1. © GSM Association 2011 Mobile Energy Efficiency A Methodology for Assessing the Environmental Impact of Mobile Networks September 2011

2. 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 4 3 2 1

3. 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 4 3 2 1 Measures mobile network energy and environmental performance Provides a common methodology, inputted in to ITU SG5 Enables MNOs to identify energy efficiency targets Will develop an annual global mobile network status report

4. Participants

5. MEE Participants in 145 countries

6. 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

7. 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

8. 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 operators control, e.g. population distribution and climatic conditions, are normalised for using multi-variable regression techniques* Key Performance Indicators 1. Energy consumption per mobile connection 2. Energy consumption per unit mobile traffic 3. Energy consumption per cell site 4. Energy consumption per unit of mobile revenue External comparisons are made anonymously * See Appendix for an explanation of multi-variable regression techniques

9. Benchmarking before normalisation Mobile operations electricity and diesel usage, per connection, 2009 ABCDEFGHIJKL kWh per connection Country 0 5 10 15 20 25 30 35 7x Diesel usage Electricity usage Key Spread of energy per connection across countries can be high DISGUISED EXAMPLE Network A inefficient? Network I efficient?

10. Benchmarking after normalisation kWh per connection ABCDEFGHIJKL Country -2 0 1 2 Difference between actual electrical and diesel energy usage per mobile connection and the expected value, 2009 -3 -4 3 4 Normalisation (against 5 variables) shows a more meaningful picture 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) Regression variables DISGUISED EXAMPLE Network A more efficient than I

11. Operators receive anonymised comparisons against other MNOs, with their networks highlighted Difference between operators actual electrical and diesel energy usage per mobile connection and the expected value, 2009 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) kWh per connection Top Mobile in South Africa Top Mobile in France Top Mobile in Japan Top Mobile in Mexico Top Mobile in India Top Mobile in Canada Top Mobile International OpCos Other Operators KeyRegression variables Top Mobile in Italy E.g. Feedback to operator Top Mobile on normalised energy per connection, which yields greater insights for energy managers Top Mobile average

12. 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 4 3 2 1 Grazie!

13. Appendix Brief explanation of regression analysis Definitions

14. Appendix: Brief explanation of regression analysis (1) Source: GSMA 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 – 7X 1 + 3X 2 + 0.03X 3 + 0.002X 4 where X 1 is % 2G connections, X 2 is number of connections per cell site, X 3 is area covered by MNO per cell site and X 4 is number of cooling degree days With the equation, we can calculate the theoretical energy per cell site for a network, using the networks values for each of the independent variables. Subtracting the networks 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

15. 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 R 2 which measures how much of the variation in the dependent variable can be explained by the independent variables – E.g. an R 2 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 R 2 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

16. Appendix: Definitions (1) Source: GSMA TermDefinition 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 countrys GDP per capita is used. GDP per capita data are 2008. Cell SiteNumber 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 days 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 connectionTotal 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. 20091st January to 31st December.

17. Appendix: Definitions (2) Source: GSMA

18. Appendix: Definitions (3) AcronymsDescription AuCAuthentication Centre BSCBase Station Controller BSSBusiness Support Systems BTSBase Transceiver Station EIREquipment Identity Register eNode B4G equivalent of a BTS GGSNGateway GPRS Support Node HLRHome Location Register IPInternet Protocol LTELong-Term Evolution (4G) MGWMedia Gateway MMEMobility Management Entity MMS-CMultimedia Message Service Centre MSCMobile Switching Centre NOCNetwork Operations Centre Node B3G equivalent of a BTS OSSOperations Support Systems Source: GSMA AcronymsDescription PSTNPublic Switched Telephone Network RANRadio Access Network RNCRadio Network Controller SGSNServing GPRS Support Node SMS-CShort Message Service Centre TETRATerrestrial Trunked Radio VASValue Added Service