Calculating carbon footprints is easy enough when the stakes are low, when accuracy doesn’t matter, or when the resulting number will cease to exist as soon as you close your browser window. Even though the long-term effects of misgauging or disregarding our carbon emissions are obvious and well documented, the immediate consequences of botching a carbon footprint analysis are basically nil.
That said, being able to ascertain the carbon footprint of a person, product or thing is a desirable goal, if for purposes of awareness and accurate offsetting. But researchers at Carnegie Mellon contend that in at least one category–electronics–figuring out this footprint is incredibly difficult.
In a paper titled ‘Uncertainty and Variability in Carbon Footprinting for Electronics: Case Study of an IBM Rack-mount Server’, Christopher Weber describes the process of calculating the carbon footprint of–you guessed it!–an IBM rack-mount server. Here’s how it went:
Uncertainty ranges from around +15% for the production and delivery phase to around +35% for cradle to grave carbon footprint, including the product’s use phase and logistics associated with delivery of products. However, given limitations in available data to access uncertainty associated with temporal variability and technological specificity, it is likely that true uncertainty is much larger.
Major sources of uncertainty in the production phase are limited, but important: Integrated circuits are hard to focus in on, and product delivery via air transport varies wildly from customer to customer. But the vast majority–up to 94%–of a product’s carbon footprint is laid during its usage phase. This hugely dominant section of the carbon footprint analysis, Weber says, is also by far the most difficult to estimate:
Uncertainty in the production phase was considerably smaller than the deep uncertainties in predicting the use phase. Unlike the production phase, where supply chains can be analyzed, the use phase is inherently predictive. It is thus 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.
Whether the market in which a product is used generates electricity with coal, nuclear or hydroelectric power plants could conceivably throw off a use-focused footprint calculation, and that’s not even taking into account varying lifespans and usage habits between different deployments.
The lesson, though, isn’t necessarily that carbon footprinting is useless, or that being mindful of carbon emissions is somehow misguided. It’s a reminder that footprinting is difficult, and that specific calculations–at least for now–should be taken with a grain of salt.
As for an overarching message, the author is fairly blunt: “[E]nergy efficiency in the use phase is the product attribute most likely to lower the product’s carbon footprint.” Time and money is better spent lowering the product’s energy usage and educating its users, he says, than attempting to work out exactly how broad its carbon footprint is.