New Advances in LCA Provide Quicker, More Robust Results—Learn More!

As Life Cycle Assessment (LCA) has been increasingly adopted as an analytical tool by corporations, organizations, and public agencies, there’s been a parallel rise in research into how LCA is done and how its utility for sustainability assessment and decision-making can be improved.

This has resulted in a number of advancements that support LCA practitioners’ ability to develop models and uncover insights more quickly, while also providing greater confidence in their results. At EarthShift Global, we strive to maximize the value of the LCAs we provide by leveraging these new techniques for our clients.

Here’s a brief review of five advanced LCA techniques we’ve found especially useful: the Application of Uncertainty, Underspecification, Anticipatory LCA, Multi-Criteria Decision Analysis, and Organizational LCA (O-LCA).

Application of Uncertainty

It might seem odd to say that more uncertainty can lead to greater confidence, but by applying appropriate uncertainty in many places across our models, we can better ascertain the robustness of a particular result. Uncertainty provides the bounds of a particular result and these bounds are what determine the confidence interval. In other words, without acknowledgement of uncertainty, there is no certainty.

To this end, we have started adding a “fit for purpose” pedigree matrix which addresses how well secondary data matches the supply chain we are attempting to model. This approach builds on the pedigree matrix work developed by Weidema and Wesnaes (Weidema & Wesnaes, 1996), which applies a pedigree matrix to the amount of resource used or emitted from a transformative process.

In some cases, we have also included uncertainty in the characterization factors to better contextualize the results. By including these in our evaluation, we can determine where one product can be more confidently said to have lower impacts and where it is indistinguishable from its competitor. We can also apply more advanced decision-support tools, such as SMAA (see below) once we quantify uncertainty of midpoint performances. The EarthShift Global team applies uncertainty in most of its projects, because it provides our clients with more certainty in their results and claims and lowers the risk of over claiming a benefit. 

Underspecification

What if you don’t know the origins of the soybeans you’re using in your product? Or what if you haven’t decided what plastic you want to use in your new widget? This is where Underspecification, a method developed by Elsa Olivetti and her team at MIT, can help accelerate and clarify the LCA process.

Underspecification allows us to stochastically choose between options to:

1. Minimize delays in an LCA project due to data collection – we can go ahead and run possible options right away
2. Target data-refinement efforts, and help quickly identify the aspects that make a difference in the model
3. Acknowledge the role of variability of a particular parameter (like testing a range of sources of soybean) in the outcome of results
4. Understand how the uncertainty affects the confidence level of a decision
5. Identify the uncertainty involved in the choice, and,
6. Support early technology and product development efforts to integrate environmental metrics with other metrics in the decision-making process.

EarthShift Global applies different levels of Underspecification as appropriate to a project; we find that it moves screening studies along faster and reduces client data collection burdens. This minimizes the number of times a client struggles to find that last piece of data, only to find out that it is statistically insignificant.

Anticipatory LCA

Anticipatory LCA offers the opportunity to use environmental impacts as design criteria as early as the ideation phase (Wender et al 2014). This provides insight that can guide the product development phases by drawing on stakeholder value assessment, underspecification, stochastic characterization factors, and decision analytic tools, based on interim data.

Applying LCA at this early stage of technology development offers greater leverage to reduce impacts and do so at the lowest cost. We’ve found that applying the Anticipatory approach in our consulting practice has resulted in more-efficient R & D efforts, lower-impact products, and even new business models.

Multi-Criteria Decision Analysis

One of the basic points of comparative LCAs is to help business organizations reconcile tradeoffs and make informed decisions. But very often, the sets of tradeoffs involved can create an “it depends” situation that makes it more difficult to produce actionable insight.

In our consulting practice we have a number of ways to address situations where our clients are trying to reconcile tradeoffs in a comparative LCA. The first is to move from assessing at the characterization level to the damage level. Often, we see that damage to human health from ozone depletion, for example, is small compared to the damage from particulates. By allowing groups of metrics to be compared using the same units, we can often reduce or even eliminate the tradeoffs.

When this doesn’t work, we turn to traditional multi-criteria decision analysis techniques, and also to novel methods. One of the latter, Stochastic Multi Attribute Analysis (SMAA), has been extensively discussed in publications, including work led by our senior analyst, Valentina Prado. SMAA allows us to rank alternatives based on the uncertainty at midpoints. We find that it helps our clients make the most of their LCAs, navigate tradeoffs, and find the most preferable compromises. 

Organizational LCA (O-LCA)

Organizations interested in creating company-wide strategies for environmental sustainability can benefit greatly from Organizational LCA (O-LCA). Similar in principle to a product LCA, O-LCA scales the system boundaries and functional units to the whole organization to identify company-level hotspots.

This approach allows identification of aspects that are contributing the most to the organization’s environmental impact. For example, O-LCA can help understand which products are creating the most impact, the role of overhead, which facilities have better results and how other facilities can learn and adapt from that knowledge. An O-LCA analysis of our consulting business highlighted the huge impact of employee travel, encouraging us to explore more virtual methods for conducting our work.

Having this information is useful in drafting sustainability strategies and identifying the most effective ways to reduce environmental impact, while also avoiding wasteful use of human and other resources on ineffective actions. Just as organization-level financial reports allow monitoring of a company’s economic performance, O-LCA provides managers with a strategic view of environmental impacts.

If you have questions about any of these techniques or their applicability for your situation, please get in touch with us. We’re glad to discuss how they can support your organization’s ability to make impactful, fact-based decisions with greater confidence.