=Paper=
{{Paper
|id=Vol-3674/CPSS4Sus-paper1
|storemode=property
|title=Method and Tool to Assess the Environmental Impacts of Cyber-Physical Systems with a Life-Cycle Approach
|pdfUrl=https://ceur-ws.org/Vol-3674/CPSS4Sus-paper1.pdf
|volume=Vol-3674
|authors=Felix Schöllhammer,Mario Cortes-Cornax,Paula Lago,Vijanti Ramautar,Claudia Roncancio,Sietse Overbeek,Sergio España
|dblpUrl=https://dblp.org/rec/conf/rcis/SchollhammerCLR24
}}
==Method and Tool to Assess the Environmental Impacts of Cyber-Physical Systems with a Life-Cycle Approach==
https://ceur-ws.org/Vol-3674/CPSS4Sus-paper1.pdf
Method and Tool to Assess the Environmental Impacts
of Cyber-Physical Systems with a Life-Cycle Approach
Felix Schöllhammer1,∗ , Mario Cortes-Cornax2 , Paula Lago3 , Vijanti Ramautar1 ,
Claudia Roncancio2 , Sietse Overbeek1 and Sergio España1,4,†
1
Utrecht University, Princetonplein 5, 3584 CC, Utrecht, the Netherlands
2
Université Grenoble Alpes, Grenoble, France
3
Concordia University, Montreal, Canada
4
Universitat Politècnica de València, Camino de Vera, s/n, 46022, Valencia, Spain
Abstract
The increasing environmental footprint of the information and communication technology sector calls
for innovative strategies for assessing and improving its sustainability. Life Cycle Assessment (LCA)
methods are suitable for estimating the negative environmental impacts of products or services. While
some LCA methods start to be applied during the engineering of Cyber-Physical Systems (CPS), some
challenges remain unresolved. For instance, the energy consumption, and therefore the greenhouse
gas emissions, of CPS is influenced by the specific configuration of components in their architecture, as
well as by the geographical location where the components are deployed. Also, the skills and efforts
required by LCA projects, remain prohibitive to many CPS engineering projects. This paper presents an
LCA-based method tailored for CPS that facilitates the analysis of the environmental impact and the
comparison of architectural and location variants. Moreover, we have developed a supporting tool that
guides the process and automates part of the data collection activity. Through an illustrative case and
expert assessment, we have been able to assess the benefits and drawbacks of our proposal. With these
contributions, we hope to lower the barrier to adopting LCA practices in CPS engineering projects, both
in industry and academia.
Keywords
Cyber-physical systems, life cycle assessment, environmental sustainability, software for sustainability
1. Introduction
Cyber-Physical Systems (CPS) integrate computational and physical resources to offer systems
that link physical devices with advanced computational capabilities [1, 2, 3]. Whereas CPS and
the Internet of Things have brought about many benefits in varied domains like smart cities,
telehealth, and smart homes [4], there is increasing concern about their negative environmental
Joint Proceedings of RCIS 2024 Workshops and Research Projects Track, May 14-17, 2024, Guimarães, Portugal
∗
Corresponding author.
†
Sergio España is supported by a María Zambrano grant of the Spanish Ministry of Universities, co-funded by the
Next Generation EU European Recovery Plan.
Envelope-Open n.f.schollhammer@uu.nl (F. Schöllhammer); mario.cortes-cornax@univ-grenoble-alpes.fr (M. Cortes-Cornax);
paula.lago@concordia.ca (P. Lago); v.d.ramautar@uu.nl (V. Ramautar); claudia.roncancio@imag.fr (C. Roncancio);
s.j.overbeek@uu.nl (S. Overbeek); s.espana@uu.nl (S. España)
Orcid 0009-0006-7638-8189 (F. Schöllhammer); 0000-0003-2635-319X (M. Cortes-Cornax); 0000-0001-5290-6486
(P. Lago); 0000-0002-3744-0013 (V. Ramautar); 0000-0002-1118-6512 (C. Roncancio); 0000-0003-3975-200X
(S. Overbeek); 0000-0001-7343-4270 (S. España)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Felix Schöllhammer et al. CEUR Workshop Proceedings 1–11
impacts. The low cost of technology is leading companies to bloat the CPS they design with
components, machine-to-machine connections, and features that are not necessary or relevant to
achieve the intended value proposition [5], worsening their environmental footprint. According
to recent predictions [6], the worldwide production of interconnected devices could potentially
result in a carbon footprint of over 1000 Mt of 𝐶𝑂2 equivalent emissions per year by 2027, in a
worst-case scenario. The rapid growth in this field underscores the urgency of the matter.
To identify and estimate the impact of CPS on the environment, it is important to consider
Life-Cycle Assessment (LCA) practices [7]. LCA entails assessing the environmental impacts
of a product, process, or service throughout its entire or partial life-cycle [8, 9]. To apply
LCA in the evaluation of CPS, some challenges remain unresolved. For instance, the energy
consumption, and therefore the greenhouse gas emissions, of CPSs are influenced by the specific
configuration of components in their architecture (due to an overall greater or lower energy
demand), as well as by the geographical location where the components are deployed (due to
the carbon intensity of the local electricity mix). Also, the skills and efforts required by LCA
projects, remain prohibitive to many CPS engineering projects.
This paper builds on the insights proposed in [7] and addresses some of its current limitations.
Section 2 describes the research method. Our main original contributions are the LCA4CPS
method and a tool (see Section 4). Our proposed method assesses the environmental impact of
CPS inspired by LCA- practices. Moreover, it facilitates the comparison of architectural and
location variants considering the hardware components’ life-cycle. It calculates environmental
emissions due to data transfer and storage and includes indicators for (i) global warming impact,
(ii) acidification of soil and water, (iii) water pollution, and (iv) freshwater usage. We chose those
indicators because they represent the critical impacts CPS components have on the environment.
We engineer a supporting tool that guides the environmental assessment process and automates
part of the data collection and the calculation of indicators. We describe the validation of our
proposal through expert assessment in Section 5. Finally, Section 6 concludes the paper and
outlines future work.
2. Research method
The research questions are: RQ1: ”How can the environmental impacts of CPS be assessed cost-
effectively?” By cost-effectiveness, we refer to the fact that CPS engineers, users, and researchers
should afford to apply the method. We aim to create an LCA-based method and a supporting
tool. RQ2: ”What are the benefits and drawbacks of the proposed LCA-based method and tool?” We
aim to validate our proposals by eliciting the expert opinions of CPS engineers and researchers,
to identify potential improvements.
Since this project aims at engineering artifacts (i.e. the LCA4CPS method and tool) while
gathering knowledge about it (i.e. current limitations and the necessary features to overcome
these, the benefits and drawbacks of our proposal), we follow a Design Science approach
[10]. We also consider this to be a re-engineering project that takes an informal version
of the LCA-based method in [7] and provides an improved and more formalized version as
well as an implementation. Figure 2 shows the process we followed. During the problem
investigation, (activity M1) we reviewed the literature on LCA (Section 3). Subsequently, (M2)
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Felix Schöllhammer et al. CEUR Workshop Proceedings 1–11
Figure 1: Overview of the research method
we expressed the pending challenges as requirements documented as user stories (Section 4).
During the treatment design, (M3) we have applied situational method engineering to create
the method (Section 4.1), documenting it with the Process-Deliverable Diagram technique [11].
This technique allows the specification of the process aspects of a method using UML Activity
Diagrams and the intermediate and final products of the method using UML Class Diagrams,
keeping both perspectives interrelated. We have then developed the supporting tool (M4) as
shown in section 4.2, first defining a Feature Model that represents the tool functionalities
and their dependencies [12]. During the treatment validation (Section 5), we have assessed
the feasibility of our proposal by applying the method and tool to assess the impacts of a CPS
(M5), which we use as an illustrative running example throughout the paper, and we have
conducted eight expert assessment interviews (M6), in which nine experts in the field have
provided feedback on the method and tool. We accompany this paper with a technical report
where the reader can find details omitted due to space constraints [13].
3. Background knowledge
Life Cycle Assessment (LCA) is a methodology to assess the environmental impacts and
resources used throughout (the entire or part of the) life cycle of products or services, i.e.,
from raw material acquisition, via production and use phase, to waste management [8], often
to identify improvements that lead to reducing the negative impacts [14]. LCA methods are
categorized as cradle-to-gate, including methods that focus on the manufacturing process, from
raw material extraction (cradle) to the consumer (gate); and cradle-to-grave, which are methods
that evaluate the impacts throughout the entire life cycle, up until disposal (grave) [8, 9, 15]. As
there are several LCA methods available the International Organization for Standardization
has established a set of standards known as the 14000 series, which includes widely recognized
procedures for conducting LCAs. ISO 14040 provides the general principles and framework for
conducting an LCA, and ISO 14044 provides more specific requirements for each step of an LCA.
Both ISO standards employ generalized terminology, allowing for broad applicability across
multiple industries [16, 17, 18]. ISO 14067 Carbon footprint standard, the latest one, offers
principles, requirements, tools, and guidelines for quantifying and communicating the carbon
footprint of products [19]. Nonetheless, these ISO standards leave some degrees of freedom to
facilitate their adoption.
Product Environmental Profiles (PEPs) are verified reports that provide environmental
declarations quantifying the environmental impacts of a product (process or service) over its
entire life cycle. While mainly intended to support business-to-business interactions, they
can also inform environmentally-conscious consumer choices. PEPs adhere to the ISO 14025
standard that regulates environmental labels and declarations [20]. This ensures that they
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Felix Schöllhammer et al. CEUR Workshop Proceedings 1–11
can effectively compare similar products and are based on reliable quantified data obtained
through LCA. In turn, information from a PEP of an electronic component type can be used
in an LCA process of a CPS that includes one or several instances of such component type.
The PEP ecopassport program allows companies to register their PEPs which, after independent
verification, become available in an online database [21].
Carbon Intensity of Electricity Production refers to the amount of 𝐶𝑂2 emissions pro-
duced per unit of something. In the case of electricity production, it refers to the emissions
produced per unit of energy generated. It is a crucial metric in understanding the environmental
impact of a nation’s electricity production. The carbon intensity is influenced by the electricity
mix, which is the proportions of primary electricity sources a region utilizes, typically including
fossil fuels, nuclear, and renewable sources [22].
4. The LCA4CPS framework
4.1. Method
As mentioned before, the main goal of our work is to help in the assessment of environmental
impacts of CPS. We have produced a list of epics and user stories that address pending challenges
in that domain1 , among them:
• As a user, I want to structure the analysis in a systematic and structured way; e.g. taking
life-cycle stages into account.
• As a user, I want to specify and track different locations (i.e. regions) where the compo-
nents of the CPS are located.
• As a user, I want to analyze environmental impact factors beyond 𝐶𝑂2 footprint; e.g.
water usage and pollution, global warming impact, and acidification of soil and water.
The proposed method comprises five major activities. Its Process Deliverable Diagram is
depicted in Figure 2. In the following, we briefly explain the process and major deliverables.
Please refer to [13] for more detail.
Global CPS definition. A succinct definition of the CPS has to be provided by the user
(activity A-TB.1 in Figure 2) including its name, a description, and its functional lifetime. This
lifetime is the period during which the system can perform its intended functions effectively
and efficiently. This is an important factor for the environmental assessment. This information
is part of the deliverable CYBER_PHYSICAL_SYSTEM (CPS) .
Component definitions. The analyst needs to register the types of components that
comprise the CPS (A-TB.2). A COMPONENT_TYPE refers to a class of components of the same
kind, which technical and environmental specifications are available. This activity involves
identifying the ENVIRONMENTAL_DECLARATION of each COMPONENT_TYPE . We assume the existence
of an ENVIRONMENTAL_DECLARATION_REPOSITORY from which the declarations can be retrieved,
such as the Product Environmental Profile (PEP Eco Passport) [21]. Additional specifications
are recorded as COMPONENT_TYPE_DETAILS . In turn, COMPONENT s are concrete devices of a given
COMPONENT_TYPE , that are later defined to be part of the CPS.
1
Refer to [13] for the full requirements specification
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<>
Calculation_mode
{Calculation_mode = functional_lifetime} {Calculation_mode = total_impact}
total_impact
functional_lifetime
0..M 0..1
A-TB 1. Defining CPS CPS 0..1
Impacts_P.A.
CYBER_PHYSICAL_SYSTEM (CPS)
Impacts_total CONFIGURATION_IMPACTS_P.A.
A-TB 1.1. Specify CPS details name: String 0..M 0..M 0..M
CONFIGURATION_IMPACTS_TOTAL
configs configs configs /global_warming_p.a: Double
description: String
CONFIGURATION /global_warming_total: Double /fresh_water_usage_p.a: Double
A-TB 1.2. Specify CPS functional lifetime functional_lifetime: Double /fresh_water_usage_p.a: Double
/fresh_water_usage_total: Double /water_pollution: Double
configuration_ID: String
Analyst data_lifetime_year: Int
/water_pollution_total: Double /acidification_soil_water_p.a.: Double
configuration_name: String
CO2_oneGBData_year: Double
A-TB 2. Defining Components 1..1 /acidification_soil_water_total: Double /data_MB_p.a.: Double
Calculation_mode: Enum_time configuration
/data_MB_p.a.: Double /data_MB_total: Double
A-TB 2.1. Create CPS component list
0..M 0..M
/data_MB_total: Double /data_GB_p.a.: Double
1..1 config_lines config_lines
DATA_INFORMATION /data_GB_p.a.: Double
information CONFIGURATION_LINE /data_GB_total: Double
samples: Boolean /data_GB_total: Double
quantity: Int /data_CO2_p.a.: Double
sampling_approach: String
A-TB 2.2. Find and link environmental declarations /data_CO2_p.a.: Double /data_CO2_total: Double
sample_rate: Double /quantity_over_functional_lifetime: Int
/data_CO2_total: Double
sample_rate_timeunit: Double /total_quantity_over_functional_lifetime: Int 0..1
[no declarations available] [declarations available]
0..M 0..M details
sample_size: Double config_lines config_lines COMPONENT_TYPE_DETAILS
A-TB 2.2.1. Find similar 0..1 0..1
component details Manufacturer_lifetime: Int details
1..1
1..1
component total_mass_g: Double 0..1
A-TB 2.3. Retrieve environmental information location details
LOCATION COMPONENT percentage_plastic: Double
Analyst 0..1
details
component_name: String percentage_metal: Double
region_name: String
A-TB 2. Defining Configurations 0..M
region_code: String components percentage_others: Double
0..1
A-TB 3.1. Specify details of configurations 0..M 1..1 1..1 0..1 details
details 0..1 0..1
location comp_type comp_type
impacts impacts
0..1 COMPONENT_TYPE
A-TB 3.2. Choose components for each configuration IMPACTS (TOTAL) IMPACTS (MANUFACTURING)
CI_electricity
component_type_ID: String
fresh_water_usage_total: Double fresh_water_usage_manufacturing:
CARBON_INTENSITY_ELECTRICITY
component_type_description: String Double
water_pollution_total: Double
A-TB 3.3. Choose quantity of component in configuration year: Datetime water_pollution_manufacturing: Double
hasPEP: Boolean
global_warming_total: Double
carbon_intensity_gCO2: Double global_warming_manufacturing: Double
LCA_link: String
acidification_soil_water_total: Double
A-TB 3.4. Specify location of component in configuration carbon_intensity_kgCO2: Double 0..M acidification_soil_water_manufacturing:
1..1
0..M comp_type comp_types energy_usage_total: Double Double
Analyst
CI_electricities 1..1 0..1 energy_usage_manufacturing: Double
repository manufacturer 0..1
A-TB 4. Defining Data-related information 0..1
MANUFACTURER impacts impacts
CARBON_INTENSITY_REPOSITORY
A-TB 4.1. Specify number of years of usage of CPS IMPACTS (DISTRIBUTION) IMPACTS (INSTALLATION)
name: String 0..M Name: String
declarations fresh_water_usage_distribution: Double fresh_water_usage_installation: Double
A-TB 4.2. Specify sampling properties URL: String
ENVIRONMENTAL_DECLARATION water_pollution_distribution: Double water_pollution_installation: Double
URL: String global_warming_distribution: Double global_warming_installation: Double
A-TB 4.3. Specify CO2-emissions for one GB of Data
comp_name: String acidification_soil_water_distribution: acidification_soil_water_installation:
Analyst Double Double
0..M
declarations 1..1 energy_usage_distribution: Double energy_usage_installation: Double
A-TB 5. Analysing method results
repository 0..1 0..1
impacts impacts
A-TB 5.1. Choose calculation mode ENVIRONMENTAL_DECLARATION_REPOSITORY
IMPACTS (USE) IMPACTS (END.OF.LIFE)
[p.a.] [total] name: String
fresh_water_usage_use: Double fresh_water_usage_end.of.life: Double
water_pollution_use: Double water_pollution_end.of.life: Double
A-TB 5.1.1 Select p.a. A-TB 5.1.2 Select total
global_warming_use: Double global_warming_end.of.life: Double
acidification_soil_water_use: Double acidification_soil_water_end.of.life:
Double
energy_usage_use: Double
energy_usage_end.of.life: Double
A-TB 5.2. Compare general footprints of configurations
A-TB 5.3. Compare data-related footprints of configurations
Analyst
Figure 2: LCA4CPS method (process and product meta-model) expressed as a Process Deliverable
Diagram [11]
CPS configurations. The analyst might consider one or several CPS configurations for
assessment (A-TB.3). A CONFIGURATION refers to a combination of geographically situated com-
ponents that constitute a CPS. They are typically architected by the CPS design team. Through
CONFIGURATION_LINE s, the analyst reflects the quantity of COMPONENT s of each COMPONENT_TYPE .
as well as their LOCATION . For example, the analyst may consider one configuration with a single
Schneider Door Sensor while another may integrate two of them. Also, one CPS configuration
could have its data center located in the Netherlands, and another configuration to have it in
Spain. The environmental impact of the number and type of devices will include their pro-
duction and their end-of-life (e.g. electronic waste). The location(s) of the CPS and functional
lifetime will be determinants for the environmental impact of the use phases.
Data footprint. Our method includes the analysis of part of the data management. The
analyst should provide data-related information (A-TB.4). This involves (i) determining the
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Felix Schöllhammer et al. CEUR Workshop Proceedings 1–11
number of years over which the data-related footprint should be calculated, (ii) defining sampling
properties for each component, including the sampling approach and frequency. To analyze
the data-related environmental footprint, scientific literature will be used to estimate the 𝐶𝑂2
emissions for storing data (e.g. storing one gigabyte of data per year) in a particular data center.
These aspects are recorded in CYBER_PHYSICAL_SYSTEM (CPS) and DATA_INFORMATION .
Environmental impacts. Based on the preceding information the impact estimations can
be calculated and the results can be subject to interpretation (A-TB.5). While the calculations
can be done manually, we recommend using the LCA4CPS tool to automate this process. The
tool facilitates the study of several configurations and provides insights to help decision-making.
Results can be presented annually (CONFIGURATION_IMPACTS_TOTAL ) or as a total over the intended
functional lifetime of the CPS (CONFIGURATION_IMPACTS_P.A. ). The analyst can also delve into
the impacts of specific COMPONENT_TYPE s. Also, the focus can be placed in a general overview or
on each of the life cycle stages (i.e. manufacturing, distribution, installation, use, end of life).
The details are recorded in the six deliverables named IMPACTS () . Moreover, when
more than one configuration has been considered, the analyst can compare their general and
data-related footprints.
4.2. LCA4CPS Tool
The structure of the tool and the guided interaction align with the method presented in the
preceding section and expressed in Figure 2. The tool allows simple interactions to handle the
CPS-related information, data repositories, and intermediate and global calculations.
Components Input. This sheet specifies component types, each having an identifier, a
name, and a description. To calculate the environmental impacts of the CPS, the tool needs
input related to the environmental declaration of its components. The analyst can choose to
enter the necessary information manually or to provide the URL of the PEP Eco Passport. In
the latter case, the tool extracts the environmental declaration data automatically and creates
and stores a separate entry for the component type. For example, the manufacturer declares
that the lifetime of the Schneider Door Sensor component type is 10 years and that the net use
of fresh water during the manufacturing of one unit is 18.9 liters.
Configuration Input. The tool supports the analyst in defining the CPS configurations
they intend to assess, following the method described above (see A-TB.2). The component
quantity and geographical location are both essential. Specifying the locations increases the
accuracy of the estimated impacts because the tool can take into account the carbon intensity
of the electricity produced in the country where the component is located. The tool has a
database detailing the average carbon intensity of electricity for various countries, allowing the
calculation of the carbon intensity for one kWh and applying this to the energy consumption
of the components2 . The component type lifetime, declared by the manufacturer and sourced
from the PEP, is used to calculate the quantity of components of each type that are needed
over the expected lifetime of the CPS. For example, if the CPS is expected to last 20 years of
service, then two Schneider Door Sensor components will be required (one after the other) as
their lifetime is only 10 years.
2
In this version, variations of the electricity mix among time are not considered
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Felix Schöllhammer et al. CEUR Workshop Proceedings 1–11
Meta-data Input. The tool offers functionality to calculate the amount of data that each
configuration generates. The analyst carries out the method activity A-TB.4; that is, inputting
data-related attributes for each component, within each configuration 3 . The tool also needs the
analyst to provide an estimated value of the 𝐶𝑂2 emissions factor for storing data. This value
is specific to the data center, or storing solution, used in the CPS. In our example, we specify
0.0379 kg𝐶𝑂2 per gigabyte of data per year, based on [23]. This information is later used to
estimate the impacts of storing the cumulative data volumes.
Overview of Environmental Footprint. The tool provides a comprehensive view of the
environmental footprint outcomes, presented either annually (P.A. stands for per annum) or as
a cumulative total over the intended functional lifetime of the CPS (A-TB.5). For example, see
Figure 3. The analyst can perform the comparative assessment of the environmental footprints
of different CPS configurations (e.g. identifying those configurations with the greatest and
smallest environmental impact). Also, each of the four impact indicators (𝐶𝑂2 impact, use of
fresh water, water pollution and acidification) has its charts to be able to perform a separate
analysis of the effects produced by alternative configurations.
Figure 3: Screenshot of LCA for CPS tool overview environmental footprint including 4 indicators
Overview of Data Footprint. The tool calculates the data footprint of the CPS usage phase
(related to A-TB.5). This helps users understand the environmental impact of data transmission
and processing and can inform decision-making. The CPS architects can perform a trade-off
analysis between CPS data-related features (or performance) and impact (e.g. lowering the
sampling rate of a sensor will result in less frequent monitoring of the environment but also in
a lower footprint).
About environmental indicators. As said before, four environmental indicators are esti-
mated by the tool. The interviewees have stressed a special interest on the Global warming
impact indicator. GlobalWarming(use) represents the impact of global warming of a configura-
tion component estimated in kilograms of 𝐶𝑂2 equivalents (𝐶𝑂2 e) due to energy consumption
over the life cycle use phase. It is estimated as follows:
3
The highlighted part in Figure 5 shows an example of this information
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Felix Schöllhammer et al. CEUR Workshop Proceedings 1–11
energy_consumption(use) × carbon_intensity
GlobalWarming(use) = × functional_LT × quantity
manufacturer_LT
The variable energy_consumption (use) denotes the energy consumption (in kWh) of a component
type consumed during the life cycle use phase. Carbon_intensity represents the amount of
𝐶𝑂2 (Kg 𝐶𝑂2 e) emitted per kWh of energy produced at the configuration component’s location.
Manufacturer_LT stands for the component lifetime which is the period given in years a
component type can operate without failure, according to its manufacturer. The functional
lifetime; i.e. the duration of time expressed in years the CPS is intended to operate, is denoted
with functional_LT. Lastly, quantity is the number of components used simultaneously for a
given CPS configuration.
Tool implementation. We have implemented the LCA4CPS prototype in Google Sheets,
extending its functionality with Google Scripts (a version of JavaScript)4 . The rationale is to
lower the barrier for adoption by CPS researchers and practitioners, who can easily access the
tool, make a copy, and run it after a simple configuration; also, it offers an interface that is
familiar to many users. The manual is also available online5 .
5. Validation
We interviewed nine participants (N=9) to assess the tool’s usefulness in general and by features.
The majority of interviewees (5) work for a research institute, and the others work for a private
company or a combination of private companies and research institutions. Moreover, most
interviewees (8) have over 10 years of experience working with CPS. We asked them about
the tool’s strengths and weaknesses. A significant majority (8 out of 9) find the tool in general
useful. One respondent expresses reservations, citing the perceived additional effort and work
when using the tool, and does not feel the need to study the numbers that are calculated with
the tool. However, the positive impressions of the tool outweigh what is also represented in the
numbers. Respondents emphasize the tool’s importance by highlighting the importance of the
topic and the usefulness of the tool. For instance, respondent 9 stated, “The tool is very effective
and very useful and provides very good insights”.
Evaluating each feature individually allows us to identify the most useful features (as in-
dicated by the interviews). Feature 1, specifying configurations, was rated extremely useful
(4.61 of 5). Feature 2 was deemed very useful (4.33) for comparing environmental impacts
and aiding in sustainability-focused decisions. Feature 3, which allows for automatic data
extraction from the PEP, received a high rating (4.44) because it reduces user effort. Feature 4
(F4) visually representing impacts, was rated very useful (4.22). Feature F5 which calculates the
data volume generated by CPS, was deemed moderately useful (3.28), whereas Feature 6, which
considers calculating the environmental impact of data was deemed very useful (4.17). Feature
4
The tool can be found here. Please make a copy, delete the data (intended for illustration pur-
poses only), and you are ready to start your own LCA processes. https://drive.google.com/drive/folders/
1Rw6YWDixBx586H3ZNyJMPiATHx2Ic0OL
5
Tool manual can be found here. https://docs.google.com/document/d/
1muqXv2GG6elV4TL-Uyc1I7NX00hNt-MM0aVtLP-2lF4
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Felix Schöllhammer et al. CEUR Workshop Proceedings 1–11
7, accounting for location-related carbon intensity of electricity, was deemed very useful (4)
because it is crucial for cross-location comparison, acknowledging energy cost variations.
A minority of participants deemed the data-related features less useful, citing a general lack
of interest in such analysis and arguing that the related costs of data are more meaningful than
its environmental impact. 7 out of 9 interviewees identified “𝐶𝑂2 footprint/global warming”
as the most critical environmental indicator out of the four impact indicators used in the tool.
Participants emphasize its widespread recognition both in the scientific community and the
industrial sector. The participants highlight that the tool effectively raises awareness about
the environmental implications of ICT as one of its strengths. Participants also commended
it offers a very structured approach to accessing the environmental footprint of CPS. Further-
more, participants highlight that the tool is both important and necessary for enabling a clear
understanding of the actual environmental impacts of CPS.
As for its weaknesses, interviewees find the tool time-consuming to use. Moreover, scalability
presents an issue due to platform limitations, and the tool makes assumptions about the analyst’s
knowledge of CPS structure. Finally, the tool currently only considers direct life cycle impacts
and does not take positive structural and enabling impacts into account.
6. Conclusions and future work
Measuring and understanding the environmental impact of IT from design phases, in general,
and CPS in particular is a major issue. CPS are very appealing from business and practical
points of view but tend to become harmful to the environment. This work is a contribution to
encourage environmental assessment when developing CPS. We developed a prototype tool to
help practitioners use the proposed environmental assessment method for CPS. The tool provides
a simple way to describe a CPS, supports automatic integration of product environmental profiles,
and estimates multiple environmental footprint indicators for various candidate configurations,
including their hardware components description and data-related information. This tool aims
to support decision-making in the design and development of CPS. The tool was validated
with 9 practitioners who confirmed the importance of this work to raise awareness about the
environmental impact of CPS. We are grateful for their time and expert opinion.
Several improvements are still needed, including enhancing the prototype and refining the
analysis by presenting hypotheses, contextual information on component type environmental
data, and characteristics of the CPS use phase. On a broader scale, it would be valuable to enable
designers to assess the potential positive environmental impacts of utilizing CPS and state their
overall net benefit.
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A. Appendix A – Tool screenshots
Figure 4: Screenshot of LCA for CPS tool configuration input sheet
Figure 5: Screenshot of LCA for CPS tool configuration data input sheet
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