Tuesday, 11 January 2011

Carbon footprinting uncertainty – an IBM rack-mounted server

Back in December researchers at Carnegie Mellon’s Green Design Institute released a paper with the title of ‘Uncertainty and Variability in Carbon Footprinting for Electronics: Case Study of an IBM Rack-mount Server.’

The research was aimed at better understanding the uncertainty in assessing carbon footprinting through a case study of a complex product - the IBM rack-mounted server. The view was that measuring a carbon footprint is not as precise an exercise as might be thought and often assumed in policy making and labelling.

Full details are in the paper, which is here, but the research found that uncertainty ranges from around +15% for the production and delivery phase to around +35% for cradle to grave carbon footprint. With the limitations in data, the paper believes the true uncertainty is much larger.

In production, relatively few components contributed to most of the uncertainty, although the report pointed out that delivery via air transport was an important factor and varied considerably between different final assembly sites and delivery locations. By contrast, the ‘use’ phase of a server represented around 94% of the uncertainty of the server’s total product carbon footprint. As Christopher Weber, Assistant Research Professor at Carnegie Mellon who wrote the paper put it; “It is impossible to know with certainty how and for how long a product will be used. On top of this, variability in the electricity mixes of different markets lead to vastly different impacts of using the product similarly in different places”.


Within the limitations of available data, the report concluded that there is a 15% uncertainty around carbon footprinting of the production phase, 25% uncertainty in the delivery phase and 50% uncertainty in the use phase.

The paper concluded that averaging the carbon impact can be very misleading, particularly for delivery and use of products. “The use phase in turn is not only geographically varying, but also an inherently prospective, scenario-­‐based calculation with deep uncertainties dependent upon how a product’s use phase compares to designed lifetime and use profile. Further, because the use phase dominates the life cycle of this product, this scenario uncertainty was dominant and is likely to be so for many energy-­‐using products. When one considers that the exact same product sold in different markets has a +50% variability in product carbon footprint due to electricity mix alone, it becomes clear that simple weighted averages are inappropriate to communicate the variation in use phase emissions to customers”.

There are a lot of general figures bandied around about the relative emissions of ICT equipment in manufacture and use and this paper puts some of the issues in context. Whilst there may be more than ten times the quantity of emissions generated in use than in production, the uncertainty in the use phase is enormous. Minimising emissions in use could easily save as much, or even more, than those generated in production.

For me it highlights two aspects. Firstly, if you are serious about assessing the overall carbon footprint of products you use then you have to do it yourself. Using standard manufacturer estimates is not enough if, for example, they don’t even account for differences in delivery distances and methods.

Secondly, carbon assessments need to be tailored for the industry of the user and the products they use - although you can use standard IT equipment estimates if you use very little IT. For example, Envido recently launched CarbonTrack, a tool to measure carbon emissions from the advertising industry. It’s claimed to be the first and only tool designed to calculate the carbon footprint of advertising campaigns. There are an increasing number of tools like this aimed at different industry sectors.

Both points highlight the fact that there continues to be a need for more sophisticated carbon management software solutions.

© The Green IT Review

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