1. Energy Efficiency Benchmarking for Mobile Networks
This slide can be circulated to GSMA Full Members to explain the EE Initiative.
Energy Efficiency Benchmarking for Mobile Networks

2. Contents Objectives and Benefits Methodology
Example Output From Pilot Phase
Our Offer
Next Steps

3. Objectives of the Energy Efficiency Initiative
Develop a benchmarking methodology, KPIs and benchmark outputs which allow mobile operators to:
benchmark themselves externally and internally, and
reduce energy consumption, emissions and costs
Coordinate with industry and regulatory stakeholders so that the benchmarking methodology is adopted as a global standard by the industry
Ensure that data confidentiality is preserved

4. - A calculation of potential cost and CO2e savings for each network
Benefits for Operators
Benchmark networks against peers, develop insight into energy use and target cuts in energy consumption
- A calculation of potential cost and CO2e savings for each network
Participate in a large dataset which leads to improved regressions and statistical significance and more useful results
Demonstrate a commitment to energy and emissions reduction, which will have a positive impact on regulators, investors, customers and other stakeholders

5. Contents Objectives and Benefits Methodology
Example Output From Pilot Phase
Our Offer
Next Steps

6. Methodology Measure mobile network energy performance by country:
Energy per mobile connection
Energy per unit mobile traffic
Energy per cell site
Energy per unit mobile revenue
Compare networks anonymously
Normalise for variables outside the energy managers’ control for example country, geography and technology factors. This process, which uses multi-variable regression analysis, enables like-for-like comparisons

7. Normalisation
There are many factors that could impact energy efficiency
EXAMPLE FACTORS
IMPLICATIONS
Country factors
Population density
Urban / rural population split
Number of cooling degree days
Topology
Market factors
Market share
Traffic, both voice and data
Geographic coverage
Population coverage
Technology factors:
2G / 3G split
Diesel vs. electricity consumption
Other
Data accuracy
Larger data set enables normalisation of multiple factors whilst retaining statistical significance
General availability and accuracy of data is important, whether from operator or public domain, e.g. country and market factors
Data that is difficult to gather e.g. age of legacy kit, cooling methods and number of transceivers can be progressively included as it becomes available
Multi-linear regression techniques test intuitive assumptions about variables
Factors must be grouped in a way that makes sense when looking for a linear relationship

8. Contents Objectives and Benefits Methodology
Example Output From Pilot Phase
Our Offer
Next Steps

9. Example: Energy Per Connection
Regression analysis captures the impact of technology by using “% connections that are 2G” as one of the factors
Numerous regressions have been tried and a good fit has been obtained from the following factors to explain variations in energy per connection:
% urban population / % country population covered
Square kilometres covered / connection
% connections that are 2G
% energy from diesel
Other variables, such as cooling degree days, can be added when the data set is larger

10. DISGUISED EXAMPLE
Prior to any “normalisation”, the spread of energy per connection across countries can be quite high
Operator X
Mobile operations average electricity and diesel usage per connection, 2009
Diesel usage
kWh per connection
Electricity usage A B C D E F G H I J K L
Country
Source: Operator X, GSMA data and analysis

11. DISGUISED EXAMPLE
The first level of normalisation is a simple regression against one variable, for example the % of 2G connections
Operator X
Scatter plot of electrical and diesel energy usage per connection versus % 2G connections, 2009
Country A
Country B
Mobile operations diesel & electricity usage per connection (kWh / connection)
Country C
Country D
Country E
% 2G connections of all mobile connections
Source: Operator X, GSMA data and analysis

12. DISGUISED EXAMPLE
However, regressing against several variables at the same time gives a true “normalisation”
Operator X
Deviation from line of best fit: average electrical and diesel energy usage per mobile
connection, 2009
Line of best fit:
R2 = 90%
kWh / connection F B I D A G K C E J L H
Country
Mobile operations diesel & electricity usage per connection regressed against:
% Mobile operations diesel usage of total diesel and electricity usage
% 2G connections of all mobile connections
Geographical area covered by all MNOs per connection
% urban population / % population covered by all MNOs
Source: Operator X, UN, GSMA data and analysis

13. DISGUISED EXAMPLE
The value comes from assessing the implications with energy managers based on their knowledge of other internal factors
After normalising for these factors, certain countries have high or low energy per connection:
High: Countries F, B, I, D
Low: Countries E, J, L, H
Various factors could explain the over or under-performance of different countries:
Energy efficiency of network equipment
Network frequency
Network design
Cooling method
Number of cooling degree days
Topology (though analytically not a factor here)
Traffic (though analytically not a factor here)
Data accuracy (to be determined)
Improving the energy efficiency of countries F, B, I and D to the average (post normalisation) will reduce energy costs in those countries by XX% on average

14. DISGUISED EXAMPLE
An anonymous comparison against other operators will allow greater insights for energy managers
Operator X
Deviation from line of best fit: average electrical and diesel energy usage per mobile
connection, 2009
Line of best fit:
R2 = 90%
kWh / connection
Canada
France
Italy
Japan
Mexico
South Africa
Key
Regression variables
Operator X
Mobile operations diesel & electricity usage per connection regressed against:
% Mobile operations diesel usage of total diesel and electricity usage
% 2G connections of all mobile connections
Geographical area covered by all MNOs per connection
% urban population / % population covered by all MNOs
Other Operators
Source: MNOs, GSMA data and analysis

15. Contents Objectives and Benefits Methodology
Example Output From Pilot Phase
Our Offer
Next Steps

16. Our offer to MNOs GSMA will:
Collect data from MNOs on electricity and diesel energy use by country for 2009 by cell site, connection, unit traffic and unit revenue
Gather external datasets that might explain variations in energy consumption along technology, country factor and market dimensions
Normalise energy use data to compare like-for-like, using multi-linear regression analyses
Feed back results bilaterally to each MNO participant and refine analyses
Combine data with other participant MNO data and feed back benchmarking results to each MNO participant
Provide each MNO with an estimate of the potential cost savings available as a result of energy reduction

17. Contents Objectives and Benefits Project Status Our Offer
Example Output From Pilot Phase
Next Steps

18. Next Steps
Agree participation of MNO, including which data can be anonymised and pooled. Start gathering data (and sign confidentiality agreement)
Data required are not onerous. We need, by country for 2009:
Mobile network electrical energy usage and diesel energy usage
Number of physical cell sites, number of mobile connections
Minutes of mobile voice traffic and bytes of mobile data traffic
Mobile revenues
Analyse and benchmark MNO countries as per the example shown for
Energy per mobile connection
Energy per unit mobile traffic
Energy per cell site
Energy per unit mobile revenue
Review the resulting analysis and conclusions with MNO
Re-check regressions using large data set, run regressions with more variables, and run regressions separately for developed and emerging market countries