# Syrupy Mouthfeel and Hints of Chocolate – Predicting Coffee Review Scores
using Text Based Sentiment

Christopher Lohse  Jeroen Lemsom  Athanasios Kalogiratos

###### Abstract

This paper uses textual data contained in certified (q-graded) coffee reviews
to predict corresponding scores on a scale from 0-100. By transforming this
highly specialized and standardized textual data in a predictor space, we
construct regression models which accurately capture the patterns in
corresponding coffee bean scores.

##  1 Motivation

Coffee is one of the most traded goods worldwide – 2021 about 141 Million
coffee bags 60 Kg each have been sold worldwide (ICO, 2022). To maintain a
uniform standard in the assessment and therefore also the price of coffee, the
Speciality Coffee Association introduced the so called q-grading system to
rank coffee beans. The ranking is conducted by licensed coffee critiques—the
so called q graders. To grade a coffee, q-graders taste the coffee at a
tasting event. The coffees during these events are always brewed and served in
the same way and scored by the q-graders based on the Speciality Coffee Taster
Wheel (see Figure 5) on a scale from 0-100 (SCA, 2022). To support the score,
the q-grader writes a short report with domain specific vocabulary to
elaborate on their experience. Given the fact that the vocabulary is highly
standardised, and that the coffees should be evaluated in a similar fashion,
our hypothesis is that text based sentiment in q-graders reviews can be used
to predict the corresponding scores. This would allow the Speciality Coffee
Association to perform anomaly detection on coffee bean reviews and to verify
whether the q-graders give scores which are in line with the standardised q
system.

##  2 Dataset

For testing our hypothesis, we generate our own dataset by using employing web
crawling techniques.

###  2.1 Dataset Generation

The data set is based on roughly 6,000 coffee bean reviews published on the
website Coffereviews going back to 1997. All of these reviews are scored with
the q-grading scale (Coffee Review, 2021).

###  2.2 Dataset Cleaning

To clean the dataset, we use the python library
beautifulSoup111https://www.crummy.com/software/BeautifulSoup/ to parse the
cleartext from the roughly one million lines of HTML texts of the pages which
have been downloaded using the python
requests222https://requests.readthedocs.io/en/latest/ library before.

After cleaning the dataset, we have two features in the dataset; one feature
$i$ which is the text of the review and the corresponding score $y_{i}$ which
is the target variable for the prediction. During the cleaning process, we had
to drop $2$ points from the dataset since they did not have a score.
Additionally, we had to remove roughly 1000 feature, which had an empty text –
resulting in 4985 unique features in our dataset.

Figure 1 shows that most of the scores in our dataset are between 86 and 97.
This implies that most of the reviewed coffee beans receive the qualification
‘speciality coffee’, which requires a score of 85 out of 100 points
(Poltronieri & Rossi, 2016).

Figure 1: Boxplot of Coffee Bean Scores

###  2.3 Feature Engineering

To capture the text more accurately, we decided to remove t stop words from
the reviews; stop words are words which occur very often in every text, e.g.
and/or and therefore do not have a high relevance of a text’s sentiment. For
the stop words removal, we used the standard English stop word set from the
python library NLTK.333https://www.nltk.org/ As input feature for our model we
take unigrams of words – which are just single words in the text and bigrams
which are the combination of two words occurring in the texts. This leads to a
significant larger number of features for models which have bigrams as their
input. (5111 features for unigram and 53341 for bigrams)

###  2.4 Data Analysis

Figure 2: Wordcloud with the most frequent bigrams and unigrams

Figure 2 shows the most frequent unigrams and bigrams contained in the text
reviews after the stopword removal – this illustrates the highly specific
domain vocabulary with terms such as ‘hint’, ‘note flower’ and ‘syrupy
mouthfeel’.

##  3 Methodology

We compare a selection of different machine learning models to predict coffee
bean scores $y_{i}$ which range between 0 and 100 for coffee bean $i$, where
$i\in\\{1,...,N\\}$ based on the sentiment in written reviews. The performance
of the model specifications in this section will be compared against a naive
predictor, which always predicts the average score.

###  3.1 Bag of Words Regression

Our first model is the bag of words regression which can be represented as

$y_{i}=\alpha+\beta_{1}x_{i,1}+\beta_{2}x_{i,2}+...+\beta_{m}x_{i,m},$ (1)

where $y_{i}$ is the numerical score corresponding to review $i$. Here,
$x_{i,j}$ equals the number of times term $j$ appears in review $i$. Moreover,
$\alpha$ defines the intercept and $\beta_{j}$ reflects the sentiment
corresponding to term $j$. If $\beta_{j}$ is positive, an occurrence of term
$j$ in a review will lead to an increase of the score. On the other hand, when
$\beta_{j}$ is negative, the term carries a negative sentiment.

###  3.2 TF-IDF Regression

####  3.2.1 Without Regularisation

In contrast to the bag of words regression discussed in Section 3.1, the TF-
IDF regression uses transformed predictors $z_{i,j}$. The Term Frequency (TF)
of term $j$ in review $i$ scales the occurrences of term $i$ by the total
number of terms counted in review $j$. This can be represented as

$TF_{i,j}=\frac{\sum_{j}x_{i,j}}{\sum_{i}\sum_{j}x_{i,j}}.$ (2)

The Inverse Document Frequency (IDF) quantifies the uniqueness of term $j$
across all documents $i\in\\{1,...,N\\}$. The IDF is defined as

$IDF_{i,j}=log\left(\frac{N}{1+\sum_{j}I(i,j)}\right),$ (3)

where $N$ denotes the total number of reviews, while indicator $I(i,j)=1$ if
term $i$ appears in document $j$ and $I(i,j)=0$ otherwise. The transformed
predictor $z_{i,j}$ is then constructed as

$z_{i,j}=TFIDF_{i,j}=TF_{i,j}IDF_{i,j}.$ (4)

Then the TF-IDF regression is specified as

$y_{i}=\alpha+\beta_{1}z_{i,1}+\beta_{2}z_{i,2}+...+\beta_{m}z_{i,m}.$ (5)

####  3.2.2 With Ridge Regularisation

The coefficients $\boldsymbol{\beta}=[\beta_{1},..,\beta_{m}]$ in Equation 5
is calculated by minimizing the mean squared error loss cost function

$\frac{1}{p}\sum_{i=1}^{p}(\boldsymbol{\beta}^{\boldsymbol{T}}\boldsymbol{z_{i}}-y_{i})^{2}.$
(6)

Note that $\boldsymbol{\beta^{T}}$ is a (1 x m) dimensional vector, while
$\boldsymbol{{z}_{i}}$ is (m x 1) dimensional444Similarly the coefficients in
the bag of words regression in Equation 1 in Section 3.1 are also calculated
by minimizing the mean squared error loss. In that case, one needs to
substitute $\boldsymbol{z_{i}}$ in Equation 6 by $\boldsymbol{x_{i}}$. A
number of p (p ¡ N) training observations is used to train the model and
obtain the coefficients in $\boldsymbol{\beta}$. When including a ridge
penalty term, the loss function becomes

$\frac{1}{p}\sum_{i=1}^{p}(\boldsymbol{\beta}^{\boldsymbol{T}}\boldsymbol{z_{i}}-y_{i})^{2}+\frac{1}{2C}\sum_{j=1}^{m}\beta_{j}^{2}.$
(7)

Adding a ridge penalty term in our optimization could prevent overfitting in
case the number of predictors $m$ is very large. An appropriate hyperparameter
$C$ should be selected based on k-fold cross validation.

###  3.3 K-NN Regression

In the K-NN Regression, $\hat{y}$ corresponding to test observation $(y,z)$
will be based on the average score of the $k$ closest points measure by the
Euclidean distance measure in a $m$ dimensional space (which is the total
number of terms in our training set). For the K-NN regression, independent
variables $z_{j}$ are equal to the independent variables in the TF-IDF
regression. The Euclidean distance from $(y,z)$ to a training point
$(y^{(i)},z^{(i)})$ is

$d(z^{(i)},z)=\sqrt{\sum_{j=1}^{m}(z_{j}^{(i)}-z_{j})^{2}}$ (8)

If $N_{k}$ contains the $k$ observations closest to $z$, then

$\hat{y}=\frac{\sum_{i\in N_{k}}y^{(i)}}{k}.$ (9)

##  4 Experimental Setup

###  4.1 K-Fold Cross Validation

The TF-IDF Ridge regression in Section 3.2.2 and the K-NN Regression in
Section 3.3 both contain a hyperparameter. These hyperparameters will be
optimized to obtain the best value; the best model will then be considered in
the benchmarking. These hyperparameters are optimized by performing k-fold
cross validation on our training set with $kf=5$ folds. This means that the
training data is split into $kf$ folds; then $kf$ models are trained — each
time with a different fold left out.

###  4.2 Performance Measures

The performance of a model is quantified using the Mean Squared Error (MSE)
measure, which can be represented as

$MSE=\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2},$ (10)

where $y_{i}$ is the actual score and $\hat{y}_{i}$ is the predicted score for
review $i$. A characteristic of the Mean Squared Error (MSE) over other
performance measures is that it penalises larger mistakes more heavily. The
MSE will be used to decide upon the hyperparameter values of the TF-IDF Ridge
Regression and K-NN regressions in our K-Fold cross validation setup.

When comparing our full range of models, we additionally compute the MAE (Mean
Absolute Error) measures

$MAE=\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|.$ (11)

The Mean Squared Error is used to select the optimal hyperparameters when
performing k-fold cross validation in Section 4.1 and the compare the out-of-
sample performance of our various model specifications.

##  5 Evaluation

###  5.1 Hyperparameter Optimization

The optimal hyperparameter values $k$ for the unigram and bigram K-NN
regressions are both 11, while the optimized value $C$ for the unigram and
bigram Ridge TF-IDF Regressions are equal to 1 and 10 respectively. The grids
of values which are used for the in-sample optimization of the hyperparameters
are shown in the Appendix and are accompanied by Figure 5 which shows a plot
containing the MSE values for different hyperparameter values. It becomes
apparent in the plot that both of the ridge regression models have a lower
standard deviation; therefore, one conclude that these models yield into more
stable results. Moreover, the unigram ridge model has again a lower standard
deviation than the bigram model. We note that the unigram model of the Ridge
regression is better with higher penalisation than the bigram model.

###  5.2 Performance

Table 1 shows the out-of-sample performance of the models discussed in Section
3.

Table 1: Out-of-sample performance of all models. Best results are printed in bold Model | MSE | MAE
---|---|---
Naive Mean | 6.624 | 1.644
BoW Regression Unigram | 3.794 | 1.277
BoW Regression Bigram | 2.980 | 1.077
TF-IDF Regression Unigram | 2.990 | 1.192
TF-IDF Regression Bigram | 2.581 | 1.046
Ridge TF-IDF Unigram | 1.508 | 0.864
Ridge TF-IDF Bigram | 2.582 | 1.034
KNN Regression Unigram | 2.277 | 1.038
KNN Regression Bigram | 3.472 | 1.111

The K-NN Regressions, which take the average score of the closest 11
observations in the training set, improves upon the Naive Mean benchmark which
always predicts the mean score of the scores in the training set. Furthermore,
Table 1 shows that also our other regression specifications achieve a better
out-of-sample performance than our benchmark model. Based on both the MSE and
MAE performance measures, the Ridge TF-IDF regression using unigrams turns out
to be most effective in predicting coffee scores based on written reviews.
This finding suggests that it is beneficial to shrink the weights
corresponding to non-significant terms using the ridge penalty term. Examples
of score predictions based on textual reviews from the Ridge TF-IDF Regression
Unigram model are illustrated in Table 2 in the Appendix. The Table contains
10 reviews randomly drawn form the test set and show that the predictions are
relatively accurate.

###  5.3 Interpretation

Figure 3 presents the bigrams with the most positive and negative sentiments
in the Ridge TF-IDF bigram regression.

Figure 3: Bigrams with Strongest Positive and Negative Sentiment

The results indicate that a q-graders experience of the ‘mouthfeel’ could
strongly affect the resulting score. In particular, ‘syrupy’ or ‘buoyant’
mouthfeels tend to describe higher-ranked coffees, while the bigram ‘leanish
mouthfeel’ carries a strong negative sentiment. Besides, q-graders appreciate
coffees with a ‘long finish’ and fruity notes such as black currant and
passion fruit carry a strong positive sentiment. On the other hand,
descriptions of a ‘salty’ or ‘meaty’ flavor decrease the score of a coffee
bean. In contrary to a ‘long finish’, a ‘bit finish’, which is used to
describe a ‘short finish’, carries negative sentiment.

##  6 Conclusion

By creating a wordcloud of the terms in the reviews and by highlighting the
terms with the strongest positive and negative sentiments in a penalized TF-
IDF regession, we show that the vocabulary used in the coffee reviews is
highly specialized.

Our findings indicate that textual data from q-graded coffee bean reviews has
strong predictive value when forecasting corresponding scores on a scale from
0-100. In particular, we find that both a bag of word regression and a
regression with TF-IDF adjusted predictors offer significant advantages over a
naive mean benchmark predictor. Additionally, we find that adding an
(optimized) ridge penalty term to the TF-IDF regression leads to an improved
out-of-sample performance.

In conclusion, the unigrams and bigrams extracted from the textual coffee
reviews show to be relevant independent variables when predicting coffee
review scores in a regression context, due to the highly standardized
terminology used by certified q-graders. We encourage further research to
assess the predictive performance of our models in combination with a more
thorough pre-processing of the textual data, including techniques such as
lemmatization, tokenization and stemming.

## References

  * Coffee Review (2021) Coffee Review.  Coffee review about us, January 2021.  URL https://www.coffeereview.com/our-story.  [Online; accessed 29. Nov. 2022].
  * ICO (2022) ICO.  Trade statistics tables, November 2022.  URL https://www.ico.org/trade_statistics.asp?section=Statistics.  [Online; accessed 29. Nov. 2022].
  * Poltronieri & Rossi (2016) Poltronieri, P. and Rossi, F.  Challenges in specialty coffee processing and quality assurance.  _Challenges_ , 7(2):19, 2016.
  * SCA (2022) SCA.  Specialty coffee association about us, November 2022.  URL https://sca.coffee/about.  [Online; accessed 29. Nov. 2022].

## Appendix

### Flavour Wheel

Figure 4: Speciality Coffee Taster Wheel555Retrieved from
https://notbadcoffee.com/flavor-wheel-en/

### Hyperparameter Optimization

For hyperparameter $C$ in the ridge TF-IDF regression, we consider the grid
$[0.0001,0.001,0.01,0.1,1,10,20]$. For $k$ in the K-NN regression, we consider
the grid
$[1,11,21,51,101,201]$666For the K-NN regression it is not required to
consider exclusively odd values in the grid. This would only be necessary for
classification problems.

(a) Ridge Unigram

(b) Ridge Bigram

(c) KNN Unigram

(d) KNN Bigram

Figure 5: Hyperparameter Optimization

### Examples on Test Set

Table 2: 10 Example Predictions: numerical predictions are rounded to the
nearest integer value

text | true | pred
---|---|---
| Luscious, deeply sweet, sumptuous. Blackberry, lavender, marjoram, roasted
cacao nib, grapefruit zest aroma cup.
---
Rich, juicy acidity; smooth, syrupy mouthfeel. The long, flavor-saturated
finish zesty sweet, offering surprise appearance
tropical fruit notes (coconut) resonate long while.
97.0 | 95.0
| Delicate, intricate, expressive. Almond butter, plumeria, roasted cacao nib,
myrrh, loquat aroma cup.
---
Lively sweet-tart acidity; silky mouthfeel. The sweet-toned, resonant finish
centers around plumeria
cocoa, suggestions myrrh short finish loquat long.
94.0 | 94.0
| Crisply sweet, cocoa-toned. Baking chocolate, hazelnut, date, cedar,
molasses aroma cup.
---
Sweet structure gentle, round acidity; full, velvety mouthfeel.
The finish consolidates baking chocolate, hazelnut cedar.
92.0 | 91.0
| Delicate yet lush, gently bright.
---
Dried peach, ripe orange, candied walnut, hints fresh-cut cedar cinnamon aroma
cup.
Roundly balanced acidity; satiny, light-footed mouthfeel.
Walnut, cedar orange particular carry crisp sweet finish.
93.0 | 93.0
| High-toned, crisply sweet. Date, cashew butter, magnolia, grapefruit zest,
cedar aroma cup.
---
Sweetly tart structure brisk acidity; full, velvety mouthfeel.
The resonant finish leads notes date magnolia short, rounding cashew butter
grapefruit zest long.
92.0 | 92.0
| Suavely bright, fragrant, deep. Flowers, pear- peach-like fruit, honey,
pistachio-like nut aroma cup.
---
Rich acidity; full, syrupy mouthfeel. Flavor consolidates resonant finish
94.0 | 93.0
| Delicately complex: black tea, lemon-lime, spicy rose, moist pipe tobacco.
---
Gently bright acidity; lean silky mouthfeel. Flavor consolidates cocoa crisp
finish.
91.0 | 91.0
| Sweetly savory, vibrant, deep. Meyer lemon zest, dark chocolate, tamarind,
sandalwood, aromatic orchid aroma cup.
---
Sweet-tart structure bright, juicy acidity; satiny-smooth mouthfeel.
Flavor-saturated-finish leads notes tamarind lemon zest, rounding deep, dark
chocolate notes long.
93.0 | 95.0
| Richly sweet, deeply spice-toned. Toffee, plum blossom, tangerine zest,
almond butter, sandalwood aroma cup.
---
Juicy-sweet structure high-toned, balanced acidity; syrupy-smooth mouthfeel.
The finish centers around notes almond, tangerine zest sandalwood.
94.0 | 94.0
| Very sweet, gently bright, delicately fruit-toned. Lush flowers, cidery
apple tart, raspberry-like fruit aroma cup.
---
Softly bright acidity; silky lightly syrupy mouthfeel. Very sweet finish.
93.0 | 93.0


[1]Didier Barret

[1]Institut de Recherche en Astrophysique et Planétologie, 9 Avenue du Colonel
Roche, Toulouse, 31028, France [2]Centre National d’Etudes Spatiales, 18
Avenue Edouard Belin, Toulouse, 31401, France 3]Scalian, 14 Rue Paul Mesplé,
Toulouse, 31100, France 4]Istituto di Astrofisica e Planetologia Spaziali, Via
Fosso del Cavaliere 100, 00133, Roma, Italy 5]Université Paris Cité, CNRS,
CEA, Astroparticule et Cosmologie (APC), F-75013 Paris, France 6]NASA Goddard
Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, United States
7]SRON, Netherlands Institute for Space Research, Niels Bohrweg 4, 2333 CA
Leiden, The Netherlands 8]Département des Systèmes Basses Températures, CEA-
Grenoble, 17 avenue des Martyrs, 38054 Grenoble cedex 99, France 9]INAF,
Osservatorio di Astrofisica e Scienza dello Spazio, via Gobetti 93/3, 40129,
Bologna, Italy 10]Centre Spatial de Liège, Liège Science Park, Avenue du Pré-
Aily, 4031 Angleur, Belgium 11]INTA, Crtra de Ajalvir km 4, 28850, Torrejon de
Ardoz, Madrid, Spain

# Life Cycle Assessment of the Athena X-ray Integral Field Unit

<EMAIL_ADDRESS>Vincent Albouys  Jürgen Knödlseder  Xavier Loizillon
Matteo D’Andrea  Florence Ardellier  Simon Bandler  Pieter Dieleman  Lionel
Duband  Luc Dubbeldam  Claudio Macculi  Eduardo Medinaceli  François Pajot
Damien Prêle  Laurent Ravera  Tanguy Thibert  Isabel Vera Trallero  Natalie
Webb  *  *  [  [  [  [  [  [  [  [  [

###### Abstract

The X-ray Integral Field Unit (X-IFU) is the high-resolution X-ray
spectrometer to fly on board the Athena Space Observatory of the European
Space Agency (ESA). It is being developed by an international Consortium led
by France, involving twelve ESA member states, plus the United States. It is a
cryogenic instrument, involving state of the art technology, such as micro-
calorimeters, to be read out by low noise electronics. As the instrument was
undergoing its system requirement review (in 2022), a life cycle assessment
(LCA) was performed to estimate the environmental impacts associated with the
development of the sub-systems that were under the responsibility of the X-IFU
Consortium. The assessment included the supply, manufacturing and testing of
sub systems, as well as involved logistics and manpower. We find that the most
significant environmental impacts arise from testing activities, which is
related to energy consumption in clean rooms, office work, which is related to
energy consumption in office buildings, and instrument manufacturing, which is
related to the use of mineral and metal resources. Furthermore, business
travels is another area of concern, despite the policy to reduced flying
adopted by the Consortium. As the instrument is now being redesigned to fit
within the new boundaries set by ESA, the LCA will be updated, with a focus on
the hot spots identified in the first iteration. The new configuration,
consolidated in 2023, is significantly different from the previously studied
version and is marked by an increase of the perimeter of responsibility for
the Consortium. This will need to be folded in the updated LCA, keeping the
ambition to reduce the environmental footprint of X-IFU, while complying with
its stringent requirements in terms of performance and risk management.

###### keywords:

Life Cycle Assessment, X-ray Integral Field Unit, Athena, Environmental
footprint

##  1 Introduction

The Athena X-ray Integral Field Unit (X-IFU) is the high resolution X-ray
spectrometer [1], studied since 2015 for operating in the mid-30s aboard the
Athena space X-ray Observatory of the European Space Agency (ESA). Athena is a
versatile facility designed to address the Hot and Energetic Universe science
theme [2, 3, 4, 5, 6] that was selected in November 2013 by ESA’s Survey
Science Committee. Based on a large format array of Transition Edge Sensors
(TES), X-IFU will provide spatially resolved X-ray spectroscopy, with a
spectral resolution of 2.5 eV (up to 7 keV) over a hexagonal field of view of
5 arc minutes (equivalent diameter).

The X-IFU is a rather complex cryogenic instrument, involving very advanced
technologies, requiring a large amount of resources and a large number of
partners spread all across the world. X-IFU is being developed right at the
time where 6 out of 9 planetary boundaries are already transgressed [7] and
when it is becoming very clear that without an immediate and drastic reduction
of our greenhouse gas emissions (by at least 50% by 2030), across all sectors,
the Paris Agreement goal of limiting global warming to 1.5 degrees will not be
reached. X-IFU is also developed when the need for building new large
scientific infrastructures having major environmental impacts (beyond the sole
greenhouse gas emission) is being debated within the community [8], this very
same community that should lead by example, because of its awareness that
there is no planet B [9].

Forging a sustainable future for astronomy requires a systemic change, and
considering the environmental impact of our research projects becomes an
ethical responsibility. Taking up this responsibility, the X-IFU Consortium
has therefore decided to conduct a life cycle assessment (LCA) of all the
components that are under its responsibility with the aim to minimise the
environmental impacts related to their development.

The LCA was performed through a contract with SCALIAN, a specialized company
with heritage in evaluating the environmental impact of space projects. As at
the time this LCA was carried out there were very few science projects which
had been subject to a LCA (see however ref. [10] for the LCA of the Giant
Radio Array for Neutrino Detection (GRAND) experiment). On the other hand, LCA
studies exist for space systems in general [11], yet they do not provide any
insights into the environmental impacts of the development of scientific
payloads. Furthermore, publicly available data in the space sector are scarce
and the LCAs performed so far are not yet homogenized in goal and scope
definitions [12, 13]. Hence it is crucial to make LCA results publicly
available that will consolidate preliminary estimates of the environmental
impact of astronomical research facilities [8]. Since LCAs are new in the
field of astronomical research, we included in this paper a description of the
most important aspects of the LCA methodology, as defined by the ISO 14040
standard [14].

X-IFU entered its System Requirement Review (SRR) in June 2022, at about the
same time when ESA called for an overall X-IFU redesign (including the X-IFU
cryostat and the cooling chain), due to an unanticipated cost overrun of
Athena. While this redesign ultimately changed the configuration of the
instrument, and the list of components provided by the X-IFU Consortium, we
had already started our life cycle assessment on the original layout. Our
assessment hence excludes components that so far were under ESA
responsibility, such as the cryostat and cooling chain. For previous
incarnations of X-IFU, when the cryostat and cooling chain were in the
Consortium perimeter, see [15, 16, 17, 18, 19].

##  2 Method and tools

###  2.1 LCA methodology

LCA is a standardized method that is defined by the norm ISO 14040 [14] that
describes the principles and framework for life cycle assessment. A LCA is
conducted in three phases, comprising the definition of its goal and scope,
the life cycle inventory analysis (LCI) phase, and the life cycle impact
assessment (LCIA) phase. We dedicate a specific section to each of the phases
(cf. sections 3, 4 and 5, respectively). Each of the three phases is followed
by an interpretation phase, with the purpose to evaluate the implications of
the hypotheses taken and to check approximations, so that the reliability of
the results can be improved.

The goal and scope definition phase identifies the reason for the assessment,
its applications and the target audience as well as its stakeholders. It
specifies in particular the system function, the functional unit, the system
boundaries, as well as reference flows and key parameters. Specifically, the
functional unit is used to describe the quantified performance of a product or
service, and specifies the reference unit. Defining a proper functional unit
is fundamental when performing a comparative LCA, as it allows for the
comparison of different scenarios or configurations that all provide the same
function.

####  2.1.1 Life Cycle Inventory (LCI)

The LCI phase consists of making an inventory of the elementary input and
output flows of the system under study, involving collection and organization
of the required data. LCI distinguishes foreground and background data, where
the former describe the system technically, while the latter describe the
system environmentally. While foreground data are specific to the system under
study, background data are in general extracted from LCI databases, which in
our case comprise the ecoinvent database and space-system specific databases
provided by ESA and SCALIAN. Matching of foreground with background data is a
critical step of this phase, as relevant materials, products or processes may
not be available in the background databases, requiring sometimes
approximations or the development of dedicated LCI models.

####  2.1.2 Life Cycle Impact Assessment (LCIA)

Finally, in the LCIA phase, tools are used (in our case the SimaPro software)
to quantify environmental impacts according to different categories (see Table
LABEL:table:categories for the categories used in this study). This involves
choosing a method for impact computation (see section 2.1.2), which can
broadly be classified into mid-point and end-point computation methods. The
former are used to assess the effects of emitted pollutants and resources
consumed on a global scale during the life cycle, with flows of similar
effects being aggregated into intermediate impact categories. For example,
emissions of CO2 and CH4, which both contribute to global warming, can be
aggregated into a climate change mid-point indicator, expressed in units of kg
CO2 eq. End-point methods evaluate the potential damages that can be linked to
intermediate categories, aggregating for example impacts on human health,
natural resources, or biodiversity.

We used the Environmental Footprint 3.0 (adapted) V1.01 method that is
extensively used in aerospace industry and comprises 16 mid-point impact
indicators, which are summarized in Table LABEL:table:categories. The first
column specifies the name of the impact indicator, the second column its unit,
and the third column the detrimental effects on the environment that are
captured by the category. NMVOC stands for non-methane volatile organic
compounds, CTUh for comparative toxic unit for humans, and CTUe for
comparative toxic unit for ecosystems.

Table 1: Environmental impact indicators considered in the LCA of X-IFU, together with their units and their description. Indicator |  Unit |  Description
---|---|---
Climate change |  kg CO2 eq |  The emission of greenhouse gases into the atmosphere which absorbs and redirects heat towards earth surface. This leads to the rise of global temperature, oceans level, extreme climatic events (storm, droughts)…
Ozone depletion |  kg CFC11 eq |  Some gas compounds released in the atmosphere may produce an increase of Chloride and Bromine concentration in the stratosphere. This reduces stratospheric ozone concentration for several years or decades. The effect on human health and ecosystem is premature ageing from sun UVB exposure.
Ionising radiation |  kBq U235 eq |  The release of radioactive substances into the air and water can be inhaled or ingested by humans and ecosystems. This may lead to cancer, severe hereditary effects, long term damage on ecosystems.
Photochemical ozone formation |  kg NMVOC eq |  Creation of ozone in the troposphere (where it is not supposed to) due to the reactions between sun UV rays and VOC/nitrogen oxides emitted. This can lead to respiratory distress for mammals and reduction of photosynthesis for ground and water based plants.
Particulate matter |  disease incidences |  The emission of particular matter or their precursors (nitrous oxides, Sulphur oxides and ammonia) can trigger respiratory diseases.
Human toxicity, non-cancer |  CTUh |  Emission of chemicals into the environment, combined with the exposition of humans, taking into account absorbed dosis and risk potential. This leads to damage of human health.
Human toxicity, cancer |  CTUh |  Emission of chemicals into the environment, combined with the exposition of humans, taking into account absorbed dosis and risk potential. This leads to damage of human health.
Acidification |  mol H+ eq |  Emission into the atmosphere of substances with acidic effects (nitrogen oxides, Sulphur oxides, and ammonia) and deposition of these substances on the ground, that increase the acidity of soils and water. This leads to a decrease of biodiversity and bio-productivity.
Eutrophication, freshwater |  kg P eq |  Release of nitrous and phosphorous substances into the ecosystems. This increases the amount of nutrients available, which leads the proliferation of microorganisms that reduce the oxygen concentration and thus biodiversity.
Eutrophication, marine |  kg N eq |  Release of nitrous and phosphorous substances into the ecosystems. This increases the amount of nutrients available, which leads the proliferation of phytoplankton that reduce the oxygen concentration and thus biodiversity.
Eutrophication, terrestrial |  mol N eq |  Release of nitrous and phosphorous substances into the ecosystems. This leads to modification of plants’ nutritive balance, which decreases their productivity.
Ecotoxicity, freshwater |  CTUe |  Emission of chemicals into the environment combined to the exposition of freshwater species, taking into account absorbed dosis and vulnerability of each species. This leads to damage to ecosystems.
Land use |  Pt |  Conversion of land for agriculture, resource extraction or dwelling. This leads to physical changes upon the environment that can modify species distributions.
Water use |  m3 depriv. |  The use of water in a specific quantity and quality may trigger usage competition: agriculture, domestic consumption, fishing and ecosystems.
Resource use, fossils |  MJ |  The use of primary energy is mainly responsible for fossil resource depletion.
Resource use, minerals and metal |  kg Sb eq |  The extraction of mineral and metal resources reduce their availability for further use.

###  2.2 Normalisation, weighting and single score

LCIA results may be presented in absolute units (as specified in Table
LABEL:table:categories), normalised to a common reference, or weighted so that
different impact categories can be aggregated into a single impact score.
Normalising and weighting depends on the LCIA method chosen, which in our case
was the Environmental Footprint 3.0 (EF3.0) method (see section 2.1.2). The
normalisation factors used in this method are the environmental impacts caused
by an average human being, without considering geographical region, based on
the world population in 2010 [20].

Weighting consists of attributing to each impact category a unit rating that
represents the global environmental footprint of the studied system. In that
way, different impact categories can be aggregated into a single impact score.
Weightings are computed against several criteria, such as current state of
science or LCA practitioner. As such, weightings introduce a significant and
subjective uncertainty, and consequently, results should be interpreted with
care. For example, EF3.0 considers climate change as particularly important,
and hence associates a large weight to this category, while effects on
toxicity and biodiversity have a smaller weight. EF3.0 normalisation and
weighting factors that were adopted in this study are summarized in Table 2.

Indicator | Normalisation factor | Weighting factor
---|---|---
Climate change | 0.0001235 | 0.2106
Ozone depletion | 18.64 | 0.0631
Ionising radiation | 0.0002370 | 0.0501
Photochemical ozone formation | 0.02463 | 0.0478
Particulate matter | 1680 | 0.0896
Human toxicity, non-cancer | 4354 | 0.0184
Human toxicity, cancer | 59173 | 0.0213
Acidification | 0.01800 | 0.062
Eutrophication, freshwater | 0.6223 | 0.028
Eutrophication, marine | 0.05116 | 0.0296
Eutrophication, terrestrial | 0.005658 | 0.0371
Ecotoxicity, freshwater | 0.00002343 | 0.0192
Land use | 0.000001220 | 0.0794
Water use | 0.00008719 | 0.0851
Resource use, fossils | 0.00001538 | 0.0832
Resource use, minerals and metal | 15.71 | 0.0755
Table 2: Normalisation and weighting factors of the EF3.0 method that were
adopted in this study.

###  2.3 Tools

The LCA was conducted using the SimaPro software version 9.3.0.3, and the life
cycle inventory was matched with specific space-systems databases from ESA
(version 3) and SCALIAN, and the ecoinvent database version 3.7.1. The ESA and
the SCALIAN databases collect LCA information that was used as background
information for aviation and space-sector specific processes (including for
example space qualified PCBs and metals for the former, and test facilities
and logistics for the latter). The ecoinvent database was used for standard
industrial processes and materials, and served as background database for the
space-sector specific databases.

###  2.4 Data collection and quality

X-IFU is still in an early development phase, implying that the project is
still subject to changes in schedule and responsibility, which may lead to
revision of scope or assumptions in the future. Even though main technologies
have been defined, the supply chains are not always completely identified,
preventing the collection of accurate data on manufacturing processes and
intermediate transportation flows. Due to the unavailability of such primary
data, we relied in this study on secondary or generic data that were collected
through dedicated interviews with sub-system leaders.

To consider the uncertainties related to the data collection, we defined a
confidence scale to trace the quality of the foreground data, and its
correspondence to entries in the background database (see Table 3).

Foreground data accuracy | Score
---|---
No data provided | 0
Unreliable data, rough estimate, remote proxy | 1
Proxy estimate | 2
Reliable data based on expert assumption or literature | 3
Direct measurement | 4
Correspondence with background database entry | Score
No entry found that can be remotely linked to the flow data | 0
The entry is a proxy that could be representative (estimate) | 1
The entry is a proxy from the same process/material family | 2
Close or similar process/material | 3
Perfect match | 4
Table 3: Confidence scale used to classify the quality of the foreground data
and their correspondence with entries of the background database.

##  3 Goal and Scope definition

###  3.1 Goal of the assessment

The aim of the LCA is the identification of significant environmental impacts
arising from the activities of the X-IFU Consortium, with the goal to take
corrective actions for impact reductions while not jeopardizing the ultimate
success of the project. The X-IFU Consortium had already committed to reduce
significantly its global travel footprint [21], starting even in the pre-covid
era, but this LCA should go one step further, by identifying additional
actions that can lead to substantial reductions in the environmental impacts
of X-IFU Consortium activities.

###  3.2 Functional unit

The functional unit defined for this study is “Provision of all X-IFU
components to ESA according to requirements, except of the cryostat and
cooling chain, enabling spatially resolved high-resolution X-ray spectroscopy
aboard the Athena satellite during a nominal lifetime of 4 years, yet with
design rules enabling the mission to operate 10 years in orbit”.

###  3.3 Boundaries of the system

This LCA was restricted to components placed under the responsibility of the
X-IFU Consortium (in the version of the X-IFU instrument extensively described
in [1]), and illustrated in Figure 1, which represents a physical and
programmatic breakdown of the instrument. Accordingly, the focal plane
assembly and its internal components, such as the transition edge sensors, the
sub-kelvin cooler, the control and readout electronics, as well as ancillary
equipments, such as filter wheel and calibration assembly, are included in the
assessment. Other elements necessary to implement the X-IFU instrument, as
well as the Athena mission in general, such as the cryostat, the cooling chain
(with the exception of the sub-kelvin cooler), the payload module, the X-ray
telescope and other instruments, the spacecraft and the launch vehicle, and
related integration activities, were not considered in this study. Table 4
summarises the sub-systems that were considered in this study together with
the geographical locations of manufacturing that were used in the LCI
modelling.

Figure 1: The physical breakdown of the X-IFU instrument, highlighting its
main components, as well as the country responsible for the procurement [1].
Elements shown as in the perimeter of ESA were excluded from the LCA of X-IFU.
This breakdown was the baseline for the system requirement review held in
2022.

The LCA is hence limited to X-IFU Consortium activities, over a period
spanning the years 2015 to 2034, covering mission phases B-D, excluding later
phases that cover launch preparation, launch, operations, science activities,
and end-of-life processes. Specifically, the LCA determines the environmental
impacts of X-IFU Consortium deliverables from cradle to gate, where the gate
corresponds to the delivery of the X-IFU components to ESA for integration.
The assessment included the supply, manufacturing and testing of sub-systems,
as well as involved logistics and manpower.

Subsystem | Description | Country
---|---|---
2KCC HybCCE | Hybrid Cooler Electronics | France
Amp SQUID | Amplification SQUIDs | Finland
ApC | Aperture Cylinder | Belgium
CAS | Calibration Assembly | Netherlands
2K Core | 2 Kelvin core | France
CryoAC | Cryogenic Anticoincidence Detector | Italy
Test bench | Cryogenic test bench | France
DRE | Digital Readout Electronics | France
EBIT | Electron Beam Ion Trap | France
EP Algorithm | Event Processor Algorithm | Spain
FW | Filter Wheel | Switzerland
FPA | Focal Plane Assembly | Netherlands
ICU | Instrument Control Unit | Italy
SQUIDs | Multiplexing SQUIDs | USA
Simulator | Scientific simulator | Germany
SubK Cooler | Sub-Kelvin cooler | France
TES Array | Transition Edge Sensor Array | USA
TGSE | Thermal Ground Support Equipment | Spain
THF | Thermal filters | Italy
WFEE | Warm Front-End Electronics | France
Table 4: X-IFU sub-systems considered in this study.

##  4 Life Cycle Inventory

###  4.1 Data collection

Foreground data of the LCI were collected through one-to-one interviews with
subsystem project leaders or their representatives. The aim of these
interviews was to get a comprehensive view on the materials and processes, as
well as the necessary conditions, for subsystems manufacturing and testing.
While data collection was iterated for all subsystems, not all subsystems were
at the same definition or maturity level, leading to heterogeneous
uncertainties in the collected foreground data. The collected foreground data
were classified into the following four categories:

  * •

Manufacturing, comprising the manufacturing aspects of the instrument, such as
raw materials, parts, Commercial Off-The-Shelf (COTS) items, manufacturing
processes, number of intermediate models and packaging.

  * •

Logistics, covering all steps necessary to ensure subsystems transportation to
testing locations and the X-IFU integration site at Centre National d’Etudes
Spatiales (CNES) premises.

  * •

Testing, including information on the use of testing equipment that can
involve energy or matter, such as the use of clean rooms, vacuum chamber,
shakers or cryogenic coolers.

  * •

Manpower, covering the number of people involved in the activities and their
travelling due to X-IFU Consortium.

In the following, the data collection for these four categories are explained
in some detail. Uncertainties in the data collection, as discussed below, were
handled through the confidence scale described in section 2.4.

###  4.2 Manufacturing

The accuracy of manufacturing data that were available depend on the maturity
level and development stage of the subsystem, eventually due to design
evolution related to project constraints, or due to an undefined manufacturing
process that may depend on the yet to be decided industrial supplier.

As illustration for material used, information may vary between “Aluminum
6062” (very detailed), “Aluminum 6XXX family” (detailed), “Aluminum box”
(rough), and “Metal” (uncertain). Some materials were also described as COTS
so only partial public information could be recovered.

The accuracy with which the manufacturing processes are known was also
variable. For example, information on surface treatment may vary between “heat
treatment followed by black anodizing” (accurate), “surface treatment”
(approximate), and “no information”.

The buy-to-fly ratio, which is the amount of material bought versus the amount
of material remaining on the final product, is also very variable, and depends
very much on the maturity of the system to build. For example, buy-to-fly
ratios may vary between “50 kg bought for 2 kg remaining after milling”
(accurate), “around twice the mass” (rough), and “no information”. Most of the
buy-to-fly ratios were actually unknown, and considered as being equal to one
when no information was provided.

Data on packaging were rarely available as they are highly dependent on the
supplier. Based on feedback from other LCAs of aerospace components, as
packaging impact is often low ($<5\%$ of overall LCA single score), it was not
included nor extrapolated in this first iteration of the LCA. There is
significant room for improvement in the next LCA iteration as resource use
contributes significantly to the environmental impacts of space projects.

###  4.3 Logistics

The necessary transportation steps to move subsystems from one location to
another or to the final integration site have been estimated even though they
may evolve with time. Mostly air and road transportation were identified at
this stage.

###  4.4 Testing

Testing activities were described following the equipment inventory carried
out during the interviews. For most subsystems a clean room has to be used for
the whole testing duration. The test campaign can be several months long even
if the test equipment might only be used actively for a few hours. Data
collected to describe facilities and equipment involved are highly variable,
as in most cases, the facilities are managed by an entity external to the
team. Four items were considered for the testing:

  * •

Equipment type;

  * •

Nominal power for active and stand by phases, and the duration of these
phases;

  * •

Consumption (helium, water);

  * •

Test duration.

When data provided were not accurate enough, assumptions were made based on
information of other subsystems or feedback from other space projects carried
out at CNES.

The impact from processing and storage of test data should in principle also
be included in this category, but no extensive data use dedicated to X-IFU was
identified during the interviews. Data use was therefore neglected in the
analysis.

###  4.5 Manpower

Manpower contributes to the environmental impact of a project due to emissions
from office buildings hosting the people (electricity use, heating, air
conditioning, waste management, water consumption), emissions related to
purchase of basic goods and services (furniture, computers, screens, office
supplies, internet, telecommunications), business travels, home-to-office
commuting and lunches. While most of these emissions are shared by different
activities and projects, we follow the ESA LCA guidelines and estimate the
fraction attributable to X-IFU by the ratio of Full Time Equivalents (FTE)
dedicated to the project compared to the total FTE working in the premises
[22].

We base our estimate on the inventory of fluxes occurring during the daily
operations of the Institut de Recherche en Astrophysique et Planétologie
(IRAP) in the year 2019 that were collected in the context of the carbon
footprint estimate of the laboratory [23]. In order to extract the
environmental impacts that are generated by hosting staff personal in the
office buildings, we exclude all fluxes that relate to project and science
activities, avoiding hence possible double-counting of environmental impacts
in the manpower category. Specifically, we consider electricity consumption,
heating of office buildings, leakage of refrigerants from air conditioning,
water consumption and waste water treatment, waste treatment, purchase of
laptops, and home-to-office commuting, excluding other fluxes such as purchase
of goods and services, business travelling, and use of observatory data. In
total, 263 FTE worked at IRAP in 2019, which results in a carbon intensity of
2.7 tCO2eq/FTE for the selected fluxes, which is about 10% of the 28
tCO2eq/FTE that is obtained when also project and science related fluxes are
included.

We specify in Table 5 the inventory of fluxes that we adopted for 263 FTE
working at IRAP. Based on this inventory, we created country-dependent LCI
models for manpower by selecting the relevant country-specific processes from
the ecoinvent database, in particular the country-specific electricity mix and
waste treatment impacts. This leads to carbon intensities, ordered by
increasing value, of 2.2 tCO2eq/FTE for Switzerland, 2.7 tCO2eq/FTE for
France, 3.7 tCO2eq/FTE for Belgium, 4.0 tCO2eq/FTE for Finland, 4.4 tCO2eq/FTE
for Spain, 5.3 tCO2eq/FTE for Italy, 5.8 tCO2eq/FTE for Germany, 6.2
tCO2eq/FTE for the Netherlands and the United States, and 10.5 tCO2eq/FTE for
Poland. To assess the manpower impacts, information was gathered from sub-
system project leaders on the size of the team dedicated to X-IFU and how they
foresaw its evolution over the project duration.

Process | Unit | Quantity
---|---|---
Electricity consumption | kWh | 2 276 000
Heating (natural gas) | MJ | 774 000
Heating (wood) | MJ | 3 085 200
Air conditioning (HFC-152a leakage) | kg | 14.06
Water consumption | kg | 4 744 000
Waste water treatment | m3 | 4744
Waste treatment | kg | 155 000
Purchase of laptops | unit | 139
Home-to-office commuting (passenger train) | pkm | 254 503
Home-to-office commuting (regular bus) | pkm | 120 105
Home-to-office commuting (tram) | pkm | 74 583
Home-to-office commuting (small size petrol car) | km | 299 985
Home-to-office commuting (small size diesel car) | km | 474 806
Home-to-office commuting (small size natural gas car) | km | 6 396
Home-to-office commuting (electric car) | km | 96 304
Home-to-office commuting (electric bicycle) | km | 33 136
Home-to-office commuting (motor scooter) | km | 32 733
Table 5: Inventory of fluxes for 263 FTE working at IRAP in 2019.

In addition to the impacts of office work, we also assessed the impacts of
projected related business travelling. Business travels by train or by plane
related to X-IFU activities have been assessed from budget forecasts for each
sub-system. A pre-pandemic scenario has been considered, which, given the
observed post-pandemic trends, probably implies that the impact of business
travels will be overestimated.

##  5 Life Cycle Impact Assessment

###  5.1 Absolute impacts

The absolute environmental impacts that we computed from the LCI are
summarised in Table 6. We estimate a total carbon footprint of 25.5 ktCO2eq
associated with the development, testing and delivery of X-IFU sub-systems.
The mass of the sub-systems that were considered in this assessment amounts to
221.3 kg [1], resulting in an emission factor of 115.2 tCO2eq per kg of
instrument mass, about twice the value of 50 tCO2eq per kg of satellite wet
mass that was inferred by ref. [8] from LCAs of space missions. We note,
however, that the latter value refers to an entire space mission, combining
elements that were not assessed in our study (full payload, satellite,
launcher, ground operations). Furthermore, the mass reference differs
(instrument mass versus satellite wet mass), hence the values are strictly
speaking not comparable. Still, the relatively large emission factor may
indicate that scientific instrumentation eventually contributes significantly
to the footprint of a scientific space mission, strengthening the necessity
for a detailed evaluation of its environmental impacts and vigorous actions
for their reductions.

Indicator | Unit | Total
---|---|---
Climate change | kg CO2 eq | 25 536 128
Ozone depletion | kg CFC11 eq | 2.299
Ionising radiation | kBq U235 eq | 15 945 955
Photochemical ozone formation | kg NMVOC eq | 81 952
Particulate matter | desease incidents | 1.001
Human toxicity, non-cancer | CTUh | 0.454
Human toxicity, cancer | CTUh | 0.01164
Acidification | mol H+ eq | 109 898
Eutrophication, freshwater | kg P eq | 11 615
Eutrophication, marine | kg N eq | 27 103
Eutrophication, terrestrial | mol N eq | 267 762
Ecotoxicity, freshwater | CTUe | 401 284 747
Land use | Pt | 200 962 056
Water use | m3 depriv. | 5 910 066
Resource use, fossils | MJ | 598 393 056
Resource use, minerals and metal | kg Sb eq | 746.4
Table 6: Absolute LCIA results.

The relative contributions of the different products and activities to the
environmental impact of X-IFU are illustrated in Figure 2. For the latter,
impacts have been split into qualification models (xM), such as manufacturing
of structural thermal models, engineering models, qualification models, proto-
flight models, and spares, the Flight Model (FM), Transport, Assembly,
Integration and Testing (AIT), Office work, and Travelling.

Clearly, for most impact categories, AIT, office work and business travels
contribute most significantly to the environmental impacts of X-IFU. AIT has
particular large contributions to ionising radiation, owing to the use of
nuclear energy for electricity production in France, as well as to freshwater
eutrophication, freshwater ecotoxicity, water use and use of fossil resources,
all related to operating clean rooms. The impact category that stands out with
substantially different contributions is the use of mineral and metal
resources, which is dominated by the manufacturing of qualification or flight
models.

Office work contributes also significantly to many impact categories, a result
that confirms the findings of ref. [24] who assessed the environmental impacts
of the Sentinel 3B mission. We observe particular important contributions to
particulate matter, non-cancer and cancer human toxicity, and land use.
Particulate matter and land use are related to heating of office buildings by
wood, which is the case for the IRAP building at the Roche-site, and hence an
artifact of the specific LCI model that we used to estimate the impacts of
office work. Human toxicity impacts arise from electricity consumption in the
office buildings.

Business travelling also contributes substantially to the environmental
impacts, in particular for ozone depletion, photochemical ozone formation, and
marine and terrestrial eutrophication. On the other hand, transport of sub-
systems contributes little to the environmental impacts of X-IFU, with the
exception of a $\sim 3\%$ contribution to water use. Also the manufacturing of
the flight model contributes only modestly to the environmental impacts, with
the exception of the use of mineral and metal resources that is dominated by
instrument manufacturing.

Figure 2: Contribution of activities to the environmental impacts of X-IFU.

###  5.2 Normalised life cycle impacts

To compare the different impact categories, we show in Figure 3 the normalised
impacts, using the normalisation factors given in Table 2 for the EF3.0
method. By multiplying absolute impacts with the normalisation factors,
environmental impacts are expressed in units of annual impacts of an average
human. For example, a normalised value of 1000 corresponds to the annual
impact generated by 1000 average humans in the world, where average refers to
the ignorance of impact inequalities among individual humans.

The most significant impact category is mineral and metal resources use, with
impacts being primarily related to the manufacturing of instrument models. The
next most significant impact category is freshwater ecotoxicity, with impacts
arising both from the manufacturing of instrument models as well as from AIT,
office work and business travelling. Next comes use of fossil resources, which
is largely dominated by AIT, office work and business travelling, and
freshwater eutrophication, again with a mix of instrument model manufacturing,
AIT, and office work.

Figure 3: Normalised environmental impacts of X-IFU.

###  5.3 Impact weighting

As alternative representation, we show in Figure 4 the weighted impacts as
derived using the normalisation and weighting factors given in Table 2 for the
EF3.0 method. This analysis suggests that climate change and resource use,
including fossil fuels as well as mineral and metal resources, are the most
significant environmental impacts of X-IFU, followed by freshwater
eutrophication, ionising radiation, and freshwater ecotoxicity. We recall that
weighted impacts depend on expert judgments on the importance of each impact
category, introducing some subjectivity in the assessment. Yet through
weighting, environmental impacts are put on a common scale (called “points”),
allowing for aggregation of results among impact categories. Adding up the
weighted impacts, we obtained a single score of 3472 points, which means that
the environmental impacts of the X-IFU systems considered in this study
correspond to the annual environmental load of 3472 humans.

Figure 4: Weighted environmental impacts of X-IFU.

In Figure 5 we break down the aggregated impacts by category, which suggests
that AIT and office work contribute most significantly to the environmental
impacts of X-IFU. Both aggregate significant environmental impacts from many
different categories, with fossil resource use and climate change contributing
to about half of the impacts. The manufacturing of instrument model is the
third most impacting activity, which is clearly dominated by use of mineral
and metal resources. Next comes travelling, with impacts in many categories.
Finally, environmental impacts from transport appear to be negligible.

Figure 5: Single score environmental impacts of X-IFU.

Figure 6 shows the aggregated environmental impacts by subsystem (cf. Table
4). Uncertainties in the total environmental impacts per sub-system are
indicated by black bars. Uncertainties range from 25% to 70%, depending on the
quality of data collection and the maturity level of the sub-system. The mean
uncertainty in the estimates is about 40%, which is acceptable, given the
maturity of the project. Prime contributors to the environmental impacts are
the TES Array and the multiplexing SQUIDs. Next come the Digitial Readout
Electronics, the 2 Kelvin core, the Cryogenic Anticoincidence Detector and the
sub-Kelvin cooler which all have comparable impacts around 300 points.

Figure 6: Single score environmental impacts of X-IFU sub-systems. The
horizontal bars indicate the uncertainty in the total environmental impact per
subsystem.

##  6 Discussion

The manufacturing of X-IFU hardware, including pre-flight (xM) and flight
models (FM), contribute about 22% to the environmental impacts of X-IFU.
Transport of subsystems turns out to have a negligible environmental impact,
below 1%, probably related to the relatively small weight of the equipment.
Testing contributes to about 37% to the environmental impacts of X-IFU, which
is explained by the significant amount of energy used over time for testing
related activities, in particular in relation to the use of clean rooms.
Office work contributes to about 32% to the environmental impacts of X-IFU,
owing primarily to power and heating office buildings and commuting of
personnel to the laboratory. Business traveling by plane is another category
with significant contribution to X-IFU’s environmental impact, estimated to be
about 10%. Next, we provide some more details about each of these items.

###  6.1 Manufacturing

The manufacturing of X-IFU hardware, including pre-flight (xM) and flight
models (FM), contribute significantly to the environmental impacts of X-IFU.
X-IFU relies on high performance state of the art technologies and
electronics, incorporating precious metals and rare earth elements which are
known for their very low availability and polluting refining processes. The
latter contribute to environmental impacts in many categories, including in
particular freshwater ecotoxicity and eutrophication. It is worth stressing
that manufacturing impacts are very likely underestimated, as most buy-to-fly
ratios, which can be very high for space applications, are not available for
most X-IFU subsystems. Getting more accurate inputs will be the challenge of
the next updates of the X-IFU LCA, specifically when major hardware suppliers
are selected. The model philosophy applying to X-IFU is intended to mitigate
the risks associated with the development of the instrument, and it is very
unlikely that intermediate models can be retired. Nevertheless, the
Protoflight Model (PFM) approach followed by the project means that PFM models
of sub-systems (instead of Engineering Qualification Models) will become
Flight Spares (fully functional with qualified parts, processes and
materials), thus saving one model production. In all cases, it appears that
the impact of the model philosophy is tightly connected to the risk management
approach, which for expensive space projects is highly constrained and often
the key point in the decision process form committing the large spending
associated with the project. This means that no major reduction of the
environmental impacts associated with the model manufacturing can be
anticipated in projects like X-IFU, yet questioning and challenging the number
of intermediate units produced should be considered, whenever deemed
acceptable.

###  6.2 Logistics

Transport of sub-systems turns out to have a negligible environmental impact,
probably related to the relatively small weight of the equipment. The small
contribution of transport to the overall impacts of X-IFU should be verified
once more detailed data on suppliers is available and the integration logic of
the instrument is fully defined.

###  6.3 Testing

A significant amount of energy is used over time for testing related
activities, in particular in relation to the use of clean rooms. Depending on
the electricity mix of the country where the task is carried out, the most
significant resulting impacts will be climate change, eutrophication,
ecotoxicity and fossil resource use for fossil-based electricity mixes, while
ionizing radiation will dominate for nuclear electricity production, which
dominates in France. As X-IFU is still in its early development phase there is
room for optimizing the testing strategy, although acceptance and
qualification tests are necessary for managing the risks of the project. Yet
the duration of the tests for each model, as well as duplication of tests at
different levels of instrument integration could eventually be streamlined,
reducing the related environmental impacts. The energy usage of the clean
rooms bears also potential for optimisation, as well as the usage of
disposable consumables needed for clean room operations. Eventually, test
campaigns could be relocated to facilities that have a demonstrable lower
environmental impact, reducing thus the overall footprint of the project.

###  6.4 Office work

Office work contributes significantly to the environmental impacts of X-IFU,
owing primarily due to power and heating office buildings and commuting of
personal to the laboratory. Lever arms to reduce the environmental impacts of
office work include energy savings in the office buildings, eventually linked
to thermal insulation of the building in case that heating and cooling are
significant contributors to energy consumption. Furthermore, reduction of
purchases of new material, either by extending the lifetime of equipment or by
purchasing refurbished or recycled goods, will reduce the environmental
footprint. Home-to-office commuting, as well as midday meals are further areas
where environmental impact reductions can be achieved, for example by
promoting active mobility (walking, cycling) or public transport instead of
use of individual cars, and by offering less red meat (beef, sheep) as well as
vegetarian and vegan options in the office canteens.

###  6.5 Travelling

Business travelling by plane is another category with significant contribution
to X-IFU’s environmental impact. We recall that a pre-pandemic scenario has
been considered in this study, and given the observed post-pandemic trends,
the contribution of this category may be actually lower. In particular, the
X-IFU Consortium has already committed to reduce significantly its global
travel footprint, for example by reducing the number of large consortium
meetings to one per two years, and progressively replacing face-to-face
working meetings by video conferences [21]. It remains to be seen how these
commitments impact the overall project footprint, and whether additional
actions can be taken to reduce X-IFU’s travelling footprint.

##  7 Revisiting the LCA of the new X-IFU

Since the completion of this first analysis, X-IFU entered a redesign phase,
leading to changes in the instrument baseline and in the perimeter of
activities of the Consortium (see Figure 7). The most notable changes are that
the Dewar is now under Consortium responsibility. Similarly, the cooling
chain, thanks to the use of passive cooling has been replaced by a single 4K
cooler and the last stage by a multi-stage Adiabatic Demagnetization
Refrigerator (ADR) cooler (the cooling chain, with the exception of the last
stage cooler, and the Dewar were previously in the perimeter of responsibility
of ESA, hence excluded from our LCA, explaining why it does not appear in
Figure 1). Most other sub-systems have changed very little compared to the
previous instrument design. Yet changes in the performance requirements for
X-IFU have led to reducing the number of readout channels, hence the number of
electronic boxes, e.g. the Digital Readout Electronics. The current LCA will
thus be updated to account for the new perimeter of the Consortium. Some
elements, e.g. the 4K cooler, considered today as a procurement by NASA, may
be protected by non-disclosure agreements, hence their environmental impacts
will have to be approximated. It is hoped that all the other elements making
up Athena will also be subject to a LCA, so that the global environmental
footprint of the mission is assessed, and further impact reductions, beyond
those of X-IFU, identified.

Figure 7: The new physical breakdown of the X-IFU instrument. The main change
compared to the previous version is that the 50K Dewar and the 4K cooler are
now within the Consortium perimeter and will have to be modeled in the updated
LCA. While in the previous incarnation of X-IFU, the Consortium was
responsible for the delivery of a 2K core, the new configuration comes with
the increased responsibility of having the Dewar and 4K core integrated before
delivery.

##  8 Conclusions

For the first time for a space astronomy instrument, a LCA was performed to
estimate the environmental impact associated with its development. This LCA
identified model manufacturing, testing, and business traveling as
environmental impact hot spots on which reduction actions should be focused.
Among the impacts categories, climate change and resource use, including
fossil, mineral and metal resources, turn out to dominate the environmental
impacts, a finding that is similar to results obtained for ground-based
science facilities [10]. More detailed analysis is required to prioritize the
actions, e.g. a detailed breakdown of the impacts associated with the use of
clean room facilities, across the whole consortium. Nevertheless, in order to
verify the efficiency of these actions, a mechanism needs to be put in place
that traces their effectiveness from the recommendations to their
implementation. So far, no such mechanism exists, neither at X-IFU Consortium
level, nor at CNES or ESA project level. One possible solution could be that a
dedicated “environmental architect”, under the responsibility of the CNES
X-IFU project manager, overlooks and tracks the implementations of the
recommendations. The architect could also have the responsibility to update
the LCA, as the maturity of the instrument design increases and real hardware
is produced and tested. The person may also act as liaison with similar
personal in other consortium institutes.

It remains to be seen by what amount environmental impacts can actually be
reduced based on LCA-informed action plans. Studies on eco-design of space
missions suggest that environmental impact reductions of several tens of a
percent may be achievable, yet that in general, improvements in some
environmental indicators can lead to a degradation of others [25]. Therefore
it seems likely that eco-design will not be sufficient to make space sciences
sustainable, and that more significant changes are needed in the modus
operandi of the community. Specifically, LCA need to inform implementation
decisions of new space missions, making sure that the limits of planetary
boundaries are no longer exceeded. Of course, space sciences do not exceed
these boundaries alone, yet they contribute together with all other human
activities that the Earth system no longer resides within the safe operating
range [26]. We are convinced that vibrant and exciting space sciences are also
possible with a more responsible and sober use of resources, by favoring
collaboration over competition, stopping the race for ever more and bigger
facilities, by enhancing the mining of existing data archives that so far
remain often poorly explored, and by investing more time and effort in
research and development activities that path the way to new ground-breaking
instruments, instead of constantly pushing for new instruments with only
incremental improvements, owing to the lack of sufficient technological
innovation.

##  9 Acknowledgments

The authors are grateful to the X-IFU sub-system leads that contributed to the
data collection used for the LCA, that led to the results presented above. We
are also grateful to Bruno Millet (CNES) for supporting the objective to make
the development of the instrument more sustainable.

The authors declare no conflict of interest. The data presented here are
available on request. All authors made substantial contributions to the
conception or design of the work; the acquisition, analysis, or interpretation
of data. They contributed to drafting the work or revised it critically for
important intellectual content. They approved the version to be published; and
agree to be accountable for all aspects of the work in ensuring that questions
related to the accuracy or integrity of any part of the work are appropriately
investigated and resolved. This work was funded by CNES, CNRS and CEA in
France and by internal resources elsewhere.

We thank the referee for comments on the paper.

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