Energy Use Implications of ICT Hardware NATO SCIENCE PROGRAMME in conjunction with the Carnegie Bosch Institute ADVANCED RESEARCH WORKSHOP: Life Cycle Analysis for Assessing Energy and Environmental Implications of Information Technology Budapest, Hungary September 1-3, 2003 by Andrius Plepys

Why the issue?  Dynamism of ICT sector  Productivity and structural impacts  Role in sustainable development  Climate change policies  Energy security

Energy and the New Economy Period Reduction rate of energy intensity Source (“old Economy”) 1.3 % (EIA 2001) (oil crisis) 2.6 % (Laitner 2000) (the New Economy) 3.4 % (N.E. incl. IT stock crash) 2.8 % (Laitner 2002)  Decoupling between GDP growth and energy consumption often attributed to ICT sector

Electricity crisis – a hoax or a reality? New York, August 15, 2003

Internet to blame for the blackouts? Electricity consumption increaseSilicon ValleyRest of California Residential consumption19.6 %18.1 % Non-residential consumption15.4 %15.1 % Total16.5 %16.0 % derived from California Energy Commission’s data (2002) El. consumption dynamics in Silicon Valley and California,  Interests of power suppliers (coal industry)  Poor planning and artificial price increase?

 From Mills to LBNL  National estimates of AEC CountryReliability Sources USA high 2.72 – 4 ADL (2002) low 2 – 82 – 50 several studies Japan medium 3.34 NTT/FRIC (2002) low ISTEC (2000) Germany medium 4n.a. Aebischer* (2003) low 0.5 – 1.76 Barthel/Turk (2001) Bottom line< 3-4%<5-6% ? ICT-related electricity consumption as % of national AEC

However…  Absolute consumption will increase  Future predictions are fuzzy  Reportedly large energy saving potential

Electricity consumption by component in non-residential sector Source: Roth et al. in ADL (2002)

The power of power management  CPU – idling >90% of the time  Hardware actively used <25% of the time (Webber, 2001)  PM already saves 25%, but additional 15% could be saved by optimal set up (US EPA, 2002)  Largest saving potential in offices: desktop computers/workstations CRT monitors Copiers & printers (Kawamoto et al., 2001)

The two legs of power management Technology solutions Behavioural solutions - software (BIOS  OS) - products (CRT  LCD) - components (CPU) - awareness - knowledge - informed choice

Relevancy of the issues - DC example  High power reliability costs dearly  Overestimated needs  Consumption insignificant on national scale, but a large share of ICT infrastructure  HVAC – largest consumer DC’s energy  Saving 20-40% technically feasible today HVAC optimisation (air  water, CHP, t o ) night switching  Economic barriers (large build-up, risk aversion)

The impacts of trends  Wireless communications  Mobile devices  LCD displays  ICT diffusion into other products  Optic fibre – broadband – data traffic  The “last mile” limitations  Voice and data n-work convergence  E-services

Shortcomings Methodological and data issues: ICT definition and system boundaries Allocation procedures Data: Behavioural data (!) Power rating Stock data and return rates

Reflections  Electricity consumption – not significant today, but future is uncertain  Growth rate and saving potential makes it important for continuous research  Supply side: energy efficiency not always a design priority (often a trade-off with costs)  Demand side: marginal role of energy costs to encourage savings (hardware costs, performance, ergonomics before environmental considerations)

 Technology can take care of some efficiency improvements  Behavioural changes are needed to fully exploit the potential savings  Market “failure”? Reflections

A role for policy makers?  Economic instruments (e.g. green taxes)  Informational voluntary instruments (performance standards, labelling initiatives)  Governmental procurement for more energy efficient equipment  more research on policy role