Calculating the footprint of a product is a tricky business. If you think about the computer on which you are reading this article: how much energy goes into its invention, design, manufacture, distribution, usage and disposal/recycling?
Let’s imagine a PC invented and designed in a studio in London, and say they are using a computer-aided-design (CAD) package like Autodesk. In this instance the designer can examine the design and materials using AMEE to get a sense of the embodied energy while they are designing.
To cover the designer’s own efforts in the process; the primary consumption is from the UK energy grid at that moment in time as well as the usage of an office, PCs and travel. As we illustrate at http://www.realtimecarbon.org, the carbon intensity of the grid varies over the course of the day.
Then in manufacturing, let’s assume that the components are shipped from all around the world (Thailand for the hard-drives, chips from Japan) and assembled in China at a factory in Guangdong, which has a different grid intensity, but also may go on and off-grid, since it has its own local generators.
We have modelled this kind of difference in our factory demo.
Getting a robust and accurate number is hard. Making the information useful or actionable is also hard. There have been some great innovation in the space (e.g. Walkers crisps and Innocent drinks) that have revealed genuine savings, and improved efficiency. From a business-value viewpoint, it is often the act of asking the questions that lead to change: the actual quantification has limited value if there is nothing to compare your results with.
And there’s a problem - very few companies have created full life-cycle emissions and published them. This makes it hard for anyone to make any rational comparison, so many innovative initiatives have waned (e.g. Tesco removing labelling). On one hand, it is (currently) expensive to do these analyses -- maybe tens of thousands of dollars for a consultant. On another hand, unless there is some form of comparison at the end of the analysis, it becomes a self-referencing exercise, with little benefit.
It’s a very chicken-and-egg situation: starting is hard and doesn’t scale -- unless everyone else has already started, and shared, their data.
With the best of intent, a raft of innovative labelling ideas have come to market to try to simplify the endpoint customer visualization, but these often miss the point because they are not ubiquitous, nor do they have credible data to back them up (not for lack of will in trying, but because getting the data is extremely hard).
That leaves consumers uninformed, and to calls of “greenwash”. It also leaves businesses unable to make any form of quantifiable decision which, for a procurement and finance officers, needs to be part of their decision process.
Energy consumption is like a katamari ball... energy accumulates as any material extracted, processed, assembled, shipped, shipped again, sold, used and disposed.
Rolling all the “product ingredients” into embedded energy and carbon is instructive -- for certain decisions. But it is only a snapshot of the entire value-chain at a time in history.
I believe that there is an underlying trend here: that “big data” and the internet of things will make the necessary data-flows easier to access, and scalable: those snapshots will become increasingly fine-grained and addressable over time. We have already built systems that convert all these activities (energy) into carbon -- in real-time or in conjunction with the relevant time-periods (e.g. reporting or financial accounting), and can connect to this emerging data ecosystem.