Is there an easier way to calculate product footprints?
I’ve dreamt often of a different way to calculate footprints, one where you would get results in seconds. 10x faster, 10x more powerful. That would make the results better, the world more transparent, and we would actually achieve several orders of magnitude more (and better) product footprints.
When measuring the environmental impact of products (or systems, more generally), Life Cycle Assessment (LCA) is the de-facto tool. LCA is a methodology to track a product or service throughout its life cycle (from materials extraction, through production and distribution, to use and finally disposal). In each part of it, we account for all exchanges with nature (emissions and waste), and use the best available science to calculate environmental impacts, e.g. global warming, resource depletion, damage to human health, etc.
It gives you a reliable way of understanding environmental impacts and why they happen.
But LCA has serious shortcomings: it’s time-consuming, unnecessarily complicated, and the results are as good as the data (which is often an approximation).
Data is the new gold… but it’s expensive to dig out!
In our time and age, data is everywhere. Yet still:
primary data (data that you measure directly) is difficult to gather;
products are often complex, and when modelling them it’s easy to leave out parts of the system... which makes primary data incomplete and more difficult to gather;
you rely often on secondary data or databases, rather than on your suppliers... because primary data is difficult to gather; and
it requires specialists and a considerable time before you get any result.
To be fair, most of those shortcomings are not problems in and of themselves. They are rather prices that you have to pay in order to get a good result. And the currency in which you pay those prices is time. It just takes a long time to do a proper LCA!
You pay the price of (slow) data silos The greatest price to pay is often that of getting information from other parties. And it’s a hefty price to pay, because you must constantly pretend to be somebody else. Let’s say for a moment that I’m a LCA consultant (internal or external, your pick).
When I include the impact of a material, I’m making a model of my suppliers’ processes. When I use an industrial machine, I’m also making a model that somebody else knows better. When I account for a service, I’m again measuring on behalf of somebody else. And I could go on and on.
But it doesn’t stop there; companies are not the only silos here. When I model the distribution of the product, I’m trying to make a model of a process that somebody in a different department is an expert on. Or when I model the manufacturing process. Or when I model the use, which my marketing or customer care knows more about. Or, in essence, when I model any other department than my own, possibly, maybe.
In most of those cases, somebody else has the answers. Often relatively easily: for them it’s in the foreground. But for you as the “LCA person”, getting all that data is time consuming. You pay the price of translations You might have felt a bit of disagreement with my statement above, that “somebody else has the answers”. And I agree with you, that statement leaves out the other half of the picture. The information that others have often doesn’t “fit” your LCA model, and needs to be worked on.
It takes some work to make the data fit. They need to “digest” the model. The often complex model. And not only that, they need to model their reality based on the assumptions of LCA. You could say that they need to “make it fit”. They work day-to-day with information that is a bit different than what you need. They can boil it down to what you need, but it takes a while for them.
They need to translate the information for you. And it doesn’t end there. Then you calculate your LCA, and… you need to do something with those results, right? So you have to translate again. You have to turn those results into something actionable for decision-makers. You need to educate the rest of the organization.
We need to aim for less (difficult footprints) All these prices are a challenge that LCA professionals (and the organizations using LCA) need to solve every time, while they slowly build competence in LCA. And we often accept these prices, and decide what (and how much) of LCA we “buy” based on that price.
I make the case that if we focus on reducing these costs (instead of just accepting them), we can make LCA 10x faster, 10x more powerful, 10x easier, 10x more expressive… let’s call that 10x LCA (let’s keep it there, I won’t multiply the factors). Less means more The first effect that I would expect to see is an increase in the total number of product footprints that companies have available. Right now, companies must decide what they measure through LCA. Many companies start their journey measuring their organizational footprint, and later move onto product LCA’s (to understand better the root cause of those impacts). And if you have many products, that jump may be a considerable one, so you often have to pick which products to focus on.
Making LCA 10x more efficient could mean, with a given budget, 10x more products being analyzed. Many companies are not more than 10x away from having footprint information about their whole product portfolio. And most would get there easily by transforming information that they already have.
But I’d argue further. When something costs 10x less, there are more cases in which the investment pays off. So initially the effect may be 10 times more products, but we would often end up with 100 times or more! Less means better But less barriers to calculating footprints also means better footprints. Most of the value we get from product footprints comes from comparing them. A LCA of a supplier is nice to have, a LCA of all my suppliers means better decisions. A LCA of one product is better product decisions, a LCA of all products means a better performing company. Data quantity has a compound effect on data quality.
With a comprehensive picture, you can also check for data gaps. Most product LCAs forget to allocate some of the departments in the organization (or make it unclear where the scope starts and ends in that regard). This often happens naturally out of the bottom-up approach of LCA. With a comprehensive picture instead, you can do LCA top-down and bottom-up. You can identify which parts of the organization have a bigger or smaller effect per product, which ones scale when you sell more, or align your business metrics with your environmental ones.
And finally, when calculating a product’s footprint is a breeze, you can demand it from basically anybody. It’s difficult to ask a supplier to spend a few weeks giving you information (at least in some sectors), but it’s much easier to ask them to spend a few hours. And that means that your information can also be up-to-date constantly.
We are in exponential times, let’s make that benefit the planet
Let’s do a thought experiment. Let’s imagine for a moment we make LCA 10x better. How would that change the effect of those LCAs?
The end effect of LCA is always for somebody to make better decisions. Typically those are inside the company (product design teams, heads of departments, etc.), but not always (e.g. in the case of customers). 10x better footprints would mean that, at the very least, they have 10x more information. They understand their products better, they understand their supply chains better. Not only the person in the organization who deals with it, but everybody who needs to.
But that’s not all. A critical part of sharing environmental information in the organization is to make everybody aware of what they can do. 10x footprints means that you can spend more time making sure people understand the results and their implications. Anybody in the organization, not just the intended decision-makers.
Having clear and transparent footprint information has a multiplicative effect. 10x footprints means thousands of better decisions on behalf of the organization.
And how about customers? Environmental claims are all over the place! And a good footprint calculation gives substance to those claims. But most of the time, the footprint is merely an approximation.
I dream of a world in which every product comes with a link to its footprint calculation. Where next to my price comparison, I have a footprint comparison (global warming, human health, biodiversity, resources, water… finding the best impact categories may be a topic for another discussion).
Imagine how powerful for the consumer! And imagine the market advantage for those who can prove how much better they are! When I started doing research in this topic, environmentally friendly products were niche. Now the market creates a strong demand for manufacturers.
And 10x better footprints means potentially millions of customers making better decisions.
Some people say that markets are a representation of our collective intelligence, especially when talking about financial markets. And in the investments field, environmental sustainability has become crucial. 10x footprints also play a role here.
It’s difficult to measure the impact of an investment on the environment. You’re predicting the future about a market, sometimes even about the products! And a full LCA would be too costly for you to ask every investment target.
But 10x footprints means you can have good data to make your investment decisions. What will the impact of this company be? How much better than the competition are they (in environmental terms)? When they grow, what is the net effect of that on the environment?
Making investments available to companies who can prove they are performing better can nudge the market in the right direction. Or rather push it!
10x footprints can create the right type of transparency. It can create the compound effect of billions, and eventually trillions of euros (or your favorite currency, I’m in Europe myself).
Combine it all, and you’ve got an economy saving the planet
You have the effect of thousands of employees, with millions of customers, and trillions of euros. 10x footprints paint a very bright picture indeed!