Addressing Data Uncertainty
As with many engineering tasks, quantifying embodied carbon involves working with uncertain data. With this comes a responsibility that is not currently addressed in common WBLCA tools such as Tally and Athena Impact Estimator for Buildings—to quantify predictions in our analysis as “uncertainty.” There’s no way for us to control everything so quantifying uncertainty allows us to highlight what we can control but still include what’s left when we report our data to maintain intellectual integrity. However, for many of us, this process is nothing new.
As structural engineers, we already quantify uncertainty in our codes and design with methods such as probabilistic design. This approach can and should be applied to reducing embodied carbon so we can make well-informed sustainable design decisions—just as we do with our structures. But even without these considerations in place, we can still operate effectively within this imprecise framework.
Before we can manage and reduce environmental impacts from building materials, we must be able to properly measure and analyze that data. In the case of embodied carbon, uncertainty in these measurements stems from a variety of sources: material volume assumptions, the usage of industry averages for EPDs, and different methodologies for developing impact factors, to name a few. However, by using a simplified approach and focusing on the largest sources of impact, we can validate the directional accuracy attained by our work.
Consider the usage of cement in concrete. Although there are broad assumptions behind the concrete mix data and impact values, we know cement is one of the largest sources of impact in a concrete building. If we focus on simple carbon reduction strategies such as using structural systems with less material or specifying concrete mixes with lower cement content, we can be more confident in our impact reductions.
WBLCA of a Concrete Structure
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This WBLCA of a concrete structure (see figure) was conducted using Tally and includes enclosure, superstructure, and foundation in the scope. By using consistent impact data from materials and transportation within Tally and only manipulating the cement content within the mix design, we focus on what we know to achieve reductions. If these reductions came from a variety of small changes to materials we are less familiar with, we would lose confidence in our results, regardless of the software’s output. Without impact data sets or software that incorporates uncertainty, we are restricted in how we can conduct a proper analysis. However, by focusing our efforts on changing the concrete mix design, we are prioritizing actual impact reduction, which is far more important than the exact value for total output.
Structural engineers employ methods like probabilistic analysis and design to ensure strength and serviceability requirements are met while still maintaining design efficiency. Although the severity of a collapsed building may be more intuitive than the negative effects of climate change, there is still an important opportunity for improving sustainable design practices. So how might we apply this same line of thinking to environmental impact data and reductions?
We must demand more statistically transparent impact data and software that is straightforward about uncertainty. It is paramount that we treat impact analysis with the same rigor we apply to structural design. While we have solutions that fit within the current framework, improvements to methodology are essential to achieving goals such as SE 2050, a charge to eliminate embodied carbon in all projects by 2050 proposed by the Carbon Leadership Forum.
We are tackling a diabolical problem in a compressed time frame. It is our responsibility as building design professionals to improve our practice by identifying shortcomings and developing progressive, forward-thinking ideals. Sustainability is not just about checking a box for a certification. It’s about being honest in our efforts and responding quickly to improve our methods as we continue to learn.