Federal Department of the Environment, Transport,
Energy and Communications DETEC
Swiss Federal Office of Energy SFOE
Energy Research and Cleantech Division

Final report dated 28 June 2021

Investigating the Inconsistencies among
Energy and Energy Intensity Estimates of theInternet
Metrics and Harmonising Values

Source: Vlad Coroama, 2021

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Date: 28 June 2021
Location: Bern
Publisher:
Swiss Federal Office of Energy SFOE
Energy Research and Cleantech
CH-3003 Bernwww.bfe.admin.ch
Co-financing:
ETH Zürich
Universitätsstr. 68092 Zürich
www.ethz.ch
Subsidy recipients:
ETH Zürich
Universitätsstr. 68092 Zürich
www.ethz.ch
Author:Vlad Coroamă, ETH Zürich, vcoroama@ethz.ch
SFOE project coordinators:
Dr. Michael Moser, michael.moser@bfe.admin.ch
Roland Brüniger, roland.brueniger@brueniger.swiss
SFOE contract number: SI/502164-01
The authors bear the entire responsibility for the content of this report and for the conclusions drawn
therefrom.

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Zusammenfassung
Der Bericht präsentiert eine eingehende Analyse von Schätzungen der Energieintensität (EI) und desGesamtenergieverbrauchs (E) des Internets. Die beiden Werte müssten über den gesamten
Internetverkehr (Englisch: traffic, T) verbunden sein, da die Multiplikation von EI mit T zum Ergebnis E
führen müsste. Diese Identität wird jedoch durch die heute vorhandenen Studien nicht erfüllt. Im
Gegenteil, die mit den jeweiligen Methoden erzielten Ergebnisse liegen einen Faktor von 5 bis 26
auseinander. Die Studie identifiziert zwei wichtige Ursachen dieser Inkonsistenz: die unterschiedliche
Behandlung von Zugangsnetzen (Englisch: access networks, ANs) und die Tendenzen hin zu Überund Unterschätzungen, die der Top-Down- bzw. der Bottom-Up-Modellierung inhärent zugrunde liegenscheinen. Das WAN (Wide Area Network), die FANs (Fixed Access Networks) und die RANs (Radio
Access Networks) einzeln betrachtend, zeigt die Studie, dass die grössten Diskrepanzenüberraschenderweise nicht auf die relativ jungen und recht heterogenen RANs zurückzuführen sind,sondern auf dem schon recht etablierten WAN.
Die Studie identifiziert zudem drei Ebenen möglicher Harmonisierung von EI und E; in der Rangfolge
von Strenge und Präzision, aber auch von zunehmenden Herausforderungen und Unsicherheiten,
sind dies: i) eine pauschale Harmonisierung über alle Netztypen, ii) eine, die individuell innerhalb desWAN und innerhalb der Zugangsnetze harmonisiert, und iii) eine, die innerhalb jedes der drei
Netztypen harmonisiert. Angesichts der Inkompatibilität innerhalb der bestehenden Literatur bezüglich
der Energie- und Energieintensitätsschätzungen für das WAN konnte nur eine globale Harmonisierung
erreicht werden. Infolge dieser gegenseitigen Validierung, bei der dennoch versucht wurde, so viel wie
möglich auch innerhalb der einzelnen Netztypen zu harmonisieren, ergeben sich folgende
Schätzwerte der heutigen Energie- und Energieintensitätswerte: der Energieverbrauch des WAN im
Jahr 2020, E2020 (WAN) = 110 TWh, E2020 (FAN) = 130 TWh und E2020 (RAN) = 100 TWh; die
Energieintensität des WAN im Jahr 2020, EI2020 (WAN) = 0,02 kWh/GB, EI2020 (FAN) = 0,07 kWh/GB
und EI2020 (RAN) = 0,2 kWh/GB. Für kurzfristige Extrapolationen in die Zukunft erscheinen jährliche
Energieintensitätsreduktionsfaktoren von 0,8 für WAN und RAN und 0,85 für das FAN sinnvoll undeine eher konservative Wahl.
Zukünftige Anstrengungen sind insbesondere erforderlich, um die Energiebewertungen des WAN zu
harmonisieren. Konsistentere Ergebnisse können beispielsweise erzielt werden, indem entweder
sowohl Top-Down- als auch Bottom-Up-Methoden eingesetzt werden, eine Hybridmethode angestrebt
wird oder zumindest die Ergebnisse über die jeweils andere Methode validiert werden. Besonders
wichtig und oft falsch interpretiert sind die Durchschnittswerte der Energieintensität des gesamten
Internets. Aus Gründen der Konsistenz müssen diese i) den additiven Charakter der Intensitäten desWAN und der Zugangsnetze, ii) den alternativen Zugang über FAN oder RAN und iii) die (sich schnelländernden) Anteile dieser beiden Arten von Zugangsnetzen berücksichtigen. Die Studie bietet
Gleichungen, die sowohl zur Berechnung dieses Durchschnitts als auch zur Überprüfung derKonsistenz zwischen Schätzungen von EI und E eingesetzt werden können.

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RésuméCe rapport analyse en profondeur les estimations de l'intensité énergétique (EI) et de laconsommation énergétique globale (E) de l'Internet. Les deux devraient être connectés via le trafic
Internet global (T), car la multiplication de EI par T devrait donner E. Cette identité, cependant, n'est
pas satisfaite par les évaluations existantes aujourd'hui ; au contraire, les résultats obtenus par lesdeux méthodes sont séparés par un facteur de 5 à 26. L'étude identifie deux sources importantes decette incohérence : le traitement différent des réseaux d'accès (AN) et les biais inhérents aux
surestimations et sous-estimations qui semblent résider respectivement dans la modélisation
descendante et ascendante. Considérant individuellement le réseau étendu (WAN), les réseauxd'accès fixe (FAN) et les réseaux d'accès radio (RAN), l'étude montre que, de manière assez
surprenante, les écarts les plus importants ne proviennent pas des RAN assez récents et assez
hétérogènes, mais du WAN bien établi.
L'étude identifie en outre trois niveaux d'harmonisation entre l'IE et l'E ; par ordre de rigueur et deprécision, mais aussi de défis et d'incertitudes croissants, ce sont : i) une harmonisation globale pour
tous les réseaux, ii) une harmonisation individuelle au sein du réseau étendu et des réseaux d'accès,et iii) une harmonisation au sein de chacun des trois types de réseaux. Étant donné l'incompatibilité
des estimations de l'énergie et de l'intensité énergétique pour le WAN dans la littérature existante,
seule une harmonisation globale a pu être réalisée. Suite à cette validation mutuelle, qui a également
tenté d'harmoniser autant que possible les différents types de réseaux, une approximation correctedes valeurs actuelles de l'énergie et de l'intensité énergétique est la suivante : la consommationd'énergie du WAN en 2020, E2020 (WAN) = 110 TWh, E2020 (FAN) = 130 TWh et E2020 (RAN) = 100
TWh; l'intensité énergétique du WAN en 2020, EI2020 (WAN) = 0,02 kWh/GB, EI2020 (FAN) = 0,07
kWh/GB et EI2020 (RAN) = 0,2 kWh/GB. Pour les extrapolations à court terme dans le futur, les facteursde réduction de l'intensité énergétique annuelle de 0,8 pour le WAN et le RAN, et de 0,85 pour leFAN, semblent significatifs et plutôt conservateurs.
Des efforts futurs sont nécessaires en particulier pour harmoniser les évaluations énergétiques duWAN. Des résultats plus cohérents peuvent être obtenus en déployant à la fois des méthodes
descendantes et ascendantes, en visant une méthode hybride, ou au moins en validant les résultats
via l'autre méthode. Les moyennes globales de l'intensité énergétique d'Internet sont particulièrement
importantes et souvent mal interprétées. Par souci de cohérence, ils doivent prendre en compte i) lanature additive des intensités du WAN et des réseaux d'accès, ii) l'accès alternatif via FAN ou RAN, etiii) les parts (en évolution rapide) de ces deux types de réseaux d'accès. L'étude fournit des équationsqui peuvent être déployées à la fois pour calculer cette moyenne et pour vérifier la cohérence entre lesestimations de l'IE et d'E.

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Summary
The report performs an in-depth analysis of estimates of the energy intensity (EI) and the overall
energy (E) consumption of the Internet. The two values should be connected via the overall Internet
traffic (T), as multiplying EI by T should yield E. This identity, however, is not satisfied by the existingassessments today; on the contrary, the results obtained via the two methods lie a factor of 5-26 apart.
The study identifies two important sources for this inconsistency: the different treatment of accessnetworks (ANs), and the inherent biases towards overestimates and underestimates that seem toreside within top-down and bottom-up modelling, respectively. Considering the wide-area network(WAN), fixed access networks (FANs), and radio access networks (RANs) individually, the study
shows that, rather surprisingly, the largest discrepancies do not stem from the fairly recent and quite
heterogeneous RANs, but from the well-established WAN.
The study further identifies three levels of harmonisation between EI and E; in order of rigour andprecision, but also of increasing challenges and uncertainties, they are: i) a global one across all typesof networks, ii) one that individually harmonises within the WAN and within the access networks, andiii) one harmonising within each of the three types of the networks. Given the incompatibility of energy
and energy intensity estimates for the WAN within existing literature, only a global harmonisation could
be achieved. Following from this mutual validation, which also tried to harmonise as much as possible
within the individual types of networks, a fair approximation of today’s energy and energy intensity
values is as follows: the energy consumption of the WAN in 2020, E2020 (WAN) = 110 TWh, E2020 (FAN)= 130 TWh, and E2020 (RAN) = 100 TWh; the energy intensity of the WAN in 2020, EI2020 (WAN) = 0.02
kWh/GB, EI2020 (FAN) = 0.07 kWh/GB, and EI2020 (RAN) = 0.2 kWh/GB. For short-term extrapolations
to the future, yearly energy intensity reduction factors of 0.8 for both WAN and RAN, and 0.85 for the
FAN, appear meaningful and rather conservative.
Future efforts are needed in particular to harmonise energy assessments of the WAN. More consistent
results can be achieved by either deploying both top-down and bottom-up methods, aiming for a
hybrid method, or at least validating the results via the other method. Particularly important and often
misinterpreted are the overall averages of the energy intensity of the Internet. For consistency, theyneed to account for i) the additive nature of the intensities of the WAN and the access networks, ii) thealternative access via FAN or RAN, and iii) the (rapidly changing) shares of these two types of accessnetworks. The study provides equations that can be used both to compute this average, and to verify
the consistency between estimates of EI and E.

Main findings-

The energy intensity (EI) and the energy (E) consumption of the Internet must be connected via
the overall Internet traffic (T): EI [TWh/EB] * T [EB/year] = E [TWh/year].

-This must hold both for the entire Internet and for each network type individually: wide-area
network (WAN), fixed-access network (FAN), and radio/mobile access network (RAN).

-

While precise data for T exist, the existing estimates of EI and E do not satisfy this identity,
yielding results that lie a factor of 5–26 apart.

-

The main sources for these discrepancies lie in the inconsistent treatment of access networks
and the inherent and converse biases of top-down and bottom-up modelling.

-

Only values from the opposing ends of their respective spectra in the existing literature, i.e. highvalues for the intensities and low values for the energies, can fulfil the identity. Yet discrepancies
remain, in particular for the wide-area network (WAN).

-

Reasonable estimates for 2020 are: E (WAN) = 110 TWh, E (FAN) = 130 TWh, and E (RAN) =100 TWh, EI (WAN) = 0.02 kWh/GB, EI (FAN) = 0.07 kWh/GB, and EI (RAN) = 0.2 kWh/GB.
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Contents
Zusammenfassung .................................................................................................................................3
Résumé ....................................................................................................................................................4
Summary .................................................................................................................................................5
Main findings ..........................................................................................................................................5
Contents ..................................................................................................................................................6
Abbreviations..........................................................................................................................................81

Introduction ................................................................................................................................9

1.1

Background information and current situation .............................................................................9

1.2

Purpose of the study and structure of the report ...................................................................... 10

2

Background: Early discrepancies and their methodological sources ............................. 10

2.1

Different system boundaries and understanding of ‘the Internet’ ............................................. 11

2.2

Efficiency gains and reference year for the assessment .......................................................... 11

2.3

Vanishing relevance of these factors today.............................................................................. 13

3

Methods ................................................................................................................................... 14

3.1

System boundaries and topics under scrutiny.......................................................................... 14

3.2

Assessment principles .............................................................................................................. 16

3.3

Period of assessment and harmonising the time perspective .................................................. 17

3.4Internet traffic ............................................................................................................................ 18

4

Analysis of existing estimates .............................................................................................. 19

4.1

Estimates of the energy intensity (EI) of WAN, FAN, and RAN ............................................... 19
Estimates referring to the past decade (year of reference: 2010 – 2019) ................................ 19
Current estimates ..................................................................................................................... 20
Current industry reports (RAN energy intensity) ...................................................................... 23
Summarising the reports on the energy intensity of the Internet .............................................. 24

4.2

Estimates of the overall energy consumption (E) of WAN, FAN, and RAN ................................ 24
Overall energy consumption of WAN, FAN, and RAN: data for 2010 – 2019 .......................... 25
Overall energy consumption of WAN, FAN, and RAN extrapolated to 2020 ........................... 26
Summarising the reports on the overall energy consumption of the Internet ........................... 26

5

Recent discrepancies and their sources ............................................................................. 27

5.1

Inconsistencies between energy intensity and energy studies, and first harmonization attempts.................................................................................................................................................. 29

5.2

Harder constraints required for better harmonisation ............................................................... 31

5.3

Methodological sources for the current discrepancies ............................................................. 33
Inconsistent system boundaries and modelling access networks ............................................ 33
Inherent biases of top-down versus bottom-up modelling ........................................................ 33

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6

Conclusions ............................................................................................................................ 35

6.1

Summary of the analysis .......................................................................................................... 35

6.2

Metrics for the different energy intensities ................................................................................ 36

6.3

Current values and short-term developments .......................................................................... 37

7

Outlook: Largest uncertainties and future research .......................................................... 39

Acknowledgements ............................................................................................................................. 39
References ........................................................................................................................................... 40

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Abbreviations
4E TCP

technology collaboration programme on energy efficient end-use equipment of the IEA

ADSL

asymmetric digital subscriber lineAN

access networkBSbase station (in RANs)

CDN

content delivery network

CPE

customer premises [networking] equipment (e.g., modems, routers)DCdata centreEB

exabyte (109 GB)

EDNA

electronic devices & networks annex of the “4E TCP” (see above) of the IEA

FAN

fixed access network

ICT

information and communication technology

IEA

international energy agency

IoTInternet-of-things

IP

Internet protocol

ISP

Internet service provider

LAN

local-area network

LCA

life cycle assessment

LTElong-term evolution (4G telecommunication network)MLmachine learning

MPLS

multi-protocol label switching

NFV

network functions virtualisation

PB

petabyte (106 GB)

PON

passive optical network

PSTN

public switched telephone network

PUE

power usage effectiveness

RAN

radio access network

RBS

radio base station (see BS)

SDN

software-defined network

TB

terabyte (103 GB)

UPS

uninterruptible power supply

VDSL

very-high-bitrate digital subscriber line

WAN

wide-area network

XG

next generation

ZB

zettabyte (1012 GB)

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1

Introduction

The broad use of digital information and communication technology (ICT), together with the profoundchanges in business operations and entire business models it triggers, is often called “digitaltransformation” – or “digitalisation” in short (Coroamă and Mattern 2019). Given its broad optimisationand substitution potential, digitalisation is increasingly envisioned as a miracle cure to tackle – or atleast mitigate – the world’s urgent environmental issues; in particular, it is seen as a possible key
factor in reducing carbon emissions and resource consumption across various economic sectors.
At the same time, due to trends such as video streaming, cloud computing, Internet-of-Things (IoT),
blockchain and machine learning (ML), there is also a growing concern with the own energy
consumption of ICT in general, and the Internet in particular. Two traditions are established in theliterature, presenting either estimates for the overall energy consumption of the Internet, or for its
energy intensity (i.e. the amount of energy needed to transfer a certain amount of data). As will beshown below, these two values are not independent, but connected through the overall volume ofInternet data traffic. However, current state-of-the-art estimates from the two traditions areincompatible, yielding results that differ substantially, often by more than an order of magnitude (i.e., afactor of 10). The resulting spread in results reaches from a relatively marginal problem to one of themost important sources for societal energy consumption and anthropogenic global warming.
This situation leads to various adverse consequences: overstated or ignored effects, misinformedpress as well as general public and policy makers, counterproductive advocacies, misplaced or lacking
policies. The current report addresses this issue by analysing the sources for the existing discrepancy
and establishing principles to harmonise the two traditions.

1.1

Background information and current situation

Two types of studies have so far tried to shed light on the energy consumption of the Internet:
i.

studies assessing the energy intensity of the Internet (EI) (i.e., the energy needed to transfer acertain amount of data), measured in either Joules per bit [J/bit] or kilowatt-hours per gigabyte[kWh/GB], andii.

studies concerned with the yearly overall energy consumption of the Internet (E), measured interawatt-hours per year [TWh/year]:

The energy intensity of the Internet (EI) has been addressed in several studies, such as (Schien et al.
2013; Coroama et al. 2013). For fixed access networks, the current academic state-of-the-art indicatesan average energy intensity of 0.007 kWh/GB for 2020 (Aslan et al. 2018). For the energy
consumption of networks as a whole, on the other hand, there is some divergence in the academic
literature, ranging from 325 TWh yearly (Malmodin and Lundén 2018) to 723 TWh (Andrae and Edler
2015).
These two values, EI and E, however, are necessarily connected via the yearly Internet traffic (T),according to the identity in Equation (1): the overall energy consumption of networks (E) is the averageenergy intensity of the Internet (EI) multiplied by the overall Internet traffic (T),
𝑇𝑊ℎ
𝑇𝑊ℎ
𝐸𝐵
𝐸 [
] = 𝐸𝐼 []∗ 𝑇 [
]
𝑦𝑒𝑎𝑟
𝐸𝐵
𝑦𝑒𝑎𝑟

(1),

whereby the dimension terawatt-hours per exabyte [TWh/EB] is equivalent to the more usual[kWh/GB], as 1 𝑇𝑊ℎ = 109 𝑘𝑊ℎ and 1 𝐸𝐵 = 109 𝐺𝐵.
According to (Cisco 2020), the worldwide Internet data traffic for 2020 was around 254 EB/month,corresponding to 3048 EB (or 3.05 zettabytes, ZB) yearly. Multiplying this value by the 0.007 kWh/GB
from (Aslan et al. 2018) would yield an annual energy consumption of only 21.3 TWh/year, one orderof magnitude below the low end of current estimates for the overall energy consumption of the Internet
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from (Malmodin and Lundén 2018). Identity (1) is thus violated, indicating that at least one of the two
numbers, E or EI, must be consistently wrong in the literature – under the assumption that the Internet
traffic T is not off by an order of magnitude, which, given Cisco’s mature methodology (Cisco 2018;2019) and direct access to router data across the world, seems highly unlikely. To the best of ourknowledge, this discrepancy is so far unaddressed in the literature.

1.2

Purpose of the study and structure of the report

In this context, the current study aims at analysing the sources for this inconsistency among the two
traditions, and at finding ways towards their harmonisation.
Some of the questions it addresses are:i)

Where do the existing discrepancies among existing assessments of the energy intensity of
the Internet (EI) and of the overall energy consumption (E) stem from?

ii)

For which of the network types is this misfit particularly prominent among wide-area networks(WAN), fixed access networks (FANs), and radio access networks (RANs)?

iii) Is a harmonisation per network type worthy or necessary, or is a global fit sufficient?
iv) Given the heterogeneous access modes, is the concept of an average Internet energy
intensity (irrespective of the access means) meaningful? If yes, what is a suitable metric?
v) For FANs, how must the customer-premises equipment (CPE, e.g., modems and routers) be
accounted for: in the same way as the equipment of the core and metro network (energy peramount of data), or as power (i.e., energy per time), as (Coroama et al. 2015; Schien et al.
2015) suggest?
vi) For all questions above, which are the ones with a high degree of data unavailability or uncertainty, and how can it be mitigated?
The remainder of the report is organised as follows: Section 2 briefly addresses early discrepancies in
the field, revealing their sources, and discussing why these cannot explain today’s mismatches.
Section 3 discusses the methods and assumptions deployed in this study, and in particular the systemboundaries of the Internet, general assessment principles, and the evolution of Internet traffic. Section
4 provides an in-depth analysis of well-known academic and industry estimates of both the energy
intensity and the overall energy consumption of the Internet. It also scrutinises the energy intensity ofFANs, and in particular the modelling of CPE, the energy intensity of RANs, and proposes a combined
metric for the two. Building on this analysis, Section 5 reveals sources for the existing discrepancies
between EI and E assessments today, and makes recommendations on how top-down and bottom-up
studies can validate each other. Section 6 summarises the results, and Section 7 discusses the largest
uncertainties and derives the necessary next steps.

2

Background: Early discrepancies and their
methodological sources

Studies on both the energy intensity of the Internet and its overall energy consumption were publishedstarting in the early 2000s. Presumably the two earliest publications were (Gupta and Singh 2003) and(Koomey et al. 2004). Despite deploying the same method and building on the same data sources,
these two early publications arrive to substantially different results, opening a long tradition of
discrepancies that persists until today.
To compute the overall energy consumption of the Internet in the US, both (Gupta and Singh 2003)
and (Koomey et al. 2004) start their analysis from a detailed inventory of computing and networking
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devices in the US (Roth, Goldstein, and Kleinman 2002). Using the number of devices in use and theiraverage yearly energy consumption for the US in 2000, the report also estimates the total yearlyenergy consumption per category of devices. Taking this data as input, (Gupta and Singh 2003)
compute the total energy consumption of networking devices in the US as 6.05 TWh/year in 2000.
Starting from the same data for the same year of reference, (Koomey et al. 2004) yield a result of 47.3
TWh/year energy consumption for the Internet in the US.

2.1

Different system boundaries and understanding of ‘the Internet’

Where does this substantial factor of almost 8 stem from? The difference is in what the authors
consider to be part of ‘the Internet’; in other words, and expressed in the language of environmental
sciences, what they include within the system boundaries (Coroama and Hilty 2014) of the Internet.
While (Gupta and Singh 2003) only include networking devices (core Internet routers 1.1 TWh, WAN
switches 0.15 TWh, LAN switches 3.2 TWh, and hubs 1.6 TWh), (Koomey et al. 2004) also include alldata centre (DCs) devices within the system boundaries – i..e, four types of servers (10.2 TWh), data
storage (1.5 TWh) as well as the uninterruptible power supply (UPS, 5.8 TWh) devices needed for
backup power. Adding all these additional DC devices and using the correct consumption figure for
LAN switches from (Roth, Goldstein, and Kleinman 2002) – 3.3 TWh and not 3.2 TWh as wrongly
indicated by (Gupta and Singh 2003) – they arrive to a total of 23.65 TWh for all devices consideredwithin system boundaries. Ultimately, this number is doubled to the final result of 47.3 TWh yearly to
account for the consumption of non-computing devices, in particular the cooling of the devices. The
PUE is a measure for the efficiency of a room or building dedicated to the operation of devices, such
as Internet routers. It is computed as the facility’s total power (including lightning, air conditioning, andso on) divided by the power needed to run the ICT equipment only (Coroama et al. 2013), andassuming a PUE of 2.0 for the year 2000 was certainly no overstatement on part of the authors.
After presenting the total yearly energy consumption of ‘networking devices’ and the Internet in theUS, respectively, both articles proceed to also compute the energy intensity of the Internet. In thissecond step, they continue using the same methods and data sources. Next to the energy
consumption of devices from (Roth, Goldstein, and Kleinman 2002), both articles use a top-down
methodology to compute the energy intensity of the Internet, dividing its overall consumption by theoverall traffic. And both use the same source for the overall data traffic in the US, the 20-35 PB
monthly in 2000 as presented by (Coffman and Odlyzko 2002). Using these data sources, a traffic
figure towards the middle of the range (29 PB/month) and the simple top-down division, (Koomey et al.
2004) yield an energy intensity of the Internet of 136 kWh/GB for the year 2000. Meanwhile, (Gupta
and Singh 2003) set the system boundaries of ‘the Internet’ even narrower than they had done with‘networking devices’, and only consider the subset of devices that constitute the WAN, excluding LAN
networking devices. They thus only divide 1.25 TWh/year by the yearly Internet traffic in order tocompute the energy intensity of the Internet. As their computation mistakenly assumes the 20-35 PB
from (Coffman and Odlyzko 2002) to represent the yearly (instead of the monthly) traffic, the result isflawed. Had they used the correct traffic value, however, the discrepancy to the result from (Koomey etal. 2004) would have been almost a factor of 40, corresponding to the ratio between 47.3 TWh and1.25 TWh (divided by the same traffic, respectively).

2.2

Efficiency gains and reference year for the assessment

Another attribute with a strong impact on the result is the reference year for the assessment. This wasmost obvious around the early 2010s: While the interest in the topic was picking up, two of the bestknown assessments could hardly have been more different. As analysed in (Coroama and Hilty 2014)
and shown below in Figure 2, the estimates reached a spread by a factor of more than 20,000 –between the 136 kWh/GB by the already presented (Koomey et al. 2004) and the 0.0064 kWh/GB put
forward by (Baliga et al. 2011).

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Figure 1 Summary Table 5-1 from (Roth, Goldstein, and Kleinman 2002), comprising types of devices together with their yearly network
consumption in the US, used by both (Gupta and Singh 2003) and (Koomey et al. 2004) to compute the energy intensity of the Internet
for the year 2000. Reprinted with the authors’ permission. Own highlighting showing how distinct system boundaries for ‘the Internet’,together with the inclusion of PUE or not, can yield vastly different results.

(Baliga et al. 2011) was published by the former Centre for Energy-Efficient Telecommunications(CEET) at the University of Melbourne, led by Kerry Hinton, an institution which published an entire
series of highly regarded articles on the topic. Its result is represented in the lower right corner of
Figure 2. This substantial influence of the year of assessment comes to no surprise: The Internet
communication infrastructure (both Internet routers and fibres) follow the efficiency gains as predicted
by Moore’s law. According to (Aslan et al. 2018), the energy intensity of the Internet has decreased on
average by 30% per year. This corresponds to a halving over 2 years, and to a reduction by a factor of
30 over a decade.
The 11 years between (Koomey et al. 2004) – which refers to the year 2000 – and (Baliga et al. 2011)– which refers to 2011 – thus explain a difference of two orders of magnitude or, more precisely, afactor of 50. Together with another factor of 18 (Aslan et al. 2018) explained by the inclusion of DCs
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within the system boundary of (Koomey et al. 2004), these two factors explain a difference of about
1,000 between these two examples, or 3 out of the 4 orders of magnitude.

1000
100
10
Energy intensity[kWh/GB]

1
0.1
0.01

0.001
1998

2000

2002

2004

2006

2008

2010

2012

Reference year
including data centres

excluding data centres

Figure 2 Important early assessments of the energy intensity of the Internet between 2000 and 2011, showing the influence of a) thesystem boundary – in particular, whether data centres are included (blue) or not (red) – and b) of the year of assessment (due tocontinuously increasing efficiency). Adapted from (Coroama and Hilty 2014).

2.3

Vanishing relevance of these factors today

Today, the two factors presented above have lost a good deal of their importance. The first one(inclusion of DCs) plays only a marginal role for two reasons: First, as argued in (Coroama and Hilty
2014), it has now become standard to account for DCs separately and not include them in calculationsof the energy consumption or the energy intensity of the Internet (they need, though, of course be
added on a per-case basis to energy computations for services that include both Internet and DCs, forexample video streaming). Second, with the advent of hyperscale DCs, the lowering of the PUE tovalues of 1.12-1.3, and other improvements, efficiency gains in DCs have been even more impressive
than for networks. Even if they were to be added, DCs would no longer dominate the result by anorder of magnitude as they used to do, but on the contrary contribute to a much lesser extent than
networks and, sometimes even less than the end-user device (Hintemann and Hinterholzer 2020).
The other factor, meanwhile (i.e. the reference year for the assessment), is still of central importancefor the resulting energy intensity. It hardly represents a challenge, though: it is conceptually rather
trivial, and pragmatically easily accounted for.
For different reasons, thus, these two sources of discrepancies are no longer main reasons forconcern. There are, however, several other sources for the current discrepancies. They will be
addressed in the reminder of this study. As will be argued below, albeit less prominent than thedefinition of ‘the Internet’ and the different year of assessment, these sources have always coexistedwith the two which are nowadays well understood. One first hint has been presented already: The
inclusion of DCs and a decade of difference explained a factor of 1,000 from the 20,000 between(Koomey et al. 2004) and (Baliga et al. 2011); a factor of 20, however, remains so far unaccounted for.

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3

Methods

Given the considerations so far, we will deploy the following methodologies to define systemboundaries, relate energy intensity to energy estimates, cope with the different reference years forexisting assessments, and assess the yearly Internet traffic.

3.1

System boundaries and topics under scrutiny

In line with most of the literature, e.g. (Baliga et al. 2009; 2011; Coroama and Hilty 2014; Coroama etal. 2015; Schien et al. 2015; Wu, Ryan, and Smith 2019), we consider the following high-level topologyof the Internet, as presented in Figure 3: data are usually transmitted from data centres (DCs) to end
devices (sometimes also called end-user devices) via the network. The network consists of the widearea network (WAN), which is “a network spanning a region, country or the globe […] comprising adiverse range of telecommunication networks” (Ryan, Smith, and Wu 2019). The literature sometimes
distinguishes between core, metro and edge networks (Baliga et al. 2009; 2011) or core and metro
networks (Coroama et al. 2015; Schien et al. 2015), but these distinctions are less relevant to thescope of our assessment, so we will use the simpler model of a WAN. The WAN consists of opticalfibres (both land-based and undersea) and large Internet routers, building thus the data transmission
highways of the Internet.

Figure 3 High-level topology of the Internet, distinguishing between data centres (DCs), end devices, and three distinct types of the
network: the wide-area network (WAN), fixed access network (FAN), and radio access network (RAN).

The data reaches the end consumers (households or businesses) via access networks (ANs), which“connect each home to one of the edge switches” (Baliga et al. 2009). Traditionally, and up to around2010, ANs have been exclusively fixed access networks (FANs), meaning that a cable (copper coaxial
cables or increasingly optical fibres) reach into the users’ homes, although there might be a wirelessWiFi component within the users’ homes. As shown in Figure 3, FANs consist of two main
components:•

core Internet connection: a street-level component for several households, which, dependingon the technology used can be e.g. a digital subscriber line access multiplexer (DSLAM
cabinet) for DSL or an optical network unit (ONU) for optical networks, and•end devices connection: the customer premises equipment (CPE), traditionally a modem
and/or a router, today almost exclusively one integrated device with both functionalities. CPE

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can also include repeaters or other specialised networking equipment. While CPE is physicallylocated within the user’s premises, it is logically part of the network (Coroama et al. 2015).
With the parallel emergence of 4G communication networks and the first smartphones in the second
part of the 2000s, their rapid spreading during the 2010s, and the later emergence of further relevant
technologies (e.g. tablets or 5G networks), mobile/radio access to the Internet became increasingly
popular. The corresponding radio access network (RAN) corresponds, as sketched in Figure 3 andpresented in detail in (ITU-T 2020) to•core Internet connection: base stations (BSs) or radio base stations (RBSs), which containseveral types of equipment such as radio controllers (RC), site equipment (e.g., airconditioning, rectifiers/batteries) as well as backhaul equipment required to connect the BS tothe core network (ITU-T 2020), and•end devices connection: as opposed to the architecture of FANs, in RANs there is no
dedicated device to connect the end devices to the BS, as the wireless communication
modules are incorporated into the end devices. Strictly speaking, their energy consumption
would need to be detached from the energy consumption of the end devices and consideredas part of the RANs energy consumption. As this energy, however, is small compared to theconsumption of both the other components of end devices (in particular to their screens) andmarginal as compared to the energy consumption of BSs, we will ignore the wirelesscommunication modules of the end devices. For the purposes of this study, the RAN thus
consists of the BSs.

As argued in (Coroama and Hilty 2014), for both conceptual and practical reasons, DCs and end
devices should not be considered as part of the Internet – although their consumption is of course
relevant, and for the assessment of individual services (such as video streaming), it must be added tothe consumption of the network. This system boundary became mainstream and is in line with thecurrent academic literature as well as recent publications within EDNA (Ryan, Smith, and Wu 2019;
Wu, Ryan, and Smith 2019).
Of course, not all Internet data transmissions follow the topology from Figure 3. Internet-mediatedhuman-to-human communication, for example, whether simple voice-over-IP (VoIP) or the increasingly
popular video-based meetings (such as Zoom, Microsoft Teams, Google Meet, WebEx, or Skype)
imply data transmission between end devices on both sides, although a data centre might be involved
as well. This distinction would be relevant for a comparison of the energy intensity of different types ofservices (say, video calls versus video streaming), but – as we exclude both DCs and end devices – isimmaterial to our analysis.
Furthermore, for energy efficiency and user experience reasons, data is increasingly mirrored as
closely as possible to the users. This is in particular true for content that will foreseeably be
downloaded by numerous users, such as recent software updates or the latest episode of a popularshow. Such mirroring servers come in many forms, they can belong to the user’s Internet service
provider (ISP), be collocated on the ISPs premises by the content producer (such as Netflix or
Amazon Prime), belong to commercial content delivery networks (CDNs) such as Akamai, whosebusiness lies in the mirroring of content for both improved user experience and energy efficiency, or
might belong to private CDNs of large content providers such as Google or Facebook. For simplicity
and because they are outside system boundaries, such mirroring servers have not been represented
in Figure 3. Their existence is nevertheless indirectly relevant to our analysis, as they make numerous
Internet data transfers shorter and thus more energy efficient, and as a consequence also the averagedata transfer more energy efficient.
After these considerations, the method of this study can be more clearly stated: review the existing
literature for estimates of both the overall energy consumption (E) and the energy intensity (EI) for
WAN, FAN, and RAN, unravelling existing discrepancies and understanding their underlying sources.
We call these
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•

E (WAN), E (FAN), and E (RAN), and• EI (WAN), EI (FAN), and EI (RAN), respectively.
To this end, we performed an extensive literature research, documenting which network part is taken
into consideration by each individual study. Relating studies of the Internet energy with studies of its
energy intensity via the Internet traffic, we then studied existing inconsistencies and aimed atharmonising them.
Throughout the study we consider that the energy intensity of the network is independent of the data
type transferred (Aslan et al. 2018). Different services (email, www, video streaming, etc) might induce
different energy consumption in the end devices, but not along the network.

3.2

Assessment principles

Most bottom-up studies under scrutiny present their results as energy intensity of data (𝐸𝐼𝐷 ), i.e., asamount of energy needed to transfer an amount of data across the Internet. The energy intensity ofdata is usually expressed in [kWh/GB], although sometimes other units are used such as [TWh/EB](which is actually equivalent), [J/bit], or [J/Mbit]. The necessary conversions were performed toexpress all values in [kWh/GB].
Some of the studies, however, present not the energy, but the carbon intensity of data (𝐸𝐼𝐷 ), i.e., theamount of greenhouse gases emitted to send an amount of data across the Internet. The (𝐸𝐼𝐷 ) is
usually expressed in [g CO2 / GB] or the equivalent [t CO2 / PB]. To transform 𝐶𝐼𝐷 to 𝐸𝐼𝐷 , we use
Equation 2 below
𝑔 𝐶𝑂2
𝐶𝐼𝐷 [
]
𝑘𝑊ℎ
𝐺𝐵 ⁄
𝐸𝐼𝐷 [
]=
𝑔 𝐶𝑂2
𝐺𝐵
𝐶𝐼𝐸 [
]
𝑘𝑊ℎ

(2)

where 𝐶𝐼𝐸 is the carbon intensity of electricity expressed in [g CO2 / kWh].
The electricity intensity of data can thus be derived by dividing the carbon intensity of data by the
carbon intensity of electricity. Of course, as a prerequisite for the correctness of Equation (2), thesystem boundaries for the intensities of data need to coincide with those of the carbon intensity ofelectricity. If the scope for data is global, for example, the average worldwide carbon intensity ofelectricity needs to be used. If the scope for data is a specific country, the carbon intensity of theelectricity mix of that specific country shall be used. If the carbon intensity of data refers to a specificcompany which, for example, uses a large share of renewables, then the average carbon intensity ofthat specific mix needs to be taken into account.
We further assume that for a specific geography (the world, a continent, or a country) and for a
specific time period (a day, a month, or an year), all traffic that is being transmitted over the WAN isthen being delivered via either a FAN or a RAN. In other words, and focusing on entire years,
𝑇𝑦 (𝑊𝐴𝑁) = 𝑇𝑦 (𝐹𝐴𝑁) + 𝑇𝑦 (𝑅𝐴𝑁)(3)

for any region, where 𝑇𝑦 (𝑋) indicates all the data transmitted over the X type of network in year y.
More and more data are not delivered from far-away DCs, but from servers at, or close to, the edge
network. They can be provider edge servers, collocated servers of content providers (such as Netflix
or Google) or servers belonging to CDNs such as Akamai. This means that for an increasing numberof Internet data flows, the WAN part of the transmission will be a rather short one, sometimes not evenreaching the core network, but only metro and edge. As this does not represent the aim of this study,
we do not model it explicitly; it is, however, implicitly represented in the ever decreasing EI (WAN).
Equation (3) remains unaffected; while the core data flows do certainly not equal the total data flows of
the ANs, the WAN consists not only of the core, but also of metro and edge networks.
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Given Equation (3) and the definition of E (X) and EI (X), the energy and energy intensity of theInternet, respectively, can be computed as follows:
𝐸𝑦 (𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡) = 𝐸𝑦 (𝑊𝐴𝑁) + 𝐸𝑦 (𝐹𝐴𝑁) + 𝐸𝑦 (𝑅𝐴𝑁)(4)

𝐸𝐼𝑦 (𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡) = 𝐸𝐼𝑦 (𝑊𝐴𝑁) + (1 − 𝑠𝑅𝐴𝑁 ) ∗ 𝐸𝐼𝑦 (𝐹𝐴𝑁) + 𝑠𝑅𝐴𝑁 ∗ 𝐸𝐼𝑦 (𝑅𝐴𝑁)(5)

where 𝐸𝑦 (𝑋) and 𝐸𝐼𝑦 (𝑋) are the total energy and average energy intensity of X (i.e., the entire
Internet, the WAN, all FANs, or all RANs) for the year Y, respectively, and 𝑠𝑅𝐴𝑁 represents the share of
RAN among all access networks for that same year (currently around 16% and steadily growing).
Equation (4) is a direct consequence of our definition of ‘the Internet’, as discussed in the previous
section. The reason behind Equation (5), on the other hand, is that the average energy intensity of theInternet is the cumulative energy intensities of the WAN and of the AN. As, however, the AN is
composed by either FAN or RAN, these need to be added corresponding to their respective relative
weights, which is what Equation (5) does.

3.3

Period of assessment and harmonising the time perspective

The early studies (such as those presented in Section 2) are hardly comparable to today. The
methods were just evolving back then, as the huge discrepancies already presented show. The
topology of the Internet was also quite different, as were the technologies used at all stages (DCs,
networks, end devices): for example, hyperscale DCs were just emerging, there was no RAN yet, norsmartphones or tablets. More desktop PCs than laptops were in use, and CRT screens were still
mainstream. As important as these early studies were for the development of the field, we do nolonger consider them as relevant (other than from a historic perspective) and will thus ignore themfrom now on.
Our main interest lies in the numbers of today (i.e., 2020). We will, however, also consider studies
referring to the last decade, 2010-2019, for three reasons: First, many of the ubiquitously cited andsometimes also societally discussed studies are already a couple of years old and still very relevant.
Second, trends are at least as important as today’s numbers, and to understand trends some historic
perspective is important (albeit not a too long one, as discussed above). Finally, the authors
themselves often make predictions that reach to today and beyond.
While looking at the last decade of data, the crucial importance of the reference year (as discussed in
Section 2.2) needs to be taken into account. In any instantiation of Equations (4) and (5) above, theyear Y must be the same in all terms of the respective instantiation. Due to the need of using the same
year in each such computation, we also rewrite Equation (1) from the introduction, introducing thesame parameter Y to highlight this fact:
𝑇𝑊ℎ
𝑇𝑊ℎ
𝐸𝐵
𝐸𝑦 [
] = 𝐸𝐼𝑦 [] ∗ 𝑇𝑦 [
]
𝑦𝑒𝑎𝑟
𝐸𝐵
𝑦𝑒𝑎𝑟

(6)

Most studies mention to which year they refer to. When this is not explicitly described, and given theusual times for data gathering, analysis, and academic publication process, we make the following
assumption: papers published in the second half of one year (i.e., after the 1 July) refer to the previous
year, papers published in the first half of the year (i.e., before 30 June) refer to two years before.
Extrapolations are typically performed by the authors themselves. For the rare instances when we
perform them ourselves, we use the growth rate computed by the authors for a past period to
extrapolate a couple of years into the future. We do not perform, nor accept from the literature, any
extrapolation reaching more than 7-8 years into the future; the uncertainties become simply too large
for any serious statement.

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3.4

Internet traffic

For more than a decade, the single, outstanding, and very reliable source of data for the Internet traffic
has been Cisco. Their reports were first called ‘Visual Networking Index’ (VNI), split later into ‘VNI:
Global IP Traffic Forecast’ and ‘VNI: Global Mobile Data Traffic Forecast’, and more recently again
reunited under the new name ‘Annual Internet Report’. From the plethora of data presented by thesestudies, we are mainly interested in the worldwide aggregates, both for the total IP traffic and theshare of mobile/radio access.

Total IP and RAN traffic[EB / year]
Total IP traffic

Radio access

3500

EB / year

3000
2500
2000
1500
1000
500
0

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Total IP traffic 240

372

528

612

720

876

1152 1464 1872 2412 3048

Radio access

7.2

10.8

19.2

33.6

56.4

88.8

2.4

144

228

348

492

Figure 4 Yearly traffic expressed in EB, as compiled from several Cisco reports. Both the total traffic (blue), and the share of trafficdelivered via RAN (red), are shown.

For the years relevant to this study, 2010-2020 (see Section 3.3 above), we compiled data fromseveral of these reports (Cisco 2013b; 2013a; 2015; 2016; 2018; 2019; 2020). The results arepresented in Figure 4. Radio access is increasing at a much faster pace than fixed network accessand WAN traffic: While Cisco foresaw an average annual growth rate of 26% for the entire IP traffic
over the period 2017-2022 (Cisco 2018), it foresaw an average annual growth rate of 46% for the
global RAN traffic over the same period (Cisco 2019). As a consequence, the share of radio access is
continuously increasing, from just 1% in 2010 to over 16% today (see Figure 5).
Given its networking equipment placed around the world, Cisco is in a unique position to deliver
reliable data on the worldwide data traffic. For more than a decade, its past forecasts have proven in
hindsight to have been remarkably accurate. Given these facts, we take the traffic numbers put
forward by Cisco as corresponding exactly to the reality. Hence, if there is an inconsistency, forexample in Equation (6), it cannot be due to the traffic factor, and the reason must lie within theimprecision of either or both of the other factors.

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Worldwide share of radio / mobile access
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Figure 5 Share of radio/mobile access among the entire worldwide IP traffic, as compiled from different Cisco sources.

4

Analysis of existing estimates

In this section, we present a number of well-known academic estimates of both the energy intensity
and the overall energy consumption of the Internet. They have been chosen and are analysed
according to the criteria defined in Section 3. These academic estimates are complemented withrecent numbers on the energy intensity of the RAN reported by telecom companies.

4.1

Estimates of the energy intensity (EI) of WAN, FAN, and RAN

We start by presenting estimates of the EI of WAN, FAN, and RAN. They are grouped by estimates forthe last decade (2010 – 2019), current academic estimates, and current telecom industry estimates,
respectively.
Estimates referring to the past decade (year of reference: 2010 – 2019)(Williams and Tang 2012) estimate the use-phase power needed for the Internet transfer at the
average broadband speed of 6.2 Mbit/s as 18.4 W (0.0184 kW). Dividing the latter 18.4 W by theformer 6.2 Mbit/s yields an energy intensity of 2.97 J/Mbit, which is equivalent to EI2010 (WAN) = 0.007
kWh/GB. The authors, however, allocate the power consumption of all network components only forthe duration of the data transfer, and assume a load of 100%, leaving thus both the idle energy
unaccounted for, and dividing the power of the network components to an unrealistically high load.
Both these aspects make the result even more of an underestimate than it would have been due to thegeneral issues of bottom-up estimates presented in our study.
Another early bottom-up study analyses the impact of the location of content (CDN vs. distant server)on the energy intensity of Internet content delivery. It yields an average energy intensity of the WAN of
EI2010 (WAN) = 0.057 kWh/GB, or 25 J/Mbit (Schien et al. 2012). As opposed to (Williams and Tang2012), the authors do not idealise the network, but take into account the actual load of networking
equipment and distributing it over the actual traffic. Moreover, they also account for “redundantly
operated edge and core routers (hardware is duplicated to cope with failure)” (Schien et al. 2012).
(Malmodin et al. 2012) is insofar an atypical study, as although deploying a top-down methodology, it
does not only present overall consumption results, but – by dividing through the yearly Swedish
network traffic for 2010 (the year of assessment) – it also presents intensities (both energy and19/42

carbon) for the Internet in Sweden. Its result for the WAN is EI2010 (WAN) = 0.08 kWh/GB. The paper
also makes a reference to the energy intensity of the AN by claiming that it is fourfold the size of theintensity of the WAN, while not distinguishing between FAN and RAN: “The use stage electricity
consumption of the assessed IP core network was about one fourth of the energy consumption of thecorresponding access networks (telephony, 2G and 3G mobile and fixed broadband)” (Malmodin et al.
2012). For 2010, there is no meaningful assumption that can be made retroactively as to the ratio of
FAN to RAN. Additionally, the paper presents figures that seem to be in contradiction to the claim
stated above: its Table 2 puts forward 19 GWh for the PSTN FAN in Sweden, 13 GWh for 2G RAN,
and 8 GWh for 3G RAN. The total of 30 GWh is 3 times smaller than the 126 GWh for the Swedish
WAN the same table lists, and not 4 times larger, as otherwise claimed throughout the paper. We thus
consider only the WAN from (Malmodin et al. 2012) and ignore its analysis of the ANs.
(Schien et al. 2013) are to our knowledge the first, and still among the few, to present individualenergy intensities for the WAN, FAN, and RAN alike, using the same system boundaries as we used
here. Their estimates are:•

EI2012 (WAN) = 0.038 kWh/GB,•

EI2012 (FAN) = 0.019 kWh/GB, and•EI2012 (RAN) =0.293 kWh/GB, RAN which was still 3G at the time.

For a top-down study of the energy intensity of the RAN, (Pihkola et al. 2018) divided the estimatedtotal electricity consumption of Finnish RANs by the total data transferred through all its mobile
networks. To this end, they used publicly available data on the total mobile data transmission volume,
which is regularly reported by the Finnish Communications Regulatory Agency, and energy
consumption data published in their sustainability reports by the three main mobile telecom operators
in Finland, which together account for 99% of mobile subscriptions (Pihkola et al. 2018). The results
show an impressive improvement of the RAN energy intensity by a factor of over 40 in just 7 years:
from EI2010 (RAN) = 12.34 kWh/GB down to EI2017 (RAN) = 0.30 kWh/GB. The number for 2012 is alsointeresting: The top-down computation yields a result of EI2012 (RAN) = 4.49 kWh/GB, as opposed to
the 0.293 GB/kWh for the same year assessed with a bottom-up methodology by (Schien et al. 2013).
Current estimates
As stated in the Introduction, the study by (Aslan et al. 2018) is the current state of the art of theacademic understanding on the energy intensity of the WAN. Extrapolating exponential past trends(up to 2017) via least squares fit yields a value of around EI2019 (WAN) = 0.01 kWh/GB and EI2020(WAN) = 0.007 kWh/GB. The study does not address the energy intensities of either FAN or RAN.
(Pihkola et al. 2018) also extrapolate their 2010-2017 results into the future, devising an energy
intensity of just below EI2020 (RAN) = 0.1 kWh/GB.
(Wu, Ryan, and Smith 2019) is a comprehensive study looking at all three components of the networkand have system boundaries compatible to the ones used in this study. They present both numbers forthe overall consumption as well as for the energy intensity. For each of the three network categories,
they define three types of deployed technologies: ‘legacy/traditional’, ‘modern’, and ‘next generation’(XG), as follows:
•

•

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WAN
o
o
o
FAN
o
o

legacy: backhaul and metro network for public switched telephone network (PSTN)
modern: fibre optic, multi-protocol label switching (MPLS)
next gen.: software-defined networking (SDN), network functions virtualisation (NFV)
legacy: asymmetric digital subscriber line (ADSL)
modern: very-high-bitrate digital subscriber line (VDSL), cable, passive optical
network (PON)o next gen.: XG PON
RANo legacy: 2G & 3G networks
o modern: 4G networks
o next gen.: 5G networks
The study deploys the following assumptions – most of them included in the study itself, somerevealed through personal communication by the lead author Anson Wu (Wu 2021): Starting fromcurrent data, the yearly energy efficiency improvements of ANs are as follows: 10% for FAN XG, 5%
for 4G (which is already quite mature), and 15% for 5G. Given the historic improvement of 22% for 4G
based on (Andrae and Edler 2015), the 15% for 5G in the future are considered a conservative
estimate. Although estimates consider 5G’s final efficiency 10 times higher than that of 4G (Nokia
2020) – and thus its energy intensity 10 times lower – 5G is a sum of evolving technologies that didn’t
unfold their potential from the outset. The authors thus assume the same energy intensity for 4G and
5G RAN until (and including) 2018, moment from which they start to diverge. By 2030, the last year of
the authors’ forecast, the two would be indeed a factor of 10 apart (0.3 kWh/GB for 4G, 0.03 kWh/GB
for 5G). For 2020, the intensities are 0.073 kWh/GB for 4G and 0.022 kWh for 5G, respectively.
•

WAN Data traffic and Intensity
40000

10

Data Traffic (EB)

35000

1

30000
25000

0.1

20000

0.01

15000
10000

0.001

5000

Energy Intensity TWh/EB

100

45000

0.000102015201620172018201920202021202220232024202520262027202820292030
Core Legacy

Core Modern

Core XG

2G

3G

4G

5G

FAN Legacy

FAN Modern

FAN XG

Core Legacy

Core Modern

Core XG

2G

3G

4G

5G

FAN Legacy

FAN Modern

FAN XG

Figure 6 Overview Fig. 15 from (Wu, Ryan, and Smith 2019), presenting the energy intensities of the different types of networks (rightvertical axis; lines in the graph, not bars). Reprinted with the authors’ permission.

Figure 6 reproduces Fig. 15 from (Wu, Ryan, and Smith 2019), which graphically shows theaggregated results of the study. Via personal communication (Wu 2021), the exact numbers werereceived. The study presents a range of results depending on the various technologies still deployedin parallel, as listed in the bullet points above. As we are interested in comparison of single numbers,
we proceeded as follows: For the energy intensities of WAN, FAN, and RAN, we chose the number in
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the middle (i.e., the modern technology as opposed to legacy or next generation) in the same way as
done for (Andrae and Edler 2015) below, keeping in mind the different semantics of these numbers:
While for (Andrae and Edler 2015) they represented the expected case scenario (as opposed to worst
and best case), here they represent the estimates for the modern technology (as opposed to legacy
and next generation technologies). With this assumption, the intensities for 2020 are as follows:•

EI2020 (RAN) = 0.0064 kWh/GB,•

EI2020 (FAN) = 0.015 kWh/GB, and•EI2020 (RAN) = 0.073 kWh/GB.

Finally, (Bieser et al. 2020) analyse the footprint of Swisscom’s mobile networks, developing two
scenarios, one for today (i.e., 2020) and one for 2030. Most details on the methodology used areincluded in the study itself, some were revealed through personal communication by one of the study
authors, Roland Hischier (Hischier 2021).
The goal of the study was to assess the direct effects of mobile networks on climate change in
Switzerland. Given the focus of the study, FANs are not considered; RANs, however, are obviously at
the core of the analysis as well as a share of the WAN that is dedicated to delivering the data
accessed via RAN. In 2020, Swisscom’s RAN consisted, next to the 2G-5G BSs, also of 33,000 km
dedicated fibres. From the ‘common backbone’ (WAN, in our terminology), i.e., 70,000 km of core andmetro fibres and the corresponding routers that Swisscom deploys throughout Switzerland, 10% wereallocated to mobile networks, corresponding to the share of data accessed via RANs (Bieser et al.
2020).
A life-cycle assessment (LCA) was used, taking into account the lifecycle climate impact of the
networks under study (mainly RAN and the corresponding share of the WAN, as discussed above).
Both the overall emissions for Swisscom’s RANs (plus share of WAN), and the carbon intensity withinthese system boundaries (i.e., kg CO2eq / GB) were computed (Bieser et al. 2020). The authors
followed a bottom-up approach, modelling an average BS site in close cooperation with the technology
partners Swisscom and Ericsson (Hischier 2021). For 2020, the average energy consumption of a joint
BS site for 2-4G technology was 17094 kWh/year and for 5G technology substantially lower at 5732
kWh/year (mainly due to the more efficient radio units). The resulting carbon intensities are 0.03 kg
CO2eq / GB for 2-4G RAN, and 1.01 CO2eq / GB for 5G (Bieser et al. 2020).
This surprisingly bad value for 5G (despite its potential to be substantially more energy efficient thanthe older technologies) is due to the incipience of the technology, and in particular to little availability of
5G-compatible end devices but an already substantial rollout of 5G BSs; the energy (and carbon)
footprint of the 5G RAN is allocated to relatively few devices and thus quite a low traffic. The 2030
scenario expects this to substantially change, 2G and 3G networks to be entirely faded out over thiscoming decade, and the carbon intensities of the remaining 4G and 5G technologies (5G being thedominating one by then) to have decreased to 0.008 kg CO2eq / GB and 0.004 CO2eq / GB,
respectively.
Given the slightly different focus and system boundaries of this study, we use the following
assumptions to transform its results and make them compatible to our analysis:•

As FANs were not at all covered, and the WAN only to the extent that it was relevant to thecarbon emissions induced through RANs, we limit our interpretation of the study to the RANs.

•

While for its research question (i.e., the carbon emissions induced through mobile networks),
the system boundaries in (Bieser et al. 2020) stand to reason, our approach is different: weconsider WAN, FAN, and RAN as three distinct parts of the network irrespective of the reason
they came into existence, and analyse their respective energy intensities. To adapt the systemboundaries, we would thus need to remove the 10% WAN considered within systemboundaries by (Bieser et al. 2020). Their effect, however, is relatively marginal. They
represent just 17.5% of the total fibre length (7,000 km among the total 40,000 km considered,

22/42

the other 33,000 km being dedicated RAN fibres and thus within our system boundary aswell), and the fibres altogether have a lower consumption than the BSs. As the overall
influence of this WAN share is below 5%, and there are more important uncertainties, we do
not explicitly correct the system boundary here but do it together with the transition from LCA
to use phase (see two bullets below).
•

Due to the very low 5G usage, the results for 2020 are an outlier and are thus ignored. We
consider the 2G-4G carbon intensity of 0.03 kg CO2eq / GB as typical for 2020 (equivalent to
30 g CO2eq / GB).

•The authors have taken an LCA approach, while all other studies consider only the use phaseof networks. Fig. 5 on page 28 of the study reveals the individual shares of 6 categories: useand production each of core, transport and access networks. We eliminate the production of
all these categories (41%) and the use phase of the core (1%, representing the share of the
WAN attributed to data accessed over RAN, as discussed two bullets above), and remain with
58% which are relevant to our study (52% use-phase of the ‘access network’, i.e., the BSs,
and 6% ‘transport network’, i.e., dedicated RAN fibres and part of the RAN in our
understanding). From the 30 g CO2eq / GB from (Bieser et al. 2020), a share of 58% (i.e., 17.5
g CO2eq / GB) is thus relevant to our boundaries.

•

Finally, we need to reverse-engineer from the carbon intensity of data to its energy intensity
using Equation (2). Switzerland has a very low carbon intensity of its electricity production of
only around 20 g CO2eq / kWh, as the overwhelming majority of its electricity production ishydro or nuclear (BFE 2020). Due to electricity imports, however, the consumption mix has asubstantially larger intensity of 149.5 g CO2eq / kWh (BAFU 2018). Using this figure in
Equation 2 together with the CID of 17.5 g CO2eq / GB, as discussed above, yields an energy
intensity of data (EID) of EI2020 (RAN) = 0.117 kWh/GB.
Current industry reports (RAN energy intensity)

Finally, we reviewed sustainability reports of leading European mobile telecom companies from
Germany, France, the UK, Switzerland, Sweden and Finland. The only one found to directly devise theenergy intensity of its RAN is the Finnish Elisa: according to the report, the intensity of its RAN
decreased from EI2019 (RAN) = 0.15 kWh/GB to EI2020 (RAN) = 0.12 kWh/GB (Elisa 2020).
The German Telekom, meanwhile, does not report on the RAN individually, but on the average energy
intensity of its entire networks: Dividing their overall 2020 energy consumption of 12,643 GWh by theentire IP data volume of 106 EB yields an energy intensity for (WAN + AN) of 119 kWh / TB (Telekom2020), or 0.12 kWh / GB. This number includes the WAN, the RAN and the (negligible) street-level
FAN, but not the CPE. To account for the CPE as well, we assume that the 49.1 million Telekom
customers with a fixed line – 27.4 million ADSL and 21.7 million broadband (Telekom 2020) – have
one WiFi router each, with a power drain of 8 W that are all continuously on. This implies an energy
consumption of 252 MJ yearly each, equivalent to 70.08 kWh/year. For all 49.1 million customers,
another 3,441 GWh need to be added to account for the CPE, leading to 16,084 GWh for all of WAN,
FAN and RAN (including CPE) of Telekom. Dividing this number by Telekom’s total 2020 traffic of 106
EB yields a slightly larger total energy intensity of 151 kWh / TB, or 0.15 kWh / GB.
No indication is given of how this is split between the WAN on one side and the access networks (FAN
or RAN) on the other. We can make a reasonable estimate using a couple of assumptions based on
the share of radio access as well as the ratios among the intensities of the individual networks
presented in the literature, i.e.: 20% of access via RAN, 80% via FAN, the intensity of the RAN 10
times higher than that of the WAN and twice as high as the FAN intensity (including CPE). This yields
a system of 3 equations with 3 variables, which can be unequivocally solved to•

EI2020 (WAN) = 0.02 kWh/GB,•

EI2020 (FAN) = 0.1 kWh/GB, and
23/42

•

EI2020 (RAN) = 0.2 kWh /GB,compatible to (albeit a bit larger than) the numbers presented by (Elisa 2020).
The Vodafone group devises the carbon intensity (expressed in t CO2eq / PB, which corresponds to g
CO2eq / GB) of its RAN in the sustainability reports: It decreased from 926 g CO2eq / GB in 2017 to577 in 2018 and 371 in 2019 (Vodafone 2019), and then further to 230 g CO2eq / GB in 2020
(Vodafone 2020). This number can be split between the carbon intensity of data outside the UK (242 g
CO2eq / GB) and the one within the UK (120 g CO2eq / GB). To transform this latter figure to theenergy intensity of data, we use it together with the 233 g CO2eq / kWh carbon intensity of electricity
from UK’s government conversion factors for company reporting of greenhouse gas emissions (BEIS
2020) in Equation (2), which yields an energy intensity of EI2020 (RAN) = 0.51 kWh / GB, a factor of 4
higher than the result of (Elisa 2020) and a factor of 2.5 higher than our estimate of Telekom’s RAN
intensity, based on the data from (Telekom 2020) and the assumptions mentioned above.
Summarising the reports on the energy intensity of the Internet
Table 1 below summarises the estimates on the energy intensity of WAN, FAN, and RAN presented
above.
Table 1 Summary of existing estimates for EI (WAN), EI (FAN), and EI (RAN) for the last decade (2010 – 2019) and for today.

PeriodLast decade
2010-2019Year ofref.

WAN(Williams and Tang 2011)

2010

0.007

Bottom-up(Schien et al. 2012)

2010

0.057

Bottom-up(Malmodin et al. 2012)

2010

0.08

Top-down(Schien et al. 2013)

2012

0.038

(Pihkola et al. 2018)(Pihkola et al. 2018)

Current
industry

0.019

RAN

Type

Bottom-up

2012

4.49

Top-down

2017

0.3

Top-down

0.007

Bottom-up
0.12

Top-down

0.0732

Unknown(Bieser et al. 2020)

0.117

Bottom-up(Elisa 2020)

0.12

Top-down

0.2

Top-down

0.51

Top-down(Pihkola et al. 2018)(Wu et al. 2019)(Telekom 2020)(Vodafone 2020)

4.2

FAN

0.293

(Aslan et al. 2018)
Current
academic

EI [kWh/GB]

Study

2020

2020

0.00640.02

0.01500.1

Estimates of the overall energy consumption (E) of WAN, FAN, and RAN

This section presents estimates of the worldwide overall energy consumption of WAN, FAN, and RAN.
They are grouped by estimates for the last decade (2010 – 2019) and current estimates, respectively.
Several of the current numbers presented in Section 4.2.2 below are extrapolations from the earlier
studies which will be presented first, in Section 4.2.1. Some of these extrapolations were put forward
by the authors themselves. For (Van Heddeghem et al. 2014) and (Malmodin and Lundén 2018), due
24/42

to their notoriety and the fact that they reach quite different results, we perform the extrapolations to2020 ourselves, using in both cases the growth rate observed by the respective authors in the past to
extrapolate several years into (their) future, i.e., to 2020.
Overall energy consumption of WAN, FAN, and RAN: data for 2010 – 2019
(Lanzisera, Nordman, and Brown 2012) are among the first top-down studies to organise their data insuch a way that what we call WAN (‘switching’ and ‘enterprise routers’) and FAN (‘enterprise WLAN’,‘security appliances’, and ‘customer access equipment’ and ‘residential CPE’) are clearly
distinguishable. The study presents numbers for both the US and the world for 2007 and 2008, andextrapolations to 2009-2012. We use here the worldwide numbers for 2010: 24.2 TWh for WAN and33.6 TWh for FAN. Given their still marginal role back then, RANs were not considered.
(Van Heddeghem et al. 2014) is a well-known top-down estimate of the development of the energy
footprint of all the ICT equipment in the world between 2007-2012. The study considers three maincategories: DCs, end devices (called ‘personal computers’, as desktop and laptop PCs together with
their screens were the main category of end devices at that time), and networks. For this latter
category, which is the one of relevance to our study, the paper distinguishes between three types of
networks: ‘telecom operator networks’, ‘office networks’ and ‘customer premises access equipment(CPAE)’:•The last category (CPAE), which we call CPE, is clearly part of the FAN.

•

The first category, telecom operator networks, consists mainly of the WAN, but also includes
the street-level part of the FAN.

•

Office networks, whose importance has decreased since, would have been back in the day amix between FAN and WAN.

Overall, the energy consumption of networks grew from 200 TWh / year in 2007 to 334 TWh / year in
2012. With 77% (257 TWh) of the total, telecom operators networks dominate this result, while office
networks contribute with 7% and the CPE with 15%. Simplifying, we attribute the study’s ‘telecom
operator networks’ entirely to WAN, although this yields a slight overestimate as they also include thestreet-level part of the FAN. To compensate, we attribute the office networks entirely to the FAN,
although they include both FAN and WAN devices. The errors induced through these two simplifying
assumptions are much less than the influence of other uncertainties, in particular the extrapolation ofthese results from 2012 to the present, as presented below. These boundaries imply E2012 (WAN) =257 TWh and E2012 (FAN) = 77 TWh. As by 2012 RANs only played a marginal role, they were notcovered in the study.
(Andrae and Edler 2015) present data for 2010-2013, albeit it is not always clear which year precisely
they refer to, and extrapolate all the way to 2030. Taking into account all three network components,
their boundary between WAN and FAN is slightly different from the one we consider here, and theyalso use different terminology. By ‘FAN’, their study means what we (along with most of the literature)
call both WAN and FAN together; while ‘our’ WAN is called ‘fixed access wired’ and the FAN ‘fixedaccess Wi-Fi’: “The core network, metro/edge network, content distribution network, wired accessnetwork, customer premises equipment and wireless local area networks, are regarded here as one
primary entity called fixed access networks (FAN). FAN is divided into two parts, fixed access wired
and fixed access Wi-Fi” (Andrae and Edler 2015).
Additionally, they only attribute the CPE to the FAN, including street-level AN (such as DSLAM
cabinets or ONU units) to the WAN: “The core network, metro/edge network, content distribution
network, wired access network, customer premises equipment and wireless local area networks, areregarded here as one primary entity called fixed access networks (FAN). FAN is divided into two parts,
fixed access wired and fixed access Wi-Fi. Fixed access Wi-Fi is here regarded as customer premises
equipment” (Andrae and Edler 2015). We would thus need to subtract the street-level AN from the
WAN and add it to the FAN. As there is, however, no indication in the paper of the underlying
25/42

assumptions or the sources for their numbers, and as presumably the CPE dominates the energy
consumption within the FAN, we do not perform any actions, but consider the ‘fixed access wired’ from
(Andrae and Edler 2015) as WAN, and the ‘fixed access Wi-Fi’ as FAN.
RANs are called ‘wireless access networks’ in (Andrae and Edler 2015), which distinguishes between
five categories: 2G/3G voice traffic, 2G data, 3G data, 4G data, and 5G data. For our analysis, we add
these numbers together for the RAN. The results for 2012 are as follows:•

E2012 (WAN) = 196 TWh•

E2012 (FAN) = 51 TWh•

E2012 (RAN) = 197 TWh

Finally, (Malmodin and Lundén 2018) present data for the years 2010 and 2015, with no future
extrapolations. In section 4.2 ‘network operations’, they report numbers on WAN (called ‘fixed
networks’) and RAN (called ‘mobile networks’). For WANs, there is a growth of 5% in these five years(from E2010 (WAN) = 100 TWh to E2015 (WAN) = 105 TWh) and for RANs, as expected, a much more
substantial growth of 61% (from E2010 (RAN) = 85 TWh to E2015 (RAN) = 137 TWh). The street-levelpart of the FAN is presumably included in the WAN, but it is indistinguishable within the data. The
other part of FANs, the CPE, is included with a larger category ‘Telephones, Home Network
Equipment, and Set-Top Boxes’, but the authors do devise their energy consumption separately(Table 3 in the paper), and thus: E2015 (FAN) = 83.5 TWh.
Overall energy consumption of WAN, FAN, and RAN extrapolated to 2020
Taking the figures for 2012 by (Van Heddeghem et al. 2014), and using the study’s own annual growth
rates of 10.2% for WAN and 10.8% for CPE (i.e., FAN), we extrapolate the results to today. This would
yield E2020 (WAN) = 559 TWh and E2020 (FAN) = 175 TWh, but of course the uncertainties over such aperiod are high.
(Andrae and Edler 2015) present estimates for the years 2010-2013 and extrapolations into the future
up to 2030. For these extrapolations, they use three scenarios: best case, expected, and worst case.
As the range between best and worst is quite large, we use the middle scenario, i.e., the expectedcase. For 2020, the results are as follows:•

E2020 (WAN) = 439 TWh•

E2020 (FAN) = 185 TWh•

E2020 (RAN) = 99 TWh

For (Malmodin and Lundén 2018), we also use the growth coefficients devised by the authors between
2010 and 2015 (5% for WAN, 61% for RAN) to extrapolate another 5 years into the future, arriving at
110 TWh for WANs and 220 TWh for RANs for 2020, respectively. In the supplementary material of
the article, the authors make an interesting observation, that the “number of fixed broadband
subscriptions and TV subscriptions can be used to estimate CPE (customer premises equipment, e.g.
modems / gateways), STB’s TV’s (and TV peripherals)” (Malmodin and Lundén 2018). We thus use
the same 5% extrapolation we used for WAN (and which is based on the authors’ own extrapolationfrom 2010 to 2015) to extrapolate the energy consumption of CPE, which yields 88 TWh for the CPE,
and thus for the FAN, in 2020.
Summarising the reports on the overall energy consumption of the Internet
Table 2 summarises the estimates on the total energy consumption of WAN, FAN, and RAN presented
above.

26/42

Table 2 Summary of existing estimates for the overall E (WAN), E (FAN), and E (RAN) for the last decade (2010 – 2019) and for today.

PeriodLast decade
2010-2019

Current

5

Study

Year ofref.

E [TWh/year]
WAN
FAN
RAN

Type

(Lanzisera et al. 2012)

2010

24.2

33.6

Top-down(Andrae and Edler 2015)

2012

196

51

(van Heddeghem et al. 2014)

2012

257

77

(Malmodin and Lunden 2018)

2015

105

83.5

(van Heddeghem et al. 2014)

2020

559

175

(Andrae and Edler 2015)

2020

439

185

99

Unclear(Malmodin and Lunden 2018)

2020

110

88

221

Top-down

197

UnclearTop-down

137

Top-down
Top-down

Recent discrepancies and their sources

The state-of-the-art study on the energy intensity of the Internet, widely used and accepted in theacademic world, is arguably the one put forward by (Aslan et al. 2018), which estimates 0.01 kWh/GB
energy intensity for 2019 and 0.007 kWh/GB for 2020. Multiplying its energy intensity value according
to Equation (6) by the entire Internet traffic for the same year, 3048 EB (Cisco 2020), yields an energy
consumption of 21.3 TWh. A naïve interpretation of the study would be to read this number as theoverall energy consumption of the entire Internet in 2020.
In fact, the study explicitly excluded RAN from its analysis. And while it seems to be considering both
WAN and FAN – “This study identifies representative estimates for the average electricity intensity of
fixed-line Internet transmission networks over time and suggests criteria for making accurate estimatesin the future” (Aslan et al. 2018) – it does actually not include CPE in its analysis. As CPE represent
the lion’s share (in terms of energy consumption) of the FAN, the estimate put forward by (Aslan et al.
2018) can thus be interpreted as the current state-of-the-art understanding of the EI (WAN). However,even when applying Equation (6) only to the WAN, the result of 21.3 TWh is still a factor of 5.2
smaller than the low end of the top-down estimates of the WAN overall energy consumption, the 110
TWh very conservatively extrapolated by us for (Malmodin and Lundén 2018).
Compared to the other estimates of E (WAN), the discrepancies are even larger:•a factor of 20.6 as compared to the 439 TWh from (Andrae and Edler 2015), and•a factor of 26.2 as compared to our extrapolation to 2020 of the data from (Van Heddeghem
et al. 2014).

These large discrepancies show that the consideration of different system boundaries alone does not
explain the difference among estimates of the energy intensity of the Internet and those assessing theits overall energy consumption. In fact, such mismatches leading to the violation of Equation (6) arepersistent among the recent and current estimates of the EI and the E of the Internet. We discuss
several examples in Section 5.1 and then address their sources in Section 5.2.

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Federal Department of the Environment, Transport,
Energy and Communications DETEC
Swiss Federal Office of Energy SFOE
Energy Research and Cleantech Division
Table 3 Juxtaposition of different estimates for the energy intensity of the WAN, FAN, and RAN (columns 3-5), which multiplied with the respective traffic numbers (columns 6-8) according toEquation (7) should yield a total energy consumption of the Internet (column 9) comparable to the one obtained by adding estimates of the energy consumption of the WAN, FAN, and RAN(columns 10-12) together (column 13). In general, the identity postulated by Equation (7) is not achieved, the directly estimated overall Internet energy (column 13) being a couple of times larger thanthat based on the intensities (column 9). The ratio (column 13 / column 9) is presented in column 14. The table compares row-wise results of existing studies as follows:* Rows 1–3 ‘WAN only’: EI (WAN) from (Aslan et al. 2018), E (WAN) from (Malmodin and Lundén 2018), (Andrae and Edler 2015), and (Van Heddeghem et al. 2014), respectively. Own extrapolationsfor (Malmodin and Lundén 2018) and (Van Heddeghem et al. 2014), as described in the text.
* Row 4: all energy intensities (EIs) from (Schien et al. 2013), all energies (Es) from (Van Heddeghem et al. 2014).
* Row 5: all EIs from (Schien et al. 2013), all Es from (Andrae and Edler 2015).
* Row 6: as above, but with EI (RAN) substituted for the much larger top-down estimate from (Pihkola et al. 2018).
* Row 7: all EI values from (Wu, Ryan, and Smith 2019) – mid-range, ‘modern’ technologies (as opposed to ‘legacy’ or ‘next generation’); E (WAN) and E (FAN) from (Van Heddeghem et al. 2014), E(RAN) from (Malmodin and Lundén 2018), extrapolated as described in the text.
* Row 8 ‘mainstream’: EI (WAN) from (Aslan et al. 2018), EI (FAN) from (Wu, Ryan, and Smith 2019), EI (RAN) from (Pihkola et al. 2018; Bieser et al. 2020; Elisa 2020), all Es from (Andrae and Edler
2015)..
* Row 9 cherry-picking to achieve Equation (7): EI (WAN) from (Aslan et al. 2018), EI (FAN) own assumption as described in the text, EI (RAN) from (Telekom 2020); E (WAN) and E (FAN) from
(Malmodin and Lundén 2018) very conservatively extrapolated from 2015 to 2020, E (RAN) from (Andrae and Edler 2015).
* Row 10: more cherry-picking to work towards also satisfying Equation (8): EI (WAN) and EI (RAN) per our computation based on (Telekom 2020), EI (FAN) own assumption as described in the text;
E (WAN) from (Malmodin and Lundén 2018) extrapolated, E (FAN) average between (Malmodin and Lundén 2018) and (Van Heddeghem et al. 2014), E (RAN) from (Andrae and Edler 2015).

Row

Year

1

2020,
WAN
only

2
3
4

EI [TWh/EB]
WAN

FAN

T [EB / year]
RAN

WAN

FAN

E [TWh/year]
RAN

Internet

E [TWh/year]
WAN

FAN

RAN

Discrepancy
FactorInternet

0.007

3048

21.3

110

110.3

5.2

0.007

3048

21.3

439

439.0

20.6

0.007

3048

21.3

559

559.0

26.2

0.038

0.019

0.293

528

517.2

10.8

33.1

257

77

0

334.0

10.1

0.038

0.019

0.293

528

517.2

10.8

33.1

196

51

197

444.0

13.4

6

0.038

0.019

4.49

528

517.2

10.8

78.4

196

51

197

444.0

5.7

7

0.0064 0.0150 0.0732

3048

2556

492

93.7

559

175

221

954.6

10.2

0.007 0.0150

0.12

3048

2556

492

118.6

439

185

99

723.0

6.1

0.007

0.07

0.2

3048

2556

492

298.7

110

88

99

296.9

1.0

0.02

0.07

0.2

3048

2556

492

338.3

110

131.5

99

340.8

1.0

5

8
9
10
28/42

2012

2020

Federal Department of the Environment, Transport,
Energy and Communications DETEC
Swiss Federal Office of Energy SFOE
Energy Research and Cleantech Division

5.1

Inconsistencies between energy intensity and energy studies, and firstharmonization attempts

In the few examples given in the beginning of Section 5, we have applied Equation (6) for the WAN
and the year 2020. These examples are also depicted in rows 1–3 of Table 3: Column 3 shows theenergy intensity of the WAN as computed by (Aslan et al. 2018), column 6 the worldwide Internet
traffic for 2020 (Cisco 2020), and column 9 the multiplication of the two according to the right side ofEquation (6). Columns 10 and 13 show both the results of E (WAN) studies: (Malmodin and Lundén
2018), (Andrae and Edler 2015), and (Van Heddeghem et al. 2014), respectively. According toEquation (6), columns 9 and 13 should coincide. In fact, the E (WAN) estimates are consistently above
the result of EI (WAN) * T (WAN) with a factor depicted in the last column 14.
Generalising from the exclusive consideration of the WAN to all three parts of the network (WAN, FAN,
and RAN), we need to use in the left side of Equation (6) the computation for the overall energy of theInternet from Equation (4), and on its right side the computation for the average energy intensity of theInternet, 𝐸𝐼𝑦 (𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡), as developed in Equation (5). Remembering also that FAN-traffic plus RANtraffic equal the WAN-traffic (Equation 3), yields the general Equation 7:
𝐸𝑦 (𝑊𝐴𝑁) + 𝐸𝑦 (𝐹𝐴𝑁) + 𝐸𝑦 (𝑅𝐴𝑁)= 𝐸𝐼𝑦 (𝑊𝐴𝑁) ∗ 𝑇𝑦 (𝑊𝐴𝑁) + 𝐸𝐼𝑦 (𝐹𝐴𝑁) ∗ 𝑇𝑦 (𝐹𝐴𝑁) + 𝐸𝐼𝑦 (𝑅𝐴𝑁) ∗ 𝑇𝑦 (𝑅𝐴𝑁)(7)

The two sides of this equation are presented in Table 3:•

Columns 3–5 list EI (WAN), EI (FAN), and EI (RAN), respectively, as listed in Table 1. The
units [kWh/GB] used in Table 1 and [TWh/EB] from Table 3 are hereby equivalent, as
1 𝑇𝑊ℎ = 109 𝑘𝑊ℎ and 1 𝐸𝐵 = 109 𝐺𝐵. We use the former in Table 1, as it is the usual andwidely used unit for the energy intensity of data transfers, and the latter in Table 3 as well as
in Equations (1), (6), and (7), to underline the correctness of the respective identities.

•

Columns 6–8 present the traffic for the individual components and the corresponding year, T(WAN), T (FAN), and T (RAN), as indicated by the various Cisco studies.

•

Columns 10–12 present E (WAN), E (FAN), and E (RAN), respectively, as estimated in theindividual studies and listed in Table 2.

•

Columns 9 and 13 compute the right and the left side of Equation (7), respectively. Inputs are
the values from columns 3–8 and 10–12, respectively.

•

Finally, column 14 computes the ration between column 13 and 9, i.e., the discrepancy factor
between the left side of Equation (7) and its right side, where the two should actually havebeen equal.

In rows 4–5 we compare the intensity estimates by (Schien et al. 2013) to two well-known estimates of
the Internet energy: (Van Heddeghem et al. 2014), which does not take RAN into consideration, and(Andrae and Edler 2015), respectively. As Table 3 shows, both yield an inconsistency of one order ofmagnitude. As the estimate for EI (RAN) from (Schien et al. 2013) seems quite low for the year 2012,
in row 6 of Table 3 we substitute it by the top-down estimate for the EI (RAN) from (Pihkola et al.
2018), which is more than one order of magnitude higher. With this, the computation of the total RAN
energy starting from its intensity also grows more than tenfold and reduces as a consequence theoverall discrepancy between the two to less than half its former value (5.7 as compared to 13.4). The
comparison of rows 5 and 6 raises some fundamental observations about bottom-up vs. top-down
modelling, which will be addressed in Section 5.3 below.
For the year 2020 and all parts of the network, row 7 compares the mid-range energy intensities for
WAN, FAN, and RAN from (Wu, Ryan, and Smith 2019) to some known estimates of the overall
energy consumption: E (WAN) and E (FAN) from (Van Heddeghem et al. 2014) and E (RAN) from
29/42

(Malmodin and Lundén 2018); all extrapolated by us as described above. Using the mid-range energy
intensities for WAN, FAN, and RAN from (Wu, Ryan, and Smith 2019) yields an overall energy
consumption of just 93.7 TWh, which is one order of magnitude less than the (extrapolation of) the
direct top-down assessments. Such extrapolations are by no means singular; the widely cited study(Belkhir and Elmeligi 2018), for example, uses precisely such extrapolations to reach similarly highvalues.
Row 8 of Table 3 presents what might be regarded as the state-of-the-art and most widely used
understanding of both the energy intensities and the energy consumptions in use today:•

EI (WAN) = 0.007 kWh/GB from (Aslan et al. 2018) as argued above,•

EI (RAN) = 0.12 kWh GB, as unanimously reported by (Pihkola et al. 2018; Bieser et al. 2020;
Elisa 2020),•

EI (FAN) = 0.015 kWh/GB as reported by (Wu, Ryan, and Smith 2019), and which
corroborates to a back-of-the-envelope calculation of 800 million CPE in the world, running
24/7 and needing 7 W of power on average, transporting the 2556 EB of T (FAN) in 2020,
which would yield an intensity of 0.019 kWh/GB.

•

Finally, E (WAN), E (FAN), and E (RAN) from (Andrae and Edler 2015).

These ‘mainstream assumptions’ lead to a discrepancy by a factor of 6.1, showing a fundamental
mismatch between the two types of estimates.
To arrive to relatively similar numbers and be able to satisfy to a large extent the identities as shown inEquations (6) and (7), some creative cherry-picking within the realistic ranges of the two types ofstudies is necessary. Table 3 shows this in its row 9, which deploys the following estimates:•

EI (WAN) = 0.007 kWh/GB from (Aslan et al. 2018), as argued earlier,•

EI (RAN) = 0.2 kWh/GB, as indicated by (Telekom 2020),•

EI (FAN) = 0.07 kWh/GB, 10 times worse than EI (WAN) but around 3 times better thanEI (RAN),•E (WAN) and E (FAN), from the most optimistic study by some margin (Malmodin and Lundén
2018), very conservatively extrapolated by us from 2015 to 2020 using just a 5% extrapolation
factor, but E (RAN) not from the same study, but from (Andrae and Edler 2015), which devise
E (RAN) = 99 TWh as opposed to our extrapolation from (Malmodin and Lundén 2018) thatwould have been 221 TWh, as shown in Table 2.

With these creative combination of rather pessimistic assumptions on one side, and the most
optimistic on the other, the identity from Equation (7) can finally be almost perfectly fulfilled (299 vs.
297 TWh).
These estimates, whose exact values are presented in row 9 of Table 3, can be rounded as follows:•

EI2020 (WAN) = 0.007 kWh/GB,•

EI2020 (FAN) = 0.07 kWh/GB,•

EI2020 (RAN) = 0.2 kWh/GB,•

E2020 (WAN) ~ E2020 (FAN) ~ E2020 (RAN) = 100 TWh

All of these values – but for EI2020 (FAN), which we computed as outlined above – are from theliterature: EI2020 (FAN) from (Aslan et al. 2018) and very similar to (Wu, Ryan, and Smith 2019),
EI2020 (RAN) from (Telekom 2020), which is (together with our assumptions) more pessimistic thanother industry studies, E2020 (WAN) and E2020 (FAN) from (Malmodin and Lundén 2018), conservatively
extrapolated from 2015 to 2020, and E2020 (RAN) from (Andrae and Edler 2015).

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5.2

Harder constraints required for better harmonisation

The identity reflected in Equation (7) and considered so far was derived from the general identity inEquation (6), by expanding the overall energy of the Internet and its average energy intensity
according to Equations (4) and (5), respectively. Equation (7) represents, however, just the simplest
constraint connecting estimates of overall energies and energy intensities. There are two further steps
of increasing rigour, increasing difficulty of compliance but also increasing uncertainties involved. They
are presented in this subsection.
The next level of rigour places an additional constraint on the ratio between the overall energy of the
WAN on one side and the energies of the ANs on the other; in other words between Ey (WAN) andEy (FAN) + Ey (RAN). It starts from the observation from Equation (3) that the traffic over FAN and
RAN equals the traffic over WAN. If we now consider the ratio(𝐸𝑦 (𝐹𝐴𝑁) + 𝐸𝑦 (𝑅𝐴𝑁))⁄𝐸 (𝑊𝐴𝑁)𝑦and replace the energy in each part of the network according to
𝐸𝑦 (𝑋) = 𝐸𝐼𝑦 (𝑋) ∗ 𝑇𝑦 (𝑋) 𝑓𝑜𝑟 𝑋 ∈ {𝑊𝐴𝑁, 𝐹𝐴𝑁, 𝑅𝐴𝑁}, this ratio becomes(𝐸𝐼𝑦 (𝐹𝐴𝑁) ∗ 𝑇𝑦 (𝐹𝐴𝑁) + 𝐸𝐼𝑦 (𝑅𝐴𝑁) ∗ 𝑇𝑦 (𝑅𝐴𝑁))⁄𝐸𝐼 (𝑊𝐴𝑁) ∗ 𝑇 (𝑊𝐴𝑁).
𝑦𝑦Further remembering that 𝑠𝑅𝐴𝑁 represents the ratio of radio/mobile access among all access networks(i.e., together with Equation (3), 𝑇𝑦 (𝑅𝐴𝑁) = 𝑠𝑅𝐴𝑁 ∗ 𝑇𝑦 (𝑊𝐴𝑁), and 𝑇𝑦 (𝐹𝐴𝑁) = (1 − 𝑠𝑅𝐴𝑁 ) ∗ 𝑇𝑦 (𝑊𝐴𝑁),
we can rewrite this ratio as(𝐸𝐼𝑦 (𝐹𝐴𝑁) ∗ (1 − 𝑠𝑅𝐴𝑁 ) ∗ 𝑇𝑦 (𝑊𝐴𝑁) + 𝐸𝐼𝑦 (𝑅𝐴𝑁) ∗ 𝑠𝑅𝐴𝑁 ∗ 𝑇𝑦 (𝑊𝐴𝑁))⁄𝐸𝐼 (𝑊𝐴𝑁) ∗ 𝑇 (𝑊𝐴𝑁),𝑦𝑦yielding the following new identity(𝐸𝑦 (𝐹𝐴𝑁) + 𝐸𝑦 (𝑅𝐴𝑁))((1 − 𝑠𝑅𝐴𝑁 ) ∗ 𝐸𝐼𝑦 (𝐹𝐴𝑁) + 𝑠𝑅𝐴𝑁 ∗ 𝐸𝐼𝑦 (𝑅𝐴𝑁))⁄𝐸 (𝑊𝐴𝑁) =⁄𝐸𝐼 (𝑊𝐴𝑁)𝑦𝑦(8)

In other words, the ratio between the added energies of access networks and that of the wide-area
network should equal the ratio between the energy intensities of the access networks (weighted bytheir respective share of data transfer) and the energy intensity of the WAN.
This additional constraint posed by the identity from Equation (8) questions the numbers put forward at
the end of Section 5.1, which were chosen in such a way as to satisfy the fundamental identity from
Equation (7). Using those numbers on both sides of Equation (8), would yield a factor of about 2 on
the left side of the identity (as the energies of the three parts of the network are considered roughly
equal, around 100 TWh yearly each), and a factor of about 13 (12.91, to be precise) on the right sideof the identity (as EI (FAN) is considered 10 times larger than EI (WAN), and EI (RAN) even 30 times
larger but with a lower weight of 16%).
The root of the issue lies in the unsolved mismatch between mainstream academic estimates of theenergy intensity of the WAN and estimates of its overall energy, as also presented in rows 1–3 ofTable 3. While even the low end of energy estimates devises over 100 TWh yearly (Malmodin andLundén 2018), several sources (Aslan et al. 2018; Wu, Ryan, and Smith 2019) – some of which are
regarded as the state-of-the-art of current understanding – indicate a low energy intensity of the WAN,
which, when multiplied by the traffic only yield 21 TWh yearly; a factor of 5 lower than the lowest
overall energy estimates.
To partially address this issue, we choose for row 10 in Table 3 the highest EI2020 (WAN) number from
Table 1, i.e., the 0.02 kWh/GB we computed based on (Telekom 2020), with the assumptions
described in Section 4.1.3. This leads to an 𝐸𝐼2020 (𝑊𝐴𝑁) ∗ 𝑇2020 (𝑊𝐴𝑁) value of 61 TWh/year, still
barely half as large as the 110 TWh from (Malmodin and Lundén 2018), but already much closer.

31/42Keeping the other EI values identical to row 9, we need to compensate on the total energy side. As
there is quite a split between the values for 𝐸𝐼2020 (𝐹𝐴𝑁) between (Malmodin and Lundén 2018) on
one side and (Andrae and Edler 2015) and (Van Heddeghem et al. 2014) on the other, we choose avalue in-between the 88 TWh from (Malmodin and Lundén 2018) and the 175 TWh from (Van
Heddeghem et al. 2014) – E2020 (FAN) = 131.5 TWh. Overall, the values thus are•

EI2020 (WAN) = 0.02 kWh/GB (→ EI2020 (WAN) *T2020 (WAN) = 61 TWh),•

EI2020 (FAN) = 0.07 kWh/GB (→ EI2020 (FAN) *T2020 (FAN) = 179 TWh),•

EI2020 (RAN) = 0.2 kWh/GB (→ EI2020 (RAN) *T2020 (RAN) = 98 TWh),•

E2020 (WAN) = 110 TWh,•

E2020 (FAN) = 132 TWh,•

E2020 (RAN) = 99 TWh.

With these values, the identity in Equation (7) is again largely satisfied, while the discrepancy between
the two sides of the identity from Equation (8) is no longer 13 to 2, but 4.54 to 2.09. As the two sides ofEquation (8) should be equal, this difference is still substantial, but a clear progress compared to theresults from the end of Section 5.1.
Finally, the final level of rigour would be to satisfy Equation (6) for all three individual types of
networks: WAN, FAN, and RAN. This is expressed with the rather trivial equations below:
𝐸𝑦 (𝑊𝐴𝑁) = 𝐸𝐼𝑦 (𝑊𝐴𝑁) ∗ 𝑇𝑦 (𝑊𝐴𝑁)(9)

𝐸𝑦 (𝐹𝐴𝑁) = 𝐸𝐼𝑦 (𝐹𝐴𝑁) ∗ 𝑇𝑦 (𝐹𝐴𝑁)(10)

𝐸𝑦 (𝑅𝐴𝑁) = 𝐸𝐼𝑦 (𝑅𝐴𝑁) ∗ 𝑇𝑦 (𝑅𝐴𝑁)(11)

If Equations (9)–(11) are simultaneously fulfilled, both Equation (7) and also the supplemental identity
from Equation (8) will be fulfilled as well. Three levels of rigour for harmonising estimates of the overall
energy with those of the energy intensity can thus be identified:
1. Fulfilling the identity from Equation (7),
2. fulfilling the identity from Equation (7) and additionally that from Equation (8), and3. fulfilling Equations (9)–(11) simultaneously.
Level 1 is already a large progress over no validation whatsoever. It implies a macro-level
harmonisation, although there can be remaining inconsistencies (with opposing directions) among theindividual network types – as we had with the values at the end of Section 5.1. Achieving level 2means an additional harmonisation within the WAN, and one within the ANs taken together, whileachieving level 3 implies a harmonisation within each type of network.
Given current estimates, we could only achieve level 1 (values in row 9 of Table 3) and make some
progress towards level 2 (values in row 10 of Table 3). As we argue below, further research is thus
required. While a higher level of harmonisation is generally beneficial, however, it must also be notedthat higher levels depend on additional uncertainties: While level 1 depends mainly on reliable overall
Internet traffic numbers, level 2 also crucially depends on the assumption that all WAN traffic is further
sent over ANs (which, due to Equation (3), is also implicit in Equation (7) at level 1, but not as critical
there). Level 3 further depends on a correct value for 𝑠𝑅𝐴𝑁 , the share of radio access among allaccess. While this number was also relevant to earlier levels, for satisfying the identities on level 3 it
becomes crucial. Worthy pursuing, the additional constraints introduced in Equation (8) and Equations(9)–(11), respectively, can thus also be a case of no longer seeing the forest for the trees.

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5.3

Methodological sources for the current discrepancies

Section 5.1 established a consistent misfit between estimates of the energy intensity of the Internet
and estimates of its overall energy consumption. According to the identity put forward in Equation (6),further refined in Equation (7), multiplying the former by the overall traffic yields consistently lower
results than the latter, often by quite a substantial factor.
Two important sources for historic discrepancies were discussed in Section 2. However, they can nolonger explain today’s mismatch: First, DCs are no longer considered within system boundaries. Andeven if they were, they would no longer dominate the end result. Second, while the reference year of
the assessment continues to be a crucial parameter, it is easily accounted for, and including theparameter Y in our Equations (3-7) reflects this need. All the mismatches described in Section 5.1
occur for the same year of reference, excluding it as a possible source.
Analysing the results, methods and assumptions of the academic and industry studies presented in
Section 4, two main sources for the current discrepancies become evident: i) modelling the accessnetworks, in particular of CPE for the FAN and of the RAN altogether, and ii) top-down versus bottomup modelling, which tend to set the boundaries too wide and too narrow, respectively, and thus yieldinconsistent results. They are both discussed below.
Inconsistent system boundaries and modelling access networks
As can be observed in the overview given by Table 1, the energy intensity of the AN is higher than thatof the WAN. This is particularly valid for the RAN, for which current state-of-the-art estimates range
around 0.12 – 0.2 kWh/GB (Pihkola et al. 2018; Wu, Ryan, and Smith 2019; Bieser et al. 2020; Elisa
2020; Telekom 2020) as compared to the 0.006 – 0.007 kWh/GB for the WAN (Wu, Ryan, and Smith
2019; Aslan et al. 2018); a factor of about 20 more.
With the wide adoption of the much more energy efficient (NGMN Alliance 2015; FernándezFernández, Cervelló-Pastor, and Ochoa-Aday 2017; Gourhant and Tuffin 2020, 5; Nokia 2020) 5G
radio, RANs might ultimately become more energy efficient than FANs (Wu, Ryan, and Smith 2019).
They will both, however, remain most likely less energy efficient than the WAN. Additionally, withsharply increasing data transfers worldwide, the trend of mirroring content at the provider edge is likely
to continue. In particular popular content such as the newest episodes of trending series or recent
software updates are likely to be delivered not across the core network, but from CDNs, edge serversof the ISP, or servers of content providers collocated at edge sites of ISPs. This will contribute to afurther increase of the energy efficiency (and thus the decrease of the energy intensity) of the WAN,
which for an increasing portion of the traffic will consist only of few edge and/or metro routers.
Accounting for the AN is thus of paramount importance for consistent system boundaries and foreloquent results that reflect the entire reality. This means in particular accounting for the CPE part of
the FAN and the entirety of the RAN infrastructure. Not considering the AN can lead to estimates suchas (Aslan et al. 2018), which are then easily and often misinterpreted as ‘the energy intensity of theInternet’ and can lead to massive understatements such as the 21.3 TWh/year we computed
according to Equation (6).
Inherent biases of top-down versus bottom-up modelling
A typical flaw of early bottom-up studies was to model an idealised network based on state-of-the-art
technology and ignore, among others, legacy or redundancy equipment. This error that underwent to,e.g., (Baliga et al. 2009; 2011), led to early underestimated outliers, such as the energy intensity of
0.0064 kWh/GB for the entire Internet in 2010 put forward by (Baliga et al. 2011). Such assumptions
have been rightfully criticised by (Aslan et al. 2018): “studies should consider the full range ofequipment in use within the network under study. This includes considering the legacy equipment
within networks. Estimates based on specific or state-of-the-art equipment, such as Baliga andcolleagues (2009), omit the less efficient legacy equipment (i.e., equipment with higher electricity use
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per GB of data transferred) in use within country-wide Internet networks, resulting in a substantial
underestimate of electricity intensity at the lower end of the observed range (0.004 kWh/GB for 2008)”(Aslan et al. 2018).
Despite this rightful criticism, however, (Aslan et al. 2018) itself puts forward an estimate of 0.007
kWh/GB which, even when correctly interpreted as valid for the WAN exclusively, still leads toestimates of the overall consumption that are a factor of 5 – 26 below top-down assessments of theenergy consumption of the entire WAN (Malmodin and Lundén 2018; Andrae and Edler 2015; Van
Heddeghem et al. 2014), as discussed in the beginning of Section 5.1.
Along similar lines, the bottom-up assessment from (Schien et al. 2013) yields an energy intensity ofjust 0.293 kWh/GB for the RAN in 2012, despite also accounting for legacy technologies. The topdown assessment in (Pihkola et al. 2018) devises an energy intensity of 4.49 kWh/GB for the RAN in2012 – a factor of 15 higher.
It thus appears that even when they aim to be as accurate as possible, accounting for legacy andredundancy equipment, aware that numerous technologies are coexisting and old technologies tend to
be much less energy efficient than newer generations, bottom-up studies nevertheless lead toconsistently smaller estimates than top-down ones. As argued by several authors (Schien et al. 2013;
Coroama and Hilty 2014; Aslan et al. 2018), part of the explanation is that bottom-up modelling often
ignores devices such as legacy technology, redundancy equipment, or simply necessary equipmentthat can easily be overseen in an assessment, such as uninterrupted power supply (UPS). In fact, due
to the sheer complexity of the networks, their topologies and the plethora of equipment involved,
bottom-up modelling might be inherently bound to ignore parts of the network, set its systemboundaries to narrow and thus contain an inherent bias towards underestimates.
Top-down modelling, however, might have the exact opposite bias at its core: setting the systemboundaries unintentionally too wide, considering equipment and processes that should not be
included, and thus contain an inherent bias towards overestimates. To stress this point, we use here
the (hypothetical) example of a top-down analysis of the energy consumption of WAN and LAN in
Switzerland based on a recent corporate report by Swisscom, a Swiss telecom (Swisscom 2019).
In quite a transparent manner, the report informs that Swisscom’s overall energy consumption in 2018has been 548 GWh. Given that Swisscom is the former monopolist and still dominating telecom
company in Switzerland, owing a market share of 60% of the Swiss mobile and 67% of the Swiss
broadband markets, one might quickly conclude that this number is composed to a large extent of theenergy consumption of the WAN, FAN, and RAN networks operated by Swisscom. A closer look at thereport, however, reveals that 40.9 GWh were in fact fuel for building heating and 35.5 GWh were thefuel consumption of the company’s fleet. While these numbers certainly belong in a company’ssustainability reporting, they do not belong to the ICT sector, as they are covered already in othersectors (buildings and transport, respectively).
Moreover, of the remaining 485 GWh electricity consumption of Swisscom, a substantial part is theconsumption of its content delivery servers – be it the cloud computing services offered by thecompany, its TV offerings (including cloud storage for the end customers), or the servers of othercontent providers such as Netflix possibly collocated on Swisscom’s premises. While certainly IT
devices, all of these all already accounted for in DC statistics and should not be double counted for
networks as well. The report does not reveal which share of Swisscom’s electricity consumption
pertains to content servers and not to networks; it states, however, that “this server virtualisation
requires less IT infrastructure than before and has enabled additional savings of 21.5 GWh in 2018”(Swisscom 2019). If only the savings in this domain were around 5% of the total electricity
consumption of the company, its share must certainly be substantial. Finally, Swisscom reused a partof its DC heat: It “supplied 8.4 GWh of thermal energy to the neighbouring areas as district heating”(Swisscom 2019). There are no methods yet established to account for such reuse of DC energy;
however, following general LCA principles, it should partially be allocated to its secondary use.

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All the distinctions in this example represent potential sources of error for a top-down assessment.
Either due to lack of fine-grained data (by contrast to our example), or due to a rushed assessment,each of the processes and devices discussed could be wrongly included within the system boundaries,
thus attributing too large an energy consumption to the WAN, FAN, and RAN of Swisscom.
Table 4 summarises these insights on top-down and bottom-up assessments. Their likely inherent
biases for overestimates and underestimates, respectively, although not conclusively proven,represent a compelling argument for the existing mismatch highlighted throughout this report.
Table 4 High-level summary of typical flaws of top-down and bottom-up assessments.

Limitations of top-down assessments

Limitations of bottom-up assessments

Devices andprocesses

Top-down assessments can easily
count devices and processes thatshould not be counted,

Bottom-up assessments can easily
forget devices and processes thatshould be counted,

Systemboundaries

thus (unwillingly) defining too wide
system boundaries,

thus (unwillingly) defining too narrowsystem boundaries,

Counting

leading to double counting,leading to ‘no counting’,Biasand thus contain an inherent bias
towards overestimates.

and thus contain an inherent bias
towards underestimates.

6

Conclusions

6.1

Summary of the analysis

In this report, we performed a comparative analysis of well-known estimates of the energy intensity
and the overall energy consumption of the Internet. Starting from the observation that for two decades
already there has been a substantial inconsistency between existing estimates, we discussed the earlysources of these discrepancies (i.e., inclusion of DCs into assessments and different years of theanalysis), but also why these reasons are only marginally relevant today.
Nevertheless, there is a continuing – and consistent – inconsistency between assessments of theenergy intensity of the Internet and estimates of its overall energy consumption. While the two should
be connected through the overall Internet traffic, as shown by the identities expressed in Equations (6)and (7), they are in fact quite far apart, as shown by Table 3. The table summarises the insights
generated from the in-depth analysis of several well-known academic studies of the last decade, butalso of a couple of current industry assessments, in particular of the RAN. In Section 3, we laid out thesystem boundaries for the WAN, FAN, and RAN, general assessment principles and the equations
deployed in the analysis. In the comprehensive analysis of Section 4, we have shown how theseprinciples were instantiated for each individual study, and have transparently addressed any further
assumption deployed.
The analysis identified two key sources for the current discrepancies: the inconsistent (and often
untransparent) treatment of the access networks (in particular of the RAN and the CPE part of theFAN), and the inherent but opposing biases of top-down and bottom-up assessments. Referring to thefirst source, bottom-up studies in particular compute the energy intensity of data transfers without
clearly stating whether this value represents just the EI of the WAN, of the WAN plus FAN, or the
average energy intensity of the entire Internet, which accounts for the WAN as well as for (weighted)
access via both FAN and RAN. As for the second source, we have shown evidence, although not
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definitive proof, that top-down assessments seem to have a bias towards overestimates, while bottomup assessments might often exhibit the opposite bias, towards underestimates. This is an artefact of
top-down vs. bottom-up modelling and not of the aim of the study, i.e., energy or energy intensity(while it is true that the EI is often computed in a bottom-up manner, and the E is often computed topdown, this is not always the case, (Pihkola et al. 2018) being an example for a top-down study thatyields energy intensity as result). One way to work towards satisfying the identity from Equation (7) is,in fact, to substitute intensities obtained from a bottom-up process with those computed in a top-downmanner; we performed such a substitution between rows 5 and 6 of Table 3.
Taken together, these two factors go a long way in explaining the existing discrepancies today. They
also imply that the true value of both the EI and the E of the Internet lies somewhere in between thetwo, albeit not necessarily in the middle. As a consequence, bottom-up analyses could use well-known
top-down studies to validate their results, and vice-versa.
The second factor probably also explains the persisting mismatch of the WAN values. Despite our best
efforts, we could find any numbers in the literature or any reasonable assumptions based on thisliterature to the specific WAN identity from Equation (9). Choosing an intensity value on the very highend of existing estimates – based on (Telekom 2020) and own assumptions – could bring the righthand side of Equation (9) to 61 TWh and thus somewhat close to the lowest current estimate for E(WAN), which is 110 TWh. Due to this persisting inconsistency, the “levels 2 and 3 or harmonisation”(as defined in Section 5.2) could not be achieved.

6.2

Metrics for the different energy intensities

As discussed in the Introduction, next to the analysis of existing inconsistencies, another important aimof this study was to establish metrics for the energy intensities of different parts of the network and forthe Internet as a whole, and principles for their computation.
A substantial part of the literature, especially while the RANs were still relatively negligible in terms ofdata delivered, did not take them into account and computed one joint value for the energy intensity of
the WAN and FAN taken together, 𝐸𝐼𝑦 (𝑊𝐴𝑁 + 𝐹𝐴𝑁). Examples for such studies include, for example,(Baliga et al. 2009; Coroama et al. 2013); a notable early exception is (Schien et al. 2013). Other
studies, such as (Aslan et al. 2018), ignored the AN altogether. While the system boundaries areclearly communicated in (Aslan et al. 2018), its title “Electricity Intensity of Internet Data Transmission”
can still be misleading, as “the Internet” is usually understood as more than just the WAN (Baliga et al.
2009; Schien et al. 2013; Coroama et al. 2013; 2015; Schien et al. 2015; Malmodin and Lundén 2018;
Ryan, Smith, and Wu 2019).
Instead, we argue that the three parts of the network be clearly distinguished. The scope of anassessment must obviously not cover them all, but it should be clearly stated which parts of the
network are under study. And any of 𝐸𝐼𝑦 (𝑊𝐴𝑁), 𝐸𝐼𝑦 (𝐹𝐴𝑁), and 𝐸𝐼𝑦 (𝑅𝐴𝑁) that is addressed in the
study, should necessarily be devised separately. With this demand for clarity and granularity, we
mirror our call from several years ago, when we similarly argued that end devices and data centres
should not be included in the computation of the energy intensity of the Internet: “It therefore seems to
be more useful and also more transparent to first estimate (a), (b), and (c) separately and adding thepartial results up when necessary, as opposed to calculating just the total from the outset. In general,knowing which fraction of the overall energy demand is caused by which part of the supply chain of a
final service is key to efficiently allocate investments in energy efficiency. If such results are to be usedby decision-makers, they should clearly differentiate between the three components” (Coroama and
Hilty 2014).
We further argue that 𝐸𝐼𝑦 (𝐹𝐴𝑁) should include not only the street-level part of the AN, but also theCPE (i.e., essentially WiFi routers nowadays). Although the CPE is located within the customers’
premises, it is equipment used to deliver the Internet and thus semantically belonging to the network,
not to end-user devices. An additionally argument is that if CPE is not modelled as part of FAN,
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comparative assessments of 𝐸𝐼𝑦 (𝐹𝐴𝑁) and 𝐸𝐼𝑦 (𝑅𝐴𝑁) will be skewed to the detriment of RANs, as thelion’s share of FAN energy consumption will be ignored. This can have important policy implications, if
for example RANs are to become more energy efficient than FANs, as envisioned by (Wu, Ryan, andSmith 2019).
This being said, the energy intensity of WAN and AN together is obviously a meaningful value tocompute for various contexts. After devising them separately, studies can thus compute the energy
intensity of the Internet accessed over fixed and mobile networks, respectively, by adding the energy
intensity of the WAN to that of the corresponding access network, with the rather trivial equations:
𝐸𝑦 (𝑓𝑖𝑥𝑒𝑑 𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡) = 𝐸𝑦 (𝑊𝐴𝑁) + 𝐸𝑦 (𝐹𝐴𝑁)(12)

𝐸𝑦 (𝑚𝑜𝑏𝑖𝑙𝑒 𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡) = 𝐸𝑦 (𝑊𝐴𝑁) + 𝐸𝑦 (𝑅𝐴𝑁)(13)

A further motivation behind this work was to study whether the average energy intensity of the Internet
is a meaningful concept and how it should be computed. After the analysis, this seems both beneficial
and feasible. As users are typically unaware of the technical infrastructure behind their Internet
connection but might be interested and motivated to reduce their energy and carbon footprints, anestimate of the average is probably valuable. It must, however, account for the general topology of theInternet, as presented in Figure 3, and in particular account fori) the additive nature of the EIs of the WAN and the AN,ii) the alternative access via FAN or RAN, andiii) the (rapidly changing) shares of the two types of access networks.
With Equation (5), we have provided a computation formula that fulfils these requirements:
𝐸𝐼𝑦 (𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡) = 𝐸𝐼𝑦 (𝑊𝐴𝑁) + (1 − 𝑠𝑅𝐴𝑁 ) ∗ 𝐸𝐼𝑦 (𝐹𝐴𝑁) + 𝑠𝑅𝐴𝑁 ∗ 𝐸𝐼𝑦 (𝑅𝐴𝑁)(5)

where 𝑠𝑅𝐴𝑁 represents the share of data accessed via RAN among all Internet-delivered data for thatsame year.
This need for a compound average energy intensity for the Internet is also one of the reasons to stick
for ANs to an energy intensity expressed as ‘energy per data’ [kWh/GB] rather than ‘energy per time’(i.e., power) [W], as suggested by (Coroama et al. 2015) and (Schien et al. 2015). The other reason
against such change is that, for RAN at least, the advanced sleep modes built into the 5G technology(Gourhant and Tuffin 2020) will increase their load elasticity, i.e. enable them to adapt their power
consumption much more to the actual load, making the ‘energy per data’ modelling a closer reflectionof reality than an ‘energy per time’ one.

6.3

Current values and short-term developments

To be able to harmonise estimates of the energy intensity and of the energy consumption of theInternet in such a way that they satisfy the identity expressed in Equation (7), and trying to work
towards satisfying the identity from Equation (8) as well, in Section 5.2 we had to use some of themore pessimistic assumptions of energy intensity estimates together with some of the most optimistic
estimates for the overall consumption of WAN, FAN, and RAN.
These estimates, whose exact values are presented in row 10 of Table 3 and in Section 5.2, can berounded as follows:•

EI2020 (WAN) = 0.02 kWh/GB,•

EI2020 (FAN) = 0.07 kWh/GB,•

EI2020 (RAN) = 0.2 kWh/GB,•

E2020 (WAN) = 110 TWh,•

E2020 (FAN) = 130 TWh,
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•

E2020 (RAN) = 100 TWh

The high EI-values were chosen higher than all academic estimates as follows: EI (WAN) and EI(RAN) follow our computation based on (Telekom 2020), EI (FAN) an own assumption as described in
Section 5.1; the low E-values are: E (WAN) from (Malmodin and Lundén 2018) very conservatively
extrapolated 2015-2020, E (FAN) an average between (Malmodin and Lundén 2018) and (Van
Heddeghem et al. 2014), E (RAN) from (Andrae and Edler 2015).
Although they are towards the two distinct ends of their respective spectra in the literature, they werechosen in such a way as to satisfy the identity reflected in Equation (7), together with trying to fulfil tosome extent also the level-2 and level-3 identities from Equations (8) and (9)–(11), respectively. Only
by selecting such numbers can estimates of the overall energy and the energy intensity of the Internet
validate each other. Additionally, top-down and bottom-up modelling (typically used for overall energy
and energy intensity estimates, respectively) tend to induce opposite biases, as discussed in Section
5.3.2. It then appears plausible to pick numbers from the end of the spectrum that is opposite to therespective biases, i.e., high values for intensities, low values for overall energy.
With all their caveats and uncertainties, these numbers are thus far more plausible than the numbers
at the opposing ends of their respective spectra, i.e., the low energy intensity and the high overall
energy numbers. Before new studies appear that will take into consideration the biases of bottom-up
and top-down assessments, and mutually validate the energy and the energy intensity results, wesuggest using them as fair approximations for the year 2020.
These numbers probably also represent a reasonable basis for extrapolation for a couple of years into
the future. The yearly efficiency gains (corresponding to the yearly decreases of the energy intensity)need to be taken into account according to the rather trivial equation:
𝐸𝐼𝑦+1 (𝑋) = 𝑒𝑖𝑟𝑓𝑦 (𝑋) ∗ 𝐸𝐼𝑦 (𝑋)(14)

for all 𝑋 ∈ {𝑊𝐴𝑁, 𝐹𝐴𝑁, 𝑅𝐴𝑁} and where 𝑒𝑖𝑟𝑓𝑦 (𝑋) represents the yearly energy intensity reduction
factor for the year Y. There are approximations of the intensity reduction factors in the literature; theircaveat, however, is that – as shown above – they typically resulted in too optimistic results in the past.
Until updated factors validated against top-down studies emerge, we suggest to use slightly more
pessimistic values than those of the literature, as follows:•

𝑒𝑖𝑟𝑓2020 (𝑊𝐴𝑁) = 0.7 (Aslan et al. 2018) + 0.1 = 0.8•

𝑒𝑖𝑟𝑓2020 (𝐹𝐴𝑁) = 0.75 (Wu 2021) + 0.1 = 0.85•

𝑒𝑖𝑟𝑓2020 (𝑅𝐴𝑁) = 0.7 (Pihkola et al. 2018) − 0.8 (Elisa 2020). Choose the more pessimistic –and directly measured – 0.8.

Of course, Equation (14) can be used for several years if assuming a constant reduction factor:
𝐸𝐼𝑦+𝑛 (𝑋) = (𝑒𝑖𝑟𝑓𝑦 (𝑋))𝑛 ∗ 𝐸𝐼𝑦 (𝑋)(15)This is a reasonable assumption for a couple of years; and equation (15) should not be used for alonger time horizon anyway, given the uncertainties of the current data (see also next section) and theneed for mutual validation between energy and energy intensity values in the new studies to come,which will thus hopefully decrease current uncertainties.

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7

Outlook: Largest uncertainties and future
research

Identifying the important sources for inconsistencies as well as plausible, harmonised data werenecessary first steps; using these insights to minimise the discrepancies is the obvious next one. From
our results and insights, we can provide valuable guidelines for sounder analyses in future research.
The most obvious implication of our work is that researchers and practitioners should be aware of theexisting inconsistencies and control the results of their study at least against Equation (7), or its
simpler form from Equation (6). The traffic data from the Cisco studies is a valuable, highly reliable,
and thoroughly tested link between EIs and Es. If possible, they should aim higher for a level 2
harmonisation (i.e., Equation (8) jointly with Equation (7)) or even level 3 (Equations (9)–(11)simultaneously). When using just one type of methodology (bottom-up or top-down), the authors
should use established results obtained via the other method to validate their results.
Tables 1 and 2, individually and in comparison, provide some interesting high-level insights that havenot been discussed so far: One is the relative coherence of the estimates for the RAN. Despite thevarious parallel generations of radio standards and the greatest traffic growth of all types of networks
that occurs within RANs, existing assessments are relatively homogeneous (between 0.1-0.5 kWh/GB
for the intensity, 100-220 TWh/year for their overall energy, and a solid connection between the two
with a general tendency towards the lower values). In particular the EI (RAN) estimates seem quite
solid, as the top-down studies (Pihkola et al. 2018) and (Elisa 2020) reach very similar results to thebottom-up study from (Bieser et al. 2020), while (Telekom 2020) is also close.
Unexpectedly, the largest data inconsistencies are for the WAN: While the intensity estimates areclose together, the values for the total energy of the WAN differ by a factor of more than 5. And eventhe lowest of these estimates is 5 times higher than the bottom-up results, leading to a divergencefactor of 5-26 between the two types of studies. This discrepancy also leads to an impossible
harmonisation of EI (WAN) and E (WAN) within the currently available studies, which also makes a“level 2” or “level 3” harmonisation, as defined in Section 5.2, impossible. This domain thus requires
immediate attention.
This also leads to another phenomenon that needs clarification: bottom-up studies clearly indicate that
the EI of the WAN is substantially lower than the EI of both types of ANs (albeit there are only few
estimates of the FAN, which in itself is another important topic for future research). Several of the topdown studies, meanwhile, seem to point towards a total energy consumption of the WAN substantiallyhigher than those of the ANs combined. Given Equation (8), these two statements cannot coexist;
future research must either raise the EI (WAN) value and/or lower the E (WAN) one.
To address these issues, it would be methodologically interesting to study whether a hybrid method
can be developed that unifies to some extent both bottom-up and top-down modelling, while mitigating
their individual biases. Related methodological questions that also need to be addressed refer to thechallenges in data availability, their age and rapid dynamics, the (cross) validation of models, and thetreatment of uncertainty.

Acknowledgements
The author wishes to thank Michael Moser and Roland Brüniger for the opportunity to perform thisstudy, Kurt Roth and Anson Wu for the permission to include their Figures 1 and 6, respectively,
Roland Hischier and again Anson Wu for the insightful personal communications which clarified key
details of their studies, and in particular Roland Brüniger and Friedemann Mattern for the thoughtfulcomments and far-reaching feedback to earlier versions of this manuscript.
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