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

Slides:



Advertisements
Similar presentations
Environmental Management & Sustainability ALEX HETHERINGTON - Benefits Beyond Compliance Libstar Manufacturing Forum May Irene Country Club.
Advertisements

© GSM Association 2011 Mobile Energy Efficiency. Mobile Energy Efficiency objectives To develop a benchmarking methodology, KPIs and benchmark outputs.
1 (c) 2008 The McGraw Hill Companies Redesigning Teacher Salary Structures School Finance: A Policy Perspective, 4e Chapter 12.
Multiple Indicator Cluster Surveys Survey Design Workshop
Economic Impacts of Climate Change
About GSMA Europe We represent the interests of the worldwide mobile communications industry and have nearly 800 operator members covering over 200 countries.
Regional Policy Changes in Common Indicators Definitions and Discussion Brussels, 14 th March
SSC-NM0053 Determination of Greenhouse Gas Emissions Reductions Based on Whole- Building Simulation of Building Mitigation Efforts Using eQUEST/DOE-2.2.
ICT Infrastructures and Climate Change Chaesub Lee Chairman of ITU-T SG 13 (ETRI, Korea)
© GSMA 2010 Jack Rowley, PhD Director Research & Sustainability GSM Association Mobiles Green Manifesto for Climate Change.
Enabling more sustainable societies How can Vodafone contribute?
1 Measuring ICT4D: ITUs Focus on Household and Individual Market, Economics & Finance Unit Telecommunication Development Bureau.
May ITU-T Workshop ICTs: Building the Green City of the Future Arthur Levin Chief, ITU-TSB ITU-T, ICTs and Climate Change United Nations Pavilion.
CoE/ARB Workshop On Infrastructure Sharing and LLU Session 4 Infrastructure Sharing Drivers and Blocks By: Isabelle Gross Khartoum – Sudan, 27 – 29 March.
1 Correlation and Simple Regression. 2 Introduction Interested in the relationships between variables. What will happen to one variable if another is.
Carbon Reduction Commitment - CRC The University of Hull Ian Gibbs (Energy Manager) – 7 th July 2009.
Clark Bockelman Cole Russert James Howe
A Roadmap to Successful Implementation Management Plans.
March, 2010 OVERVIEW April, Scrap / Recycling Steel Mills Downstream Gerdau Ameristeel | Efficient vertical integration.
On Comparing Classifiers : Pitfalls to Avoid and Recommended Approach
Pennsylvania Value-Added Assessment System (PVAAS) High Growth, High Achieving Schools: Is It Possible? Fall, 2011 PVAAS Webinar.
Cost-Volume-Profit Relationships
The European Lighting Industry Position on How to Maximise the Potential Benefits of European Policy on Energy Efficiency in Lighting January 2008.
Oil & Gas Final Sample Analysis April 27, Background Information TXU ED provided a list of ESI IDs with SIC codes indicating Oil & Gas (8,583)
7. PV System Sizing Herb Wade Consultant
2009 Foster School of Business Cost Accounting L.DuCharme 1 Determining How Costs Behave Chapter 10.
Committed to connecting the world Overview of ITU-T Study Group 5 “Environment and Climate Change” Ahmed ZEDDAM France Telecom Orange Chairman, ITU-T Study.
Place your chosen image here. The four corners must just cover the arrow tips. For covers, the three pictures should be the same size and in a straight.
Statistical Analysis SC504/HS927 Spring Term 2008
Chapter 15 ANOVA.
Employment Trendswww.ilo.org/trends Theo Sparreboom Employment Trends International Labour Organization Geneva, Switzerland Working poverty in the world.
The EU Emission Trading System (ETS) Henriëtte Bersee Henriëtte Bersee Environment Counselor Environment Counselor Royal Netherlands Embassy Royal Netherlands.
Title: Energy and Environmental Benefits of Bus Rapid Transit in APEC Economies Presenter’s Name: Walter Kulyk Economy: United States 35th APEC Transportation.
Simple Linear Regression Analysis
Multiple Regression and Model Building
WP7: Environmental impact assessment of present and potential future lifestyle and economic alternatives
A National Wireless Network for HEI Glenn Wearen Terena TF-Mobility Meeting Dec’08.
BA 555 Practical Business Analysis
Hyderabad from a Climate Change Mitigation Perspective – Possible Changes in Consumption and Lifestyle Lutz Meyer-Ohlendorf.
Chapter 11 Multiple Regression.
On Comparing Classifiers: Pitfalls to Avoid and Recommended Approach Published by Steven L. Salzberg Presented by Prakash Tilwani MACS 598 April 25 th.
Correlation and Regression Analysis
Energy Efficiency Benchmarking for Mobile Networks
© GSM Association 2011 Mobile Energy Efficiency Mobile and Environmental Sustainability 20 September 2012.
1 Load Forecast and Scenarios David Bailey Customer Energy & Forecasting Manager Soyean Kim Rate Design Manager.
Lecture 15 Basics of Regression Analysis
Inference for regression - Simple linear regression
EWG47 12.c. RE Share Doubling Goal - 1/17 The 47 th Meeting of APEC Energy Working Group (EWG) Kunming, China, May c. Memorandum for Renewable.
Technical aspects of NAMAs: Options and methodologies for developing baselines for different categories of NAMAs* Neha Pahuja Associate.
Presentation to the: Pennsylvania Public Utility Commission Demand-Side Response Working Group December 8, 2006 Gas Utility Decoupling in New Jersey A.
1 ‘Social Sharing’ By Means of Distributed Computing: Some Results From A Study of Hans-Jürgen Engelbrecht Massey University August 2005
LIEN – Reporting Energy & CO 2 Emissions for Carbon Tax and Emissions Trading. Fuel Use and Distribution in Ireland.
Statistical analysis Outline that error bars are a graphical representation of the variability of data. The knowledge that any individual measurement.
Chapter 4 Linear Regression 1. Introduction Managerial decisions are often based on the relationship between two or more variables. For example, after.
Role of Integrated Assessment Modelling (IAM) in climate change policy analysis The Global Integrated Assessment Model (GIAM) An ABARE-CSIRO joint initiative.
1 HETEROSCEDASTICITY: WEIGHTED AND LOGARITHMIC REGRESSIONS This sequence presents two methods for dealing with the problem of heteroscedasticity. We will.
Improving performance, reducing risk Dr Apostolos Noulis, Lead Assessor, Business Development Mgr Thessaloniki, 02 June 2014 ISO Energy Management.
Striving to achieve Cristina Bueti Advisor. What does the future hold? 2.
ITU Regional Standardization Forum for Africa Livingstone, Zambia March 2016 ITU-T SG5 Activities A special focus on L.1440: Methodology for environmental.
Carbon Emission Reduction Strategy Analysis Using Geographic Information System Dr. John P Deevairakkam TenneT.
Big Data for Measuring the Information Society INTERNATIONAL TELECOMMUNICATION UNION BIG DATA PROJECT - INNOVATIVE WAYS TO UTILIZE BIG DATA AS A NEW DATA.
Statistical analysis.
Statistical analysis.
What is Correlation Analysis?
Understanding Standards Event Higher Statistics Award
Mobile’s Green Manifesto
Forum on Energy Efficiency and Future Network Infrastructure
Behavior Modification Report with Peak Reduction Component
Regression Analysis.
Collaborative regulation in the digital economy
Presentation transcript:

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

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

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 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

Participants

MEE Participants in 145 countries

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 – 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

Benchmarking before normalisation Mobile operations electricity and diesel usage, per connection, 2009 ABCDEFGHIJKL kWh per connection Country x Diesel usage Electricity usage Key Spread of energy per connection across countries can be high DISGUISED EXAMPLE Network A inefficient? Network I efficient?

Benchmarking after normalisation kWh per connection ABCDEFGHIJKL Country Difference between actual electrical and diesel energy usage per mobile connection and the expected value, 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

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

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 Grazie!

Appendix Brief explanation of regression analysis Definitions

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 X X 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

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

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 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 st January to 31st December.

Appendix: Definitions (2) Source: GSMA

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