The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations

Abstract

In this paper, we critique ICT's current and projected climate impacts. Peer-reviewed studies estimate ICT's current share of global greenhouse gas (GHG) emissions at 1.8%–2.8% of global GHG emissions; adjusting for truncation of supply chain pathways, we find that this share could actually be between 2.1% and 3.9%. For ICT's future emissions, we explore assumptions underlying analysts' projections to understand the reasons for their variability. All analysts agree that ICT emissions will not reduce without major concerted efforts involving broad political and industrial action. We provide three reasons to believe ICT emissions are going to increase barring intervention and find that not all carbon pledges in the ICT sector are ambitious enough to meet climate targets. We explore the underdevelopment of policy mechanisms for enforcing sector-wide compliance, and contend that, without a global carbon constraint, a new regulatory framework is required to keep the ICT sector's footprint aligned with the Paris Agreement.

Full text

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Therealclimateandtransformative
impact of ICT: A critique
of estimates, trends, and regulations
Charlotte Freitag,
1
Mike Berners-Lee,
1
Kelly Widdicks,
2,
*Bran Knowles,
2
Gordon S. Blair,
2
and Adrian Friday
2
1
Small World Consulting, Gordon Manley Building, Lancaster Environment Centre, Lancaster University, Lancaster, Lancashire LA1 4YQ, UK
2
School of Computing and Communications, InfoLab21, Lancaster University, Lancaster, Lancashire LA1 4WA, UK
*Correspondence: k.v.widdicks@lancaster.ac.uk
https://doi.org/10.1016/j.patter.2021.100340
SUMMARY
In this paper, we critique ICT’s current and projected climate impacts. Peer-reviewed studies estimate ICT’s
current share of global greenhouse gas (GHG) emissions at 1.8%–2.8% of global GHG emissions; adjusting
for truncation of supply chain pathways, we find that this share could actually be between 2.1% and 3.9%. For
ICT’s future emissions, we explore assumptions underlying analysts’ projections to understand the reasons
for their variability. All analysts agree that ICT emissions will not reduce without major concerted efforts
involving broad political and industrial action. We provide three reasons to believe ICT emissions are going
to increase barring intervention and find that not all carbon pledges in the ICT sector are ambitious enough to
meet climate targets. We explore the underdevelopment of policy mechanisms for enforcing sector-wide
compliance, and contend that, without a global carbon constraint, a new regulatory framework is required
to keep the ICT sector’s footprint aligned with the Paris Agreement.
INTRODUCTION
The Information and Communication Technology (ICT) sector
has seen massive and accelerating growth in the last 70 years.
ICT is now so significant that there is an increasing awareness
of the potential environmental effects of ICT, particularly on
climate change. ICT has a growing ‘‘carbon footprint’’ arising
from greenhouse gases (GHG) released from all its life cycle
stages. This includes embodied emissions (the GHG emissions
released from the extraction of raw materials required, the
manufacturing process and transport to the business or user),
use or operational emissions (from energy use and maintenance)
and end-of-life emissions (disposal). Yet estimates of ICT’s foot-
print and whether it is in fact growing in impact, or held stable or
even reducing by efficiency gains and Moore’s Law, is very much
a topic of lively debate. Many increasingly point also to ICT’s po-
tential to decarbonize other sectors. It is argued that this ‘‘ena-
blement’’ is a key ingredient in the pathway to carbon neutrality,
and in many ways exempts or justifies the footprint of ICT itself.
In this paper we look at accepted estimates of climate change
impacts of ICT now and in the future (Estimating the carbon foot-
print of ICT) and ask critical questions concerning efficiency:
THE BIGGER PICTURE To avoid catastrophic consequences from climate change, all sectors of the global
economy, including Information Communication Technology (ICT), must keep their greenhouse gas (GHG)
emissions in line with the Paris Agreement. We examine peer-reviewed estimates of ICT’s GHG emissions,
which put ICT’s share of global GHG emissions at 1.8%–2.8%. We find pronounced differences and much
debate concerning the underlying assumptions behind the peer-reviewed studies, which could suggest
that global emissions from ICT are as high as 2.1%–3.9%. All study analysts agree that ICT emissions will
not reduce without major concerted political and industrial efforts, and we provide three reasons for antici-
pating that ICT emissions are actually going to increase without intervention. Our analysis suggests not all
ICT carbon pledges are ambitious enough to meet climate targets, and that policy mechanisms for enforcing
sector-wide climate target compliance are lacking. Without a global carbon constraint, sector-wide regula-
tions are required to keep ICT’s carbon footprint aligned with the Paris Agreement. With a global carbon
constraint, ICT would be a greater enabler of productivity and utility, creating opportunity for the sector to
be financially successful as a critical part of a global net zero society.
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This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

whether efficiency gains could reduce emissions in the ICT sector
and global economy over time, or whether these are more than
offset by possible ‘‘rebound effects.’’ In this context, we take a
broad view of rebound effects to include any increases in emis-
sions due to the introduction of ICT or the efficiencies it enables,
and include an example of a rebound effect (Jevons Paradox) in
our supplemental information (this supplemental information in-
cludes an appendix for this paper, which goes into more depth
about our literature review method, analysis, and additional infor-
mation relevant to this work—specifically: the methodology, esti-
mates of ICT emissions, video streaming, narratives (Six common
narratives for ICT’s role in climate change), truncation error, the
European Commissions’ investment in ICT, carbon pledges,
renewable energy purchases, and Jevons Paradox). In this paper
we also explore the importance of emerging trends in ICT (big
data, data science, and artificial intelligence [AI]; the Internet of
Things [IoT]; and blockchain) that could provide opportunities
for environmental sustainability yet threaten global emissions
reduction (ICT Trends: Opportunities and threats), as well as sug-
gest important areas of regulation and governance (Current policy
developments and governance in ICT).
Given the topic importance, there are surprisingly few studies
analyzing the environmental impact of ICT and they are often
characterized by a lack of interrogatability, potential for conflict
of interest, a limited scope that leaves out growing ICT trends
and an underestimation of ICT’s carbon footprint as significant
proportions of total emissions are omitted. We draw on peer-re-
viewed journal articles published from 2015 on the topic (Esti-
mating the carbon footprint of ICT), and analyze trends in ICT
and their environmental implications (ICT Trends: Opportunities
and threats). For this, we include literature on the energy or car-
bon impacts of ICT, its major components (e.g., data centers,
networks), its major application areas (e.g., AI, IoT), and the
impact ICT has on energy or carbon consumption in other sec-
tors. We go also beyond the literature: including consultations
with the lead authors of the main studies who are included in
this review, as well as other experts, to better assess ICT emis-
sion estimations and the associated complexities; and drawing
upon research by Small World Consulting (SWC) to account for
emissions omitted in many assessment methodologies (see
our supplemental information for further details on the model
used for assessment). For our policy analysis (European policy
and ICT), we focus on European Commission documents and
websites, supplemented by an analysis of industry pledges
(Self-regulation in the ICT industry) drawn from analysis of annual
reports, blog posts, and web pages from major ICT companies.
While there are limitations to our study in review scope and the
uncertainties of carbon calculations, we are confident we have
captured the main debates, and contribute through our focus on
GHG emissions. We specifically focus on GHG emissions rather
than electricity consumption as the former drives climate change
and the latter does not capture important factors surrounding
ICT’s environmental impact. Through our analysis, we have found
broad agreement on the size of ICT’s current carbon footprint, yet
there are a range of different views with regard to ICT’s future role
in climate change, both in terms of ICT’s own carbon footprint and
its effect on the wider economy’s emissions—we discuss the ar-
guments and assumptions underpinning these different views
and their policy implications. Nevertheless, analysts included in
our investigations agree that ICT emissions willnot reduce without
major concerted efforts involving broad political and industrialac-
tion, and we provide three reasons that indicate ICT emissions are
actually going to increase without intervention. It is clear from our
study that too much reliance is placed on a switch to renewables,
and efficiency gains within and beyond the ICT sector, for
achieving carbon targets; significant action through a global
constraint (e.g., a carbon cap on extraction), and more assess-
ment of ICT’s rebounds and governance are required.
Estimating the carbon footprint of ICT
In this section, we provide a broad overview of the estimates for
ICT’s carbon footprint before 2015, and an in-depth analysis of
three major peer-reviewed studies of ICT’s estimated emissions.
We identify the key arguments and assumptions underpinning
the different estimates, noting the essential points of agreement
and crucially the major points for and against growth in ICT sec-
tors emissions into the future.
ICT’s carbon footprint
Historically, ICT emissions have grown continuously alongside
global emissions. Several studies prior to 2015 have estimated
the carbon footprint of ICT (summarized in Figure 1). These
show an increase in ICT’s carbon footprint over time, even
without considering the full life cycle emissions, with the trend
line showing a 40% increase 2002–2012. The growth in ICT’s
emissions has coincided with consistent growth in our total
global carbon footprint,
1
where global GHGs have grown by
1.8% per year
2
(approximately 20% per decade). This indicates
ICT’s footprint has likely grown faster than global emissions, with
a very uncertain best estimate of twice as fast. Going back in
time further, ICT’s footprint will have grown faster than global
emissions since the sector started from zero mid-last century.
Scientific debate over ICT’s emissions has intensified in the
last 5 years. We therefore focus on research since 2015—espe-
cially studies by three main research groups led by Andrae,
3–6
Belkhir,
7
and Malmodin.
8,9
Andrae and Edler
3
estimate ICT’s
emissions for every year 2010–2030, Belkhir and Elmeligi
7
for
2007–2040 and Malmodin and Lunde
´n
8,9
for 2015. Malmodin
has also provided additional estimates for 2020 to us in personal
communication. We summarize the arguments in this section.
ICT’s current carbon footprint
ICT is estimated at ca. 1.8%–2.8% of global GHG emissions in
2020. Estimates of ICT’s emissions in 2020 (see Figure 2) vary
between 0.8 and 2.3 GtCO
2
e. The highest estimates (Andrae
and Edler
3
‘‘worst case’’) put ICT’s share of global GHG emis-
sions around 6.3%, but Andrae now believes that the Andrae
and Edler
3
‘‘best case’’ scenario of around 1.5% is more realistic
for 2020 (personal communication). Belkhir and Elmeligi
7
esti-
mates are higher at 1.9%–2.3%, especially considering they
omit TVs in their total estimate. Malmodin’s estimates sit in be-
tween the others at 1.9% of global emissions. When adjusting
for differences in scope, these studies point toward a footprint
of 1.0–1.7 GtCO
2
e for ICT, TVs, and other consumer electronics
in 2020; this is 1.8%–2.8% of global GHG emissions. We stress
that this estimate carries some uncertainty but gives us a reason-
able idea of the impact of ICT. Across studies, roughly 23% of
ICT’s total footprint is from embodied emissions, yet the share
of embodied emissions for user devices specifically is ca.
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50%. This is because, unlike networks and data centers, user
devices are only used for parts of the day and use less electricity,
but are exchanged often, especially in the case of smartphones.
Electricity consumption of user devices and domestic equip-
ment has decreased over the last 15–20 years driven by legisla-
tion and public procurement policy, such as the EU ERP directive
and EnergyStar (Chris Preist, personal communication). Howev-
er, efficiency improvements will not reduce embodied emissions
drastically. While production processes are becoming more effi-
cient, the manufacturing footprint of smartphones is increasing
because of more advanced integrated circuits, displays, and
cameras (Malmodin, personal communication). With a large
share of their footprint coming from their manufacture, extending
smartphones’ lifetime is the best way to reduce their footprint.
Most studies reviewed here assume an average lifetime of 2
years, partly driven by phone contracts that promise users the
newest models.
7
There are some signs, though, that this might
be increasing slightly. For example, the NPD
10
reported that in
the US, the average use has increased to 32 months in 2017
up from 25 months in 2016. Legislation encouraging repair,
e.g., the EU Waste Electrical and Electronic Equipment Directive,
can help, alongside business models centering around service
rather than product provision or selling repairable products to
markets in the Global South (Preist, personal communication).
There are important differences in how analysts arrived at
these estimations. There is a lack of agreement about which
technologies ought to be included in calculations of ICT’s GHG
emissions—particularly TV. All studies include data centers, net-
works, and user devices as the three main components of ICT,
but there are pronounced differences of opinion regarding the
proportional impact of each. A comparison of the different pro-
portions in 2020 estimates (excluding TV) is provided below
(Figure 3).
Regardingdata centers, Belkhir himself notedthat his projection
of 495 MtCO
2
e for data centers in 2020 is overestimated (personal
communication). Recent evidence by Masanet etal.
11
of 205 TWh
total energy use in 2018 seems to converge with Malmodin’s esti-
mate of 127 MtCO
2
e in 2020. Assuming a global electricity mix at
0.63 kgCO
2
e/kWh, Masanet et al.’s
11
estimate comes to ca. 129
Figure 1. Estimates of ICT’s carbon footprint
from studies published before 2015
The linear best fit line shows the increase in emis-
sions with time, although the growth is not neces-
sarily linear.
MtCO
2
e —higher than Andrae and Edler’s
3
best case estimate of 217 MtCO
2
e.
Studies systematically underestimate the
carbon footprint of ICT due to the ‘‘trunca-
tion error.’’ This error arises from the partial
exclusion of supply chain pathways by the
traditional process of life cycle analysis
(LCA). Malmodin’s studies are the most
comprehensive as they include operator
activities and overheads (e.g., offices and
vehicles used by data center and network
operators), as well as considering life cycle
emissions of equipment (i.e., from produc-
tion, use, to disposal) rather than just production energy
3
or only
material extraction and manufacturing energy.
7
However, Andrae
and Edler,
3
Belkhir and Elmeligi,
7
and Malmodin and Lunde
´n
8,9
all
follow LCA methodologies, which are unable to include the infinite
number of supply chain pathways of a product, thereby incurring
‘‘truncation error’’ in their carbon accounting. Similarly, but of
less significance, they also do not consider the full supply chain
carbon footprint of electricity used to run ICT equipment. Howev-
er, in the assessment of emissions from products, including elec-
tricity, the system boundary can be expanded to include all supply
chain pathways by combining traditional LCA with environmen-
tally extended input output (EEIO) methodologies. By mapping
the LCA’s system boundary onto the EEIO model, an EEIO-based
estimate can be made of the truncated supply chain pathways.
When truncation error has been adjusted for in this way, the
carbon footprint for ICT, including TVs and other consumer elec-
tronics, rises to 1.2–2.2 GtCO
2
e (2.1%–3.9% of global GHG emis-
sions) in 2020 with ca. 30% comingfrom embodied emissions and
70% from use phase emissions. We stress once more that these
are rough estimates with a significant degree of uncertainty.
ICT’s future carbon footprint
There is broad agreement by analysts in the field on certain key
assumptions:.
dthe world’s carbon footprint needs to decrease to avoid
climate catastrophe
ddata traffic is continuing to grow
denergy demand by ICT is increasing
ddemand for data centers and network services will increase
dthe shift to smartphones is decreasing emissions from PCs
and TVs
dusing more renewable energy would reduce ICT emissions
dICT could reduce emissions in other sectors but not by
default and only under certain conditions (contrasting to
GeSI
12
SMARTer, 2030 claims)
dICT has the potential to increase its own emissions and
facilitate rising emissions in other sectors
Opinions are more divided regarding future trends in emissions.
From 2015to 2020, Belkhir and Elmeligi’s
7
and Andraeand Edler’s
3
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estimates of ICT emissions have increased due to an increase in
data traffic and the number of user devices (see Figure 2). In
contrast, Malmodin’s estimates have decreased slightly—mostly
for data centers (by 10%) due to an increased adoption of renew-
able energy, and for networks (by 8%) due to decreases in over-
heads, despite increases in their electricity consumption.
Malmodin (personal communication) argues that: GHG emis-
sions from ICT have stabilized for now; ICT and the entertain-
ment and media sector growth is starting to decouple from
GHG emissions; and that ICT could even halve its 2020 emis-
sions by 2030 through renewable energy transformation and col-
lective effort
13
to 365 MtCO
2
e in 2030.
14
In contrast, Belkhir and
Elmeligi
7
and Andre and Edler
3
believe that emissions from ICT
will continue to grow (see Figure 4).
All analysts think that it would be possible in theory for ICT to
decrease its emissions with broad political and industry action—
but Malmodin is more optimistic that this will happen than Belkhir
and Elmeligi
7
and Andrae and Edler.
3
A recent report by Erics-
son
15
based on Malmodin and Lunde
´n
8,9
claims that ICT’s emis-
sions could be reduced by 80% if all its electricity came from
renewable sources.
Differences in predictions could be due toage of data used. The
data underlying Andrae and Edler’s
3
and Belkhir and Elmeligi’s
7
work is somewhat older (Andrae and Edler
3
use some data from
2011 for data centers and networks, while Belkhir and Elmeligi
7
use data from 2008 for data centers and from 2008 to 2012 for
networks) considering ICT’s fast pace of development, meaning
A
B
Figure 2. Estimates for global ICT’s carbon
footprint in 2015 and 2020
(A) Estimatesfor global ICT’s carbon footprintin 2015.
(B) Estimates for global ICT’s carbon footprint in
2020. Note that for Malmodin and Lunde
´n’s
8,9
esti-
mates, TV includesTV networks and other consumer
electronics, whereas for Andrae and Edler’s
3
esti-
mates, only TVs themselves and TV peripherals are
included. Belkhir and Elmeligi
7
did not include TVs.
Malmodin and Lunde
´n’s
8,9
original estimates for the
ICT and entertainment and media sector includes
paper media, which we have excluded here.
their projections are potentially based on
historical trends that might no longer apply,
such as the assumed exponential growth of
energy consumption by data centers and
networks. In contrast, Malmodin and Lun-
de
´n
8,9
might better capture recent changes
in emission trends given their estimates are
based on data measured directly from
industry (Malmodin and Lunde
´n’s
8,9
esti-
mates are based on 2015 data; Malmodin’s
more recent estimates provided in personal
communication are based on data from
2018 onward). Malmodin and Lunde
´n
8,9
also have the most inclusive scope in terms
of ICT equipment, life cycle stages and sup-
ply chain emissions considered.
However, this access to industry data
inevitably comes at the price of a lack of
data interrogatability. Part of Malmodin’s
data were obtainedby ICT companies under confidentiality agree-
ments, preventing others from reviewing the original data and the
model’s assumptions and calculations. There are also potential
risks of conflicts of interest as both authors work for network oper-
ators (Malmodin works for Ericsson, Lunde
´n works for Talia). This
arguably makes the Malmodin and Lunde
´n
8,9
paper open to con-
cerns that claims are less reliable due to selective reporting and
assumptions that cannot be properly assessed. We are not sug-
gesting that they cannot be trusted, but the lack of transparency
makes independent data and analysis difficult, and transparency
is necessary for important policy decisions.As employees of Hua-
wei, Andrae and Edler
3
also have potential for conflict of interest,
but their study is transparent about their data sources, calcula-
tions, and assumptions. Belkhir and Elmeligi
7
have no obvious
conflict of interest and they use only peer-reviewed and publicly
available sources.
Due to the trade-off between data interrogatability and up-to-
date data, it is impossible to judge which study makes the most
reliable predictions about ICT’s future emissions based on
methodology alone. It is possible, however, to examine their
arguments and the underlying assumptions to assess which
projection is more likely.
ICT’s future carbon footprint: unpacking the studies’
assumptions
In the key studies reviewed here, there is disagreement on
whether or not:
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denergy efficiencies in ICT are continuing
denergy efficiencies in ICT are reducing ICT’s carbon
footprint
dICT’s carbon footprint will stabilize due to saturation in ICT
ddata traffic is independent of ICT emissions
dICT will enable emissions savings in other industries
drenewable energy will decarbonize ICT
These assumptions have a critical influence on what we can
conclude about ICT’s role in climate change. We therefore
explore the arguments on both sides of the debate to shed
some light on the most likely path of ICT’s future emissions. In
doing so, we draw on several other much-cited sources and
direct consultation with key experts.
Are energy efficiency improvements in ICT continuing?
There has been a long history of ICT equipment becoming more
efficient (and thus cheaper and more productive) with time.
Moore’s Law allowed the ICT industry to exponentially increase
chips’ performance, speed, and reduce their power consump-
tion. The exponential improvements of processors has kept the
exponential growth in demand partly in check in terms of energy
consumption.
While Malmodin and Lunde
´n
8,9
acknowledge that Moore’s
Law has slowed down since 2012, they note that there is usu-
ally a time lag before the effects are felt outside of research
labs—therefore arguing that efficiencies are continuing for
now. Masanet et al.
11
argue that there is scope for further effi-
A
B
C
Figure 3. Proportional breakdown of ICT’s
carbon footprint, excluding TV
(A) Andrae and Edler (2015): 2020 best case (total of
623 MtCO
2
e).
(B) Belkhir and Elmeligi (2018): 2020 average (total
of 1,207 MtCO
2
e).
(C). Malmodin (2020): 2020 estimate (total of 690
MtCO
2
e).
Andrae and Edler’s
3
best case is displayed because
more recent analysis by the lead author suggest
that this scenario is most realistic for 2020. Note that
Malmodin’s estimate of the share of user devices is
highest; this is mostly because Malmodin’s network
and data center estimates are lower than those of
the other studies.
ciency improvements in data centers
through: improvements in server
virtualization; efficiency gains in servers,
storage devices, and data center cooling
technology; and the move toward large
data centers that are more energy effi-
cient due to efficiencies of scale and the
ability to invest in AI to optimize en-
ergy use.
For efficiency improvements in user de-
vices, there is evidence of carbon savings
from TVs: older, more energy-intensive
CRT and plasma TVs have been replaced
by more efficient LED TVs; and TV sales
have dropped due to users now watching
video on laptops and smartphones (Belkhir
and Elmeligi,
7
Malmodin). However, smart TVs could change this
trend if they become a popular way to access streamed media
(Preist, personal communication).
However, efficiency improvements might be coming to an
end—a view echoed by some of the experts we have consulted
(e.g., Peter Garraghan, Belkhir, Andrae). As transistors have
shrunk in size and increased in speed, they have begun to
heat up; this led to manufacturers putting a speed limit on pro-
cessing in 2004. The problem now is ‘‘quantum entanglement’’
where transistor layers become so thin that electrons jump be-
tween them, making transistors increasingly unreliable.
16
Other
avenues may exist for improving efficiencies (e.g., decreasing
semiconductor use stage power and nanophotonics),
17
but
possibly not on the same timescales
18
or with the same effi-
ciency gains.
If processor efficiencies are reaching a limit, data centers’
power consumption will likely rise as increasing demand will
no longer be counterbalanced by increasing efficiency. Despite
some remaining scope for further efficiency improvements,
Masanet et al.
11
note that there are limits to efficiency improve-
ments and that energy demand will not stabilize by itself—
arguing that urgent policy action and investment are needed
to limit increases in energy use driven by increasing demand.
Furthermore, efficiencies in ICT do not always guarantee
replacement of the older, less efficient equipment (e.g., the
development of 5G networks while 2G, 3G, and 4G networks
still exist) and new devices or user habits may conflict with
replacement gains. For example, some new ICT devices,
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such as smart watches and smart speakers, are used by people
in addition to smartphones and laptops, and Court and Sorrell
19
also highlight the issue of incomplete substitution of e-material-
ization trends like e-news or e-books. Multiple user devices in
the home have also led to a third of UK households watching
separate video content simultaneously in the same room
once a week
20
where people may have watched content using
thesameTVbefore.
Are energy efficiencies in ICT reducing ICT’s carbon
footprint?
Malmodin argues that so far, efficiency improvements are
continuing, and data center emissions are expected to stay at
1% of global electricity and at the same level of emissions as in
2015 in the next 5 years. Furthermore, Masanet et al.
11
reported
that data centers’ operational energy consumption has increased
only marginallyfrom 194 TWh in 2010 to 205 TWh in 2020 despite
global data center compute instances increasing by 550% over
the same time period—showing the effectiveness ofefficiency im-
provements in ICT. Masanet et al.
11
also note that these efficiency
improvements would be able to offset a doubling of data center
demand relative to 2018; beyond that point, energy demand will
rise rapidly. This is in line with what Belkhir (personal communica-
tion) believes, although he is less optimistic about the remaining
scope for efficiency improvements.
As highlighted above, ICT has seen rapid and continuous ef-
ficiency gains. Yet increases in demand for computation and
the number of ICT-enabled devices per person have outpaced
these energy efficiency improvements, resulting in growth in
ICT’s energy consumption and carbon footprint year-on-year.
This pattern fits with the rebound effect described by Jevons
Paradox whereby an efficiency improvement leads to an even
greater proportionate increase in total demand, meaning total
resource requirements rise rather than falls, as is often
assumed. While Jevons Paradox has not been proved to apply
within the ICT industry, it is risky to assume it does not apply
given historical evidence of ICT emissions consistently rising
despite significant improvements in efficiency (ICT’s carbon
footprint).
Figure 4. Projections of ICT’s GHG emissions
from 2020
(A) Andrae, (B) Belkhir, (C) Malmodin, personal
communication. Belkhir and Elmeligi
7
judge their
exponential scenario as most realistic, while the
linear growth scenario is more conservative and
reflects the impact of mitigating actions between
now and 2040. Malmodin and Lunde
´n
8,9
did not
make concrete estimates beyond 2020, but Mal-
modin suggests that ICT’s carbon footprint in 2020
could halve by 2030—offering a 2030 estimate of
365 MtCO
2
e in a recent techUK talk.
14
It would be surprising if rebound effects
in ICT—and Jevons Paradox in partic-
ular—were to end in the future without a
foundational change.
21
There is a theoret-
ical alternative scenario (the reverse of Je-
vons Paradox) where stalled energy effi-
ciency growth leads to a plateau in ICT
emissions due to prohibitive costs as
increasing demand cannot be counterbalanced by efficiency
improvements any longer. There is little precedent for this in prior
work.
Are ICT’s emissions likely to stabilize due to saturation?
The studies reviewed here all agree that the number of smart-
phones is increasing. According to Cisco,
22
there will be 5.7
billion mobile subscribers by 2023–71% of the world population.
However, within a few years, every person on earth might have a
smartphone and the total number might not further increase
(Malmodin, personal communication). There is some evidence
suggesting that the average lifetime of smartphones is
increasing too,
10
which will decrease the yearly embodied car-
bon associated with people replacing their smartphones. In
addition, Malmodin argues that there is a limited time per day
that people can be using their phones, theoretically capping en-
ergy consumption. The same pattern of saturation could be true
for other ICT equipment, which could stabilize ICT’s emissions.
However, ICT companies generally have a strong incentive to
prevent saturation from happening as this would cut their income
growth. There is economic pressure for them to create new tech-
nologies for individuals and organizations to buy. An example of
this is the increase in IoT devices, which require little person time
and can operate in the background, driving both embodied and
use phase emissions from the production of billions of IoT de-
vices, the networks allowing them to communicate and from
data centers that analyze the IoT data (see The Internet of
Things). Other important trends (ICT Trends: Opportunities and
threats), such as the growth in AI, would also escape this natural
saturation. The history of ICT does not provide precedents for a
saturation effect; it is therefore unlikely to occur without active
intervention. Furthermore, there is still scope for more ICT infra-
structure growth beyond smartphones before this innovation cy-
cle even begins, e.g., for data centers in the Global South (Preist,
personal communication).
Is data traffic independent of ICT emissions?
The amount of data traffic on the internet at a given time does not
correspond with simultaneous increases in ICT’s emissions.
Instead, network operators plan capacity for peak data traffic,
23
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meaning emissions from ICT are fixed regardless of the amount
of data traffic until growth in peak capacity is required. In Malmo-
din and Lunde
´n’s
8,9
view, data traffic is not directly proportional
to emissions due to efficiency gains and use of renewable energy
in data centers and networks that allow them to process increas-
ingly more data with similar emissions. Malmodin and Lunde
´n
8,9
(reiterated by Ericsson)
15
believe the energy consumption of ICT
is instead linked to the number of users and time spent using ICT
because of the energy consumption of user devices and access
equipment, such as modems and routers, and that data traffic
growth is slowing down to a more linear than exponential growth.
Andrae and Edler
3
and Belkhir and Elmeligi
7
both agree that
data traffic is a driver in ICT growth and emissions. Growth in
the internet’s infrastructure capacity allows for new data-inten-
sive services and applications; these offer more affordances to
users, driving demand for the services and therefore further infra-
structure growth.
24
Peak data traffic is one driver for this infra-
structure growth due to increased demand for data-intensive
services; other influences include ensuring technology is always
accessible to all users (Preist, personal communication).
Video streaming is a particularly prominent driver in data
traffic. During the COVID-19 pandemic, Netflix agreed with EU
regulators to reduce their traffic and ease the load on the
network, allowing network provision for homeworkers.
25
Belkhir
(personal communication) pointed out that this agreement be-
tween Netflix and EU regulators makes it difficult to argue that
data traffic is independent of ICT infrastructure growth and
therefore that data traffic has little effect on emissions.
Is ICT enabling carbon savings in other industries?
In their report SMARTer 2030, the Global eSustainability Initia-
tive,
12
which represents ICT companies, claim that ICT could
save 9.1 GtCO
2
e in 2020 and 12.08 GtCO
2
e in 2030 in other in-
dustries, such as health, education, buildings, agriculture, trans-
port, and manufacturing—mostly due to improved efficiency.
This would allow a 20% reduction of global CO
2
e emissions by
2030, holding emissions at 2015 levels and decoupling eco-
nomic growth from emissions growth. Relative to their estimate
of ICTs own emissions of 1.27 GtCO
2
e in 2020 and 1.25 GtCO
2
e
in 2030, GeSI
12
argue that ICT is net carbon negative and that
governments and businesses should invest more into ICT. Ac-
cording to them, already in 2015, ICT saved 1.5 times its own
emissions. There is also a strong argument that ICT will accel-
erate the use of renewable energy in the grid and hence lead
to decarbonization of the energy supply.
The GeSI
12
report is sponsored by several large ICT com-
panies and there is a lack of transparency in their analysis, raising
concerns over possible conflict of interest. So far, there is little
evidence that these predictions have come true. History has
shown us that growth in the global economy and its carbon foot-
print has continuously risen, even with ICT creating efficiencies in
other industries. It is risky to assume that further ICT-enabled ef-
ficiencies will suddenly start to create significant carbon savings
in the wider economy without governance and intervention.
Rather, it is more likely that ICT enables emission increases in
other sectors because it enables efficiencies, leading to growth
in the very areas into which ICT delivers those efficiency
gains—including growth in industries that are already carbon-
intensive (Preist, personal communication). By efficiencies
here, it is important to note that we go beyond just energy-spe-
cific efficiencies as described by Jevons Paradox; rather, we
take into account ICT’s emission impacts and rebound effects
more widely
cf.26
and refer to any potential route for rebound
ICT brings to our society (e.g., consider how ICT has made it
far easier to book flights online, contributing to the growth of
the aviation industry).
While GeSI
12
mention rebound effects, this is only in the
appendix and given very limited treatment. Their estimate of
an increase of global emissions by 1.37 GtCO
2
e due to
rebound effects is not included in overall calculations for
emission savings by ICT and is almost certainly a serious un-
derestimation. This is highlighted by their example of video
conferencing
12
estimating that ‘‘E-Work technologies like
videoconferencing could save around 3 billion liters of fuel.’’
by cutting workers’ commutes. It is difficult to quantify the
exact balance of ICT-enabled savings and increased emis-
sions, but one clue is that while video traffic has been expand-
ing rapidly to the extent that it is one of the main contributors of
internet traffic,
22
emissions from flights were simultaneously
increasing (save for pandemics).
27
Therefore, ICT only enables
efficiencies in other industries if it completely substitutes more
traditional carbon-intensive activities rather than being offered
in addition to them.
Will renewable energy decarbonize ICT?
While the exact share of renewable energy used for the ICT
sector is not known, some ICT operators generate renewable en-
ergy on-site and the ICT sector overall is a major purchaser of
renewable energy—leading the way for a global shift to this en-
ergy source. In a recent Ericsson blogpost building on Malmo-
din’s work, Lo
¨vehagen
28
claims that ICT’s carbon footprint could
be reduced up to 80% if all electricity came from renewable en-
ergy. Renewable energy has a much lower carbon footprint than
fossil fuel energy at ca. 0.1 kgCO
2
e/kWh. Compared to
0.63 kgCO
2
e/kWh for the global electricity mix, a switch to
100% renewable energy would reduce emissions by ca. 86%.
Both of these kgCO
2
e/kWh figures are based on SWC’s EEIO
model that draws on official data from the UK government’s
Department for Business, Energy and Industrial Strategy.
With unlimited growth in energy demand, even the relatively
small carbon footprint from renewable energy compared to fossil
fuel would add up significantly. In addition, there might be limits
to the amount of renewable energy that can be generated with
present technology, such as the availability of silver, which is
used in photovoltaic panels. An average solar panel requires
ca. 20 g of silver
29
and there are currently 2.6 billion solar panels
in the world generating a total of 865 TWh.
30
From 2019 to 2020,
135 TWh of solar energy was added; the manufacture of these
requires 52,000 tons of silver. Worldwide, 27,540 tons of silver
were being mined in 2020, and the amount increases by ca.
2% every year.
30
On this trajectory, solar panels would use
100% of global silver supplies in 2031 leaving none for electric
car batteries and other uses.
While investments into renewable energy currently have the
effect to reduce the price of renewable energy for other sectors,
as soon as there are limits to the amount of renewable energy
that can be generated, any additional energy used by ICT will
take energy away from other purposes. There are also practical
constraints on the extent that renewable energy can be used to
power ICT equipment. Even data centers that are powered by
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100% renewable energy usually have fossil fuel-powered
backups for unexpected demand increases. Powering networks
with renewable energy is a lot harder due to their decentralized
nature,
7
and powering user devices depends largely on the
greening of national grids—a trend that is ongoing in the UK
but still far from complete. Thus, while a shift to more renewable
energy is crucial, it does not provide an unlimited supply of en-
ergy for ICT to expand into without consequences.
Six common narratives for ICT’s role in climate change
The assumptions from the studies and unpacked in this section
can be summarized into six narratives of ICT’s future role in
climate change (see Figure 5): four around future trends in effi-
ciency and demand and their effect on ICT’s own emissions,
and two on ICT’s effect on emissions in the wider economy.
Summary of ICT’s carbon footprint
To meet climate change targets, the ICT sector needs to drasti-
cally decrease its own emissions and deliver vast savings in
other sectors. Despite some variability in estimates, research
studies reviewed here agree that ICT is responsible for several
percent of global GHG emissions and that its footprint has grown
until recently. The world needs to reduce its GHG emissions to
stay within 1.5
+
C warming.
31
If the ICT sector should decrease
its emissions in line with other parts of the economy, it would
have to: reduce its CO
2
emissions by 42% by 2030, 72% by
2040, and 91% by 2050 (see Figure 6) and net zero by 2050;
32
or deliver equivalent savings in other sectors in addition to the
savings these sectors will have to deliver themselves to meet
these targets, making sure that rebound effects do not offset
these savings. Global CO
2
emission cuts to 2050 needed to
stay within 1.5
+
C warming by 2100 are based on modeling by
Baskerville-Muscutt
33
based on the Shared Socio-Economic
Pathway 2 as outlined by the International Institute of Applied
A
B
Figure 5. Narratives of ICT’s role in climate
change and the critical assumptions
underlying these
(A) ICT’s carbon footprint.
(B) ICT’s effects on emissions in the wider economy.
The proponents of each narrative are in italics. Ef-
ficiency is here defined as GHG emissions per
equivalent ICT use. This includes Moore’s Law but
also higher renewable energy use, energy efficiency
of the infrastructure, etc.
Systems Analysis;
34
this is the ‘‘middle of
the road’’ or average scenario for the tra-
jectory the world will follow, and cuts are
relative to global CO
2
in 2010. Note that
this is CO
2
only, assuming ICT emissions
are mostly CO
2
as a large part if electricity
and there are no agricultural components.
The comparison to CO
2
emissions was
chosen because reliable budgets do not
exist for GHG emissions at this point.
Under business as usual, increases
in emissions are likely. Major concerted
effort would be needed to reduce emis-
sions. All the analysts we spoke to agree
that to decrease ICT’s emissions—even
assuming emissions have stabilized—a strong and unified effort
would be needed (Current policy developments and governance
in ICT). Without this effort, even if ICT’s emissions were to stay
stable at the 2020 level over the next decades, the relative share
of ICT’s emissions in global emissions would increase to more
than a third as other sectors reduce their emissions in line with
1.5C warming (see Figure 6).
There are three reasons to believe that ICT’s emissions are
higher than estimated and that they are going to increase.
Reason 1: rebound effects have occurred since the beginning
of ICT,and they will likely continue without intervention. Even if
efficiency improvements are continuing (see Are energy effi-
ciency improvements in ICT continuing?), this will not completely
counterbalance growth in demand for ICT; in fact, efficiency
gains might spur further growth in emissions by allowing the
ICT sector to grow further due to rebound effects (see Are energy
efficiencies in ICT reducing ICT’s carbon footprint?). We believe
that a natural peak in ICT emissions due to saturation of demand
is unlikely (see Are ICT’s emissions likely to stabilize due to satu-
ration?). To the extent that ICT enables efficiency gains in other
sectors, there is the risk that rebound effects more than offset
any savings following Global Rebounds (see Is ICT enabling car-
bon savings in other industries?). Renewable energy will help
decarbonize ICT but is not a silver bullet (see Will renewable en-
ergy decarbonize ICT?).
Reason 2: current studies of ICT’s carbon footprint make
several important omissions surrounding the growth trends in
ICT. The studies reviewed here make several important omis-
sions in areas of ICT growth, such as blockchain and partial
consideration of IoT. This leads to an incomplete picture.
Some analysts argue that blockchain is not part of ICT because
it requires specific hardware, not regular servers. However, we
believe that it should be in scope of ICT as it is an ICT-facilitated
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algorithm (see Blockchain); having specific hardware for block-
chain is similar to how graphics-intensive services (e.g., online
games) require graphics processing units (Preist, personal
communication). Malmodin and Lunde
´n
8,9
include some IoT
and concluded that the impact of IoT is small. However, this is
a small share of all IoT and they only accounted for the con-
nected devices, not the energy consumption that IoT creates
in data centers and networks (based on the assumption that
data traffic and energy are not closely related, see Is data traffic
independent of ICT emissions?). Such trends, as well as AI,
could help reduce global carbon emissions, but they will also
add to ICT’s carbon footprint; we discuss this trade-off for prom-
inent ICT trends in the next section (see ICT Trends: Opportu-
nities and threats).
Reason 3: there is significant investment in developing and
increasing uptake of blockchain,IoT and AI. Despite question-
able evidence that ICT growth trends will save more carbon
emissions than it will introduce (see ICT Trends: Opportunities
and threats), blockchain, IoT, and AI are seeing increased in-
vestment and uptake. As we explore in Current policy develop-
ments and governance in ICT’, the European Commission
discuss these trends as a way to spur economic growth and
yield emission reductions; yet, they expect ICT will only enable
15% reductions, which is insufficient for meeting climate
change targets (see European policy and ICT). Some large
technology corporations are setting their own carbon pledges,
which might help reduce the emissions from ICT’s growth
trends; however, these pledges are often not ambitious enough
to meet net zero emissions by 2050 (see Self-regulation in the
ICT industry). Until ICT corporations become net zero, any in-
vestment in the ICT industry will be associated with an increase
in emissions.
With a global carbon constraint, ICT will be a vital sector to
ensure transition to a net zero world. If a global carbon
constraint was introduced, we could be certain that rebound ef-
fects would not occur, meaning that productivity improvements
through ICT-enabled efficiencies both within the ICT sector and
the wider economy would be realized without a carbon cost.
Figure 6. ICT emissions, assuming the 2020
level (adjusted for truncation error) remains
stable until 2050, and global CO
2
emissions
reduced in line with 1.5
+
C under scenario
SSP2-19
Numbers on the blue slope indicate global CO
2
cuts
needed relative to 2010 and labels at the bottom
indicate ICT’s share of global CO
2
emissions in
percent. We assume most of ICT’s emissions are
from CO
2
because a large proportion of its footprint
is from electricity consumption and there are no
agricultural components. The comparison to CO
2
emissions was chosen because reliable budgets do
not exist for GHG emissions at this point.
Under these conditions, ICT would be a
key means by which productivity is main-
tained or increased despite the carbon
constraint, and therefore ICT’s role in
enabling the whole economy can be ex-
pected to be even greater than it is today.
Given these reasons, under a carbon
constraint, ICT’s share of global emissions could justifiably be
allowed to rise.
ICT trends: Opportunities and threats
Three recent and emerging innovations may have profound im-
plications for the carbon footprint of the ICT sector: (1) big
data, data science, and AI; (2) the IoT; and (3) blockchain and
cryptocurrencies. In this section, we explore the opportunities
and threats for each, as well as the potential mitigation of such
threats.
Big data, data science, and AI
Big data is one of the most significant technology trends, made
possible by the vast data and computational capabilities of cloud
computing. Arguments have been made for both the opportu-
nities of realizing a ‘‘smart’’ future and potential growth in ICT’s
carbon footprint.
Opportunities
Big data, data science, and AI could contribute to a lower carbon
smart future. Big data/data science/AI and IoT can help bring
about a smart and sustainable future encompassing smart grids,
cities, logistics, agriculture, homes, etc.
35–38
For example, by
finding optimal routes through cities and reducing traffic conges-
tion, or by optimizing energy use for building heating and lighting.
As these areas rely on IoT, we defer discussion on these oppor-
tunities until Internet of Things.
There is a willingness across industry and academia to apply
such technologies for the benefits of society. There is a signifi-
cant move toward data science and/or AI for social good,
including applications in health
39
and the environment, although
this work is in its infancy and generally not in everyday practice.
The role of big data in supporting green applications has been
discussed in the areas of energy efficiency, sustainability, and
the environment;
40
and the field of computational sustainability
is emerging, using technologies, such as AI, in support of the
United Nations (UN) sustainable development goals.
41
There is
also an emerging research community looking at the role of
such technologies in supporting environmental sciences as
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they seek a deeper understanding of our changing natural envi-
ronment. See, for example, research in Toronto, Exeter and the
Center of Excellence in Environmental Data Science, a joint
initiative between Lancaster University and UK Center For Ecol-
ogy & Hydrology program called ‘‘data science for social good.’’
Threats
The world’s data are doubling every 2 years. Data has been
described as ‘‘the new oil’’
42
given its commercial impact—yet
as data storage and data centers grow to meet demand, this
description could have a double meaning due to its environ-
mental impacts. Data can help solve complex world problems,
but there are concerns over the resources required to facilitate
data science and AI, especially the carbon footprint of data cen-
ters (see Estimating the carbon footprint of ICT). The total size of
the world’s digital data was estimated to be 59 zettabytes in
2020, with the amount of data created in the following 3 years ex-
pected to be more than the data created in the last 30 years.
43
AI
and data science are therefore an important trend that drives
growth in data storage and processing (data processing will be
the larger contributor to ICT’s energy use, as simply storing
data is environmentally cheap in comparison [Preist, personal
communication]) and in data centers, which some experts argue
leads to an increase in ICT’s carbon footprint (Is data traffic inde-
pendent of ICT emissions?).
Emissions associated with processing this data are increasing
due to growing computational complexity. Data science and AI
offer additional threats over and above the potential growth of
data center emissions. AI has the greatest potential for impact
given the complexity of training and inferencing on big data,
and especially so-called deep learning. Researchers have esti-
mated that 284,019 kg of CO
2
e are emitted from training just
one machine learning algorithm for natural language processing,
an impact that is five times the lifetime emissions of a car.
44
While
this figure has been criticized as an extreme example (a more
typical case of model training may only produce around 4.5 kg
of CO
2
),
45
the carbon footprint of model training is still recog-
nized as a potential issue in the future given the trends in compu-
tation growth for AI:
45
AI training computations have in fact
increased by 300,0003between 2012 and 2018 (an exponential
increase doubling every 3.4 months).
46
Further adding to the
threat of AI, ICT companies have been found to use such compu-
tationally intensive algorithms for advancing the fossil fuel in-
dustry.
47
Threat mitigation
Sustainability needs more consideration in ethical guidelines of
AI. Due to this growth of computation, Schwartz et al.
48
argue
the need for ‘‘Green AI’’ that focuses on increasing the efficiency
of AI computation rather than the current focus on what they
describe as ‘‘Red AI,’’ i.e., accurate AI models trained without
consideration of resource costs. Sustainability is currently one
of the least represented issues associated with ethics guidelines
in AI,
49
although a framework and ‘‘leaderboard’’ to track the en-
ergy consumption and carbon emissions of machine learning
has recently been offered in the hope that this will encourage en-
ergy efficiency to be considered.
50
Improvements in efficiency
and opportunities may exist, such as addressing the processing
requirements of AI algorithms by using idle PCs as a distributed
supercomputer.
51
However, we reiterate the earlier concerns
that an efficiency-focused endeavor without a carbon or con-
sumption constraint may fail to mitigate rebound effects (see
Are energy efficiencies in ICT reducing ICT’s carbon footprint?).
The IoT
The IoT represent a set of everyday internet-connected objects
from wearable technologies through to appliances, cars, and
other transport vehicles. This has led to a substantial and
ongoing growth of the internet as documented below.
Opportunities
IoT technologies can enable efficiency improvements outside of
the ICT sector. IoT applications are often viewed as ‘‘smart tech-
nology,’’ especially when combined with data science/AI in ways
that optimize energy usage more widely. Smart cities aim to pro-
vide better public services at a lower environmental cost,
52
e.g.,
location-based services from smart city IoT sensing and data
analysis can reduce transportation pollution through more effi-
cient driving routes.
53
Govindan et al.
54
also investigate how
such developments can support smarter logistics, including
reducing energy requirements. As mentioned in Will renewable
energy decarbonize ICT?, ICT has the potential to decarbonize
the energy supply and a combination of IoT and the power grid
has real potential to enable the Smart Grid, e.g., by dealing
with intermittency of renewable supply.
55
IoT deployments
have been tested in schools with the aim of raising awareness
of energy consumption and ‘‘promoting sustainable behav-
iors,’’
56
and IoT has also been harnessed to enable energy effi-
ciency improvements within ICT, e.g., by using IoT to reduce
air conditioning for data centers.
57
These few examples highlight
the breadth of IoT opportunities to reduce GHG emissions, as
long as the IoT applications substitute more carbon-intensive ac-
tivities rather than act alongside them.
Threats
IoT enablement comes at a cost of rapidly rising numbers of de-
vices, device traffic, and associated emissions. The sheer num-
ber of IoT devices and the associated data traffic is growing
significantly. Innovation in IoT is expected to create a 5-fold in-
crease from 15.41 billion internet-connected devices in 2015 to
75.44 billion in 2025.
58
Cisco estimate machine-to-machine
(M2M) connections will grow from 6.1 billion in 2018 to 14.7
billion by 2023 (a compound annual growth rate [CAGR] of
19%), representing 1.8 M2M connections per member of the
global population in 2023.
22
The majority of these connections
is expected to be formed by IoT in the home for automation, se-
curity, and surveillance (48% of connections by 2023), yet con-
nected cars (30% CAGR between 2018 and 2023) and cities
(26% CAGR) are the fastest growing IoT sectors.
22
IoT’s carbon footprint is under-explored, but will have signifi-
cant implications for embodied emissions. While the footprint
of IoT is uncertain and often unexplored in studies of ICT carbon
emissions (Are ICT’s emissions likely to stabilize due to satura-
tion?), it has been estimated that the energy footprint of IoT semi-
conductor manufacturing alone might be 556 TWh in 2016 and
increase 18-fold to 722 TWh in 2025.
59
This does not include
other aspects of embodied carbon in IoT, such as material
extraction and transport, or sources of GHG emissions other
than electricity; it also does not consider energy use of running
systems, although Das
59
estimates that this would be a lot
smaller than the embodied carbon in manufacturing, at perhaps
118 TWh in 2016 and decreasing to only 1 TWh in 2025 as we see
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more energy efficient technologies. This study has also, howev-
er, been questioned as being vastly overestimated by Malmodin
(personal communication). Assuming a global electricity mix of
0.63 MtCO
2
e/TWh, this would be a total of 424 MtCO
2
ein
2016 and 6,125 MtCO
2
e in 2025 for the manufacture and use
of the semiconductors; this is without emissions from the entire
IoT device, associated sensors, and the emissions in data cen-
ters and networks that IoT communicate with. It is also worth
noting that the introduction of IoT could lead to an initial rise in
obsolescence for other non-ICT products, as society makes
the transition to an IoT-focused life (e.g., replacing a working ket-
tle with an internet-connected kettle).
Threat mitigation
Lower energy IoT systems are a way forward, but may lead to
energy-intensification and fuel greater emissions overall. Re-
searchers are already looking to create lower energy IoT sys-
tems, considering both devices
60
and communication technolo-
gies. One focus is on Low Power Wide Area Networks
(LPWANs)
61
to reduce the energy requirements of M2M commu-
nication, but at a trade-off of lower bandwidth. There is an
associated field of study referred to as ‘‘Green IoT,’’
62–65
which
focuses on ensuring that IoT’s own environmental costs are
considered as we move toward a smarter society and environ-
ment. Yet we should be careful of IoT applications that could
lead to rebound effects. For example, smart home technologies
have the potential to reduce energy consumption (e.g., through
remote-controlled heating or lighting), but could perhaps lead
to ‘‘energy-intensification’’ once adopted through offering new
services (e.g., pre-heating homes, continuously running security
systems) or intensifying current services (e.g., internet connec-
tivity, audio/visual entertainment)
66
—the latter adding to ICT’s
carbon footprint through additional user devices and data traffic.
Blockchain
Blockchain is an example of a decentralized algorithm designed
to avoid a centralized authority or central point of failure. Block-
chain allows for potentially important new uses, e.g., for decen-
tralized financial systems. Cryptocurrencies are the most
popular application for blockchain, with Bitcoin being the
biggest cryptocurrency available today.
Opportunities
Blockchain could offer some opportunities for reducing carbon,
but there are no emissions-reducing applications of these tech-
nologies yet. A decentralized electronic currency could offer a
real disruption in the management of market transactions and
in the possibility of handling decentralized energy exchanges,
67
although there are no real examples of demonstrable emissions
savings yet. Kouhizadeh and Sarkis
68
discuss the potential of
blockchain technologies to enhance sustainability in the supply
chain, for example, by supporting transparency in the early
stages of supply chain management (e.g., vendor selection
and evaluation); this work, however, is speculative at this stage,
leading to researchers offering directions to further explore
adoption of blockchain in this domain.
69
Threats
The energy consumed by single cryptocurrency is equivalent to
that of entire nations. Blockchain is underwritten by energy: the
algorithm, if based on ‘‘proof of work,’’ creates high levels of
replication and redundant computation.
70
The methodology
and assumptions behind Mora et al.’s
70
projections of block-
chain’s future energy use have been questioned by Masanet
et al.,
11
but proof of work is widely accepted to be energy-inten-
sive. Energy consumption can also increase through escalation
of the ‘‘mining arms race’’ due to improving risk sharing for proof
of work blockchains.
71
Focusing on cryptocurrencies, one study
indicates that Bitcoin’s annual electricity requirements of 68.7
TWh in 2020 are equivalent to powering 7 million US house-
holds,
72
associated with a footprint of 44 MtCO
2
. This is based
on a global average electricity intensity of 0.63 kgCO
2
e/kWh,
which is likely an underestimate since the energy used to mine
Bitcoin often draws on a higher share of coal than the global
average.
73
Due to the inefficiency of transactions, a single trans-
action could be ca. 750 kWh, enough to power 23 households for
1 day,
72
or 473 kgCO
2
e—also based on the (likely underesti-
mated) 0.63 kgCO
2
e/kWh global average electricity intensity.
Bitcoin currently has a market dominance of 64% of all crypto-
currencies.
74
Under the assumption that other cryptocurrencies
have the same carbon intensity as Bitcoin, the carbon footprint of
all cryptocurrencies would be ca. 69 MtCO
2
e, 0.1% of global
emissions. Another study estimated the Bitcoin network elec-
tricity consumption at 2.55 gigawatts (GW) in 2018 (a value
that is nearly as much as Ireland at 3.1 GW), but that this could
rise to 7.67 GW in the future (making it comparable with Austria
at 8.2 GW).
75
Other researchers argue an annual electricity con-
sumption of 48.2 TWh and annual carbon emissions ranging
from 23.6 to 28.8 MtCO
2
for Bitcoin in 2018.
73
Stoll et al.
73
also
estimated that other cryptocurrencies would add another 70
TWh in 2018, bringing the total carbon footprint to ca. 73 MtCO
2
e
in 2018.
Threat mitigation
Fiscal policy intervention may be needed to mitigate energy con-
sumption of decentralized algorithms. Alternatives to proof of
work exist that could reduce the resources required for block-
chain, e.g., proof of stake reduces computation and Byzantine
protocols remove consensus mining.
76,77
Carbon offset mecha-
nisms for blockchain also exist, such as SolarCoin, whereby so-
lar energy producers are rewarded with a free SolarCoin for each
MWh of solar-based electricity they produce.
78
Renewable en-
ergy can also be used to power these technologies and it is
argued to form 73% of Bitcoin’s mining,
79
although it is important
to note that CoinShares Research who published the report run a
cryptocurrency investment fund, so there is a potential conflict of
interest. However, de Vries
80
does not think Bitcoin can be sus-
tainable due to: (1) the seasonality of hydropower in Sichuan,
China (a region that supposedly supports nearly half of global
mining capacity)
81
meaning energy is required from alternative
sources such as coal; and (2) the e-waste associated with mining
machines once they reach their end-of-life (if the cryptocurrency
collapses, mining machines cannot be repurposed as a generic
data centers since they are so specialized [Preist, personal
communication]), estimated at an annual 10,948 metric tons
(comparable to Luxembourg at 12 kt) assuming Koomey’s effi-
ciencies law.
82
Despite being the most popular use of blockchain
technology, there are, and will continue to be, blockchain appli-
cations beyond Bitcoin and cryptocurrencies. To mitigate the en-
ergy consumption of blockchain technologies and applications,
Truby
83
has proposed a series of fiscal policy options, such as
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introducing a customs duty or excise tax on imports of miners’
verification devices based on its energy consumption.
Summary of ICT trends
If unchecked, ICT trends could drive exponential growth in GHG
emissions. The three trends we have discussed could lead to
substantial growth in ICT’s footprint (see Figure 7 and note that
in this section we expand ‘‘user devices’’ to ‘‘devices’’ to include
embedded devices). While we have discussed the trends inde-
pendently, it is important to note that these trends are in fact in-
terlinked. For example, IoT involves collecting more data from
sensors, requiring more analytics and adding to the issues raised
by big data, data science, and AI, with the potential to further in-
crease ICT’s emissions. Such growth trends will also be facili-
tated through innovations in the ICT infrastructure, e.g., the
move from 4G to 5G cellular networks would enable faster,
data-intensive network transmissions for IoT devices—allowing
for even more data to be collected, communicated, and pro-
cessed. If not restrained, these above trends all have potential
to help drive further exponential growth, unlikely to be out-
weighed by the ICT-enabled carbon reductions in other sectors.
COVID-19 has shown a consumption constraint that could
disrupt these trends. As many activities have been restricted or
avoided during the pandemic, ICT has shown the significant ben-
efits and value it can bring to society—allowing families to
communicate, people to work from home, and conferences to
be held online. Under these circumstances, ICT serves as a
substitution rather than an addition to our regular activities. Coin-
ciding with this, there has been a temporary drop in carbon emis-
sions. A recent study in Nature estimates that daily global CO
2
emissions temporarily decreased by 17% in early April relative
to 2019 levels, largely due to changed transport and consumption
levels, and that 2020 annual emissions could decrease by 4% if
restrictions remain inplace until the end of 2020, and 7% if restric-
tions end in June relative to 2019.
84
However, this is negligible if it
does not lead to lasting changes after the pandemic. The key
question is what society will do when the COVID-19 crisis is
over. Will the world embrace some of the new ways of living and
working instead of their traditional counterparts and reap the car-
bon benefits, or return to the old ways, or a mix of the two?
There are important policy decisions to be made that deter-
mine the future of ICT’s carbon footprint. There is an increasing
awareness of the impacts of ICT, but we note the need to expand
our awareness to the full range of narratives and their underlying
assumptions (see ICT’s future carbon footprint: Unpacking the
studies’ assumptions). We also note that ICT and its trends
can bring a lot of value to many people worldwide. Society is
very much at a crossroads in terms of the choices faced, and
there are some positive signals. For example, in AI research,
there have been calls for the EU to incentivize AI applications
that are ‘‘socially preferable (not merely acceptable) and environ-
mentally friendly (not merely sustainable but favorable to the
environment),’’ recognizing the need for a methodology to
assess these characteristics.
85
Without a global carbon constraint, avoiding unsustainable
growth in ICT becomes a debate of what we should prioritize in
the ICT sector, what problems can and should be solved using
computing, and who can access the required ICT resources for
such solutions—supporting valued use of ICT (for example, for
uses that lead to carbon reductions in the economy) while con-
straining consumption and minimizing the ICT sector’s carbon
footprint. Anexample of such prioritization in practice is the recent
Netflix agreement with EU regulators to reduce its bitrate to ease
the burden on the internet during the COVID-19 outbreak,
enabling more people to work online fromhome.
25
This in turn pla-
ces the spotlight on policy makers and governance structures at
all levels, including in industry, governments, and academia. We
look at this important issue in the next section.
CURRENT POLICY DEVELOPMENTS AND
GOVERNANCE IN ICT
Self-governance and the policy landscape is changing. Europe is
leading the world in implementation of and experimentation with
climate policy,
86
making the EU Green Deal and the European
Commission’s (EC) rhetoric particularly worthy of analysis as a
bellweather of global climate policy. In this section, we explore
such European policy, and also look at self-regulation of ICT
emissions by top technology companies to understand whether
they are sufficiently ambitious to meet carbon targets without the
need of top-down regulation.
European policy and ICT
ICT is a central pillar of Europe’s climate strategy. Under the EC’s
Green Deal, Europe is committed to becoming carbon neutral by
2050, and climate neutral later this century.
87
The EC use the
term ‘‘carbon neutral’’ to refer to no net emissions of carbon di-
oxide, and the term ‘‘climate neutral’’ to refer to no net emissions
of GHG emissions. This is different from the way most ICT com-
panies use the term ‘‘carbon neutral,’’ which includes all GHG
emissions. ICT features prominently in policymaking around
the climate: (1) because of recent efforts to lead the world in a
sustainable, human-centric approach to innovation,
88
and (2)
to drive down GHGs across the economy.
European ICT emissions policy emphasizes efficiency, renew-
ables, and circular waste. The EC’s official figures put ICT’s cur-
rent share of global GHG emissions at more than 2%,
89
and a
study commissioned by the EC anticipates that ‘‘the energy con-
sumption of data centers and telecommunication networks will
grow with an alarming rate of 35% and 150% respectively over
Figure 7. The impacts that trends in ICT have
on growth in emissions from data centers,
networks, and devices
Note that the thicker lines depict prominent threats,
thinner lines depict secondary threats, and the
dotted lines depict the links between the trends.
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9 years’’ (from 2018).
90
Rather than seeking to directly affect this
consumption trend, policy focuses on mitigating the impacts of
rising consumption, specifically through improved efficiency
and renewable energy. Three fundamental assumptions are
evident in this approach: (1) there is scope for energy efficiency
improvements in ICT to continue, at least through 2050 (Are
energy efficiency improvements in ICT continuing?); (2) energy
efficiency gains in ICT can reduce ICT’s carbon footprint (Are en-
ergy efficiencies in ICT reducing ICT’s carbon footprint?); and (3)
renewable energy will decarbonize ICT (Will renewable energy
decarbonize ICT?). As we have discussed in Summary of ICT’s
carbon footprint, there are strong arguments against each of
these premises that may impede successful decarbonization of
ICT unless simultaneously curbing demand or adding a global
carbon constraint. However, publicly facing policy statements
do not attend to these counter-assumptions.
Data centers are a particular focus of European policy. The EC
has committed to carbon neutral data centers by 2030, through a
mixture of continued efficiency improvements, transitioning to-
ward reliance on renewable energy sources, and developing
methods of reusing the heat that servers generate.
91
This is an
ambitious proposal, as currently there is no indication that data
center emissions are decreasing despite continuous efficiency
improvements (see ICT’s carbon footprint). The EC also does
not specify whether this must be achieved through on-site re-
newables or can include purchasing of offsets.
Other noteworthy policy covers e-waste, which is recognized
by the World Economic Forum as the fastest growing category of
waste.
92
As part of Europe’s New Circular Economy Action Plan,
the EC plans to put forward a ‘‘Circular Electronics Initiative’’ by
the end of 2021 to improve the lifespan, repairability, and recy-
clability of ICT products.
93
This initiative would help decrease
the embodied carbon of ICT but would be partly offset if the total
number of devices continues to increase (i.e., innovation will pro-
hibit saturation in ICT, see Are ICT’s emissions likely to stabilize
due to saturation?).
Except for this Circular Electronics Initiative, which will likely
include a reward scheme for consumers who recycle their old
devices,
94
the Green Deal is notable for its lack of clear incentiv-
ization or enforcement mechanisms regarding decarbonization
of ICT. It may be believed that efficiency naturally improves as
technology advances (e.g., through Moore’s Law), and/or that
market forces will compel industry to drive these improvements,
as there is no discussion of either penalties to be applied or
assistance to be offered to the sector toward achieving carbon
neutrality by 2050. Also not provided within the Green Deal are
estimates of the emissions reductions needed within the ICT
sector itself to meet this ambition, which may be incompatible
with continuing growth expected of ICT’s electricity consump-
tion (see ICT’s carbon footprint).
Europe seeks to supercharge enablement through significant
investment in ICT. While policies clearly acknowledge ICT’s
share of global emissions and commit to reducing them, the pri-
mary thrust of Europe’s climate strategy is the use of ICT to
enable emissions savings in other industries (‘‘enablement’’).
An EC commissioned report states vaguely that ICT ‘‘probably
saves more energy than it consumes.’’
90
The wording of the
Green Deal, however, is unambiguous: ‘‘Digital technologies
are a critical enabler for attaining the sustainability goals of the
Green deal in many different sectors.’’
95
This includes various ini-
tiatives and major funding schemes intended to foster innovation
in and uptake of AI, IoT, and blockchain.
The Green Deal does not provide a detailed roadmap for how
these technologies will in fact deliver against these goals, nor fig-
ures regarding expected savings to be achieved. These are un-
doubtedly difficult to estimate, but as yet there is no evidence
in the multi-decade history of ICT-driven efficiency savings that
enablement works for reducing overall emissions (see Is ICT
enabling carbon savings in other industries?). In the absence of
an intervention, such as the introduction of a global carbon
constraint, claims of the feasibility of this strategy should be ap-
proached with skepticism. As a baseline, staying below 1.5C
warming would require the global economy to reduce by 42%
by the year 2030, including the ICT sector (see Summary of
ICT’s carbon footprint); so if ICT’s emissions do not shrink by
42% by 2030, then it would have to enable reductions in other
sectors—beyond the 42% that other industries will have to cut
anyway—to compensate for this shortfall. This may prove a deli-
cate balancing act. To facilitate this work, complete and accu-
rate estimates of ICT’s footprint need to be captured regularly,
alongside careful accounting of the emissions ICT is driving or
saving in other sectors, with sector targets adjusted accordingly
to ensure regional and global targets are met. For this, consistent
carbon accounting standards would need to be established
across the sector; this would avoid the variability of carbon esti-
mations, as we found with current studies in Estimating the car-
bon footprint of ICT, from differences in the approaches, bound-
aries, and data used.
We note the competing policy priorities of the EC. Europe
faces pressures to remain competitive in the global technology
market and seeks to lead the way in rapidly growing technolo-
gies that would otherwise be capitalized by Asian and US com-
petitors.
96
By stimulating innovation in these areas, Europe
seeks to maintain both the health of its economy and the health
of the planet. But critically, in the current policy environment, and
lacking a global carbon constraint, economic growth would likely
further spur consumption and therefore emissions.
Self-regulation in the ICT industry
Companies need net zero carbon targets that cover supply chain
emissions. Several big ICT companies have recently announced
carbon pledges to self-regulate their emissions (e.g., Amazon,
Apple, BT, Microsoft, Sky). These pledges fall into three main
categories: (1) carbon neutral (least ambitious); (2) net zero;
and (3) carbon negative (most ambitious). To limit global warming
to 1.5C,
31
we will need to reach net zero emissions by 2050
globally.
97
Companies should aim for net zero or, even better,
carbon negative. To make this possible carbon neutral targets
are not enough because they do not cover supply chain emis-
sions. Yet only a few firmly aim to be net zero (e.g., Microsoft,
Sky, Amazon, BT), and only Microsoft aims to be carbon
negative.
98
Carbon offsetting requires truly additional carbon removal
methods. Companies need to prioritize reducing the total emis-
sions as much as possible
99
—only then should the rest of their
emissions be offset by permanent, verifiable, and additional car-
bon removal methods. For a company’s emissions to be truly
offset, the same amount of carbon that the company emits
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needs to be removed from the atmosphere (e.g., through affores-
tation, reforestation, planting seagrass, taking in landfill gas), not
simply avoided. An example of an avoided emission offset is an
area of forest that is protected from logging; the amount of car-
bon that would have been released if the forest was cut down is
counted as offset. However, there needs to be some certainty
that it would have been removed if it had not been purchased,
otherwise these offsets cannot be considered additional. Even
genuine ‘‘avoided’’ emissions may end up ‘‘leaking’’ out at
another point in the system (e.g., a protected area of forest
may just lead to more logging somewhere else in the world).
100
Only 2% of offsets result in truly additional removals.
101
Furthermore, some offsetting projects may not be permanent:
where forests or peatlands are used to sequester carbon, these
carbon stores must be protected from fires or logging—other-
wise the carbon removals are negated. Efficiency enablement
cannot count as offsetting because it is hard to show that any
enabled savings are not negated by rebound effects (see Is
ICT enabling carbon savings in other industries?).
Only some renewable energy helps to cut emissions. Some
companies also claim, or aim for, power provision from 100%
renewable energy without specifying whether they aim to cut
emissions. Companies need to detail which type of renewable
energy they use (e.g., biofuels, solar, wind, hydro), and what pro-
portion of their renewable energy comes from on-site renewable
power generation, Power Purchasing Agreements (PPAs), and
Renewable Energy Guarantees of Origin (REGOs), as these differ
in their additionality. For a company to claim they are 100%
renewable, they should source 100% of their energy through
PPAs, on-site renewables, and investment in off-site projects
but not unbundled REGOs, because the latter cannot claim ad-
ditionality. Renewable energy projects should not be considered
a removal but rather a scope 2 reduction (see Will renewable en-
ergy decarbonize ICT?).
The new ITU standard encourages ICT companies to become
net zero by 2050. In collaboration with GSMA, GeSI, and SBTi,
the International Telecommunication Union (ITU),
32
a UN agency
focused on the ICT industry, released a new standard in
February 2020. The standard aims to reduce ICT’s GHG emis-
sions by 45% by 2030, and net zero by 2050, in line with limiting
global warming to 1.5C. The scope of ITU’s recommendation
includes ‘‘mobile networks, fixed networks, data centers, enter-
prise networks, and end-user devices, but excludes ICT ser-
vices.’’ The ‘‘voluntary’’ standard comes with reduction targets
for each ICT sub-sector for the next decade. Sub-sectors are
defined as per other ITU documentation, specifically clauses
A2 to A6 of ITU-TL.1450.
102
Data center operators adopting
the science-based target will need to reduce emissions by at
least 53%, mobile network operators by 45% and fixed network
operators by 62%.
12
The targets have been approved by the
SBTi and require companies to set targets for scope 1 and 2
emissions and some supply chain scope 3. Most of these reduc-
tions between 2020 and 2030 are expected to come from a shift
to more renewable and other low-carbon energy sources. The
targets are less ambitious than pledges by individual companies,
such as BT, Sky, and Microsoft, which commit to reach net zero
by 2030 or 2040, but they send a strong signal that the world
needs net zero and science-based targets and provide a tem-
plate that policy makers could adopt.
Key implications for policy moving forward
The full climate impacts of ICT need to be considered systemat-
ically, accounting for end-to-end life cycles and supply chain
emissions. It is critical that complete and accurate estimates
are used to guide climate policy making and target setting within
the sector. Studies of ICT’s carbon footprint should strive for in-
terrogatability, but also need to disclose potential conflicts of in-
terest that may affect boundary setting for such calculations.
Where technologies are unlikely to be included within the esti-
mates of other sectors’ carbon footprints, it is essential that
they are included in estimates of ICT’s footprint so that climate
impacts can be accurately monitored across the economy. It is
also vital that calculations do not conflate efficiency improve-
ments with emissions reductions, and that they use methods
that allow for objective, high-quality, and up-to-date data and
analysis—rectifying the issues of current estimates (see ICT’s
carbon footprint). This also supports the recommendations by
Dobbe and Whittaker
47
who lobby for carbon transparency, as
well as consideration of the full supply chain and rebound effects
in carbon accounting.
While ICT offers opportunities to enable reductions in CO
2
emissions in other sectors, evidence does not support their abil-
ity to achieve the significant carbon savings required by 2050. It
is important not to overhype ICT’s potential to reduce emissions
across the economy, thus additional research is sorely needed
to provide robust estimates to policy makers. Continued growth
in the carbon footprint of the ICT sector cannot be justified on the
basis that these technologies may enable sufficient savings in
other sectors—particularly as estimations of ICT-enabled emis-
sions savings in other sectors fall short of what is required for
meeting agreed targets, and there is a risk that ICT’s expansion
into other sectors could increase those sectors’ emissions (see
European policy and ICT). This fundamentally calls into question
the presumed role of efficiency within climate strategy. There is
clear need to detail sector by sector the savings ICT is expected
to produce—reflecting careful balancing of sector footprints
within the contexts of regional and global targets—along with
developing a detailed roadmap toward delivering on those ex-
pectations.
The ICT sector must adopt science-based net zero targets in
line with, or better than, the ITU standard; but industry self-regu-
lation may not be sufficient to yield necessary emissions reduc-
tions. With growing awareness of the climate emergency, public
pressure may be enough to get more ICT companies to
announce net zero emissions by 2050. However, there is a lack
of net zero pledges thus far. Some companies that have pledged
net zero are not on target, or do not have detailed and trans-
parent action plans. Note that this piecemeal approach of indi-
vidual companies making commitments also comes at a
competitive cost for the foreriders, with others gaining financially
from being free from such commitments. The way forward for a
reduction in ICT’s emissions is a sector-wide commitment to net
zero that is enforced through incentives and compliance mech-
anisms, such as procurement clauses that set out carbon criteria
and consequences for non-compliance. We flag this as an
important issue for the sector but detailed consideration of the
form of regulation is beyond the scope of this paper. We also
note that an ICT-focused net zero commitment is unlikely to limit
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the emissions from ICT’s impact on the wider economy, unless
upstream scope 3 emissions are included in the targets.
There is a pressing need to devise a strategy for constraining
consumption of ICT so that efficiency improvements lead to
actual emissions reductions and enable productivity to be main-
tained in a carbon-constrained world. It is likely that unabated
growth in demand for ICT will more than offset the emissions
saved through improved efficiency of these technologies. The
only condition under which these rebound effects would not
apply is if a constraint were applied, such as a constraint on con-
sumption or an economic constraint through rising carbon costs
(e.g., a carbon tax or a cap on emissions). Policy-enforced car-
bon caps on global emissions, or carbon pricing for all industries,
would help avoid the risk of Global Rebounds; but without a
global carbon constraint, policies will be needed to enforce cred-
ible and ambitious carbon pledges within the ICT sector (see
Self-regulation in the ICT industry). We have outlined below
five criteria specifically for ICT sector targets, all of which will
need to pervade the ICT sector and be subjected to tough,
well-resourced, and independent scrutiny:
1 targets should be inclusive of scope 1, 2, and 3 emissions
2 reduction trajectories should be in line with IPCC recom-
mendations for limiting warming to 1.5C
3 where transition to renewable energy is part of the decar-
bonization pathway, a careful test should be applied that
the renewables are provably additional
4 emissions offsets need to pass tests of permanence,veri-
fiability, and additionality
5 where ‘‘net zero’’ or‘‘carbon neutral’’targets are announced,
these should be disaggregated into an emissions reduction
component and an offsetting component so that offsets are
not allowed to replace reduction responsibilities
6 emission reduction targets should not be replaced by ena-
blement claims due to the risk of rebound effects
Top-down, deliberate direction of ICT research and develop-
ment may be needed to meet global carbon targets. In a world
where consumption of ICT needs to be constrained, ‘‘worthy’’
uses of ICT may need to be weighed against other ‘‘less worthy’’
ones. The ICT sector plays an essential role in helping people live
better, and it needs to continue to do so while carefully managing
demand. Binding commitments to emissions targets for the ICT
sector are needed to force decision making that prioritizes the
environment over profit when these are in conflict. Unprece-
dented coordination across the sector in collaboration with pol-
icy makers is required to design and enact a plan for achieving
net zero emissions from ICT by 2050.
DISCUSSION AND CONCLUSIONS
As we have explored in this report, there are two central issues
for the ICT industry with respect to the climate emergency:
ICT’s own carbon footprint; and ICT’s carbon impact on the
rest of the global economy. There has been surprisingly little
research into these questions given their significance in
response to climate change. The evidence that does exist needs
to be interpreted with awareness of problems arising from the
following issues: (1) the age of the data; (2) a lack of data interrog-
atability; (3) a potential for conflict of interest (especially where
researchers are employed by ICT companies, and data and anal-
ysis is not freely available); and (4) varying approaches to, and
lack of agreement on, the boundaries of the analysis of specif-
ically what constitutes the ICT industry in terms of inclusion in es-
timates of its carbon footprint (e.g., whether or not growth trends
in ICT such as blockchain are included, how scope 3 emissions
in the supply chain are included to avoid truncation error).
Historically we can be sure that four phenomena have gone
hand in hand: ICT has become dramatically more efficient;
ICT’s footprint has risen to account for a significant proportion
of global emissions; ICT has delivered increasingly wide-ranging
efficiency and productivity improvements to the global econ-
omy; and global emissions have risen inexorably despite this.
Looking to the future, our concerns are that this growth in emis-
sions will continue at a time when emissions must shrink. All ana-
lyses reviewed in this report concur that ICT is not on a path to
reduce emissions in line with recommendations from climate sci-
ence unless additional steps are taken by the sector, or legislators,
to ensure that this happens. Prevalent policy emphasis on effi-
ciency improvements, use of renewables and circular electronics
is likely insufficient to reverse ICTs growth in emissions. There are
real concerns that the period governed by Moore’s Law is coming
to an end, and there is huge investment in trends that can signifi-
cantly increase the carbon footprint of ICT, including in AI, IoT,
and blockchain. Recently there are encouraging signs that some
ICT giants may be moving in a positive direction (e.g., through
net zero and carbon-negative targets that include their supply
chains), yet there is a lack of policy mechanisms for enforcing
sector-wide climate target compliance. Our hope is that with the
right policy to enforce these commitments, ICT companies will
be able to deliver on their pledges and that other industries will
follow ICT’s example, allowing us to stay within 1.5Cwarming.
Based on the evidence available, it is also key that regulators
move away from the presumption that ICT saves more emissions
than it produces—at the very least it would seem unsafe to as-
sume that ICT efficiencies bring about carbon savings by default.
While ICT offers opportunities to enable reductions in GHG emis-
sions in other sectors, evidence does not support their ability to
achieve the sustained significant carbon savings we require by
2050. And while ICT might make lower carbon living possible,
this will not in itself help to bring about a cut in carbon, and
conceivably may lead to rebound effects leading to higher emis-
sions overall. The argument of enablement simply does not
exempt the ICT sector from addressing its own emissions, and
the sector could certainly do more to understand its enablement
and rebound effects. To ensure current technologies have a truly
positive impact on the environment, the climate emergency re-
quires a global constraint such as a carbon cap on extraction, a
price on carbon emissions, or a constraint on consumption, to
rule out reboundsin emissions. With this in place, the ICT-enabled
carbon reductions could be realized, and the ICT industry could
become a vital sector for the transition to a net zero world.
EXPERIMENTAL PROCEDURES
Resource availability
Lead contact
Any queries related to our review resources should be directed to Kelly Wid-
dicks (k.v.widdicks@lancaster.ac.uk).
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Patterns 2, September 10, 2021 15
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Materials availability
No new unique reagents were generated as a result of our review.
Data and code availability
The data from our figures is available on Lancaster University’s Pure research
repository here: https://doi.org/10.17635/lancaster/researchdata/477. Belkhir
requested their raw data were kept confidential for Figure 4, so this is not avail-
able for the relevant.csv file in the repository. No code was used for the anal-
ysis of the data in this review, but we did draw on research by Small World
Consulting (SWC) Ltd. into sector emissions to adjust estimates by the key
studies in Estimating the carbon footprint of ICT for truncation error; details
about this research are provided in the supplemental information (Appen-
dix A.5.4).
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.
patter.2021.100340.
ACKNOWLEDGMENTS
This work was developed following discussions at the Royal Society project on
Digital Technology and the Planet. The research was partially supported by the
DT/LWEC Senior Fellowship (to G.S.B.) in the Role of Digital Technology in Un-
derstanding, Mitigating and Adapting to Environmental Change, EPSRC: EP/
P002285/1, and by the EPSRC Doctoral Prize (to K.W.) to enable K.W. to
continue her research in sustainability and digital wellbeing, EPSRC: EP/
R513076/1. The research was also partially sponsored by an independent sci-
entific academic body committed to the advancement of high-qualit y science,
who would prefer not to be named. We thank the experts we consulted for this
research as well as those who provided feedback, particularly: Anders Andrae,
Lotfi Belkhir, Livia Cabernard, Peter Garraghan, Jens Malmodin, and Chris
Preist. We also thank the Patterns reviewers for their insightful comments
and feedback.
AUTHOR CONTRIBUTIONS
Conceptualization, all authors; methodology, C.F. and M.B.-L.; investigation,
C.F., M.B.-L., K.W., B.K., and G.S.B.; data curation, C.F., M.B.-L., and K.W.;
writing – original draft, all authors; visualization, C.F.; supervision, B.K.,
G.S.B., M.B.-L., and A.F.; project administration, C.F. and M.B.-L.; funding
acquisition, C.F., M.B.-L., G.S.B., K.W., and B.K.
DECLARATION OF INTERESTS
Charlotte Freitag is an employee at Evenlode Investment Ltd. Mike Berners-
Lee is the founder and principle consultant of Small World Consulting. Bran
Knowles is a member of the ACM Europe Council, and the ACM Europe Tech-
nology Policy Committee, where she leads the standing group on climate
change. Gordon Blair is a Research Fellow in the UK Center for Ecology and
Hydrology (UKCEH) and is a member of the Patterns advisory board.
REFERENCES
1. Ritchie, H., and Roser, M. (2019). How Have Global CO
2
Emissions
Changed over Time? (Our World in Data). https://ourworldindata.org/
co2-and-other-greenhouse-gas-emissions#how-have-global-co2-
emissions-changed-over-time.
2. Jarvis, A.J., Leedal, D.T., and Hewitt, C.N. (2012). Climate–society feed-
backs and the avoidance of dangerous climate change. Nat. Clim.
Change 2, 668–671.
3. Anders, S.G.A., and Edler, T. (2015). On global electricity usage of
communication technology: trends to 2030. Challenges 6, 117–157.
4. Anders, S.G.A. (2019). Prediction studies of electricity use of global
computing in 2030. Int. J. Sci. Eng. Invest. 8, 27–33.
5. Anders, S.G.A. (2019). Comparison of several simplistic high-level ap-
proaches for estimating the global energy and electricity use of ICT net-
works and data centers. Int. J. 5,51.
6. Anders, S.G.A. (2019). Projecting the Chiaroscuro of the Electricity Use of
Communication and Computing from 2018 to 2030 (Researchgate.net).
7. Belkhir, L., and Ahmed, E. (2018). Assessing ICT global emissions foot-
print: trends to 2040 & recommendations. J. Clean. Prod. 177, 448–463.
8. Malmodin, J., and Lunde
´n, D. (2018). The energy and carbon footprint of
the global ICT and E&M sectors 2010–2015. Sustainability 10, 3027.
9. Malmodin, J., and Lunde
´n, D. (2018). The Electricity Consumption and
Operational Carbon Emissions of ICT Network Operators 2010-2015,
Technical report (Report from the KTH Centre for Sustainable Com muni-
cations Stockholm).
10. NPD (2018). The Average Upgrade Cycle of a Smartphone in the U.S. Is
32 Months, According to NPD Connected Intelligence. https://www.npd.
com/wps/portal/npd/us/news/press-releases/2018/the-average-
upgrade-cycle-of-a-smartphone-in-the-u-s–is-32-months—according-
to-npd-connected-intelligence/.
11. Masanet, E., Shehabi, A., Lei, N., Smith, S., and Koomey, J. (2020). Re-
calibrating global data center energy-use estimates. Science 367,
984–986.
12. GeSI Smarter. (2020). ICT Solutions for 21st Century Challenges, Tech-
nical report, 2015. http://smarter2030.gesi.org/downloads.php.
13. Malmodin, J. (2019). Energy Consumption and Carbon Emissions of the
ICT Sector (Energimyndigheten).
14. Malmodin, J. (2020). The ICT Sector’s Carbon Footprint. Presentation at
the techUK Conference in London Tech Week on ‘decarbonising Data’.
https://spark.adobe.com/page/dey6WTCZ5JKPu/.
15. Ericsson. (2020). A Quick Guide to Your Digital Carbon Footprint – De-
constructing Information and Communication Technology’s Carbon
Emissions, Technical report.
16. Waldrop, M.M. (2016). The chips are down for Moore’s Law. Nature
https://www.nature.com/news/the-chips-are-down-for-moore-s-law-
1.19338.
17. Anders, S.G.A. (2020). Hypotheses for Primary Energy Use, Electricity
Use and CO
2
Emissions of Global Computing and its Shares of the Total
between 2020 and 2030 (WSEAS Transactions on Power Systems).
18. Simonite, T. (2016). Moore’s Law is dead. Now what? Technol. Rev.
https://www.technologyreview.com/s/601441/moores-law-is-dead-
now-what/.
19. Court, V., and Sorrell, S. (2020). Digitalisation of goods: a systematic re-
view of the determinants and magnitude of the impacts on energy con-
sumption. Environ. Res. Lett. 15, 043001.
20. Ofcom. (2017). Box Set Britain: UK’s TV and Online Habits Revealed.
https://www.ofcom.org.uk/about-ofcom/latest/media/media-releases/
2017/box-set-britain-tv-online-habits.
21. Hilty, L., Lohmann, W., and Huang, E.M. (2011). Sustainability and ICT—
an overview of the field. Notizie di Politeia 27, 13–28.
22. Cisco. Cisco annual internet report (2018-2023). Technical report, 2020.
accessed February 2020.
23. Sandvine. (2014). 1h 2014. Global Internet Phenomena Report, Technical
report (Sandvine Incorporated ULC.).
24. Preist, C., Schien, D., and Blevis, E. (2016). Understanding and mitigating
the effects of device and cloud service design decisions on the environ-
mental footprint of digital infrastructure. In Proceedings of the 2016 CHI
Conference on Human Factors in Computing Systems, pp. 1324–1337.
25. Sweney, M. (2020). Netflix to Slow Europe Transmissions to Avoid
Broadband Overload (The Guardian). https://www.theguardian.com/
media/2020/mar/19/netflix-to-slow-europe-transmissions-to-avoid-
broadband-overload.
26. Rivera, M.B., Ha
˚kansson, C., Svenfelt, A
˚., and Finnveden, G. (2014).
Including second order effects in environmental assessments of ICT. En-
viron. Model. Softw. 56, 105–115, Thematic issue on Modelling and eval-
uating the sustainability of smart solutions.
27. Graver, B., Zhang, K.K., and Rutherford, D. (2018). CO
2
Emissions from
Commercial Aviation, Technical report.
ll
OPEN ACCESS
16 Patterns 2, September 10, 2021
Review

28. Lo
¨vehagen, N. (2020). What’s the Real Climate Impact of Digital technol-
ogy? (Ericsson). https://www.ericsson.com/en/blog/2020/2/climate-
impact-of-digital-technology.
29. Apergis, I., and Apergis, N. (2019). Silver prices and solar energy produc-
tion. Environ. Sci. Pollut. Res. 26, 8525–8532.
30. IEA (2019). Solar Pv. Technical Report. https://www.iea.org/reports/
tracking-power-2019/solar-pv.
31. IPCC (2018). Special Report – Global Warming of 1.5C, Technical report.
https://www.ipcc.ch/sr15/.
32. ITU. ICT (2020). Industry to Reduce Greenhouse Gas Emissions by 45 Per
Cent by 2030, Technical report. https://www.itu.int/en/mediacentre/
Pages/PR04-2020-ICT-industry-to-reduce-greenhouse-gas-emissions-
by-45-percent-by-2030.aspx.
33. Baskerville-Muscutt, K. (2019). Setting Science-Based Supply Chain
Greenhouse-Gas Emission Targets: Aligning Corporate Climate Action
with the 1.5C Target, Thesis.
34. International Institute of Applied Systems Analysis (2018). Welcome to
the Ssp Database (Shared Socioeconomic Pathways) - Version 2.0.
https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=40.
35. Al-Ali, A.-R., Zualkernan, I.A., Rashid, M., Gupta, R., and AliKarar, M.
(2017). A smart home energy management system using IoT and big
data analytics approach. IEEE Trans. Consum. Electron. 63, 426–434.
36. Minoli, D., Sohraby, K., and Occhiogrosso, B. (2017). IoT considerations,
requirements, and architectures for smart buildings—energy optimiza-
tion and next-generation building management systems. IEEE Internet
Things J. 4, 269–283.
37. Sofana Reka, S., and Dragicevic, T. (2018). Future effectual role of energy
delivery: a comprehensive review of Internet of Things and smart grid.
Renew. Sustain. Energy Rev. 91, 90–108.
38. Saleem, Y., Crespi, N., Rehmani, M.H., and Copeland, R. (2019). Internet
of Things-aided smart grid: technologies, architectures, applications,
prototypes, and future research directions. IEEE Access 7,
62962–63003.
39. Raj, P., Raman, A., Nagaraj, D., and Duggirala, S. (2015). Big data ana-
lytics for healthcare. In High-Performance Big-Data Analytics, P. Raj, A.
Raman, D. Nagaraj, and S. Duggirala, eds. (Springer), pp. 391–424.
40. Wu, J., Guo, S., Li, J., and Zeng, D. (2016). Big data meet green chal-
lenges: big data toward green applications. IEEE Syst. J. 10, 888–900.
41. Gomes, C., Dietterich, T., Barrett, C., Conrad, J., Dilkina, B., Ermon, S.,
Fang, F., Farnsworth, A., Fern, A., and Fern, X. (2019). Computational
sustainability: computing for a better world and a sustainable future.
Commun. ACM 62, 56–65.
42. James, J. (2019). Data as the New Oil: The Danger behind the Mantra
(The Enterprises Project). https://enterprisersproject.com/article/2019/
7/data-science-data-can-be-toxic.
43. IDC. (2020). IDC’s Global Datasphere Forecast Shows Continued Steady
Growth in the Creation and Consumption of Data. https://www.idc.com/
getdoc.jsp?containerId=prUS46286020.
44. Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy con-
siderations for deep learning in NLP. arXiv.
45. Biewald, L. (2019). Deep Learning and Carbon Emissions. Towards Data
Science. https://towardsdatascience.com/deep-learning-and-carbon-
emissions-79723d5bc86e.
46. Amodei, D., and Hernandez, D. (2019). AI and Compute. https://openai.
com/blog/ai-and-compute/.
47. Dobbe, R., and Whittaker, M. (2019). AI and Climate Change: How
They’re Connected, and what We Can Do about it (AI Now Institute).
https://medium.com/@AINowInstitute/ai-and-climate-change-how-
theyre-connected-and-what-we-can-do-about-it-6aa8d0f5b32c.
48. Schwartz, R., Dodge, J., Smith, N.A., and Etzioni, O. (2019). Green
AI. arXiv.
49. Jobin, A., Ienca, M., and Vayena, E. (2019). The global landscape of AI
ethics guidelines. Nat. Mach. Intelligence 1, 389–399.
50. Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., and Pineau,
J. (2020). Towards the systematic reporting of the energy and carbon
footprints of machine learning. arXiv.
51. Folding@Home. Folding@home, 2020. https://foldingathome.org/, ac-
cessed March 2020.
52. Mohanty, S.P., Choppali, U., and Kougianos, E. (2016). Everything you
wanted to know about smart cities: the Internet of Things is the back-
bone. IEEE Consum. Electron. Mag. 5, 60–70.
53. Elias Bibri, S. (2018). The IoT for smart sustainable cities of the future: an
analytical framework for sensor-based big data applications for environ-
mental sustainability. Sustain. Cities Soc. 38, 230–253.
54. Govindan, K., Cheng, T.C.E., Mishra, N., and Shukla, N. (2018). Big data
analytics and application for logistics and supply chain management.
Transport. Res. E Log. Transport. Rev. 114, 343–349.
55. Collier, S.E. (2017). The emerging enernet: convergence of the smart grid
with the internet of things. IEEE Ind. Appl. Mag. 23, 12–16.
56. Mylonas, G., Amaxilatis, D., Chatzigiannakis, I., Anagnostopoulos, A.,
and Paganelli, F. (2018). Enabling sustainability and energy awareness
in schools based on IoT and real-world data. IEEE Pervasive Comput.
17, 53–63.
57. Liu, Q., Ma, Y., Alhussein, M., Zhang, Y., and Peng, L. (2016). Green data
center with IoT sensing and cloud-assisted smart temperature control
system. Comput. Netw. 101, 104–112.
58. Statista Research Department (2020). Internet of Things (IoT) Connected
Devices Installed Base Worldwide from 2015 to 2025 (In Billions). https://
www.statista.com/statistics/471264/iot-number-of-connected-devices-
worldwide/.
59. Das, S. (2019). Global Energy Footprint of IoT Semiconductors, Technical
report.
60. Kaur, N., and Sood, S.K. (2015). An energy-efficient architecture for the
Internet of Things (IoT). IEEE Syst. J. 11, 796–805.
61. Raza, U., Kulkarni, P., and Sooriyabandara, M.h. (2017). Low power wide
area networks: an overview. IEEE Commun. Surv. Tutorials 19, 855–873.
62. Karim Shaikh, F., Zeadally, S., and Exposito, E. (2015). Enabling technol-
ogies for green Internet of Things. IEEE Syst. J. 11, 983–994.
63. Arshad, R., Zahoor, S., Shah, M.A., Wahid, A., and Yu, H. (2017). Green
IoT: an investigation on energy saving practices for 2020 and beyond.
IEEE Access 5, 15667–15681.
64. Alsamhi, S.H., Ou, M., Ansari, M.S., and Meng, Q. (2019). Greening
internet of things for greener and smarter cities: a survey and future pros-
pects. Telecommun. Syst. 72, 609–632.
65. Solanki, A., and Nayyar, A. (2019). Green Internet of Things (G-IoT): ICT
technologies, principles, applications, projects, and challenges. In Hand-
book of Research on Big Data and the IoT (IGI Global), pp. 379–405.
66. Wilson, C., Hargreaves, T., and Hauxwell-Baldwin, R. (2017). Benefits
and risks of smart home technologies. Energy Policy 103, 72–83.
67. Enerdata. Between 10 and 20% of electricity consumption from the ICT*
sector in 2030? Technical report, 2018. accessed March 2020.
68. Kouhizadeh, M., and Sarkis, J. (2018). Blockchain practices, potentials,
and perspectives in greening supply chains. Sustainability 10, 3652.
69. Saberi, S., Kouhizadeh, M., Sarkis, J., and Shen, L. (2019). Blockchain
technology and its relationships to sustainable supply chain manage-
ment. Int. J. Prod. Res. 57, 2117–2135.
70. Mora, C., Rollins, R.L., Taladay, K., Kantar, M.B., Chock, M.K., Shimada,
M., and Franklin, E.C. (2018). Bitcoin emissions alone could push global
warming above 2C. Nat. Clim. Change 8, 931–933.
71. Cong, L.W., He, Z., and Li, J. (2019). Decentralized Mining in Centralized
Pools, Technical Report 0898-2937 (National Bureau of Economic
Research).
ll
OPEN ACCESS
Patterns 2, September 10, 2021 17
Review

72. Digiconomist. (2019). Bitcoin Energy Consumption Index.
73. Stoll, C., Klaaßen, L., and Gallersdo
¨rfer, U. (2019). The carbon footprint of
bitcoin. Joule 3, 1647–1661.
74. CoinMarketCap. Global charts, 2020.https://coinmarketcap.com/charts/,
accessed March 2020.
75. Alex de Vries. (2018). Bitcoin’s growing energy problem. Joule 2,
801–805.
76. Monrat, A.A., Schele
´n, O., and Andersson, K. (2019). A survey of block-
chain from the perspectives of applications, challenges, and opportu-
nities. IEEE Access 7, 117134–117151.
77. Saleh, F. (2021). Blockchain without waste: Proof-of-stake. The Review
of financial studies 34 (3), 1156–1190. https://academic.oup.com/rfs/
article-abstract/34/3/1156/5868423.
78. Peter, H. (2019). Tackling climate change with blockchain. Nat. Clim.
Change 9, 644–645.
79. Bendiksen, C., and Gibbons, S. (2019). The Bitcoin Mining Network:
Trends, Average Creation Costs, Electricity Consumption & Sources.
December 2019 Update, Technical report (CoinShares Research).
80. Alex de Vries. (2019). Renewable energy will not solve Bitcoin’s sustain-
ability problem. Joule 3, 893–898.
81. Bendiksen, C., Gibbons, S., and Lim, E. (2018). The Bitcoin Mining
Network. Technical Report (CoinShares Research).
82. Koomey, J.G., Berard, S., Sanchez, M., and Wong, H. (2011). Web extra
appendix: implications of historical trends in the electrical efficiency of
computing. IEEE Ann. Hist. Comput. 33, S1–S30.
83. Truby, J. (2018). Decarbonizing Bitcoin: law and policy choices for
reducing the energy consumption of Blockchain technologies and digital
currencies. Energy Res. Soc. Sci. 44, 399–410.
84. Le Que
´re
´, C., Jackson, R.B., Jones, M.W., Smith, A.J.P., Abernethy, S.,
Andrew, R.M., et al. (2020). Temporary reduction in daily global CO
2
emissions during the covid-19 forced confinement. Nat. Clim. Change
10, 647–653.
85. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum,
V., Luetge, C., Madelin, R., Pagallo, U., and Rossi, F. (2018). AI4people—
an ethical framework for a good AI society: opportunities, risks, princi-
ples, and recommendations. Minds Mach. 28, 689–707.
86. Delbeke, J., and Vis, P. (2016). EU Climate Policy Explained, Technical
report.
87. European Commission (2018). A Clean Planet for All. A European Stra-
tegic Long-Term Vision for a Prosperous, Modern, Competitive and
Climate Neutral Economy, Technical report.
88. European Commission (2019). On Artificial Intelligence - a European
Approach to Excellence and Trust, Technical report. https://ec.europa.
eu/info/sites/info/files/commission-white-paper-artificial-intelligence-
feb2020_en.pdf.
89. European Commission (2020). Supporting the Green Transition, Tech-
nical report. https://ec.europa.eu/commission/presscorner/detail/en/
fs_20_281.
90. PEDCA (Pan-European Data Centre Academy) (2015). Final Report Sum-
mary, Technical report (European Commission). https://cordis.europa.
eu/project/id/320013/reporting.
91. Kayali, L., Heikkil€
a, M., and Delcker, J. (2020). Europe’s Digital Vision, Ex-
plained (Politico). https://www.politico.eu/article/europes-digital-vision-
explained/.
92. World Economic Forum (2019). A New Circular Vision for Electronics.
Time for a Global Reboot, Technical report. http://www3.weforum.org/
docs/WEF_A_New_Circular_Vision_for_Electronics.pdf.
93. European Commission (2020). Changing How We Produce and
Consume: New Circular Economy Action Plan Shows the Way to a
Climate-Neutral, Competitive Economy of Empowered Consumers.
https://ec.europa.eu/commission/presscorner/detail/en/ip_20_420.
94. European Commission (2020). Eco-innovation at the Heart of European
Policies (Rewards for Recycling). https://ec.europa.eu/environment/
ecoap/about-eco-innovation/policies-matters/rewards-recycling_en.
95. European Commission (2019). The European Green Deal, Technical
report. https://ec.europa.eu/info/sites/info/files/european-green-deal-
communication_en.pdf.
96. Palmer, M. (2019). Eu Launches V2bn AI and Blockchain Fund (Sifted).
https://sifted.eu/articles/eu-2bn-ai-blockchain-fund/.
97. Carrillo Pineda, A., and Faria, P.O. (2019). Towards a Science-Based
Approach to Climate Neutrality in the Corporate Sector, Technical report
(Science Based Targets report). https://sciencebasedtargets.org/wp-
content/uploads/2019/10/Towards-a-science-based-approach-to-
climate-neutrality-in-the-corporate-sector-Draft-for-comments.pdf.
98. Smith, B. (2020). Microsoft Will Be Carbon Negative by 2030 (Microsoft).
https://blogs.microsoft.com/blog/2020/01/16/microsoft-will-be-
carbon-negative-by-2030/.
99. Stephens, A. (2019). Net Zero: An Ambition in Need of a Definition (Carbon
Trust). https://www.carbontrust.com/news-and-events/insights/net-zero-
an-ambition-in-need-of-a-definition.
100. Childs, M. (2020). Does Carbon Offsetting Work? (Friends of the Earth).
https://friendsoftheearth.uk/climate-change/does-carbon-offsetting-
work.
101. Cames, M., Harthan, R.O., F€
ussler, J., Lazarus, M., Lee, C.M., Erickson,
P., and Spalding-Fecher, R. (2016). How Additional Is the Clean Develop-
ment Mechanism? Analysis of the Application of Current Tools and Pro-
posed Alternatives, Technical report.
102. ITU (2018). ITU Recommendations: Itu-T l.1450 (09/2018), Technical
report. https://www.itu.int/ITU-T/recommendations/rec.aspx?id=13581.
ll
OPEN ACCESS
18 Patterns 2, September 10, 2021
Review