Lifecycle Analysis: Moving from Black-box to Transparency
Lifecycle analysis, or LCA, is all the rage. Academics and consultants tout the amazing insights that an LCA can provide companies. Labeling organizations solicit companies to run LCAs to produce carbon or other environmental footprint numbers. And itâ€™s true â€“ mostly â€“ that LCAs can provide incredible and sometimes truly revolutionary insights into a companyâ€™s impacts that highlight waste and illogical supply chain steps.
There are two very real problems about LCA that everyone needs to know about (but which, in my opinion, shouldnâ€™t actually prevent anyone from running an LCA). The more you know, the more you know – right? So it makes sense to know as much as you can about your productâ€™s lifecycle. However, you also need to know about LCA and the best way to make it work for you.
Problem #1: Whoâ€™s running your LCA and how are they doing it?
The title of this piece references a magical, mysterious â€śblack-box,â€ť because many LCAs today are shrouded in secrecy. And the secrecy is being defended as â€śintellectual propertyâ€ť or â€śproprietary dataâ€ť or â€śjust too complicatedâ€ť and â€śweâ€™re the experts.”
And while some of that may be true, how can LCA become common practice if the only way to do it is to hand over your private information to a magician, pay him (or her) a vast sum of money, and sit back and wait for him to reappear with spreadsheets and spreadsheets of data in teeny tiny fonts that only he understands? And when you pay him another vast sum of money, heâ€™ll give you his interpretations and recommendations about what he sees in all those numbers and youâ€™ll have to take his word concerning the data. And then, hopefully, you go away with a feeling of satisfaction that now you have run an LCA and have some insight too. (Of course, youâ€™ve also got all those spreadsheets that you donâ€™t really know what to do with but that you suspect could possibly have additional insights somewhere in them but unfortunately youâ€™ve run out of your budget and canâ€™t afford to pay another vast sum of money to get another reading!)
Problem #2 â€“ The data itself.
Building on Problem #1 is the common assumption that the data used in an LCA is precise and must be down to a bunch of decimal places and must be absolutely defendable in every instance. For the vast majority of LCAs, that may be the line you hear; but thatâ€™s not actually whatâ€™s going on behind the scenes. Imagine a wheat field on a hill. If youâ€™re measuring the water footprint of wheat, should you measure the water use at the top of the hill or at the bottom of the hill? What about in the shade of that tree? Or, what about in the last dry year? Or during the 100-year flood year? What about calculating productivity (tons per hectare)? Which is the key variable? This depends on the soil type, texture and moisture level. The reality is that productivity varies from plot-to-plot. Despite the scientific communityâ€™s efforts to relate LCA results and productivity, unfortunately there is no real correlation.
There are so many problems and angles that it would actually be scientifically more reasonable to take an average of water consumption of this type of wheat, in this type of climate, in perhaps the past 10, 20 or 30 years. Even though it may seem a little scary and difficult to defend, the average water consumption would actually provide more accuracy than the actual measurement of water usage in that field today. Â And if we expand that example to carbon, do you really need to know the exact carbon emissions of a truck in Argentina versus the same type of truck used in Spain? Wouldnâ€™t it be more cost and time effective to use average measurements from readily available data?
The key to a defendable and useful LCA is transparency. Transparency of methodology, transparency of data sources, and transparency of assumptions. Without transparency, the results mean very little. Recently I went through a bunch of retailersâ€™ websites and pulled their published carbon footprints â€“ from Japan, to France, to the UK, and beyond. Thereâ€™s actually quite a lot out there and definitely a lot of investment behind these numbers, but there is absolutely no transparency about how these numbers were calculated and what they actually mean. Unfortunately, despite the hype and PR, the numbers end up being fairly useless and undermine real measurement models that can inspire real, sustainable change in a world that really needs it.
So, do yourself a favor. Recognize that LCA is going to become a common business practice in the not too distant future. And demand transparency and full disclosure from your magicians and their assistants.
Sara Pax is the president of Bluehorse Associates, a developer of sustainability metrics specialized in the food and beverages industry with its smart product-level lifecycle assessment solution, Carbonostics (cost + carbon + nutrition). www.carbonostics.com
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