Energy Efficiency Benchmarking for Mobile Networks

1. www.gsmworld.com/ee energyefficiency@gsm.org 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. 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 IMPLICATIONS EXAMPLE FACTORS • 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. Prior to any “normalisation”, the spread of energy per connection across countries can be quite high DISGUISED EXAMPLE 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. However, regressing against several variables at the same time gives a true “normalisation” DISGUISED EXAMPLE 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. The value comes from assessing the implications with energy managers based on their knowledge of other internal factors DISGUISED EXAMPLE • 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. An anonymous comparison against other operators will allow greater insights for energy managers DISGUISED EXAMPLE 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 0 Canada France Italy Japan Mexico South Africa Key Regression variables • 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 Operator X 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