A Methodology for Assessing
the Environmental Impact of
Mobile Networks
September 2011
Mobile Energy Efficiency
© GSM Association 2011
Public sector goals


2009: Commission Recommendation for the ICT sector to:
1– Develop a framework to measure its energy and environmental
performance
2– Adopt and implement common methodologies
3– Identify energy efficiency targets
4– Report annually on progress
2010: Digital Agenda Key Action 12:
–
Assess whether the ICT sector has complied with the timeline to
adopt common measurement methodologies for the sector's own
energy performance and greenhouse gas emissions and propose
legal measures if appropriate
Mobile Energy Efficiency objectives and status


MEE analysis:
1
Measures mobile network energy and environmental performance
2
Provides a common methodology, inputted in to ITU SG5
3
Enables MNOs to identify energy efficiency targets
4
Will develop an annual global mobile network status report
MEE started a year ago as a pilot with Telefonica, Telenor and China
Mobile. Today we are working with 29 MNOs accounting for more than
210 networks that serve roughly 2.5 billion subscribers
Participants
MEE Participants in 145 countries
Greenland
Alaska
Norway
Iceland
Finland
Great Germany
Britain
Belarus
Ireland
Poland
Ukraine
France
Romania
Italy
Turkey
Greece
Portugal Spain
Lebanon Syria
USA
Morocco
Mexico
Belize
Russia
Sweden
Canada
Algeria
Bahamas
Cuba
Dominic. Rep.
Mauritania
Guatemala
Honduras
Jamaica
Nicaragua
El Salvador
Venezuela
Guyana
Costa Rica
Panama
Surinam
Colombia
Fr. Guyana
Ecuador
Mali
Niger
Senegal
Guinea
Sierra Leone
Libya
Chad
Burkina Faso
Liberia
Ivory Coast
Nigeria
Peru
Somalia
Uganda
D. R. of
Congo
Congo
Brazil
Mongolia
Uzbekistan
Kyrgyzstan
Nord Korea
Tadzhikistan
Japan
Turkmenistan
China
South Korea
Afghanistan
Iraq
Iran
Bhutan
Israel
Qatar
Nepal
Pakistan
Saudi
V.A.E
Egypt
Myanmar
Taiwan
Arabia
India
Laos
Oman
Eritrea
Bangladesh
Vietnam
Yemen
Sudan
Cambodia
Philippines
Thailand
Ethiopia
Cameroons
Ghana
Gabon
Kazakhstan
Kenya
Malaysia
Indonesia
Papua New Guinea
Tanzania
Angola
Zambia
Mozambique
Bolivia
Paraguay
Zimbabwe
Namibia
Botswana
South Africa
Chile
Madagascar
Australia
Lesotho
Uruguay
Argentina
New Zealand
Benefits for MNOs
1.
A detailed analysis of the relative network performance against a
large and unique dataset
–
Energy cost and carbon emissions savings of 20% to 25% of costs per
annum are typical for underperforming networks
2.
Suggested high level insights to improve efficiency
3.
The opportunity to participate annually, to map improvements over
time and quantify the impacts of cost reduction initiatives
4.
Demonstrate a commitment to energy and emissions reduction to all
stakeholders
5.
In addition, we are piloting an initiative with an MNO and vendor to
use the MEE results to identify actions to reduce energy and hope to
offer this additional service more widely soon
How are the benefits achieved and which data
are required from operators?


How the benefits are achieved
1.
Share energy consumption data with GSMA in confidence
2.
Review GSMA analysis and validate
3.
Use the benchmarking results and high level insights to refocus or
refine current and future energy efficiency improvement initiatives
The data required from operators:
–
Mobile network electrical energy usage and diesel energy usage
–
Number of physical cell sites and number of technologies
–
% coverage (geographic, population)
–
Number of mobile connections, mobile revenues
–
Minutes of mobile voice traffic, bytes of mobile data traffic
Methodology

Unique analytical approach allows MNOs to compare their networks
against one another and against their peers on a like-for-like basis
–

Key Performance Indicators
1.
2.
3.
4.

Variables outside the operator’s control, e.g. population
distribution and climatic conditions, are normalised for using
multi-variable regression techniques*
Energy consumption per mobile connection
Energy consumption per unit mobile traffic
Energy consumption per cell site
Energy consumption per unit of mobile revenue
External comparisons are made anonymously
* See Appendix for an explanation of multi-variable regression techniques
DISGUISED EXAMPLE
Benchmarking before normalisation
Spread of energy per connection across countries can be high
Mobile operations electricity and diesel usage, per connection, 2009
35
Network “A” inefficient?
Network “I” efficient?
30
25
7x
kWh per 20
connection
15
10
5
0
A
B
C
D
E
F
G
Country
Key
Electricity usage
Diesel usage
H
I
J
K
L
DISGUISED EXAMPLE
Benchmarking after normalisation
Normalisation (against 5 variables) shows a more meaningful picture
Difference between actual electrical and diesel energy usage per mobile
connection and the expected value, 2009
4
3
2
kWh per 1
connection
0
-1
-2
Network “A” more efficient than “I”
-3
-4
F
B
I
D
A
G
K
C
E
J
L
Country
Regression variables
Mobile operations diesel & electricity usage per connection regressed against:
- % 2G connections of all mobile connections
- Geographical area covered by MNO per connection
- % urban population / % population covered by MNO
- Number of cooling degree days per capita (population weighted)
- GDP per capita (adjusted)
H
Operators receive anonymised comparisons against
other MNOs, with their networks highlighted
E.g. Feedback to operator “Top Mobile” on normalised energy per
connection, which yields greater insights for energy managers
Difference between operators’ actual electrical and diesel energy usage per mobile
connection and the expected value, 2009
Top Mobile average
kWh per
connection
Top Mobile
in South
Africa
Top Mobile
Top Top Mobile
in Mexico Mobile in Canada
in India
Top
Mobile in
Italy
Top Mobile
in France
Top Mobile in
Japan
Key
Top Mobile International OpCos
Other Operators
Regression variables
Mobile operations diesel & electricity usage per connection regressed against:
- % 2G connections of all mobile connections
- Geographical area covered by MNO per connection
- % urban population / % population covered by MNO
- Number of cooling degree days per capita (population weighted)
- GDP per capita (adjusted)
Next steps for MEE

1
Feed back 2009 results to MNOs and finalise 2010 data and
validation exercise
2

Wish the ITU well for Korea!
3
Calculate the first annual global aggregate data for mobile network
energy consumption and CO2, with a view to developing a time
series of data for the coming years
4

Continue to engage with key stakeholders and share our knowledge
and expertise as required

Grazie!
Appendix

Brief explanation of regression analysis

Definitions
Appendix: Brief explanation of regression analysis (1)

Regression analysis mathematically models the relationship between a dependent
variable (in this case either energy per connection or energy per cell site) and one or
more independent variables. E.g.:
–
–

For energy per connection the independent variables are % 2G connections, % urban
population / % population covered by MNO, adjusted GDP per capita, number of cell sites per
connection and number of cooling degree days per capita
For energy per cell site they are % 2G connections, number of connections per cell site,
geographical area covered by MNO per cell site and number of cooling degree days per capita
The regression analysis produces a set of results which enable a mathematical
equation to be written to explain the relationship. An example equation for energy per
cell site is:
Energy per cell site = 16 – 7X1 + 3X2 + 0.03X3 + 0.002X4
where X1 is % 2G connections, X2 is number of connections per cell site, X3 is
area covered by MNO per cell site and X4 is number of cooling degree days


With the equation, we can calculate the theoretical energy per cell site for a network,
using the network’s values for each of the independent variables. Subtracting the
network’s actual value from the theoretical value gives a measure in MWh per cell site
of whether the network is over or under-performing versus the theoretical value. This
approach can be extended to multiple networks
Therefore the effect of differing values of independent variables for multiple networks
can be removed, and so networks can be compared like-for-like
Source: GSMA
Appendix: Brief explanation of regression analysis (2)

The regression analysis also produces statistics, which show amongst other things:
–
How well the equation fits the data points: this is denoted by the coefficient of
determination R2 which measures how much of the variation in the dependent
variable can be explained by the independent variables
–
–
E.g. an R2 of 62% means that approximately 62% of the variation in the dependent variable
can be explained by the independent variable
The remaining 38% can be explained by other variables or inherent variability
The probability that the coefficient of the independent variable is zero, i.e. that the
independent variable is useful in explaining the variation in the dependent variable.
These probabilities are given by the P-values. A P-value of 12% for the coefficient of
the independent variable ‘% 2G connections’ means that this coefficient (value -7)
has a 12% chance of being zero, i.e. a 12% chance that this independent variable is
not useful in explaining the variation in the dependent variable
As the dataset increases we would hope to provide a higher R2 and lower P-values, and
also to be able to include additional independent variables
Note that regression analysis does not prove causality but instead demonstrates
correlation (i.e. that a relationship exists between the dependent and independent
variables), and also that we are assuming a linear relationship over the ranges of
variables covered in this analysis
Sensitivity analysis is conducted in two ways: running regressions with slightly different
independent variables; and re-running the regressions with subsets of the dataset (e.g.
developed vs. emerging countries)
–



Source: GSMA
Appendix: Definitions (1)
Term
Definition
Adjusted GDP per
capita
GDP per capita is used as a proxy for mobile call / data quality. Developed countries are assumed to have equally high quality and
so an average ‘Developed country GDP per capita’ figure is used of $49,000. Developed countries are defined as those with GDP
per capita over $21,000. For all other countries, the country’s GDP per capita is used. GDP per capita data are 2008.
Cell Site
Number of physical Cell Sites averaged over the calendar year, equal to [Number of Cell Sites on 1st January + Number of Cell Sites
on 31st December]/2. A Cell Site includes a BTS and/or a Node B and/or eNode B. Femtocells, repeaters and picocells are
excluded. A co-located site (e.g. 2G or 3G ) equals one Cell Site.
Cooling degree
days per capita
(population
weighted)
Based on departures from an average temperature of 18 °C, cooling degree days are defined as T – 18 °C, where T is the average
temperature. Accordingly, a day with an average temperature of 25 °C will have 7 degree cooling days. T for a particular day is
calculated by adding the daily high and low temperatures and dividing by two, and each day’s figure is summed over the year. A
national average is calculated by weighting by population distribution and the result is divided by total population.
Diesel energy
consumption
Energy consumed by diesel generators used to power Radio Access Network (RAN) and Core Network. This includes prime and
standby diesel energy usage from RAN and Core Network, but does not include diesel consumption from travel, delivery trucks or
buildings which are unrelated to the network. An average diesel generator efficiency of 20% has been used to convert from MWh of
diesel to MWh of electricity generated by the diesel generator.
Mobile connection
Total number of SIMs or, where SIMs do not exist, a unique mobile telephone number that has access to the network for any purpose
(including data only usage), except telemetric applications. SIMs that have never been activated and SIMs that have not been used
for 90 days should be excluded. Total number of SIMs includes wholesale SIMs but excludes mobile Machine to Machine (M2M)
connections. Average number of mobile connections is the Number of mobile connections averaged over the calendar year, equal to
[connections on 1st January + connections on 31st December]/2.
RAN energy
consumption
Energy consumed by RAN including BTS, Node B and eNode B energy usage and all associated infrastructure energy usage such as
air-conditioning, inverters and rectifiers. It includes energy usage from repeaters and all energy consumption associated with
backhaul transport. It excludes picocells, femtocells and Core Network energy usage, as well as mobile radio services such as
TETRA. Mobile Network Operators (MNOs) should include an estimation of the proportion of energy consumption from shared Cell
Sites, including the shared proportion of infrastructure (DC, air-conditioning, etc.) if it cannot be measured.
Revenue of mobile
operations
Revenues from mobile operations including recurring service revenues (e.g. voice, messaging and data) and non-recurring revenue
(e.g. handset sales) as well as MVNO, wholesale and roaming revenues. It excludes fixed line and fixed broadband revenues.
2009
1st January to 31st December.
Source: GSMA
Appendix: Definitions (2)
Source: GSMA
Appendix: Definitions (3)
Acronyms
Description
Acronyms
Description
AuC
Authentication Centre
PSTN
Public Switched Telephone Network
BSC
Base Station Controller
RAN
Radio Access Network
BSS
Business Support Systems
RNC
Radio Network Controller
BTS
Base Transceiver Station
SGSN
Serving GPRS Support Node
EIR
Equipment Identity Register
SMS-C
Short Message Service Centre
eNode B
4G equivalent of a BTS
TETRA
Terrestrial Trunked Radio
GGSN
Gateway GPRS Support Node
VAS
Value Added Service
HLR
Home Location Register
IP
Internet Protocol
LTE
Long-Term Evolution (4G)
MGW
Media Gateway
MME
Mobility Management Entity
MMS-C
Multimedia Message Service Centre
MSC
Mobile Switching Centre
NOC
Network Operations Centre
Node B
3G equivalent of a BTS
OSS
Operations Support Systems
Source: GSMA