calculated independently for the two smaller and two
larger LHC experiments. The _Emissions_ column lists the per person annual
emission for the smaller and larger LHC experiments. This is calculated by
dividing the Overall emissions from Table 3 by the respective values in the
_Mean_ column.

###  3.2 DESY

The research centre _Deutsches Elektronensynchrotron_ (DESY) in Hamburg,
Germany, is the German national laboratory for accelerator development and
operation, photon science, particle physics and astroparticle physics. Due to
the presence of a significant accelerator and experimental complex, emissions
from DESY are used as proxy for emissions from smaller HEP experiments.

The first DESY sustainability report for 2019–2021 [27] has been published in
2022, but does not include detailed numbers on the emissions from scope 1, 2
or 3 sources as provided in the CERN environmental reports [18, 19, 20]. The
DESY webpage, however, provides a figure on the energy consumption at DESY in
2021 [28] which is reproduced in Figure 1.

Figure 1: DESY energy consumption in 2021 [28]. Electrical power consumption
(yellow) is split into consumption by the European XFEL (brown) and DESY
(yellow). Heat consumption (orange) contains a contribution from heat recovery
from cryogenics plants which is provided for heating and hot water supply to
buildings on the DESY campus.

Electricity consumption in the year 2021 at DESY, excluding the electricity
needed for the European XFEL, amount to
$152.5\text{\,}\mathrm{G}\mathrm{W}\mathrm{h}$. This includes: the accelerator
complex with the accelerators: PETRA, FLASH, LINAC, HERA and DESY (55%),
cryogenics (25%), as well as offices, workshops and laboratories, computing
centre, cooling centre, canteen and others (20%). Subtracting the contribution
from offices, workshops and laboratories, canteen and others as institute
footprint, and the computing centre as computing footprint, which are treated
separately in the Kyf calculator, the remaining experimental electricity
consumption is $128.3\text{\,}\mathrm{G}\mathrm{W}\mathrm{h}$ for 2021, which
is considered as representative for the annual consumption.

Two conversion factors for electricity consumption to $\mathrm{CO}_{2}$
emissions are employed, based on data of Ref. [29]: The specific
$\mathrm{CO}_{2}$ emissions averaged over the German grid in 2023 correspond
to ; emissions from green energy production assume $100\text{\,}\%$
photovoltaic (PV)-based electricity production with a footprint of . Emissions
from wind- () and water- () based production are lower, so $100\text{\,}\%$
PV-based production is considered to be a conservative estimate for green
electricity production.

The number of guest scientists using the DESY facilities is listed as 3000 in
the DESY environmental report [27], which is complemented by an additional 200
accelerator operators. The latter is based on an internal estimate by one
accelerator operator, and is considered reasonable given the size of the DESY
facilities. Refinements of these numbers are possible in a future version of
the Kyf calculator.

Based on the above conversion factors and the number of scientists and
operators, the total emissions from DESY are () for conventional (green)
electricity production, corresponding to () emissions per person per year,
given a conventional (green) electricity supply. DESY itself switched to a
green electricity contract at the start of 2023, significantly reducing its
environmental footprint.

###  3.3 European Southern Observatory

Since 2020, the annual reports for ESO include a section on the environmental
impact of the observatory [30, 31, 32]. In order to estimate the annual
$\mathrm{CO}_{2}$ emissions per person, only the emissions for 2021 were used
to stay with the most up-to-date values. This, however, involves contributions
from business travels, commute and waste, which do not fit well under this
category and need to be subtracted. The breakdown for the various categories
was extracted using PlotDigitizer [22] from the figure: _ESO $\mathrm{CO}_{2}$
emissions 2018–2021_, on page 111 in ESO’s annual report 2022 [32] and is
shown in Table 5. The overall emission after correcting for the contributions
from business travels, commute and waste is estimated to be
$19\,363\text{\,}\mathrm{tCO}_{\mathrm{2}}\mathrm{e}$.

Category | Emission
---|---
Energy (E) | $8599\text{\,}$
Purchase (P) | $5924\text{\,}$
Freight (F) | $2675\text{\,}$
Travel (T) | $191\text{\,}$
Commuting (C) | $1083\text{\,}$
Capital Goods (CG) | $2166\text{\,}$
Waste (W) | $127\text{\,}$
Total | $20\,764\text{\,}$
Corrected | $19\,363\text{\,}$
Table 5: Breakdown of the total $\mathrm{CO}_{2}$ emissions of ESO for 2021.
The contribution of the various categories are extracted using PlotDigitizer
[22] from the figure: _ESO $\mathrm{CO}_{2}$ emissions 2018–2021_, on page 111
in ESO’s annual report 2022 [32]. The corrected value is calculated as
$\text{Corrected}=\text{Total}-\text{T}-\text{C}-\text{W}$. All emission
values are provided in .

The public webpage of ESO lists more than $22\,000\text{\,}$ users of the
observatory who share this overall emission [33]. The average annual emission
per person is hence estimated as
$$19\,363\text{\,}\mathrm{tCO}_{\mathrm{2}}\mathrm{e}$/$22\,000\text{\,}$=$0.88\text{\,}\mathrm{tCO}_{\mathrm{2}}\mathrm{e}$$.

More refined evaluation of individual ESO facilities and experiments would be
very interesting for future versions of the Kyf calculator, if additional
information becomes available.

##  4 Institute or research centre footprint

Institutes within universities or research centres, as key academic
institutions, are an integral part of a researcher’s environmental footprint.
As benchmarks, the University of Freiburg, Leibniz University Hannover, and
CERN are considered. The choice is based on the location of the universities
in Germany, or CERN as central European HEP laboratory, and the publication of
environmental reports by the institutions with sufficient information for a
calculation.

In the Kyf calculator, the estimate from the University of Freiburg is used as
proxy for a German university, the estimate for CERN is used as proxy for a
research centre. Our analysis is based on the respective environmental
reports, covering the years 2017–2019 for the Leibniz University Hannover, the
years 2019–2020 for the University of Freiburg, and the years 2021–2022 for
CERN. We base our estimate of the institutional footprint on one example year
each (outside of the COVID-19 pandemic), and consider this to be
representative for the annual footprint. Future assessments will test this
assumption.

The carbon footprint of the institutions is assessed across several categories
including electricity, heating/cooling, water, waste, and procurement. The
section offers a detailed comparison between these entities, highlighting the
diversity and scale of their emissions. Special attention is given to the
significant role of procurement in contributing to the overall carbon
footprint of universities and research centres.

###  4.1 University of Freiburg

The environmental footprint for the University of Freiburg is based on the
environmental report for 2019/2020 [34], published in 2021. The numbers for
2019 are used as the most recent, pre-pandemic numbers considered to be
representative for the annual consumption of the university.

Emissions of the University of Freiburg are included for the categories:
Electricity, heating/cooling, water, waste, and procurement. Numbers for
vehicle fleet and business travel are provided as well, but are considered
separately in the Kyf calculator and are therefore not listed here. The
numbers provided for each of the used categories are reproduced in Table 6.
Additional detail on the numbers for procurement can be found in B.

Category | Emissions [$\mathrm{tCO}_{2}\mathrm{e}$ ]
---|---
Electricity |  $2431\text{\,}$ ($19\,224\text{\,}$)
Heating/Cooling | $13\,584\text{\,}$
Water | $14\text{\,}$
Waste | $577\text{\,}$
Procurement | $14\,486\text{\,}$
Total |  $31\,092\text{\,}$ ($47\,885\text{\,}$)
Table 6: Breakdown of the total $\mathrm{CO}_{2}$ emissions of the University
of Freiburg for 2019 [34], summing all categories except _Vehicle fleet +
business travel_ which is considered separately in the Kyf calculator.
Electricity values are provided nominally using green electricity supply. The
values in brackets provide the numbers using electricity produced with the
German electricity mix. The conversions to $\mathrm{tCO}_{2}\mathrm{e}$ are
conducted by the University of Freiburg.

The total annual footprint of the University of Freiburg, excluding vehicle
fleet and business travel which are considered separately in the Kyf
calculator, is estimated as () using green electricity (the German electricity
grid mix). The University of Freiburg obtains green electricity from certified
water-power plants [34]. The largest individual contributor for the University
of Freiburg, when considering green electricity supply, is procurement which
corresponds to $87\text{\,}\%$ of the emissions from other categories (excl.
vehicle fleet + business travel).

The environmental footprint of the University of Freiburg is distributed over
the members of the university (students and employees) which are determined as
$31\,147\text{\,}$ from the environmental report [34]. The institutional
footprint per person per year therefore amounts to () using green electricity
(the German electricity grid mix).

###  4.2 Leibniz University Hannover

The environmental footprint of the Leibniz University Hannover is estimated
based on the environmental report for 2017–2019 [35], using numbers from 2019
for direct comparison to the numbers from the University of Freiburg in
Section 4.1. Numbers for the Leibniz University Hannover, provided in the
environmental report, largely specify the original consumption, hence the
conversion to $\mathrm{tCO}_{2}\mathrm{e}$ is performed in this study.

Emissions for the Leibniz University Hannover are provided for the categories:
Electricity, Heating/Cooling, Water, and Waste. Numbers for procurement are
not provided. While the total footprint is therefore not comparable to the one
from the University of Freiburg, for the categories except procurement, the
numbers from the Leibniz University Hannover provide an interesting cross-
check.

Consumption numbers, as provided by the Leibniz University Hannover,
conversion factors and $\mathrm{CO}_{2}$ emissions are listed in Table 7. The
conversion factors for green (conventional) electricity are used as () [29] as
in Section 3.2. The conversion factor of [36] (including green house gas
emissions other than $\mathrm{CO}_{2}$) for heating is obtained from the
energy provider _enercity_ of the Leibniz University Hannover, which is
specified to use district heating. The climate impact for water consumption
assumes that the used water is drinking water, and the conversion factor of is
obtained from a study comparing the climate impact of bottled vs. tap water
[37]. The total amount of waste in 2019, as well as the amount of toxic waste
(_Sonderabfall_) in 2019, are read off from two figures contained in the
environmental report 2017–2019 by the Leibniz University Hannover [35]. The
conversion factors for the $\mathrm{CO}_{2}$ emissions for waste are not
provided in that report, however, in the environmental report by the
University of Freiburg [34] sufficient information is provided to calculate
conversion factors222For the University of Freiburg, $88\text{\,}\mathrm{t}$
of toxic waste are specified to correspond to $24\text{\,}\%$ of the total
amount of emissions from waste, which are given as . This results in a
conversion factor of for toxic waste. The remaining $1019\text{\,}\mathrm{t}$
of waste for the University of Freiburg are specified to generate
$76\text{\,}\%$ of the emissions, resulting in a conversion factor of for
other waste. These conversion factors are transferred to the waste of the
Leibniz University Hannover, assuming implicitly a similar waste composition
within the two categories and a similar treatment by the two universities..

Category | Consumption | Conversion factor | Emissions [$\mathrm{tCO}_{2}\mathrm{e}$ ]
---|---|---|---
Electricity | $58.66\text{\,}\mathrm{G}\mathrm{W}\mathrm{h}$ | () [29] |  $2053\text{\,}$ ($24\,403\text{\,}$)
Heating/Cooling | $50.01\text{\,}\mathrm{G}\mathrm{W}\mathrm{h}$ |  [36] | $4936\text{\,}$
Water | $696\,000\text{\,}\mathrm{m}^{3}$ |  $0.35\text{\,}\mathrm{g}\mathrm{/}\mathrm{l}$ [37] | $244\text{\,}$
Waste | $1620\text{\,}\mathrm{t}$ |  |
… Toxic waste | $63\text{\,}\mathrm{t}$ |  [34] | $99\text{\,}$
… Other waste | $1557\text{\,}\mathrm{t}$ |  [34] | $670\text{\,}$
Total |  |  |  $8001\text{\,}$ ($30\,351\text{\,}$)
Table 7: Breakdown of the total $\mathrm{CO}_{2}$ emissions of the Leibniz
University Hannover. Consumption values are taken from the environmental
report 2017–2019 [35]. Electricity values are provided nominally using green
electricity supply. The values in brackets provide the numbers using
electricity produced with the German electricity mix. Sources for the
conversion factors are indicated, and further explained in the text. Note that
the sums are calculated with more decimal places than indicated, resulting in
small differences to the sums of the listed values.

The total emissions for the Leibniz University Hannover are estimated to be
(), with green (conventional) electricity production. Comparing only the
equivalent categories, the emissions of in case of green electricity are in
contrast with for the University of Freiburg. The main difference results from
the emissions for heating/cooling, where district heating for the Leibniz
University Hannover generates significantly fewer emissions than the
heating/cooling used at the University of Freiburg. In the latter case, the
usage of ground water for cooling is specified, but no information on the type
of heating by the University of Freiburg is provided.

The number of students at the Leibniz University Hannover is given in the
environmental report 2017–2019 [35] as $29\,781\text{\,}$, and the number of
employees as $4948\text{\,}$, summing to $34\,729\text{\,}$ members of the
Leibniz University Hannover. This results in emissions per person per year of
() with green (conventional) electricity supply, with the caveat of not
accounting for procurement.

Given the lack of information on procurement for the Leibniz University
Hannover, the larger emissions for heating/cooling by the University of
Freiburg and approximate similarity in the required other numbers, the values
obtained for the University of Freiburg are used as conservative estimate for
the institutional footprint of a university.

###  4.3 Research centre CERN

The environmental footprint for CERN as research centre is based on the values
for 2022 (post-pandemic), listed in the newest environmental report for
2021–2022, published in 2023 [20].

The categories considered are similar to the ones for University of Freiburg
and Leibniz University Hannover: _Electricity_ , _Heating (gas+fuel)_ and
_Other (scope 1)_ , _Water purification_ , _Waste_ , _Procurement_. A value
for business travel is provided as well, but will be considered separately in
the professional footprint of the Kyf calculator and is therefore dropped
here. Values for catering and personal commutes are provided by the CERN
environmental report, in addition; however, in the Kyf calculator, food, diet
choices and commute are considered as part of the personal footprint, and are
therefore removed from the institutional footprint to avoid double counting.
The numbers for each of the employed categories are provided in Table 8.

Category | Emissions [$\mathrm{tCO}_{2}\mathrm{e}$ ]
---|---
Electricity | $3158\text{\,}$
Heating (gas+fuel) + Other | $11\,250\text{\,}$
Water purification | $176\text{\,}$
Waste | $1875\text{\,}$
Procurement | $104\,974\text{\,}$
Total | $121\,433\text{\,}$
Total without Procurement | $16\,459\text{\,}$
Table 8: Breakdown of the total $\mathrm{CO}_{2}$ emissions of CERN as
research institute for 2022 [20]. The categories Business travel and Personnel
commutes are considered separately in the Kyf calculator, and thus not listed
here. The conversions to $\mathrm{tCO}_{2}\mathrm{e}$ are conducted by CERN.

Emissions for electricity correspond to $5\text{\,}\%$ of the total
electricity-related emissions for 2022, which is specified in the CERN
environmental report 2021–2022 [20] as electricity component required for
powering the campus. Though not explicitly specified in the report, the
effective conversion factor for electricity consumption to $\mathrm{CO}_{2}$
emissions can be derived as from the total electricity consumption in 2022 of
$1215\text{\,}\mathrm{G}\mathrm{W}\mathrm{h}$, corresponding to emissions of ,
specified for scope 2 in 2022. Electricity at CERN largely comes from low-
carbon nuclear-power plants from France, resulting in a conversion factor
significantly lower than the one from the conventional German grid mixture,
but still slightly larger than electricity based on renewables, discussed in
Section 3.2. The numbers for _Heating (gas+fuel) and Other_ (scope 1) are
obtained from the total scope-1 emissions minus emissions for _LHC
experiments: Particle detection_ and _LHC experiments: Detector cooling_ as
well as _Other experiments_ , where the LHC-related emissions are considered
as part of the experimental contribution, discussed in Section 3.1. The value
for the sum of the LHC and non-LHC experimental contributions has been
obtained using PlotDigitizer [22] (also see A). Additional detail on the
composition of the procurement category are given in B.

The total emissions for CERN as a research centre in 2022 sum to () including
(excluding) procurement. They are dominated by the footprint of procurement,
followed by heating (gas+fuel), where heating reaches a similar level as for
the University of Freiburg. A split up of the emissions from procurement is
provided by the CERN environmental report 2021–2022 [20], and discussed
further in B. It is important to note that the emissions from procurement at
CERN do not only cover those related to CERN as an institute, but also those
related to the experiments, accelerator facilities, as well as future
accelerator developments. Some procurement categories such as _Civil
engineering, building and technical services_ appear more related to the
infrastructure of the accelerator and experimental complex; different
categories such as _Other_ that includes office supplies, furniture, etc. seem
more related to CERN as an institute. A clear separation of the procurement
categories belonging to CERN as an institute is however not possible. In the
future, improved assignment can be done if more granular information becomes
available. For the moment, procurement is fully assigned to the CERN institute
footprint.

Emissions from electricity and waste play a smaller role in the carbon
footprint of CERN as an institute. Differences in the electricity consumption
to the university footprints can be primarily attributed to different
conversion factors for the type of used electricity. The electricity
consumption of the CERN campus in GWh is comparable to that of the University
of Freiburg and the Leibniz University Hannover333The electricity consumption
for the CERN campus is $5\text{\,}\%$ of
$1215\text{\,}\mathrm{G}\mathrm{W}\mathrm{h}$ in 2022, corresponding to
$60.75\text{\,}\mathrm{G}\mathrm{W}\mathrm{h}$ vs. about
$50\text{\,}\mathrm{G}\mathrm{W}\mathrm{h}$ for the University of Freiburg and
$58.66\text{\,}\mathrm{G}\mathrm{W}\mathrm{h}$ for the Leibniz University
Hannover.

The evaluation of an _effective CERN population_ needs to consider that at any
time during the year, a certain fraction of CERN users is present at CERN,
using electricity, water, heating, etc. and thus contributing to the
institutional footprint. The total number of CERN personnel employed by CERN
(Employed Members of Personnel - MPE) in 2022 amounts to
$N_{\text{MPE}}=$3558\text{\,}$$ [38], including staff and CERN fellows. The
total number of Associated Members of Personnel (MPA), including CERN users as
largest subcategory, corresponds to $N_{\text{MPA}}=$13\,376\text{\,}$$ in
2022 [38]. The average presence for MPAs at CERN is evaluated as a weighted
average of the _Yearly Presence_ of CERN users at CERN, using the midpoint of
the percentage bins in Table 11 of Ref. [38] for weighting. This results in an
average presence $\bar{p}=$27.9\text{\,}\%$$ for MPAs at CERN. The effective
CERN population, $N_{\text{eff}}$, is thus calculated as:

$N_{\text{eff}}=N_{\text{MPE}}+\bar{p}\cdot N_{\text{MPA}}\,,$

and, numerically, determined to be $N_{\text{eff}}=$7295\text{\,}$$.

The institutional footprint per person per year for CERN as research institute
is estimated to be () including (excluding) procurement. This is, also
excluding procurement, larger than the institute footprint of the University
of Freiburg, discussed in Section 4.1. Given the larger experimental
facilities at the research centre, higher per-researcher emissions at a
research centre compared to a university are expected. The impact of
procurement at CERN as an institute is artificially increased, since it also
includes contributions relevant to the experiments, accelerators and future
developments, which cannot be separated at the moment. The estimate of
emissions from procurement at CERN are based on the Greenhouse Gas Protocol
spend-based method, which – though already rather advanced – is acknowledged
in the CERN environmental report 2021-2022 [20] to have limitations and
variable degrees of uncertainty. CERN started the _Environmentally Responsible
Procurement Policy Project (CERP3)_ in September 2021, to improve the
environmental impact of procurement in the future.

##  5 Computing footprint

Most of research in physical sciences today relies on having High Performance
Computing (HPC) Clusters to enable breakthroughs and make advancements within
the various sub-fields of physics. Hence, it is essential to consider the
environmental impact of such computing clusters. Sustainable research and
accelerating scientific progress is very much dependent on rise of awareness
of our resource usage [39].

Typical HPC usage involves researchers submitting jobs to computational
clusters via a scheduler or interactively. For each job, specific requirements
are defined, including the number of CPU-cores or GPUs 444We do not detail GPU
power consumption by individual CUDA cores [40] for two main reasons: the
standard power specification of 250W for a PCIe A100 GPU offers a reliable
estimate, and the real-world application of GPUs does not involve partitioning
by CUDA cores, rendering such a breakdown impractical here. needed and a
defined time window for execution. For a carbon footprint, average idle times
as well as surplus-power, e.g. due to HPC cooling, need to be considered as
well.

The carbon footprint averaged over a year is thus estimated as:

$\text{Total Footprint [$\mathrm{tCO}_{2}\mathrm{e}$ ]}=f_{\text{PUE}}\cdot
f_{\text{overh}}\cdot n_{\text{WPC}}\cdot f_{\text{conv}}\,,\\\ $ (5)

where $f_{\text{PUE}}$ denotes the HPC’s _Power Usage Effectiveness_ (PUE)
[41], $f_{overh}$ is an overhead factor to account for power consumption of
the HPC when its computing cores are idle555 Generally, an HPC has to estimate
the number of cores it operates by predicting the requirements by its users.
If too few cores are operated, it leads to long waiting times for jobs to
start, but no idle operation time. It too many cores are available, idle times
become too large. HPCs will thus aim for a small fraction of idle time, which
needs to be considered in the carbon footprint., $n_{\text{WPC}}$ accounts for
the _Workload Power Consumption_ (WPC), and $f_{\text{conv}}$ is the
conversion factor from kWh to $\mathrm{gCO}_{2}\mathrm{e}$.

The yearly WPC is estimated taking into account the usage of CPUs and GPUs
separately as:

$n_{\text{WPC}}=p_{\text{CPU-core}}\cdot l_{\text{core-h,
CPU}}+p_{\text{GPU}}\cdot l_{\text{h, GPU}}\,,$ (6)

where $p_{\text{CPU-core}}$ is the power consumption in kW for each CPU core
and $l_{\text{core-h, CPU}}$ is the CPU workload measured in core hours. GPU
usage is calculated with $p_{\text{GPU}}$ representing the power consumption
in kW per GPU and $l_{\text{h, GPU}}$ is the total number of hours the GPU is
used for.

For the Kyf calculator, the following values are used as default values for an
individual researcher’s computing footprint. For $f_{\text{PUE}}$, a value of
1.5 has been chosen, which is listed in the CERN environmental report
2021–2022 [20] as global average PUE for large data centres, and which has
been declared as the target to be reached by 1 July 2027 for computing centres
starting operations before 1 July 2026 in the German law for the improvement
of energy efficiency (_Energieeffizienzgesetz_ \- EnEfG, §11(1)) [42]. The
idle-time overhead factor $f_{\text{overh}}$ has been estimated by comparing
the system power consumption of the _Hawk_ supercomputer at the HPC Stuttgart
[43] at full load (LinPack operation) ($4.1\text{\,}\mathrm{M}\mathrm{W}$) to
normal operation ($3.5\text{\,}\mathrm{M}\mathrm{W}$). The resulting factor of
$1.17\text{\,}$ is used as default idle-time overhead factor. As conversion
factor $f_{\text{conv}}$, the same values as discussed in Section 3.2 are
employed, based on Ref. [29]: () for a conventional (green) electricity
provision at the HPC. Finally, $7.25\text{\,}\mathrm{W}$ is the default value
set for the CPU power consumption per core along with a default power
consumption of $250\text{\,}\mathrm{W}$ per GPU. The CPU value is obtained
from the DESY Maxwell cluster with AMD EPYC 75F3 CPU cores [44]. The GPU value
is the median of a range, $150\text{\,}$–$350\text{\,}\mathrm{W}$, reported on
a forum of NVIDIA GPU users [45]. Both these systems are fairly recent, so the
power usages should be representative of newer HPC processing units. The usage
load in core-hours for CPU and GPU usage, $l_{\text{core-h, CPU}}$ and
$l_{\text{h, GPU}}$, needs to be provided by the researcher when using the Kyf
calculator.

The computing footprint estimate can be further personalised by adjusting
$f_{\text{PUE}}$, if calculations have been done, for example, at the very new
CERN computing centre which targets a PUE value of 1.1 [20], or by changing
$f_{\text{overh}}$ to a different value, if better or worse HPC load factors
have been obtained. It can also be switched to green electricity, with
conventional electricity being used as default.

In recent years, HPC centres have been implementing system-generated reports
that inform their users about their usage with high transparency. Resources
like the _Production and Distributed Analysis_ (PanDA) client [46] or API [47]
can also be used by users or software developers to monitor group or
individual workload on a system. If the annual carbon footprint is thus known
precisely, this can also be entered directly in $\mathrm{tCO}_{2}\mathrm{e}$.

For convenience, four scenarios discussed in the following are provided, based
on the estimated usage level: low, medium, high and extremely high. Several
factors come into play when deciding whether to utilise a CPU or a GPU for a
particular computational task. These factors include the availability of
suitable software and libraries, memory requirements, considerations related
to parallelisation, as well as potential bottlenecks in data transfer rates.
As a result, simulations and analyses are tailored to the specific needs and
research objectives of individual researchers. The provided examples thus
serve as rough approximations only, and CPU vs. GPU usage cannot necessarily
be interchanged easily.

  * 1.

Case for low usage

Example: A graduate student submits several jobs per week each needing between
one to four CPU cores. The consumption corresponds to an an average of
$4000\text{\,}\text{CPU core-h}$ used per month resulting in an annual
footprint as follows:

Footprint $\displaystyle=$1.5\text{\,}$\times
1.17\times$0.007\,25\text{\,}\mathrm{k}\mathrm{W}$$ $\displaystyle\times
12\times$4000\text{\,}\text{CPU core-h}\mathrm{/}\text{month}$$
$\displaystyle\times$416\text{\,}\mathrm{gCO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{k}\mathrm{W}\mathrm{h}$$
$\displaystyle=$0.25\text{\,}\mathrm{tCO}_{\mathrm{2}}\mathrm{e}$$

  * 2.

Case for medium usage

Example: A doctoral student or post-doctoral researcher, involved in data
analysis 70-100% of their time, submits jobs over the year. For this case, the
top five ranked users at the Uni-Freiburg HPC called the Black-Forest Grid
(BFG) are used as a blueprint, giving
$30\,000\text{\,}\mathrm{C}\mathrm{P}\mathrm{U}\text{core-h}$ per month. The
resulting annual footprint is as follows:

Footprint $\displaystyle=$1.5\text{\,}$\times
1.17\times$0.007\,25\text{\,}\mathrm{k}\mathrm{W}$$ $\displaystyle\times
12\times$30\,000\text{\,}\text{CPU core-h}\mathrm{/}\text{month}$$
$\displaystyle\times$416\text{\,}\mathrm{gCO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{k}\mathrm{W}\mathrm{h}$$
$\displaystyle=$1.91\text{\,}\mathrm{tCO}_{\mathrm{2}}\mathrm{e}$$

  * 3.

Case for high usage

Example: An accelerator scientist studies accelerator performance with
particle tracking codes and semi particle-in-cell (PIC) codes. The studies are
both memory and computationally demanding. We assume $2500\text{\,}\text{GPU
h}$ used per month with code optimised for GPUs (equivalent to
$80\,000\text{\,}\text{CPU core-h}$ per month).

Footprint $\displaystyle=$1.5\text{\,}$\times
1.17\times$0.25\text{\,}\mathrm{k}\mathrm{W}$$ $\displaystyle\times
12\times$2500\text{\,}\text{GPU h}\mathrm{/}\text{month}$$
$\displaystyle\times$416\text{\,}\mathrm{gCO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{k}\mathrm{W}\mathrm{h}$$
$\displaystyle=$5.48\text{\,}\mathrm{tCO}_{\mathrm{2}}\mathrm{e}$$

  * 4.

Case for extremely high usage

Example: A researcher runs PIC simulations or high-resolution imaging
analysis. This corresponds to a usage of $8000\text{\,}\text{GPU h}$ per month
(equivalent to $300\,000\text{\,}\text{core-h}$ per month on CPUs).

Footprint $\displaystyle=$1.5\text{\,}$\times
1.17\times$0.25\text{\,}\mathrm{k}\mathrm{W}$$ $\displaystyle\times
12\times$8000\text{\,}\text{GPU h}\mathrm{/}\text{month}$$
$\displaystyle\times$416\text{\,}\mathrm{gCO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{k}\mathrm{W}\mathrm{h}$$
$\displaystyle=$17.52\text{\,}\mathrm{tCO}_{\mathrm{2}}\mathrm{e}$$

It is important to acknowledge that the calculations presented here are based
on the assumption of optimal core utilisation in HPCs. In practice, however,
it is often the case that not all cores are used efficiently, leading to
unnecessary energy consumption.

The environmental costs associated with data centres for long-term storage
require significant amounts of electricity and water to operate servers and
cooling systems. The large scale resources used by experimental collaborations
or at institutes are assumed to be accounted for within their respective
categories. Within the individual computing footprint, the environmental
contribution of data storage is assumed to be small compared to that of
actively running jobs and is therefore neglected. If we obtain data that such
costs for an individual are high, we will include this contribution in future
iterations. If significant amounts of data are stored at a location other than
the institute, such as on a commercial cloud, e.g. Microsoft OneDrive [48] or
Google Drive [49], an additional $\mathrm{CO}_{2}$ emission contribution can
however be added, based e.g. on the Microsoft emissions impact dashboard [50],
or the Google’s Carbon Footprint app [51].

##  6 Business travel footprint

Travel constitutes another crucial component of High Energy Physics (HEP). The
importance of in-person events became notably evident during the COVID-19
pandemic, where their absence made discussions and collaborations more
challenging, prompting a reconsideration of our methodologies. In light of
this realisation, a meticulous assessment of travel-associated
$\mathrm{CO}_{2}$ emissions is imperative. Business travel, including
transportation and accommodations, significantly contributes to
$\mathrm{CO}_{2}$ footprints. Efforts to comprehend and mitigate these
emissions involve scrutinising various transportation modes, such as long-
distance buses, trains, personal cars, and air travel, each exerting diverse
impacts on our environmental footprint [11, 52]. Comparison of
$\mathrm{CO}_{2}$ emissions from various modes of transport are briefly
discussed in C. Additionally, the emissions from factors such as accommodation
and event venues play a role, emphasising the need to evaluate the average
$\mathrm{CO}_{2}$ emissions of hotel rooms per night and event venues per day
for an individual [53]. Benchmark values used for the Kyf calculator are
documented in Table 9. These values are estimated using travel, hotel stay and
meeting room usage scenarios in Germany. The footprint corresponding to the
event venue assumes an area of $4\text{\,}\mathrm{m}^{2}$ per person.

Source of Emission | Emission Factor
---|---
Long-distance Buses | $0.031\text{\,}$ |
Long-distance Trains | $0.031\text{\,}$ |
Personal Car | $0.17\text{\,}$ |
Flights within Europe | $130\text{\,}$ |
Transcontinental Flights | $170\text{\,}$ |
Hotel room | $12\text{\,}$ |
Event venue | $0.19\text{\,}$ |
Table 9: List of dominant contributors to the $\mathrm{CO}_{2}$ footprint of a
business trip with the respective $\mathrm{CO}_{2}$-equivalent emission value
for an average individual in the German scenario.

It is important to note that some institutes, such as the University of
Freiburg, compensate for all flights taken by its employees for business
trips. The Kyf calculator provides an option for showing the amount of carbon
footprint, which was compensated. This might be extended to other sections of
the Kyf calculator in future iterations.

Using the values from Table 9, it is possible to obtain a rudimentary estimate
for a business trip. Three benchmark scenarios are presented here.

###  6.1 Train travel within Europe

For someone in Freiburg attending a conference in Hamburg lasting
$5\text{\,}\mathrm{d}\mathrm{a}\mathrm{y}\mathrm{s}$ and travelling by long-
distance trains, the total $\mathrm{CO}_{2}$ emissions are a sum of the
following three contributions:

  * 1.

Travel: Given a one-way distance of $800\text{\,}\mathrm{k}\mathrm{m}$, the
footprint for the travel with long-distance trains (both ways) is:

Footprint (travel)
$\displaystyle=2\times$800\text{\,}\mathrm{k}\mathrm{m}$\times$0.031\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{k}\mathrm{m}$$
$\displaystyle=$49.6\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

  * 2.

Hotel: For a conference from Monday to Friday, a hotel reservation of
$5\text{\,}\mathrm{n}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{s}$ with
check-in on Sunday and check-out on Friday is assumed. The footprint for the
hotel room is:

Footprint (hotel)
$\displaystyle=$5\text{\,}\mathrm{n}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{s}$$
$\displaystyle\times$12\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{n}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}$$
$\displaystyle=$60\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

  * 3.

Venue: Assuming that the conference venue is occupied for
$8\text{\,}\mathrm{h}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{s}$ on all
$5\text{\,}\mathrm{d}\mathrm{a}\mathrm{y}\mathrm{s}$ of the conference, the
footprint for the event venue is:

Footprint (venue)
$\displaystyle=$5\text{\,}\mathrm{d}\mathrm{a}\mathrm{y}\mathrm{s}$\times$0.19\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{d}\mathrm{a}\mathrm{y}$$
$\displaystyle=$0.95\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

The total $\mathrm{CO}_{2}$ emission for this benchmark scenario is given by
the sum of the three contributions:

Total Footprint $\displaystyle=\text{Footprint(travel + hotel + venue)}$
$\displaystyle=$49.6\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$+$60\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$+$0.95\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$$
$\displaystyle\approx$111\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

###  6.2 Flight travel within Europe

Considering the same scenario as in the previous benchmark, but changing the
mode of transport from train to flight, which requires
$1.5\text{\,}\mathrm{h}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{s}$ for the same
distance of $800\text{\,}\mathrm{k}\mathrm{m}$ one way, the travel
contribution increases to:

Footprint (travel)
$\displaystyle=2\times$1.5\text{\,}\mathrm{h}$\times$130\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{h}$$
$\displaystyle=$390\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

The change to flight as transport mode therefore yields a travel footprint
that is $5.4\text{\,}$ times larger compared to travel by train. The total
footprint for the entire business trip in this case is estimated as:

Total Footprint
$\displaystyle=$390\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$+$60\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$$
$\displaystyle+$0.95\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$$
$\displaystyle\approx$451\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

To consider an example where travelling by train would not be considered
practical, another scenario for a person based in Freiburg attending a
conference lasting $5\text{\,}\mathrm{d}\mathrm{a}\mathrm{y}\mathrm{s}$ in
Thessaloniki, Greece is considered. A direct flight from Basel to Thessaloniki
takes about $2.5\text{\,}\mathrm{h}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{s}$
for one way. This results in a travel contribution of:

Footprint (travel)
$\displaystyle=2\times$2.5\text{\,}\mathrm{h}$\times$130\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{h}$$
$\displaystyle=$650\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

Taking the same contributions for hotel and venue calculated in Section 6.1,
the total footprint for the entire business trip in this case is estimated as:

Total Footprint
$\displaystyle=$650\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$+$60\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$$
$\displaystyle+$0.95\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$$
$\displaystyle\approx$711\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

###  6.3 Flight travel across continents

This benchmark scenario considers a person travelling to Seoul (South Korea)
from Freiburg for a summer school lasting
$2\text{\,}\mathrm{w}\mathrm{e}\mathrm{e}\mathrm{k}\mathrm{s}$. As in the
previous benchmark scenarios, the three contributions are estimated as
follows:

  * 1.

Travel: A one-way travel requires a long-distance train from Freiburg to
Frankfurt ($275\text{\,}\mathrm{k}\mathrm{m}$) followed by an transcontinental
flight from Frankfurt to Seoul
($12\text{\,}\mathrm{h}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{s}$). The
footprint of the return trip is estimated as:

Footprint (travel)
$\displaystyle=2\times($275\text{\,}\mathrm{k}\mathrm{m}$\times$0.031\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{k}\mathrm{m}$$
$\displaystyle+$12\text{\,}\mathrm{h}$\times$170\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{h}$)$
$\displaystyle=$4097\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

  * 2.

Hotel: For a summer school lasting
$2\text{\,}\mathrm{w}\mathrm{e}\mathrm{e}\mathrm{k}\mathrm{s}$, a Hotel
reservation of
$14\text{\,}\mathrm{n}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{s}$ is
assumed. The footprint for the hotel room is:

Footprint (hotel)
$\displaystyle=$14\text{\,}\mathrm{n}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{s}$$
$\displaystyle\times$12\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{n}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}$$
$\displaystyle=$168\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

  * 3.

Venue: Assuming that the conference venue is occupied for
$8\text{\,}\mathrm{h}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{s}$ on all
$10\text{\,}\mathrm{b}\mathrm{u}\mathrm{s}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{s}\mathrm{s}\
\mathrm{d}\mathrm{a}\mathrm{y}\mathrm{s}$ of the summer school, the footprint
for the event venue is:

Footprint (venue)
$\displaystyle=$10\text{\,}\mathrm{d}\mathrm{a}\mathrm{y}\mathrm{s}$\times$0.19\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}\mathrm{/}\mathrm{d}\mathrm{a}\mathrm{y}$$
$\displaystyle=$1.9\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

The total footprint for the entire trip is estimated as:

Total Footprint
$\displaystyle=$4097\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$+$168\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$$
$\displaystyle+$1.9\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$$
$\displaystyle\approx$4267\text{\,}\mathrm{k}\mathrm{g}\mathrm{CO}_{\mathrm{2}}\mathrm{e}$\,.$

The duration of this trip is extended from the typical 5 day duration observed
in other scenarios to recognise that longer-distance trips often require more
justification to be funded, e.g. longer educational programs. It must be noted
however, that the footprint of hotel stay and event venue remains small
compared to that of the travel itself. This implies that the carbon footprint
of a shorter trip would be similar as long as the footprint of the flight
travel is not compensated. Note that hotel and venue footprints are based on
German numbers and are assumed to be valid in the international context as
well. This assumption might be refined in a future version of the Kyf
calculator.

##  7 Professional footprint example

An example professional footprint of an early-career researcher in Germany is
calculated for a benchmark doctoral student working on one of the large LHC
experiments at CERN, being employed by a university in Germany, which is
supplied with conventional electricity, medium computing level, where the HPC
also uses conventional electricity, and who does two 1-week trips in Germany
by train (for example, a DPG spring meeting and a national collaboration
meeting), one flight travel within Europe for a week (for example, a
conference in Thessaloniki, Greece), and one two-week flight travel across
continents (for example, a summer school in Seoul, South Korea) during one
particular year. The combined professional footprint is listed in Table 10,
and shown in Figure 2, in comparison with the personal footprint and the
remaining carbon budget per person per year, given the climate warming
limitation goals discussed in Section 1.

Category | Emissions [$\mathrm{tCO}_{2}\mathrm{e}$ ]
---|---
Large LHC experiment | $11.91\text{\,}$
University (German electricity mix) | $1.54\text{\,}$
Computing (medium) | $1.91\text{\,}$
Travel (2xT, 1xF(E), 1xF(C)) | $5.20\text{\,}$
Total | $20.56\text{\,}$
Table 10: Professional $\mathrm{CO}_{2}$ footprint of a benchmark doctoral
researcher, working on one of the large LHC experiments, employed by a German
university, with medium computing usage level and two intra-Germany travels by
train (T), one intra-Europe flight travel (F(E)) and one cross-continental
flight travel (F(C)) per year. Figure 2: Professional $\mathrm{CO}_{2}$
footprint of a benchmark doctoral researcher, working on one of the large LHC
experiments, employed by a German university, with medium computing usage
level and travel as discussed in the text, in comparison with the average
personal footprint in Germany, as well as the remaining carbon budget per
person per year of () for a maximum temperature increase of
$1.5\text{\,}{}^{\circ}\mathrm{C}$ ($2.0\text{\,}{}^{\circ}\mathrm{C}$).

The annual professional footprint of the benchmark doctoral researcher amounts
to , which can be compared to the average private footprint of $10\text{\,}$–
per person per year in Germany [8, 9, 11]. The professional footprint is
clearly dominated by work in a large LHC experiment, followed by travel. Work
on a smaller HEP experiment using a green electricity supply or an astronomy
experiment would have resulted in a smaller experimental footprint, leaving
the travel footprint dominant. Several cross-continental business travels per
year also would result in a significantly larger travel footprint, turning it
into the leading contribution. A high or extremely high usage computing level
would significantly increase the computing footprint, making it instead the
leading contribution. Similarly, employment at a research centre with larger
laboratory facilities could result in the institutional footprint becoming
dominant.

For the benchmark doctoral researcher and several other scenarios, the
professional footprint thus exceeds the personal footprint, both of which by
far exceed the remaining carbon budget per person per year of () for a maximum
temperature increase of $1.5\text{\,}{}^{\circ}\mathrm{C}$
($2.0\text{\,}{}^{\circ}\mathrm{C}$), as discussed in Section 1 and displayed
in Figure 2. It is therefore crucial, that not only personal consumption
patterns, including the societal boundaries and incentives, are adjusted, but
that also the way research is performed is transformed towards a more
sustainable and environmentally friendly practice. The Kyf calculator provides
a first idea for an individual researcher where these transformations have to
happen first and most urgently, such that the biggest and easiest
contributions are addressed first, immediately followed by tackling the more
challenging ones. Environmental reports by universities, research centres,
experiments, computing centres, etc. provide key information for this purpose,
and should become mandatory as soon as possible, followed by action (plans)
for the reduction of the carbon footprint. It is clear that changes towards a
more sustainable research practice require resources, both financially and
personnel-wise. This fact needs to be considered by funding agencies in their
future decision-making. The costs for inaction will eventually be higher than
those for coordinated and targeted action now.

##  8 Conclusions

_Know your footprint_ is an essential early step in tackling climate change
and for motivating sustainable behaviour. This holds both in the personal as
well as in professional life.

In an energy-hungry sector, such as High Energy Physics (HEP) and related
fields, awareness is key to start moving towards a more sustainable research
practice. The _young High Energy Physicists_ (yHEP) association launched the
_Know your footprint_ (Kyf) campaign to evaluate the
$\mathrm{CO}_{2}$-equivalent emissions generated by HEP-related research.

In the four categories: _Experiments_ , _Institutes_ , _Computing_ and
_Travel_ , we have evaluated the carbon footprint of research activities. A
user-oriented tool is provided [10] to allow for the simple evaluation of the
professional footprint of an individual researcher in, or associated to,
Germany. The results show that for various combinations, the professional
footprint significantly exceeds the personal footprint of an average citizen
in Germany, which already exceeds the maximal remaining carbon budget per
person per year, allowed for a maximum temperature increase of
$1.5\text{\,}{}^{\circ}\mathrm{C}$ ($2.0\text{\,}{}^{\circ}\mathrm{C}$), by
factors of around $6\text{\,}$ (around $2\text{\,}$). Targeted and prompt
action is therefore essential. Every gram of $\mathrm{CO}_{2}$ saved makes a
difference.

While it is encouraging to see a growing awareness among individuals and
institutions of their carbon footprint, our study shows that significant work
in the HEP-related research lies before us. Given that the next 10–20 years
will be crucial to mitigate global warming, this work needs to start now.
Carbon-footprint accounting provides us with the data to shape strategies and
target measures in order to effectively reduce our carbon footprint.
Scientific research, starting now, needs to ensure that our pursuit of
scientific and technological progress aligns harmoniously with a strong
commitment to environmental sustainability.

## Acknowledgements

Our warm thanks go to Ruth Jacobs, Srijan Sehgal, and Dima El Khechen from the
yHEP management board for their constructive input on this paper and to
Michael Lupberger from the yHEP management board on his feedback to the Kyf
calculator. Many thanks also go to further current and former yHEP management
board members for their support and constructive input to the Kyf campaign
efforts: Farah Afzal, Leonel Morejon, Meike Küßner, Felipe Peña, Philipp
Krönert, Alina Nasr-Esfahani, Annika Thiel. Additionally, we would like to
express our gratitude for feedback on the paper and the calculator to the
following colleagues: Lutz Feld (for the committee for particle physics (KET)
in Germany), Uli Katz (for the committee for astro-particle physics (KAT) in
Germany), Michael Block (for the committee for physics of nucleons and hadrons
(KHuK) in Germany), Eric Bründermann (for the committee for accelerator
physics (KfB) in Germany), Denise Völker (as head of the DESY Sustainability
group), Sonja Kleiner and Luisa Ulrici (as leaders of the CERN Environment
group), Frank Schlünzen (for computing inputs), and Maria Zeitz (from
KlimAktiv). We thank KlimAktiv especially for the permission to reference and
forward to the UBA carbon calculator for the private footprint. The authors of
this paper take responsibility for any errors in this paper.

##  Appendix A Digitisation of CERN scope 1 emissions

Numbers from the digitisation of the figure: _CERN scope 1 emissions for
2017–2022 by category_ , from page 18 of the CERN environmental report
2021–2022 [20], reproduced in Figure 3, can be found in Table 11. The total
scope 1 emissions from 2022 determined with PlotDigitizer [22] agree within
$0.05\text{\,}\%$ with the numerical value for the total 2022 scope 1
emissions stated in the environmental report. Given resolution precision, this
is considered sufficiently close.

Figure 3: CERN scope 1 emissions for 2017–2022 by category [20]. The category _Other_ includes air conditioning, electrical insulation, emergency generators and the fuel consumption of the CERN vehicle fleet. Category | Cumulative emissions [$\mathrm{tCO}_{2}\mathrm{e}$ ]
---|---
LHC experiments: Particle detection | $101\,981\text{\,}$
LHC experiments: Detector cooling | $152\,444\text{\,}$
Other experiments | $172\,923\text{\,}$
Heating (gas+fuel) | $181\,857\text{\,}$
Other | $184\,090\text{\,}$
Total (specified in the report) | $184\,173\text{\,}$
Table 11: Digitised values for 2022 of the CERN scope 1 emissions, extracted
with PlotDigitizer [22] from Figure 3. The values correspond to the upper edge
of the bar including the specified category. The value for _Other_ should
therefore correspond to the total scope 1 emissions value specified in the
CERN environmental report 2021–2022 [20], listed under _Total (specified in
the report)_ , which - given resolution precision - is sufficiently close.

##  Appendix B Emissions from procurement

Procurement is a major source of emissions, in particular for organisations
such as a university, whose core functionalities: research, teaching and
knowledge transfer, can be assigned to the services sector.

The procurement-related emissions by the University of Freiburg, discussed in
Section 4.1 and obtained from the environmental report 2019/2020 [34], are
based on procurement data from 2017, analysed in a master thesis [54], which
is unfortunately not available online. A subsequent report is accessible in
Ref. [55], for which the relevant figure has been reproduced. Figure 4
displays the scope 3 procurement distribution of the categories _Goods and
services_ and _Capital goods_ , contained in Ref. [55]. The combined emissions
listed as do not exactly equal the value of , quoted in the environmental
report 2019/2020 by the University of Freiburg [34], but are very close. The
displayed split-up can therefore be seen as representative of the value,
quoted in the environmental report 2019/2020.

Figure 4: Distribution of scope 3 greenhouse gas emissions of the two
categories _Goods and services_ and _Capital goods_ for the University of
Freiburg in 2017 [55]. _Construction_ is listed as separate scope 3 category
for comparison, but it is unclear which category in the environmental report
2019/2020 [34] this has been added to.

Largest contributors to the emissions from procurement are the categories:
_Chemicals_ (), _Medical & Precision Instruments_ (), _Furniture & Goods
n.e.c._ (), and _Machinery n.e.c._ (), which amount to about $64\text{\,}\%$
of the procurement-related emissions.

The large number of categories gives an idea of how difficult it is to address
the reduction of procurement-related emissions; due to its large overall
contribution, it remains critical though. Two approaches are possible for this
purpose: demand management by the university, and _green_ procurement. The
choice of the best approach might differ depending on the category.

Details on procurement-related emissions by CERN are provided in the
environmental report 2021–2022 [20]. The figure _Emissions by procurement
family 2021–2022_ from page 23 of the CERN environmental report 2021–2022 [20]
is reproduced in Figure 5.

Figure 5: Emissions by procurement family 2021–2022 [20]. The category _Other_
includes office supplies, furniture, transport, handling and vehicles;
centralised expenses and codes for internal use; particle and photon
detectors; health, safety and environment; optics and photonics.

The largest contributors to procurement-related emissions in 2022 are the
following categories: _Civil engineering, building and technical services_ ,
_Permanent services on the CERN site_ , and _Mechanical engineering and raw
materials_. These could be related to construction regarding buildings or
machinery, which can be seen either as part of the infrastructure of the
accelerator complex, usual construction and maintenance of buildings of an
organisation, or engineering work, which could also be performed at a
university. It is not possible to explicitly assign the procurement categories
to the footprints of accelerators, experiments, upgrade activities, or CERN as
an institute.

A direct comparison of the procurement categories to those of the University
of Freiburg is not possible.

##  Appendix C Emissions for various transportation modes

The German Federal Environtment Agency (_Umweltbundesamt_ \- UBA) provides a
comparison of $\mathrm{CO}_{2}$ emissions for various modes of transport per
person per kilometre in a table on their webpage [52]. This table represents
the data collected for 2022 and is reproduced in Table 12. For the calculation
of the average emission values, an average occupancy is also estimated to
obtain a comparable value for an individual across all modes of transport.

For the first version of the Kyf calculator, only long-distance trains
(_Eisenbahn, Fernverkehr_), long-distance buses (_Linienbus, Fernverkehr_) and
personal car (_Pkw_) were considered. The $\mathrm{CO}_{2}$ emissions for
intra-Europe and transcontinental flights were obtained from Ref. [11] in
order to have the emission values corresponding to the duration of the flight
rather than the distance covered by it. This was considered to be more user-
friendly for the Kyf calculator. Other modes of transportation listed in Table
12 are primarily relevant for short-distance commute and were therefore not
incorporated in the first version of the Kyf calculator. These could
potentially be included in future iterations.

Table 12: Comparison of average $\mathrm{CO}_{2}$-equivalent emissions for
various modes of transport in Germany for 2022 listed in [52]. For the first
version of the Kyf calculator, only long-distance trains (_Eisenbahn,
Fernverkehr_), long-distance buses (_Linienbus, Fernverkehr_) and personal car
(_Pkw_) were used.

## References

  * [1] S. Arrhenius, _XXXI. On the influence of carbonic acid in the air upon the temperature of the ground_, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 41 (251) (1896) 237–276.  doi:10.1080/14786449608620846.
URL https://doi.org/10.1080/14786449608620846

  * [2] UC San Diego and SCRIPPS Institution of Oceanography, _The Keeling Curve - Full Record_, accessed: 2023-10-22 (2023).
URL https://keelingcurve.ucsd.edu

  * [3] D. Lüthi, M. Le Floch, B. Bereiter, et al., _High-resolution carbon dioxide concentration record 650,000–800,000 years before present_ , Nature 453 (2008) 379–382.  doi:https://doi.org/10.1038/nature06949.
  * [4] Copernicus Climate Change Service/ECMWF, _Surface air temperature for December 2023_, accessed: 2024-02-09 (2023).
URL https://climate.copernicus.eu/surface-air-temperature-december-2023

  * [5] Intergovernmental Panel on Climate Change (IPCC), Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 2023, Ch.  _Summary for Policymakers_ , pp. 3–32.  doi:10.1017/9781009157896.001.
  * [6] United Nations, _Paris Agreement_, United Nations Treaty Series 3156 (2015) 79.
URL
https://treaties.un.org/doc/Treaties/2016/02/20160215%2006-03%20PM/Ch_XXVII-7-d.pdf

  * [7] United Nations, _Global issues: Population_, accessed: 2023-10-22 (2023).
URL https://www.un.org/en/global-issues/population

  * [8] K. Schächtele, H. Hertle, _Die $\mathrm{CO}_{2}$ Bilanz des Bürgers - Recherche für ein internetbasiertes Tool zur Erstellung persönlicher CO2 Bilanzen_, Umweltbundesamt Förderkennzeichen: 206 42 110 (2007).
URL
https://www.umweltbundesamt.de/sites/default/files/medien/publikation/long/3327.pdf

  * [9] S. Schunkert, J. Siewert, P. Pitz, et al., _Der UBA- $\mathrm{CO}_{2}$-Rechner für Privatpersonen - Hintergrundinformationen_, Umweltbundesamt Forschungskennzahl: 3718 16 313 0 (2022).
URL
https://www.umweltbundesamt.de/sites/default/files/medien/479/publikationen/texte_97-2022_der_uba-
co2-rechner_fuer_privatpersonen.pdf

  * [10] young High Energy Physicists Association (yHEP), _Know your footprint_, accessed: December 2023.
URL https://yhep.desy.de/sustainability/

  * [11] Umweltbundesamt, _Carbon Calculator_, accessed: 2023-11-01 (2023).
URL https://uba.co2-rechner.de/en_GB/

  * [12] _LHC Machine_ , JINST 3 (2008) S08001.  doi:10.1088/1748-0221/3/08/S08001.
  * [13] Home — cern, accessed: 2024-02-09.
URL https://home.cern/

  * [14] ATLAS Collaboration, _The ATLAS Experiment at the CERN Large Hadron Collider_ , JINST 3 (2008) S08003.  doi:10.1088/1748-0221/3/08/S08003.
  * [15] CMS Collaboration, _The CMS Experiment at the CERN LHC_ , JINST 3 (2008) S08004.  doi:10.1088/1748-0221/3/08/S08004.
  * [16] ALICE Collaboration, _The ALICE experiment at the CERN LHC_ , JINST 3 (2008) S08002.  doi:10.1088/1748-0221/3/08/S08002.
  * [17] LHCb Collaboration, _The LHCb Detector at the LHC_ , JINST 3 (2008) S08005.  doi:10.1088/1748-0221/3/08/S08005.
  * [18] CERN, _CERN Environment Report – Rapport sur l’environnement 2017–2018_ (2020).  doi:10.25325/CERN-Environment-2020-001.
URL https://doi.org/10.25325/CERN-Environment-2020-001

  * [19] CERN, _CERN Environment Report – Rapport sur l’environnement 2019–2020_ (2021).  doi:10.25325/CERN-Environment-2021-002.
URL https://doi.org/10.25325/CERN-Environment-2021-002

  * [20] CERN, _CERN Environment Report – Rapport sur l’environnement 2021–2022_ (2023).  doi:10.25325/CERN-Environment-2023-003.
URL https://doi.org/10.25325/CERN-Environment-2023-003

  * [21] LHCb Collaboration, Framework TDR for the LHCb Upgrade II: Opportunities in flavour physics, and beyond, in the HL-LHC era (2021).
  * [22] Plotdigitizer: Version 3.1.5 (2024).
URL https://plotdigitizer.com

  * [23] ALICE Collaboration, _Collaboration Map_, accessed: 2024-01-05 (2024).
URL https://alice-collaboration.web.cern.ch/collaboration/map

  * [24] LHCb Collaboration, _The LHCb Collaboration_, accessed: 2024-01-05 (2021).
URL https://lhcb-outreach.web.cern.ch/collaboration/

  * [25] CMS Collaboration, _People Statistics_, accessed: 2024-01-05 (2022).
URL https://cms.cern/index.php/collaboration/people-statistics

  * [26] ATLAS Collaboration, _The Collaboration_, accessed: 2024-01-05 (2024).
URL https://atlas.cern/Discover/Collaboration

  * [27] DESY, _First Sustainability report_ (2022).
URL https://nachhaltigkeit.desy.de/sustainability_report/index_eng.html

  * [28] DESY, _Energy monitoring_, accessed: 2024-01-07 (2023).
URL
https://nachhaltigkeit.desy.de/energy_management/energy_monitoring/index_eng.html

  * [29] Electricity maps, _Germany_, accessed: 2024-01-07 (2023).
URL https://app.electricitymaps.com/zone/DE

  * [30] European Organisation for Astronomical Research in the Southern Hemisphere, _ESO Annual Report 2020_, accessed: 2023-10-16 (2021).
URL https://www.eso.org/public/products/annualreports/ar_2020/

  * [31] European Organisation for Astronomical Research in the Southern Hemisphere, _ESO Annual Report 2021_, accessed: 2023-10-16 (2022).
URL https://www.eso.org/public/products/annualreports/ar_2021/

  * [32] European Organisation for Astronomical Research in the Southern Hemisphere, _ESO Annual Report 2022_, accessed: 2023-10-23 (2023).
URL https://www.eso.org/public/products/annualreports/ar_2022/

  * [33] European Southern Observatory, _About ESO_, accessed: 2024-01-03 (2023).
URL https://www.eso.org/public/about-eso/

  * [34] University of Freiburg, _Umweltbericht 2019/2020_ (2021).
URL https://www.nachhaltige.uni-
freiburg.de/de/umweltberichte/umweltbericht-2019-2020-2

  * [35] Leibniz University Hannover, _Umweltbericht 2017-2019_ (2021).
URL https://www.uni-
hannover.de/fileadmin/luh/content/webredaktion/universitaet/publikationen/umweltbericht/umweltbericht_17_19.pdf

  * [36] Technische Universität Dresden, _Certification for the district heating system from enercity AG Hannover_ (2022).
URL https://www.enercity.de/assets/cms/enercity-
de/Privatkunden/Produkte/Waerme/Fernwaerme/Downloads/FW309-6.pdf

  * [37] GUTcert, _Vergleich des $\mathrm{CO}_{2}$-Fußabdrucks von Mineral- und Trinkwasser_ (2020).
URL https://atiptap.org/files/studie_gutcert_pcf_wasser.pdf

  * [38] CERN Annual Personnel Statistics 2022CERN Annual Personnel Statistics 2022 (2022).
URL https://cds.cern.ch/record/2858688

  * [39] B. Bruers, M. Cruces, M. Demleitner, et al., Resource-aware research on universe and matter: Call-to-action in digital transformation (2023).  arXiv:2311.01169.
  * [40] Nvidia cuda cores explained, accessed: 2024-03-04.
URL https://www.techcenturion.com/nvidia-cuda-cores/

  * [41] Pue: A comprehensive examination of the metric, accessed: 2024-01-16 (2014).
URL https://www.thegreengrid.org/en/resources/library-and-
tools/20-PUE:-A-Comprehensive-Examination-of-the-Metric

  * [42] Gesetz zur steigerung der energieeffizienz in deutschland, effective date: 18 November 2023.
URL https://www.gesetze-im-internet.de/enefg/BJNR1350B0023.html

  * [43] Hpe apollo (hawk), accessed: 2024-01-24.
URL https://www.hlrs.de/solutions/systems/hpe-apollo-hawk

  * [44] F. Schluenzen, Computing cluster power consumption data, private communication (2023).
  * [45] How much electricity does the a100 gpu use?, accessed: 2024-01-16.
URL https://wifocusonenergy.com/optimizing-power-consumption-understanding-
the-nvidia-a100s-energy-usage

  * [46] Panda dashboard, accessed: 2024-01-16.
URL https://bigpanda.cern.ch/dash/region/

  * [47] Panda system python api reference, accessed: 2024-01-16 (2020).
URL https://panda-wms.readthedocs.io/en/latest/client/rest.html

  * [48] Microsoft onedrive, https://www.microsoft.com/en-us/microsoft-365/onedrive/online-cloud-storage.
  * [49] Google drive, https://www.google.com/drive/.
  * [50] Microsoft onedrive emisions impact dashboard, https://www.microsoft.com/en-us/sustainability/emissions-impact-dashboard.
  * [51] Google drive footprint calculator, https://cloud.google.com/carbon-footprint.
  * [52] Umweltbundesamt, Emissionsdaten, accessed: 2024-02-12 (2023).
URL https://www.umweltbundesamt.de/themen/verkehr/emissionsdaten

  * [53] UBA CO2-Rechner für Veranstaltungen (2024), accessed: 2024-03-02 (2024).
URL https://uba-event-free.co2-rechner.pro/de_DE/

  * [54] M. Eichler, _Evaluating Environmental Impacts of University Procurements – An Environmentally Extended Multiregional Input Output Analysis of Albert-Ludwigs-Universität Freiburg_ (2020).
  * [55] S. Pauliuk, M. Eichler, B. E. Durán, et al., _Treibhausgasbilanz der Universität Freiburg im Breisgau 2017_, Industrial Ecology Freiburg (IEF) Working Paper 1 (2021).  doi:10.6094/UNIFR/176419.
URL https://www.blog.industrialecology.uni-freiburg.de/wp-
content/uploads/2021/03/Klimabilanz_Uni_Freiburg_IEF_Working_Paper_1_2021.pdf