How To Get Better At Life Cycle Assessments (Seriously)
Knowledge is power, and any company pursuing more sustainable business practices needs to know the impact of its products at every stage—from raw materials to reuse, recycling or disposal.
This article was originally published in Triple Pundit on December 12, 2019. We think it's so good we are republishing it, here!
Knowledge is power, and any company pursuing more sustainable business practices needs to know the impact of its products at every stage—from raw materials to reuse, recycling or disposal. Fortunately, there is a proven, powerful technique for the gathering and presentation of impact information: the life cycle assessment (LCA).
Like a carbon footprint report on steroids, LCAs include data on any environmental footprint you can imagine—water, deforestation, pollution, you name it. By identifying hotspots in the supply chain, LCAs point out where environmental impacts are high. And counter to popular belief, most of these hotspots are also cost drivers—meaning steps to reduce impact are also steps to reduce costs.
Enterprises of all sizes can leverage this cradle-to-grave accounting to weigh the benefits and trade-offs of alternative materials, design for sustainability, modify production processes, and make other improvements in their supply chain and operations.
Like any research-oriented process, LCAs require care and attention to uncover previously hidden data from sources inside and outside your company. Even if you've done a life cycle assessment before, there's always room for improvement. These seven factors—compiled after almost two decades of conducting this type of analysis for hundreds of organizations—can help make your next LCA as efficient, effective and, above all, as easy as possible.
Save time and effort by identifying the questions you really need to answer in your life cycle assessment.
When you undertake a life cycle assessment, you’ll analyze one or more supply chains—and each can be incredibly complex. Narrowing your scope to focus on specific questions allows you to identify which aspects of the life cycle need high-accuracy modeling and which can be assessed in more general terms. This saves a great deal of time and may make the difference between completing a study or not
Make interactions easier with a customized data collection workbook.
Most LCA data comes from colleagues whose primary expertise centers on things other than the life cycle assessment. As designers, engineers, distributors or managers, they typically don’t think like LCA practitioners. Everyone will have their own metrics, and their functional units are likely to be things like “minutes [or months] of operation,” “measurement above or below threshold,” or “number of workers required.”
Projects go much more smoothly when data collection workbooks reflect these differing perspectives. It simplifies the work and doesn’t force people to learn a new vocabulary. In many cases, you can speed things up even more by using a flow diagram rather than a workbook. These approaches improve the chances of getting the right data in a reasonable time frame—and the chances of winning colleagues’ cooperation next time.
Document what you’re modeling.
It’s all too easy, in the heat of a project, to rely on your memory and skip the sometimes-tedious process of recording how you arrived at a value or data choice. But this is inherently self-limiting.
You may know exactly what you did today, and why, but what about your co-worker who comes to help out next week? Next year, when you have to update the study, will you remember all the nuances? Clear documentation is an investment of time that pays off as an accelerator in the long run, boosting the chances for effective collaboration and long-term value from your study.
Keep (and share) a log of your assumptions.
A list of your assumptions provides a quick way to convey your starting point to colleagues, partners and decision-makers, among other groups. Looping them in can help them spot inconsistencies or confirm that you’re on the right track. It’s also a way of tapping into their knowledge of areas that could change. Early feedback ensures a solid study and reduces the risk of late-in-the-game surprises on all sides.
Be consistent in your data and methods.
We all strive to meet high standards, but there is no perfect life cycle inventory (LCI) data, no perfect LCA tool, no perfect allocation method, and no perfect impact-assessment method. To get maximum insight, avoid mixing and matching any of these, because you won’t know if the impact differences you’re reporting are due to a true difference in the life cycle or to flawed interactions.
Moreover, using a consistent set of data and methods over time makes you a more effective analyst by giving you a deep grasp of the set's strengths and weaknesses, as well as the types of trends you can expect to see with different types of projects.
Be open to different perspectives.
Yes, I just said you should be consistent. But sometimes different data, tools and methods can provide different insights—and new learning. If you’re feeling stuck or uncertain, don’t be afraid to test a different assessment method or look at different inventory. If you don’t learn anything, leave it out of your report. But if you do, the insights you get could make a real difference. Just be sure to apply the method consistently and document your process, as noted above.
Don’t work in a silo.
LCA work requires intensive research and diligent attention to detail. As a result, many LCA practitioners are most at ease when working alone. It’s almost a necessary personality trait. But mistakes are easy to make when you’re handling the quantities of data needed for even a simple study. It’s also easy to get deep into the weeds and miss the big picture.
Protect yourself and the overall effort by getting someone to check your data entry. Share your impact assessment with a big-picture thinker to get a different perspective. And if parts of your study involve technologies that are foreign to you, seek out an expert who can answer questions, even basic ones. You will almost certainly save time and reduce the risk of everyone’s worst-case situation: that your study doesn’t really show what you think it does.