Q&A - Green Business Transformation And The Role Of LCA

Here we have collected the answers to some questions that were not all answered in our webinar on May 10th 2023 because of time constraints.

Q: There is a big demand of making LCA for materials or products but not enough LCA verifications in the industries. How can this be changed? What can be done to make it easier for consultants to not wait on verifications and continue making LCAs?

A: This is a fantastic question, because it has many layers. The first one is the concept of verification in LCA. Traditionally, LCA have undergone what's called a critical review, where some independent party reviews the study, methodological coherence, etc. It typically serves as a sanity/integrity check, rather than actually verifying the numbers. Attempts at actually verifying the numbers (e.g. contrasting those with accounting numbers, for example) are few and sparse.

Our vision in Earthster is that verification should be much more common and much less of a chore, so sharing a cycle with a third party (e.g. one who audits accounting) to verify the actual numbers becomes a low-effort possibility. And of course, critical reviews even more so, since the reviewer has access to the complete model and logic.

Another angle to it is the reason for the need for verification. Verifying assumptions and coherence in a critical review implies there is uncertainty in results. And there are more and more efforts to unify assumptions in a way that results would be consistent (EPDs, PEF, etc.).

The combination of both "fronts" means that we are advancing towards a world in which LCA data is more reliable model-wise, and more verifiable details-wise. Which leaves more time for LCA practitioners to actually answer the relevant questions. It's a very exciting time to be in LCA!

Q: Avoided products to the Technosphere is only allowed under a consequential LCA system, and not attributional, according to ISO. Wouldn't it be greenwashing to claim an avoided impact to the environment from an activity that is not creating changes in the global market (condition for consequential thinking?

A: There's an inaccuracy in the statement above. ISO presents a hierarchy of strategies to deal with co-product allocation, the first and preferable one being system expansion. This is typically implemented through avoided co-products (or rather, product substitutes). Far from greenwashing, ISO considers it the best strategy, and the one to follow if at all possible. Now comes the "catch": it isn't always, and you can't pick just anything. It's also one of the most difficult to justify in traditional co-product allocation.

Let's take an example in which it can be challenging: let's say we have a process that produces both human food and animal feed, and we want the impact of only the human food. The decision of which animal feed is avoided could have dramatic effects on the actual "numbers" of the impact. Of course, it would have much less of an impact on the conclusions, but we know how people like their "final numbers". For that reason, it is common to do sensitivity analysis of any of such type of difficult-to justify allocation challenges.

Now let's go to the example of the talk: a new business that displaces the equivalent business of that same company. There is no uncertainty on what is being displaced. There is no sensitivity analysis to be done, because there is nothing else that it could be displacing (I'm assuming, like in most products, that what we produce is not saturating the market). In such a scenario, there's no room for greenwashing. Not to mention that innovation strategies are not something companies tend to publish much about (which is the problem of greenwashing).

Q: Hi Daniel, a very interesting LCA application. I'm curious what are the LCI databases included in Earthster? If there's data that is not available in the databases, can we add manually?

A: Thank you! In Earthster you have full access to Ecoinvent, USEEIO, and some user data. If any data is missing, it is quite easy to generate or import manually, or with help of our customer support (for high-volume). In fact, we offer to our customers bulk-import of data (as long as it can be linked, of course), since typically, the more data, the better the experience. If you see a database missing, please do reach out, and let's make it happen!

Q: Hello Daniel, we can use this model for any case study we have and what are the limiteations of this model if any exist?

A: I'll give two answers, in an attempt to cover all the ground for this question.

Firstly, in abstract and theoretical terms, you can do a back-of-the-napkin calculation of any business model change (or of any business model). The limitations are those are analog to any early-stage modeling (e.g. economic business cases): you have high uncertainty. If two alternatives are 20% apart, you can consider them to be similar.

Secondly, in terms of what you can model with LCA itself (ranging from back-of-the-napkin to everything-included), you can really model anything. You can define inputs and outputs for any human activity or combination thereof (production, processes, services, changes, projects), and include there clearly displaced activities. The limitation is the trade-offs you end up doing: do you make a rougher model, or do you spend more time and get more precise results. Other disciplines have been taken by storm with agile practices (start with something, and progressively evolve as you gain understanding), and that has certainly served as inspiration for us at Earthster.

Q: Hi Arjan, could you please elaborate a bit how we can use LCA for setting science-based target based on SBTi's framework?

A: It mainly depends on what kind of targets a company wants to achieve. Basically there are 2 perspectives - one is the company's best estimate (a long-term perspective), and the second is NetZero. So you can have a company that is going for a long-term objective and not NetZero, or you can have a company aiming at NetZero.

Now in the science part of it is, of course anybody can say that they want a decrease by 10%, but to make it scientifically you need much more data. At the company I have worked at, we used LCA to find the data to substantiate our claims that we would decrease by a certain percentage of metric tons.

Science based targets initiative has accepted LCAs as a way to substantiate scientifically what you're doing. Interesting is that there is also a preconception about LCA, that it will stop you from innovation. Now with the SBT framework suddenly that's not our venue, but it is another very good reason to have LCA.

I think there's now 3000 companies worldwide that are publishing their targets in SBT so it becomes bigger. So what you see is an increase of management teams that want to be on SBTi. It is also beneficial for them because banks now are looking more and more for companies to be in SBTi. So it creates a demand within the company. Once "the boss" makes a decision that a company now will be committed to SBT, the question arises - how to do it? Luckily there are LCA teams who can help with this.

Q: How do you deal with uncertainties in a "back-of-a-napkin" model? Do you just create more scenarios? How robust will your outcome will be?

A: I think of model uncertainty in 3 different layers.

There's a first "invisible" layer of uncertainty, that comes from getting immediate feedback while modeling. If I make an estimate, and the impact is very low, I might try overshooting to a "maximum conceivable". If that's still very small in comparison with other impacts, I accept that such uncertainty will not affect my conclusions, leave a note, and move on.

There's a second visible layer of design uncertainty. Sometimes, when we make the model, some of the numbers we input are decisions that we haven't fully made. After all, we're modeling the future. Here it's important to "play" with the numbers, the same way one can play with a cash flow projection in a business case. And it's important to document this playing, so that it guides the decisions.

Finally, there's a third layer of model and background uncertainty. There are some processes where a small variation can have a considerable impact. Here, we have uncertainty because of our estimates, as well as because of the background data (picking a different supplier could have a dramatic effect). This is a conclusion of its own, but I tend to recommend to model enough scenarios to be confident, and compare them with each other as well as with the reference.

Of course, the beauty of starting with the back of the napkin is that... you can adjust that model as you learn, decide, and iterate. So the more you know, the more you can reduce the second and third types of uncertainty.

Q: LCA used in fundraising. More and more investors want to know where they get the biggest environmental bang for the buck. LCA, seen from our perspective, is a fundamental requirement for attracting green investments and lower cost of capital. Are any LCA 'dynamic' tools recognized and aligned with investment frameworks?

A: I certainly agree: if an investor would have a rough (or not) LCA of all investment cases, they would certainly maximize their positive impact on the planet. They could even measure their carbon return on investment (or ecosystem return on investment, or water return on investment, etc.). I think we're seeing a renaissance of LCA for this type of purpose. Unfortunately, there are also some hurdles to overcome. LCA has a legacy of being slow, data-hungry, prone to variability from assumptions, and requiring high expertise. All those aspects are changing, but change in human behavior (e.g. tools becoming "recognized") is often slower than in technology.

Q: How can I get in contact with the speakers?

A: You can for example connect with them on LinkedIn - Arjan Rensma and Daniel Collado-Ruiz

Q: How can my business get started with doing Environmental Life Cycle Assessment?

A: Hire a young Environmental talent from the Environmental Impacts Academy LCA internship program, or have one of your current employees attend it. More information is available here.