Carbon-Aware Process Execution for Green Business Process
Philipp Hehnle
, Maximilian Behrendt
, Luc Weinbrecht
and Carl Corea
envite consulting GmbH, Stuttgart, Germany
Research Group Process Science, University of Koblenz, Koblenz, Germany
Green BPM, Carbon-Aware Process Execution, Green ICT, Camunda.
Traditional business process management (BPM) focuses on the improvement of performance dimensions such
as time, costs, and quality. Ecological aspects are usually not considered as an equal performance dimension.
In this context, Green BPM approaches have been proposed to strengthen the awareness among people and
organisations about the impact of business processes on the climate. However, little research in Green BPM
covers the runtime of digitised processes, or provides concrete means to reduce carbon emissions during
process execution. Therefore, we present an approach for carbon-aware process execution, which allows to
automatically postpone energy-intensive activities to times when energy with low CO
emissions, e.g. solar
energy, is better available. Importantly, our approach considers and complies with external regulations such as
Service Level Agreements (SLAs) when postponing activities. Our approach is implemented in Camunda and
has been evaluated in interviews with domain experts.
Climate change has the potential to cause un-
precedented natural disasters that impact, inter alia,
food production and thereby threatens livelihoods
(Matemilola et al., 2020). In result, governments
across the world adopted the Paris Agreement in 2015
(Matemilola et al., 2020), committing to implement
measures in order to keep the global temperature in-
crease below 2°C compared to pre-industrial levels.
To limit the global temperature increase, it becomes
necessary to drastically reduce carbon (CO
) emis-
sions, so that there are net zero emissions in the long
run (Matemilola et al., 2020). To this end, there is
a broad consensus that Information and Communi-
cation Technology (ICT) can play an important role
in reducing CO
emissions (Kim et al., 2009; Oloo
Ajwang and Nambiro, 2022; Gohar et al., 2020), e.g.
web meetings may reduce the need for transportation.
However, it is not safe to assume that the use of ICTs
will always save emissions. In particular, the sheer
execution of the ICTs produces emission itself. Ac-
cording to (Freitag et al., 2021), ICTs produce 2.1%-
3.9% of global green house gas emissions. There-
fore, the ICT sector itself must also strive to reduce
its emissions to contribute towards the overall goal
of the Paris Agreement. In this work, we investi-
gate how digitised business processes can contribute
towards this goal. In particular, we present an ap-
proach for carbon-aware process execution, which al-
lows to automatically execute activities of a business
process at times at which energy sources with low
emissions (green energy) are available, e.g. so-
lar energy, lowering the CO
emissions. Importantly,
our approach leverages predictive insights to ensure
that the business process execution remains compli-
ant with external requirements such as service level
agreements (SLA).
In traditional BPM, the main performance dimen-
sions for which processes are optimised for are time,
cost, and quality (van der Aalst, 2013). In this work,
we aim to raise awareness that the dimension of ecol-
ogy should be seen as an equal. To this aim, our
approach supports companies in considering carbon
emissions as a process performance dimension.
Our approach is implemented in Camunda, and
can be used “out-of-the-box” by companies. We show
that our approach can be used to reduce CO
sions with a comprehensive case-study and evaluate
our approach in interviews with domain experts. For
our investigation, we follow a design science oriented
approach as proposed in (Peffers et al., 2007), in that
we identify important requirements (Section 3.1) and
develop/evaluate our approach based on these.
Hehnle, P., Behrendt, M., Weinbrecht, L. and Corea, C.
Carbon-Aware Process Execution for Green Business Process Management.
DOI: 10.5220/0012557100003690
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 2, pages 659-666
ISBN: 978-989-758-692-7; ISSN: 2184-4992
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
In Section 2, fundamentals on Sustainability,
Green BPM and Predictive Process Monitoring are
stated. In Section 3, we present our approach to re-
duce CO
emission during process execution and a
proof of concept implemented for the Workflow Man-
agement System (WfMS) Camunda. In Section 4, the
presented approach is evaluated. Finally, this paper
concludes with a summary and outlook.
In this section, we introduce concepts to save carbon
when using ICTs, SLAs, predictive process monitor-
ing, and Green BPM.
2.1 Sustainability in Information and
Communication Technology
There are various approaches to operate ecologically
sustainable ICTs, mainly, by trying to improve the ef-
ficiency of the software or the hardware. As an exam-
ple, improving the hardware software is operated on
can reduce the energy consumption and thereby the
ecological impact (Freitag et al., 2021). Furthermore,
the choice of technology (e.g. programming language
(Pereira et al., 2017)) and the programming style (e.g.
used data structures (Hasan et al., 2016)) affects the
energy consumption of software.
Besides these approaches, the time when software
is executed may be chosen carbon-aware. For in-
stance, (Radovanovic et al., 2023) discusses how data
centre computing may be run in time windows when
green energy is available. For this purpose, the Green
Software Foundation’s Carbon Aware SDK
may be
used to get a forecast about when and where addi-
tional energy consumption causes the least CO
sions. The SDK takes into account whether there are
enough sustainable energy sources available for the
additional energy consumption. Building on this fore-
cast, it therefore becomes possible to execute energy-
intensive activities when green energy is available.
Consequently, in this work, we focus on this latter line
of approaches, i.e. trying to reduce emissions by op-
timising the execution time.
2.2 Service Level Agreements in
Business Process Management
An SLA (Frankova et al., 2011) constitutes a contract
between a service provider and a consumer describ-
ing a non-functional requirement. SLAs of a business
process may be the maximal execution time of the en-
tire (Frankova et al., 2011) or parts of the business
process (del R
ıo-Ortega et al., 2015). By monitoring
the SLAs of a business process, it becomes possible to
prevent a violation of an SLA, e.g. by alerting experts
or by automatically removing/adding activities of the
business process (del R
ıo-Ortega et al., 2015).
In the context of this work, we implement an SLA-
based demand shifting to ensure that any alterations in
process execution (in favour of less CO
still remain compliant to the SLAs.
2.3 Predictive Process Monitoring
Predictive Process Monitoring (PPM) (Di Francesco-
marino and Ghidini, 2022) is a discipline aimed at
predicting future aspects of running business pro-
cesses. Here, history data from process executions
(event logs) are considered to train machine learn-
ing models. These models can then take as input a
running instance and predict different aspects such as
the next activity, the remaining time, or possible out-
comes (Di Francescomarino and Ghidini, 2022).
To train the models, traces from the event log, i.e.
a sequence of executed activities, are usually consid-
ered to recognise emerging patterns and behaviours.
To this end, process variables can be taken into ac-
count, such as the assigned employee.
In the approach presented in this work, remain-
ing time predictions (i.e. the time until the end of the
entire process instance) are leveraged to estimate the
potential degree of freedom for postponing current ac-
tivities (e.g. the postponement of an activity plus the
estimated remaining time should not exceed SLAs).
As our approach is applied to WfMSs, event logs
tracked by a WfMS can be used for model training.
For this work, we build on our previous results for
training machine learning models in Camunda (Bart-
mann et al., 2021). The tool presented in that work
can be used to train models in a user-friendly way and
provides an interface to predictions in Camunda.
2.4 Green Business Process
In broad terms, Green BPM is a specialisation of tra-
ditional BPM, aimed to lessen the impact of processes
on the natural environment (Seidel et al., 2012). Due
to an increasing societal and organisational attention
on topics such as climate change (cf. e.g. (Couckuyt
and van Looy, 2021)), the topicality of “Green BPM”
has again gained some recent momentum, with recent
works broadly agreeing on potential benefits and the
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
need for future research in this area (Couckuyt, 2018;
Couckuyt and van Looy, 2021). Also, there seems
to be a growing awareness that organisations do in
fact contribute to environmental degradation through
their processes and should therefore aim to implement
more environmentally sustainable processes (Seidel
et al., 2012; Couckuyt and van Looy, 2021). Follow-
ing (Opitz et al., 2014), Green BPM can be described
to have two main goals, namely 1) the reduction of the
environmental impact of business processes, and 2)
inducing cultural change w.r.t. more eco-friendly be-
haviour. This work focuses on the first goal, namely
means for reducing the environmental impact, con-
cretely, reducing the carbon footprint.
Regarding the existing works on reducing the en-
vironmental impact of business processes, most re-
search can be found in the area of design-time anal-
ysis (cf. (Maciel, 2017; Couckuyt and van Looy,
2020; Roohy Gohar and Indulska, 2020; Hoesch-
Klohe and Ghose, 2010; Ghose et al., 2010)). That
is, most works focus on facilitating means for mod-
elling processes in a more eco-aware way, for exam-
ple, by means of extensions of modelling notations
to indicate fuel consuming activities (Recker et al.,
2011), or design patterns for modelling processes in a
more ecologically sustainable way (Alexander Nowak
et al., 2014). Works such as (Ghose et al., 2010)
and (Hoesch-Klohe et al., 2010) annotate activities
in process models with carbon emissions at design
time to substitute fragments of the process models
with ones that preserve the behaviour but reduce the
carbon impact. However, the authors (Ghose et al.,
2010) acknowledge the fact that, at runtime, carbon
emissions may vary depending on the execution time.
Despite the clear benefits of such approaches, we do
see a current lack of Green BPM research in the area
of runtime monitoring, i.e. support for Green BPM
once processes are actually running. And in fact,
this research gap has also been identified in recent re-
search agendas, i.e. (Maciel, 2017; Couckuyt, 2018)
state that more attention should be paid to “[green]
process implementation and execution”, and the “de-
velopment of instruments”. Further, the authors in
(Ghose et al., 2010) argue that “process execution
management can also contribute to carbon footprint
minimization”. To close this existing research gap,
in this work, we develop new means for the opera-
tional support of reducing the environmental impact
of running processes, concretely, by developing eco-
aware monitoring techniques in combination with re-
sults from prescriptive process monitoring.
The Activity-Based Emissions (ABE) approach
presented in (Recker et al., 2011) is developed for
analysing the carbon footprint of processes and allows
to conceptualise the emissions of individual activities.
In this work, we follow the ABE approach and focus
on activities with high emissions due to high energy
consumption. Our work lifts the ABE approach to a
runtime perspective. In particular, our approach al-
lows to reduce the environmental impact of carbon-
aware process models at runtime, by orchestrating the
execution of individual activities s.t. these activities
are executed in time windows where green energy is
better available. This is mainly done by postponing
activities, as has also been proposed by (Zhu et al.,
2015). While other approaches comprise manufactur-
ing business processes (Ghose et al., 2010; Hoesch-
Klohe et al., 2010), we focus on digitised business
processes only.
Importantly, by leveraging predictive insights, ac-
tivities are postponed while still ensuring that pro-
cesses remain compliant, e.g. are in line with SLAs.
In such, our approach represents a novel form of
“Carbon-Aware” Prescriptive Process Monitoring.
Note that CO
emissions are not the only KPI
measurable in regard to the environmental impact (cf.
e.g. the dimensions mentioned in (Roohy Gohar and
Indulska, 2020) like water consumption, waste, etc.).
However, reducing CO
can be seen as an important
part of lowering the environmental impact of pro-
cesses (Roohy Gohar and Indulska, 2020), which is
why we focus on this aspect in this work.
In this section, we will present our approach to reduce
the environmental impact of business processes. We
begin by exploring important requirements for such
an approach and then continue with the concrete pre-
sentation and discussion.
3.1 Requirements
The main goal of our approach is to reduce the CO
emissions of digitised business processes. We pro-
pose to shift the execution of energy-intensive activi-
ties to time windows when green energy is available.
For such a run-time approach, we see the following
general requirements:
Typically, a WfMS is used to orchestrate various
services. The required energy for these services varies
widely. Getting an entry in a database might not use
much energy compared to complex tasks such as im-
age processing. Therefore, an approach is required
that is simple to use and takes a holistic view of the
business process instead of single services separately.
Carbon-Aware Process Execution for Green Business Process Management
Furthermore, the process instances need to con-
tinue to comply with the specified SLAs. In result,
when moving the execution of an activity to a later
time window, the overall process execution has to re-
main within these bounds.
Based on these observation, we therefore raise the
following requirements.
R1: The adjustment effort must be low. In princi-
ple, it would be possible to configure every ser-
vice orchestrated by a process individually to con-
sider the carbon intensity of the current energy
supply. However, that would entail a lot of ad-
justment efforts in many services. Ideally, only
small changes to the original process model are
R2: The business process must be optimised holisti-
cally. Rather than optimising services individu-
ally, the optimisation should aim for the business
process as a whole.
R3: The process instances need to comply with speci-
fied SLAs. When business processes are executed,
the specified SLAs must be adhered to. For ex-
ample, if the maximal allowed execution time for
a process instance is 3 hours, the approach needs
to ensure that even if execution is postponed in
favour of carbon savings, the process instance is
executed within this frame.
3.2 Proposed Approach for
Carbon-Aware Process Execution
To reduce CO
emissions produced by energy-
intensive activities, we propose to shift the execution
of these activities to time windows where green en-
ergy is available. For instance, before executing an
energy-intensive activity (e.g. image processing), it is
verified if green energy is available entailing immedi-
ate execution or otherwise it may be postponed.
Two main problems in this use-case are 1) get-
ting data on the availability of green energy, and 2)
deciding if and how long to postpone activities. For
problem 1), we build on the publicly available Car-
bon Aware SDK by the Green Software Foundation
(cf. Section 2.1). Regarding problem 2), a method is
needed for deciding how to postpone activities, e.g.
SLAs must be kept. In the following, we present our
proposed method for this decision task. See process
model in Figure 1 as a running example.
Following the ABE approach (Recker et al.,
2011), activities causing high emissions have to be
identified by domain experts, e.g. based on met-
rics such as energy consumption. In Figure 1, this
maximum execution time
time window for
② duraon of the
remaining tasks
③ duraon since
process start
execution for
greener energy
energy intense
Figure 1: Exemplary process model containing energy-
intensive activity adapted from (Hehnle et al., 2023).
is schematically shown as the activity “energy in-
tense work”. In case of such an activity, a new
model element, denoted a postponement activity, can
be added before this element (see “postpone execu-
tion for greener energy” in Figure 1). The postpone-
ment activity decides whether to postpone subsequent
activities to time windows when energy is less carbon-
intensive. To allow for making this decision, the post-
ponement activity is called with the following data:
the region in which the process is executed
the start time of the process instance
the maximal runtime until the end of the process
instance (SLA)
the (predicted) duration of the remaining activities
(cf. Section 2.3)
The region is required to determine the carbon-
intensity in the energy forecast. It is assumed that the
entire process and its called services are executed in
the same region. The time information (start time,
maximal execution time according to SLA, predicted
remaining time) are needed to calculate the possible
time window for postponing the carbon-intensive ac-
tivity. To prevent SLA violations, the execution may
not be postponed arbitrarily. Equation (1) describes
how the time window for postponement is calculated:
= t
) (1)
: time window for postponement, i.e. the maxi-
mal time subsequent activities may be postponed.
: maximal execution time of the business
process (i.e. the SLA)
: duration since process start
: predicted duration of the remaining activities
The calculation of the time window for postpone-
ment is visualised in Figure 1. The maximal execu-
tion time of a process instance, which must not be ex-
ceeded, can be seen at the arrow with the mark
according to an SLA). The (predicted) duration of the
remaining activities following the postponement-task
) as well as the time since the process in-
stance start (mark
) need to be deducted from this
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
maximal execution time, which results in the time
window for the postponement (mark
). In result,
the execution of activities can be postponed within
this time window while still considering the specified
maximal execution time. This ensures that carbon op-
timisation does not result in SLA violations.
Finally, the API by the Green Software foundation
is used to retrieve a forecast of the carbon-intensity
within the time window for the postponement. Based
on this forecast, the WfMS postpones the execution
of the energy-intensive activity until the optimal time
with regard to green energy.
3.3 Proof of Concept
To demonstrate our approach, we implemented a
proof of concept (PoC) using Camunda together
with the Green Software Foundation’s Carbon Aware
SDK. In the following, we refer to our PoC as the
Camunda Carbon Reductor. The Camunda Carbon
Reductor is publicly available
and can be used “out-
of-the-box” with any Camunda (7 or 8) installation.
In the following, the functionality of the Camunda
Carbon Reductor is described in interaction with Ca-
munda 8. Connectors are a special type of task in
Camunda. They act as reusable templates consisting
of a model element and a small application that inter-
acts with third party systems. For our PoC, the Ca-
munda Carbon Reductor is implemented via a con-
nector. It can be added to a process model before
energy-intensive activities. Configured accordingly,
it can automatically decide to postpone the energy-
intensive activity to a better time window, while still
ensuring the SLAs. Figure 2 highlights the configura-
tion possibilities of the Camunda Carbon Reductor:
1. The location where the energy-intensive activity
is executed in, which is required for the forecast.
2. An estimated duration of the remaining activities,
which may be specified manually or retrieved via
a remaining time prediction. The concrete meth-
ods for obtaining such a prediction are beyond the
scope of this report. We build on our previous tool
presented in (Bartmann et al., 2021), which allows
to retrieve such predictions directly in Camunda.
3. The milestone is a timestamp and the starting
point for the SLA based duration calculation.
4. The maximal duration of a process instance from
the milestone to finish to be SLA compliant.
Once the Camunda Carbon Reductor determined
the optimal time window for execution, a timer is set
to pause the execution until green energy is available.
Figure 2: Showcase of configuration possibilities of the Ca-
munda Carbon Reductor adapted from (Hehnle et al., 2023).
For analysis in the monitoring tool Camunda Op-
timize, results are written as variables into the process
instance (also shown in Figure 2)
3.4 Discussion
In the following, we discuss the fulfilment of the re-
quirements (cf. Section 3.1) as well as limitations.
3.4.1 Requirements Fulfilment
The benefit of the proposed approach is that only
the process model has to be adjusted in order to
save carbon emissions, and no further adjustments
are necessary for the called services. Therefore, R1
is met. The Camunda Carbon Reductor orchestrates
the called services and is thus able to achieve the
maximum carbon reduction for the business process
through carbon-aware optimisation. Here, R2 is sat-
isfied as well. The developer can specify the SLA
of the maximal execution time of the business pro-
cess, which is considered when postponing energy-
Carbon-Aware Process Execution for Green Business Process Management
intensive activities. Based on the specified SLA, the
predicted remaining time, and the duration since a
specified milestone, the time window for postpone-
ment is calculated. In result, R3 is met as well.
3.4.2 Limitations
If a business process is run in a region where the de-
mand of energy is higher than green energy is avail-
able postponing activities will not reduce emissions.
Furthermore, only activities whose consumption is
higher than that of the Camunda Carbon Reductor
should be postponed or otherwise more carbon emis-
sions are produced than saved. However, measuring
the energy consumption of software is difficult and
still a subject of research (Ardito et al., 2019). Inte-
grating the Camunda Carbon Reductor multiple times
in one process model may result in SLA violations
as all Camunda Carbon Reductors might exploit the
maximal execution time. When subsequent activi-
ties after the Camunda Carbon Reductor include user
tasks it becomes difficult to predict the remaining time
of the process. When the Camunda Carbon Reductor
postpones the execution of a business process and in
addition to that later on in the business process it takes
more time than anticipated for a human to complete a
user task it may result in an SLA violation as well.
Finally, the current approach favours only the process
performance dimension of emitted carbon. However,
it might be necessary to consider a trade-off among
time, cost, quality, and carbon emissions.
The approach as well as the Camunda Carbon Re-
ductor are evaluated in the following. First, a case
study explores the fitness in production use. Then, we
present feedback from experts gained in interviews.
4.1 Case Study
We conducted a case-study to investigate the poten-
tial benefits of our approach. For this, we created
an exemplary process model and simulated its exe-
cution within a Camunda installation. The process
model (shown in Figure 3) consists of a start event,
an energy-intensive activity and an end event. The
Camunda Carbon Reductor was included as a prede-
cessor activity of the energy-intensive activity. We
then used Camunda to run concrete process instances
of this process over the course of one week (every 30
minutes, a process instance was started). Importantly,
the execution was performed in real-time, allowing to
also obtain real-time energy forecasts (meaning the
data obtained from the case study reflects the actual
savings!). In total, 173 process instances were
executed in this time. For the energy-intensive ac-
tivity, an execution time of one hour was assumed.
Furthermore, an exemplary SLA of 23 hours was as-
sumed. In result, there is a time window of 22 hours
to postpone the energy-intensive activity. The activity
was assumed to be run in a data centre in the region
West US. The results of the case study are depicted in
Figure 3.
Figure 3: Case study results (Camunda Optimize Dash-
board) adapted from (Hehnle et al., 2023).
The data collected by the Camunda Carbon Recu-
tor allow to report KPIs such as the amount of emis-
sions reduced per kWh. As can be seen, 17.9 thou-
sand gCO
/kWh could be saved over all instances. On
average, 103.4 gCO
/kWh were saved.
Furthermore, the dashboard allows to track the
progress of goals, e.g. an instance should ideally save
at least 10% of carbon. 74.72% of all (173) instances
satisfied this goal. Among all instances on average
23% CO
emissions could be saved.
4.2 Expert Interviews
To evaluate the plausibility of our approach, we con-
ducted interviews with eight domain experts who
were approached from a pool of industrial partners,
but not associated with the affiliations of the authors.
An overview of the experts is shown in Table 1.
As can be seen, all experts had a strong back-
ground in BPM and Camunda. Especially, inputs
from experts A, B, D, E and H, who explicitly work on
implementing Camunda in industrial settings, make
us confident the interview partners were very suitable
to provide feedback. With each expert, we conducted
an interview via Microsoft Teams. At least two re-
searchers were present in every interview and an pro-
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
Table 1: Expert panel overview.
ID Job Title Experience
with BPM/
A Developer Advocate 9 years Software Vendor
B Developer Advocate 1 years Software Vendor
C Senior Consultant 12 years IT Consulting
D BPM Consultant 6 years IT Technology
E IT Consultant 5 years IT Consulting
F Principal Scientist 11 years Software Vendor
G Data Scientist 3 years Tech Company
H Software Architect
Process Development
3 years Insurance Company
tocol was followed to support internal validity: First,
the general approach was motivated. Then, the PoC
was shown to the experts, followed by a roughly 20-
30 minute discussion. After the interview, the ex-
perts were shown four statements (e.g. “I found the
tool useful”), to which they should indicate whether
they agreed or not (on a 5-point Likert scale from
strongly agree to strongly disagree). The statements
were based on the technology acceptance model. Fig-
ure 4 shows our survey results.
easy to
Figure 4: Results of the conducted user survey (Statements:
“I found the tool...”), on a 5-point Likert scale from strongly
agree (+2) to strongly disagree (-2).
As can be seen from Figure 4, our approach was
uniformly seen as useful and easy to use. It was es-
pecially mentioned that the approach was very well
integrated, which is in line with requirement R1. The
experts did see a slight need for training when using
such a tool, but this was seen as expectable.
Also from the open questions/interview, the over-
all approach was uniformly well-received. The ex-
perts were specifically asked whether they could
imagine to use the PoC in one of their industrial
project, to which ve experts answered yes. Expert
C emphasised that the PoC is especially useful and
necessary in sectors that run huge simulations, e.g in-
surance companies run simulations before many con-
tracts. Expert D indicated that the approach should
also be able to transfer the execution of an activity to
a different location where green energy is available.
Experts F and G proposed a configuration to specify
a minimum saving of CO
which need to be achieved
to postpone the execution or otherwise the execution
will be continued immediately. Expert B argued that it
might become necessary for a human process partici-
pant to manually resume a paused process instance.
Climate change is a challenge for society as a whole.
The ICT Sector also needs to contribute to overcome
it by reducing carbon emissions (Gohar et al., 2020;
Seidel et al., 2012). Research has unveiled various
strategies to reduce carbon emissions of ICTs, one of
which is them being executed carbon-aware , i.e. time
windows can be chosen for running the ICT solutions
in which green energy is available.
In this work, we applied the well-established ap-
proach of carbon-aware execution from the field of
sustainable ICTs to business processes, i.e. energy-
intensive activities are executed in time windows in
which green energy is available. While traditional
BPM focuses on the performance dimensions time,
cost, and quality, Green BPM has evolved to reduce
the environmental impact of business processes. Pre-
vious approaches of Green BPM push for design time
optimisation, whereas we focus on the runtime.
The Camunda Carbon Reductor is a proof of con-
cept that implements the presented approach and is
publicly available. A case study revealed that the use
of the Camunda Carbon Reductor can achieve sub-
stantial CO
reduction. In this context, the conducted
expert interviews underline these findings, where the
majority of experts confirmed the need, usefulness
and plausibility of our approach.
Despite the achievements in the case study and the
good response in the expert survey, the Camunda Car-
bon Reductor may be improved. Currently, an ele-
ments needs to be integrated into the process model,
whereas in the future, no element shall be needed.
Also, in future work, the Camunda Carbon Reductor
shall be able to relocate the execution of business pro-
cesses to data centres where green energy is available.
Currently, it is reasonable to pause the execution of a
business process only at one position. However, mul-
tiple energy-intensive activities in a process model
shall be considered. Also, we plan to integrate results
from multi-instance predictive process monitoring to
verify whether activities from parallel instances can
be postponed in a batch, e.g. image processing tasks
from different process instances can be batched, s.t.
the hardware used for the image processing does not
have multiple (energy-intensive) “cold-starts”.
As stated, CO
emissions are not the only KPI in
regard to the environmental impact (e.g. water con-
sumption, waste (Roohy Gohar and Indulska, 2020)).
However, reducing the carbon footprint is uniformly
viewed as important in the context of Green BPM
(Gohar et al., 2020), which makes us confident that
the presented approach is a step towards improving
the environmental impact of business processes.
Carbon-Aware Process Execution for Green Business Process Management
Alexander Nowak, Uwe Breitenb
ucher, and Frank Ley-
mann (2014). Automating green patterns to compen-
sate co2 emissions of cloud-based business processes.
In Proc. 8th Int. Conf. ADVCOMP., pages 132–139.
Xpert Publishing Services.
Ardito, L., Coppola, R., Morisio, M., and Torchiano, M.
(2019). Methodological guidelines for measuring en-
ergy consumption of software applications. Scientific
Programming, 2019:1–16.
Bartmann, N., Hill, S., Corea, C., Drodt, C., and Delfmann,
P. (2021). Applied predictive process monitoring and
hyper parameter optimization in camunda. In Proc.
CAiSE Forum 2021, pages 129–136. Springer.
Couckuyt, D. (2018). An overview of challenges and re-
search avenues for green business process manage-
ment. In On the move to meaningful internet systems,
volume 10697 of LNCS Inf. sys and applications, incl.
internet/web, and HCI, pages 270–279. Springer.
Couckuyt, D. and van Looy, A. (2020). A systematic review
of green business process management. Business Pro-
cess Management Journal, 26(2):421–446.
Couckuyt, D. and van Looy, A. (2021). An empirical study
on green bpm adoption: Contextual factors and perfor-
mance. J. of Software: Evolution and Process, 33(3).
del R
ıo-Ortega, A., Guti
errez, A. M., Dur
an, A., Resinas,
M., and Ruiz-Cort
es, A. (2015). Modelling service
level agreements for business process outsourcing ser-
vices. In Advanced inf. sys. eng., volume 9097 of
LNCS, pages 485–500. Springer.
Di Francescomarino, C. and Ghidini, C. (2022). Predictive
process monitoring. In Process Mining Handbook,
volume 448 of LNBIP, pages 320–346. Springer In-
ternational Publishing.
Frankova, G., S
eguran, M., Gilcher, F., Trabelsi, S.,
orflinger, J., and Aiello, M. (2011). Deriving busi-
ness processes with service level agreements from
early requirements. J. Syst. and Softw., 84(8):1351–
Freitag, C., Berners-Lee, M., Widdicks, K., Knowles, B.,
Blair, G. S., and Friday, A. (2021). The real climate
and transformative impact of ict: A critique of esti-
mates, trends, and regulations. Patterns, 2(9):100–
Ghose, A., Hoesch-Klohe, K., Hinsche, L., and Le, L.-S.
(2010). Green business process management: A re-
search agenda. Australasian J Inf. Sys., 16(2).
Gohar, S. R., Indulska, M., et al. (2020). Environmental
sustainability through green business process manage-
ment. Australasian J. Inf. Sys., 24.
Hasan, S., King, Z., Hafiz, M., Sayagh, M., Adams, B., and
Hindle, A. (2016). Energy profiles of java collections
classes. In Proc. 38th Int. Conf. Softw. Eng., pages
225–236. ACM.
Hehnle, P., Weinbrecht, L., and Behrendt, M. (2023). The
camunda 8 connector for carbon-aware process ex-
aware-process-execution-connector/ (retrieved
Hoesch-Klohe, K. and Ghose, A. (2010). Carbon-aware
business process design in abnoba. In Proc. 8th
Int. Conf. Service Oriented Comp., pages 551–556.
Hoesch-Klohe, K., Ghose, A., and L
e, L.-S. (2010). To-
wards green business process management. In Int.
Conf. Services Comp., pages 386–393. IEEE.
Kim, S., Kim, H.-K., and Kim, H. J. (2009). Climate change
and icts. In INTELEC 09, pages 1–4. IEEE.
Maciel, J. C. (2017). The core capabilities of green business
process management—a literature review. In Proc.
Int. Conf. Wirtschatsinformatik, pages 12–15.
Matemilola, S., Fadeyi, O., and Sijuade, T. (2020). Paris
agreement. In Encyclopedia of Sustainable Manage-
ment, pages 1–5. Springer International Publishing.
Oloo Ajwang, S. and Nambiro, A. (2022). Climate change
adaptation and mitigation using information and com-
munication technology. Int. J. Comp. Sci. Research,
Opitz, N., Krup, H., and Kolbe, L. M. (2014). Green busi-
ness process management a definition and research
framework. In 47th Hawaii Int. Conf. Sys. Sci., pages
3808–3817. IEEE.
Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chat-
terjee, S. (2007). A design science research methodol-
ogy for information systems research. J. management
inf. sys., 24(3):45–77.
Pereira, R., Couto, M., Ribeiro, F., Rua, R., Cunha, J., Fer-
nandes, J. P., and Saraiva, J. (2017). Energy efficiency
across programming languages: how do energy, time,
and memory relate? In Proc. 10th ACM SIGPLAN Int.
Conf. Softw. Language Eng., pages 256–267. ACM.
Radovanovic, A., Koningstein, R., Schneider, I., Chen, B.,
Duarte, A., Roy, B., Xiao, D., Haridasan, M., Hung,
P., Care, N., Talukdar, S., Mullen, E., Smith, K.,
Cottman, M., and Cirne, W. (2023). Carbon-aware
computing for datacenters. IEEE Transactions on
Power Systems, 38(2):1270–1280.
Recker, J., Rosemann, M., and Gohar, E. R. (2011). Mea-
suring the carbon footprint of business processes.
In BPM 2010 Int. Workshops and Education Track,
pages 511–520. Springer.
Roohy Gohar, S. and Indulska, M. (2020). Environmental
sustainability through green business process manage-
ment. Australasian J. Inf. Sys., 24.
Seidel, S., Recker, J., and vom Brocke, J. (2012). Green
business process management. In Green business pro-
cess management, Springer Management/Business for
professionals, pages 3–13. Springer.
van der Aalst, W. M. P. (2013). Business process manage-
ment: A comprehensive survey. ISRN Software Engi-
neering, 2013:1–37.
Zhu, X., Zhu, G., vanden Broucke, S., and Recker, J.
(2015). On merging business process management
and geographic information systems: Modeling and
execution of ecological concerns in processes. In Geo-
informatics in resource management and sustainable
ecosystem, volume 482 of Communications in Com-
puter and Inf. Sci., pages 486–496. Springer.
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