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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 it’s true – mostly – that LCAs can provide incredible and sometimes truly revolutionary insights into a company’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, shouldn’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 product’s lifecycle. However, you also need to know about LCA and the best way to make it work for you.

Problem #1: Who’s running your LCA and how are they doing it?

The title of this piece references a magical, mysterious “black-box,” because many LCAs today are shrouded in secrecy. And the secrecy is being defended as “intellectual property” or “proprietary data” or “just too complicated” and “we’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, he’ll give you his interpretations and recommendations about what he sees in all those numbers and you’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, you’ve also got all those spreadsheets that you don’t really know what to do with but that you suspect could possibly have additional insights somewhere in them but unfortunately you’ve run out of your budget and can’t afford to pay another vast sum of money to get another reading!)

Problem #2 – 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 that’s not actually what’s going on behind the scenes. Imagine a wheat field on a hill. If you’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 community’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.  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? Wouldn’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 retailers’ websites and pulled their published carbon footprints – from Japan, to France, to the UK, and beyond. There’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

3 thoughts on “Lifecycle Analysis: Moving from Black-box to Transparency

  1. dont you mean life cycle assessment? perhaps you are mixing that up with an application of LCA – LCIA (Life Cycle Impact Analysis). sorry about the semantics

  2. I agree that LCA has been a black box approach to environmental assessment until now. Fortunately, there is an emerging national ANSI LCA standard that is intended to provide complete transparency to the LCA profiling of products and service with growing NGO support. This development is intended to answer the major criticisms that the author points out. It will not take an expert to understand the information provided by standard.

  3. I agree with Sara about life cycle inventory data and life cycle assessments needing to be transparent. This is why we always encourage our clients to submit their LCI data to publicly available data sets that are peer reviewed, documented and transparent, such as ecoinvent or the US LCI databases. For LCA to become more cost effective and efficient manufacturers and industry associations need to support these organizations so we have a large body of transparent data for use in our LCA’s. The more publicly available data we have the easier it will be to perform low cost screening LCA’s that inform design decisions, material selections and process improvements before they are implemented which I believe is where the power of an LCA is truly meaningful and effective.

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