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Energy Use Implications of ICT Hardware by Andrius Plepys 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 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 Decoupling between GDP growth and energy consumption often attributed to ICT sector Period Reduction rate of energy intensity 1960-96 (“old Economy”) 1.3 % 1973-86 (oil crisis) 2.6 % 1996-99 (the New Economy) 3.4 % 1996-01 (N.E. incl. IT stock crash) 2.8 % Source (EIA 2001) (Laitner 2000) (Laitner 2002) Electricity crisis – a hoax or a reality? New York, August 15, 2003 Internet to blame for the blackouts? El. consumption dynamics in Silicon Valley and California, 1990-2000 Electricity consumption increase Silicon Valley Rest of California Residential consumption 19.6 % 18.1 % Non-residential consumption 15.4 % 15.1 % Total 16.5 % 16.0 % derived from California Energy Commission’s data (2002) Interests of power suppliers (coal industry) Poor planning and artificial price increase? From Mills to LBNL National estimates of AEC ICT-related electricity consumption as % of national AEC Country USA Japan Germany 2000 2010 Sources high 2.7 2–4 ADL (2002) low 2–8 2 – 50 medium 3.3 4 low 4.3 30 4 n.a. Aebischer* (2003) 0.5 – 1.7 6 Barthel/Turk (2001) < 3-4% <5-6% ? Reliability medium low Bottom line several studies NTT/FRIC (2002) ISTEC (2000) However… Absolute consumption will increase Future predictions are fuzzy Reportedly large energy saving potential Electricity consumption by component in non-residential sector UPSs 6% Other* 10% Monitors & displays 22% Printers 6% Data networks 7% Telecomm networks 7% Copiers 10% PCs & workstns. 20% Servers 12% 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 (BIOSOS) - awareness - products (CRTLCD) - knowledge - components (CPU) - 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 (airwater, CHP, to) 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) Reflections Technology can take care of some efficiency improvements Behavioural changes are needed to fully exploit the potential savings Market “failure”? 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