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A Methodology for Assessing the Environmental Impact of Mobile Networks September 2011. Mobile Energy Efficiency. Public sector goals. 2009: Commission Recommendation for the ICT sector to: Develop a framework to measure its energy and environmental performance

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mobile energy efficiency

A Methodology for Assessing the Environmental Impact of Mobile Networks

September 2011

Mobile Energy Efficiency

public sector goals
Public sector goals
  • 2009: Commission Recommendation for the ICT sector to:
    • Develop a framework to measure its energy and environmental performance
    • Adopt and implement common methodologies
    • Identify energy efficiency targets
    • 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

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mobile energy efficiency objectives and status
Mobile Energy Efficiency objectives and status
  • MEE analysis:
  • 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
  • Measures mobile network energy and environmental performance

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  • Provides a common methodology, inputted in to ITU SG5

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  • Enables MNOs to identify energy efficiency targets

3

  • Will develop an annual global mobile network status report

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slide5

Greenland

Norway

Alaska

Iceland

Finland

Russia

Sweden

Canada

Great Britain

Germany

Belarus

Ireland

Poland

Kazakhstan

Ukraine

France

Mongolia

Romania

Uzbekistan

Kyrgyzstan

Italy

Nord Korea

Spain

USA

Turkey

Greece

Tadzhikistan

Portugal

Japan

Syria

Turkmenistan

China

South Korea

Lebanon

Afghanistan

Iraq

Morocco

Iran

Bhutan

Israel

Qatar

Nepal

Algeria

Pakistan

Libya

SaudiArabia

Bahamas

V.A.E

Egypt

Myanmar

Taiwan

Mexico

India

Cuba

Laos

Belize

Mauritania

Oman

Dominic. Rep.

Eritrea

Bangladesh

Mali

Niger

Vietnam

Guatemala

Senegal

Chad

Honduras

Yemen

Jamaica

Sudan

Burkina Faso

Cambodia

Nicaragua

El Salvador

Guinea

Venezuela

Philippines

Nigeria

Thailand

Ethiopia

Guyana

Costa Rica

Sierra Leone

Panama

Cameroons

Surinam

Somalia

Liberia

Malaysia

Colombia

Uganda

Ghana

Fr. Guyana

Gabon

Ecuador

D. R. of

Congo

Ivory Coast

Kenya

Indonesia

Congo

Papua New Guinea

Tanzania

Brazil

Angola

Peru

Mozambique

Zambia

Bolivia

Zimbabwe

Namibia

Madagascar

Paraguay

Botswana

Australia

Lesotho

South Africa

Chile

Uruguay

Argentina

New Zealand

MEE Participants in 145 countries

slide6

Benefits for MNOs

  • 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
  • Suggested high level insights to improve efficiency
  • The opportunity to participate annually, to map improvements over time and quantify the impacts of cost reduction initiatives
  • Demonstrate a commitment to energy and emissions reduction to all stakeholders
  • 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 are the benefits achieved and which data are required from operators?
  • How the benefits are achieved
    • Share energy consumption data with GSMA in confidence
    • Review GSMA analysis and validate
    • 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
Methodology
  • Unique analytical approach allows MNOs to compare their networks against one another and against their peers on a like-for-like basis
    • Variables outside the operator’s control, e.g. population distribution and climatic conditions, are normalised for using multi-variable regression techniques*
  • Key Performance Indicators
    • 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

benchmarking before normalisation
Benchmarking before normalisation

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30

25

20

15

10

5

0

DISGUISED EXAMPLE

Spread of energy per connection across countries can be high

Mobile operations electricity and diesel usage, per connection, 2009

Network “A” inefficient?

Network “I” efficient?

7x

kWh per connection

A

B

C

D

E

F

G

H

I

J

K

L

Country

Key

Diesel usage

Electricity usage

benchmarking after normalisation
Benchmarking after normalisation

DISGUISED EXAMPLE

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

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3

2

kWh perconnection

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0

-1

-2

Network “A” more efficient than “I”

-3

-4

F

B

I

D

A

G

K

C

E

J

L

H

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)
operators receive anonymised comparisons against other mnos with their networks highlighted
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 in Mexico

Top Mobile in India

Top Mobile in Canada

Top Mobile in Italy

Top Mobile in France

Top Mobile in Japan

Key

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)

Top Mobile International OpCos

Other Operators

next steps for mee
Next steps for MEE
  • Feed back 2009 results to MNOs and finalise 2010 data and validation exercise
  • Wish the ITU well for Korea!
  • 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
  • Continue to engage with key stakeholders and share our knowledge and expertise as required

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  • Grazie!
appendix
Appendix
  • Brief explanation of regression analysis
  • Definitions
appendix brief explanation of regression analysis 1
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
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

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