Gauging Carbon Footprint of AI/ML
Implementations in Smart Cities: Methods and
Challenges
2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC) | 979-8-3503-3452-4/22/$31.00 ©2022 IEEE | DOI: 10.1109/FMEC57183.2022.10062634
Rajkumar P.V.
Computer Information Systems and Analytics
University of Central Missouri
Warrensburg, MO, USA.
rajkumarpv@ucmo.edu
Abstract—A smart city aspires to enhance quality of life, optimize city operations, and promote economic growth with the use
of AI/ML techniques. However, the AI/ML techniques themselves
often produce carbon emission due to their high demand for
computations during their training. Environmentally sustainable
Smart Cities require systematic measure of its carbon footprint
and approaches to reduce carbon emission from cities backbone
edge networks and cloud data centers. This work studies the
methods and challenges in gauging the carbon footprint produced
by the AI/ML implementations in smart cities.
Index Terms—Smart City, Planning, Artificial Intelligence,
Machine Learning, Model Training, and Carbon Footprint.
I. I NTRODUCTION
Big data and smart cities have recently gained popularity.
The idea that data can be used to make cities smarter over
many spatial and temporal scales is the synergy of both
concepts. Over the past few years, ideas for “smart cities”have
attracted a lot of attention as they incorporate technologies
such as Cloud, Edge, and their combinations to improve the
caliber and effectiveness of smart services. Smart city planning
and implementation applies Information and Communication
Technologies for collecting, storing, and processing data generated at various temporal and spatial points within the city.
AI implementation can leverage the local computing infrastructures and public cloud resources along with high volume
of available data sets to develop and train AI models and
deploy smart services inside smart city. Since Cloud combined
with Edge computing supports near real-time data processing and intelligent response to various service requests, this
combination is expected to grow in coming years. In general,
effective AI/ML model construction processes often require
data center scale hardware units that consumes lot of electricity
for computations. Further, accuracy improvements of AI/ ML
based algorithms also depend on the availability of real-time
data feeds and continuous feedback loop to tune the model.
For example, accuracy improvements in neural networks
largely depend on the availability of exceptionally large computational resources. Deep Learning (DL) has achieved notable
gains across many automated tasks in smart cities, but this
often comes at the cost of training the models for extensive periods on specialized hardware accelerators. Although
training AI/ ML models can require a lot of energy, they
are usually used to improve the efficiency of many tasks
that would otherwise require more time, space, human effort,
and potentially electricity too [1]. Given all these issues,
machine learning (ML) training may soon become a significant
contributor to climate change if this exponential trend continues. Specifically, the training phase is computationally more
demanding and energy intensive. This work explores various
factors contributing to AI/ML carbon emission and the reviews
existing carbon emission measurement methods.
A few crucial aspects of training the models that have a
major impact on the quantity of carbon that it emits include:
the location of the server used for training and the energy grid
that it uses, the length of the training procedure, and even the
make and model of hardware on which the training takes place
[1]. If the current trend continues, carbon emission by the
cloud-based data centers has been projected to contribute to
almost 3.2 percent of global carbon emissions. Even though
the big cloud vendors have started using renewable sources
of energy and have invested in waste heat recycling and
carbon offsetting schemes, environment aware utilization of
AI by the individual applications and end users still requires
improvement.
Efficient and systematic methods need to be implemented
in order to bring down the ballooning of carbon footprint of
AI/ ML training and inference models [2]. Thus, there is an
impending need to conduct more research on effective usage
of energy by the edge data centers within smart cities, besides
reporting to reporting to end consumers of AI. Though some
considerable study has been conducted about gathering the
amount of carbon generated by AI/ ML training models in
the cloud data centers, yet an in-depth study is required to be
performed on the various parameters considered to determine
the carbon footprint of various AI/ ML model training used in
various edge data centers that are applicable for smart cities.
This work is focused on reviewing some of the available
carbon footprint measurement methods relevant to AI/ML.
Then, it presents the key observations and future work.
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II. BACKBONE I NTELLIGENT E DGE FOR S MART C ITIES
Smart cities are designed to improve quality of life for
citizens, and they are based on a set of goals [23]: sustainability, inclusivity, and social and economic growth. Smart
cities invest in technology infrastructure they deploy the
required information and communication technology (ICT)
platforms across the city; and doing so in such a way as to
support the integration of information and activity across city
systems. Smart cities are about more than just technology.
They are about the way we use it to transform our cities and
communities.
Smart cities are built on the premise that technology can
help us solve problems and enhance our daily livesand that
includes the way we design and implement that technology.
Smart platforms include networks such as 5G and broadband
[24]; communication tools such as telephony, social media and
video conferencing; computational resources such as cloud and
edge servers; information repositories analytic and modeling
tools that can provide deep insight into the behavior of city
systems as shown in Figure 1.
efficient, suitable response from various smart services, good
AI/ML models with adequate training are required.
As discussed in the introduction section such AI/ML produces significant carbon emissions. Eco-friendly smart cities
require accurate measure of carbon emissions due to artificial
intelligence generation from the citys back end intelligent
network and the connected cloud. Two types of carbon meters
shown in the figure 1 are notional. First type is meant for
measuring carbon emissions from the edge network and the
cloud backend connected to the smart city. This readings in
this meter must be details such that it helps to fine tune
the ML model’s parameters to minimize the carbon emission.
Second type is meant for reporting the carbon emission to the
individual smart applications such as that end users become
aware and responsible to make an eco-friendly smart city by
adopting to need based and environmentally responsible use
of AI.
III. C ARBON F OOTPRINT M EASUREMENT & E FFORTS TO
M AKE E CO -F RIENDLY AI
Power Usage Effectiveness (PUE) is a standard measurement used for optimize DC energy efficiency. The nonlinearity of the different DCs subsystem interactions and their
inter-dependencies make the prediction of PUE difficult since
it is affected by complex factors such as workload variation,
weather conditions and humidity etc. A PUE of 1 indicates that
system is operating at maximum efficiency, excluding energy
for cooling, lighting, other non-IT systems. In most cases, a
PUE of 1.5 to 1.8 is regarded to be effective [3].
A. Green House Gas Protocol
Figure 1: AI/ML Powered Smart City.
Smart network coverage boundary of a city includes smart
vehicles, smart parks, smart buildings along with connectivity
to the edge and cloud that provides access to information
systems, digital marketplace platforms, and local currencies
that reinforce regional economic synergies. To meet stringent
reliability and efficiency requirements more resources will be
required leading to higher overheads. For example, in case of
smart traffic management system each car must be fitted with a
sensor and thousands of roadside units must be installed. Such
system cannot afford a downtime and must be highly efficient
and reliable. In a typical urban city. Critical systems cannot
afford downtime and requires very high availability. Challenge
of meeting the tough requirements of availability is addressed
by city backbone intelligent edge with intent connect cloud.
The key layers of a smart city are ICT infrastructure, egovernance and the city departments.
ICT infrastructure is the foundation of a smart city, which
forms the basis on which all other components rely. It comprises high speed wired and wireless network connectivity,
high end data centers, physical space enrichment with smart
devices, sensors, actuators and much more. To offer fast,
The work in [4] states that though the Green House Gas
Protocol provides a standardized methodology for assessing
data center emissions it may not be feasible for customers
to get access to all sort of data required for assessing the
carbon footprint of their workload in the Cloud. This work
performs an analysis showing that data required to calculate
carbon emissions as per the standards of the GHG Protocol
is available exclusively to the Cloud Vendors. Even though
the Power Usage Effectiveness (PUE) data is being regularly
published by these vendors it is still not detailed enough to
provide the inputs required for calculation by the customers.
Thus, it is not possible for cloud customers to follow the GHG
Protocol for calculation of emissions from their cloud computing workloads. Besides, GHG protocol does not take into
consideration metrics such as the Water Usage Effectiveness
(WUEsource), Renewable Energy Factor (REF) and Energy
Reuse Factor (ERF) and heavily depends on PUE as a metric
of calculating carbon emissions
B. Quantifying Carbon Emission of Machine Learning Models
Quantifying the energy required to train four popular offthe-shelf NLP models in [4] has: Tensor2Tensor (T2T), ELMo,
BERT, GPT-2. As a result of training these models, the cost
of training these models have been listed in terms of kilowatthours, carbon emissions, and cloud compute cost. Training of
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BERT costs lower than training other models. Even though
training a single model may seem inexpensive but the cost of
tuning a model as per the input data set often becomes pricey.
Evaluation of the energy use and carbon footprint of several recent large models like T5, Meena, GShard, Switch
Transformer, and GPT-3 are given in [6]. The authors have
also worked on refining the earlier estimates for the neural
architecture Evolved Transformer. According to this work,
the authors have discussed that if these models are deployed
sparsely, the consumed energy would be less than 1/10th
of what it consumes when deployed densely. Cloud based
data centers with ML specific hardware are more efficient
in handling such compute intense ML workloads. Moreover,
the choice of specific regions, locations and datacenter infrastructure also contributes differently in the emission of carbon.
Besides evaluating the carbon footprint of ML models, the
authors are working on collaborating with MLPerf developers
from industries to include energy usage during training and
inference.
A preliminary exploration of the energy use profile of
ML training in the cloud is conducted in [7]. Based on the
observations this work has explained on how transfer learning
can be used to optimize energy consumption by AI/ ML
training models. A Machine Learning Emissions Calculator is
presented in [1]. This work had developed a software tool to
get an estimation of the amount of carbon emissions that occur
after training the ML tools. The factors considered for estimation of emissions includes the CO2 -equivalents (CO2 eq),
this standardized measure used for expressing the globalwarming potential of various greenhouse gases measurement
was taken from [17]. Data regarding CO2 eq emissions of
different grid locations was collected from Google Cloud
Platform, Microsoft Azure and Amazon Web Services. This
Emissions Calculator tool helps to calculate direct carbon
emissions resulting from ML research and thus helps in
taking minute decisions about choosing appropriate resources
in model training.
C. Carbon Tracker and Green Algorithms
A tool for tracking and predicting the energy consumption
and carbon emissions of training Deep Learning models and
named it as Carbontracker [8]. The tool Carbontracker measures carbon footprint of AI/ ML models in a way similar
to the tool developed in [9] with some advanced features;
this tool supports predictions in order to estimate the time
the training should stop and additionally this tool runs on a
wide range of software platforms such as clusters, desktop
computers and Google Colab notebooks. The authors have
suggested that if the total energy and carbon footprint of AI/
ML model development and training can be reported alongside
accuracy and similar metrics then it would promote responsible
computing in ML and research.
A simple framework experiment-impact-tracker that facilitates real time tracking of energy consumption and carbon
emissions introduced in [9]. Apart from that the framework
can also generate standardized online appendices. Realtime
carbon intensity is quantified in terms of gCO2eq/kWh. The
main goal of developing this framework is to ease the burden
of standardized reporting and encourage the positive impacts
of adopting carbon mitigation strategies. The authors have
provided a list of suggestions for mitigating the effects of
carbon emission. Survey of the existing tools and techniques
developed for quantifying energy usage and CO2 emissions
of AI algorithms that are used to train Natural Language
Processing (NLP) methods is given in [10]. Six software
tools developed for measuring carbon footprint of AI/ ML
in data centers carbontracker [8], Experiment Impact Tracker
[9], Green Algorithms [9] have been enlisted and the detailed
scope and limitations of these tools have been discussed. The
online tools Green Algorithms [11] and ML CO2 impact [1]
are very convenient to use for measuring the carbon footprint
of NLP methods as these tools do not require installation.
Various methods and tools/techniques used to quantify the
impact on climate and weather change as a result of using
AI/ ML tools are given in [12]. This work has also assessed
the various advancements made in the field of Health care by
National Health Service (NHS) with the aid of AI/ML tools.
Alongside they have assessed the potential use of AI/ML tools
in improving the climate and offsetting carbon emissions.
Method to enables a user to estimate and report the carbon footprint of their computation is given in [11]. These
authors have developed a generalized software framework
tool known as ‘Green Algorithm’(www.green-algorithms.org)
which easily integrates with computational processes. This
tool processes minimal information while being adaptable on
a large number of hardware configurations and moreover it
does not interfere with existing code. This tool can assess the
GHG emissions/ carbon footprint of nearly any computational
algorithm. According to this work, the carbon footprint of an
algorithm depends on two factors: ‘the energy needed to run
it’and ‘the pollutants emitted when producing such energy’.
The computing resources such as the number of cores, running
time, and data center efficiency decides the energy needed
to run the algorithms and the pollutants specifically known
as carbon intensity, depends on the location and production
methods used such as nuclear, gas, coal etc.
D. Cloud Services and Sever-less Computing Sustainability
AWS is over 2.5 times more energy efficient according
to 451 Researchs Voice of the Enterprise survey on cloud,
hosting and managed services as compared to all surveyed
US enterprises, on the ground of data center facility efficiency
AWS is over 3.6 times more energy efficient as compared to
its peer vendors. The power usage effectiveness (PUE) for
AWS data-centers ranged from 1.63 to 1.70 as per industry
data collected by the Uptime Institute. Coal and gas are the
major energy sources for the US electrical grid. In most of
the major US regions the carbon intensity ranges between 300
and 500 grams of CO2 per kilowatt-hour. AWS mostly uses
renewable sources of energy, hence working on extensively
reducing the carbon intensity of its electrical purchase. It
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has renewable power-purchase agreements (PPAs) besides
associated renewable energy credits (RECs) [13].
Method to evaluating the quality of server-less computing
sustainability is presented in [14]. In server-less computing, the
client can pay per-sub-second use of computational resources.
In order to be able to ensure that the server-less computation
services retain warm containers, the resource providers often
keep passing in synthetic data to keep the containers up and
running. This study has delved into the sustainable execution
of deep neural network-based AI inference jobs on server-less
platforms. The authors have suggested that these server-less
platforms can enable run-time-specific energy-aware optimization.
A neural network framework to model plant performance
and predict PUE is given in [15]. This neural network model
was trained based on data obtained from actual operations data
with error of 0.4% for a PUE of 1.1. After extensive training,
testing and validation of the model at Google data centers, the
author concluded that these trained models can be leveraged
for further modeling of data center performance with an aim
to improve energy efficiency. Development of such models
help in the comparison of actual vs predicted data center
performance under any given set of conditions. Any data center
operator may benefit from such results regarding automatic
performance alerting, real time plant efficiency targets and
troubleshooting. Also, it may help to evaluate PUE sensitivity
to operational data center parameters.
E. AI for Eco-friendly AI
AI techniques have widespread range of cross-cutting use
cases. AI tools and techniques can be used in environmental
planning and decision making, AI can be used to predict
diseases, improve resource, energy and material efficiency,
improve planning of transport systems and infrastructure,
enhance processes in automobile industries to charging of
electric vehicles, predicting weather and climate changes,
management of railway systems etc. On the flip side, training
these compute-intense AI models have direct negative environmental effects. By 2030, the ICT sector would be consuming
20% of the worlds total electricity. Greenhouse gas emissions
(GHG) from the ICT sector was projected to be about 1.3
Gt CO2 eq in 2020. Immediate measurements are required
to optimize such emissions with the use of energy-efficient
techniques, use of renewable sources of energy should be
promoted in the AI innovation process. The European Green
Deal, in particular the European data strategy with its plans
for a specific Common Europe Green Deal Data Space and
the initiative GreenData4All intends to promote fairness and
prosperity and an efficient, competitive, and more sustainable
economy for Europe [2].
The factors that influence the emissions of carbon in AI/
ML research and mainly focused on deciding the tradeoffs
between GHG emissions generated as a result of using AI/
ML tools vs the notable gains made in different sectors of
science, engineering and applications with the advent of AI/
ML tools are assessed in [16]. Finally, they discussed the
gambits of adopting AI/ ML in research vs the carbon footprint
generated by undertaking this type of research and provided
13 recommendations. They have highlighted the need for
European Union to play a key role in deciding on the design
for harnessing opportunities via AI tools to combat climate
change.
IV. C HALLENGES IN M EASURING AI/ML C ARBON
F OOTPRINT IN S MART C ITIES
AI/ ML training jobs in large Cloud data-centers are becoming one of the major sources of Smart Citys carbon emissions.
Though a lot of initiatives have been taken by the major Cloud
vendors, still much more efficient system needs to be developed and implemented for controlling the carbon emissions
and keeping the planet green. The main obstacle in reducing
AIs climate impact is proper measurement and quantification
of its energy consumption and carbon emission. Basically, the
solutions available to mitigate the carbon emissions problem
are as good as the accuracy of measurements that are in place.
One more important requirement is that the customers and end
users of AI based smart applications should have access to the
carbon emission data individualized for each smart application.
Given the obvious benefits that the AI methods brings along
with its ever-developing new techniques and algorithms, AI/
ML methods are anyways going to be the driving force of
smart city technologies in future; hence there is no way to stop
their AI/ML training in order to completely avoid the risks. A
pragmatic approach is to develop more systematic methods to
measure and feed emission data to the carbon meters designed
for both end users and edge-cloud service operators.
V. K EY O BSERVATIONS AND F UTURE W ORK
A perspective on green intelligence for smart cities is
presented in [29]. Problems such as (a) systematically mapping
the quantity of carbon produced by the ML mode training
task and the artificial intelligence utilized by the various smart
applications within the smart city, (b) metrics for quantifying
the utility of artificial intelligence in different applications
for the smart city occupants, (c) localized and distributed
artificial intelligence support for smart applications, (d) data
reduction with minimal loss of intelligence precision require
development of new suitable methods for carbon footprint
measurement.
This work presents a preliminary study on measuring carbon
emission due to AI/ML models for smart applications. Limitations, feasibility, and applicability of different carbon emission
measurement methods for specific smarty city applications
require further analysis and study. Usage control models
provide continuous authorization of processes and devices
while using smart city data and services. In future work, we
would like to develop decentralized security and usage control
authorization framework based on our earlier works in [18]–
[20], [25]–[28]. Safe implementation of smart applications for
smart cities based on [21] and [22] are also considered for
future work.
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VI. C ONCLUSION
A number of software tool and application software have
been developed to measure the carbon footprint of AI/ ML
models. A number of frameworks have been developed to
support the evaluation and measurement activities for tracking
carbon emissions from different AI/ ML training and inference
jobs in major data centers. However, smart city specific carbon
emissions measuring methods and carbon meters to report
them are still in early stage of development. It requires
systematic planning and approach stating from mapping the
ML parameters, training data, and accuracy requirements for
various smart applications within smart cities.
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