In this episode of the Decarbonizing Commerce Podcast, host Keith Anderson sits down with David Bryngelsson, CEO and founder of CarbonCloud. With a background in climate science, David shares his journey from academia to founding CarbonCloud, a climate intelligence platform for the food industry. They delve into the challenges and opportunities of product-level climate footprinting and labeling, exploring why it's gaining traction and how it can drive sustainability in the food value chain. Tune in to discover how technology is revolutionizing climate transparency in the food sector and why it's becoming a crucial consideration for retailers and brands.

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TRANSCRIPT BELOW:
Keith Anderson: Welcome to Decarbonizing Commerce, where we explore what's new, interesting, and actionable at the intersection of climate innovation and commerce. I'm your host, Keith Anderson, and together we'll meet entrepreneurs and innovators reinventing retail, e commerce, and consumer products through the lenses of low carbon and commercial viability.

Keith Anderson: Hello, this is the Decarbonizing Commerce Podcast. I'm your host, Keith Anderson. Today's guest is David Bryngelsson, PhD. David is CEO and founder of CarbonCloud, a climate intelligence platform for the food industry. Essentially, they help anyone across the food value chain calculate and reduce the climate footprint of food products.

Previously, David was a climate scientist. He spent eight years immersed in climate change mitigation. As a published researcher and lecturer at Chalmers University of Technology before he made a career pivot and started CarbonCloud. We cover a lot of ground in this conversation. It's a topic I've been following for, around a year and was really eager to learn more about.

 We talk about why product level climate footprinting and labeling has historically been so challenging and why new technology makes it a lot more achievable. We talk about why comparatively few retailers and brands calculate product level footprints and label their products with climate footprints today, but why that's likely to change in the years to come.

And we unpack the business case and who typically has a seat at the table when retailers and brands are deciding and deploying, this type of technology. I really learned a ton, and I think you will too. So let's meet David Bryngelsson, CEO and founder of CarbonCloud.

Hi, David. Thanks so much for joining me. Good to see you.

David Bryngelsson: Good to see you. Thank you for having me.

Keith Anderson: Well, I always love starting these discussions with a little bit of background on you and how you came to do what you're doing now. And, as I said to you a bit earlier, I find it super fascinating when somebody who comes from a, deeply technical or scientific background, sees opportunity to have an impact in industry.

So maybe we could start there. Why don't you tell us a bit about, who you are and how you came to start CarbonCloud?

David Bryngelsson: All right, let's do that. That goes actually a pretty long way back, but yeah, so now we are a source company. I've been running this for a bit over four years. And we're now going to a growth phase. And before that I was in academia for, roughly eight years doing climate change mitigation research.

 And that is maybe an unusual trajectory, but sort of how did this happen? So let's go back a little bit further. I decided when I was like 14 or something like that. So first half of the nineties that I wanted to dedicate my life to fighting climate change and doing it as effectively as possible. And then I thought about like, how do I make the best impact?

And, As I was doing studies at university, I was doing physics and I was like thinking about what to do. There was some, a division there that was doing some teaching where they had some professors that were very influential, both in sort of public outreach and to prime ministers, you know, IPCC lead authors, stuff like that.

And I thought, well, that, that is definitely a way of making an impact and some really cool people. So I joined that research division and I did research for a while before I realized that for most verticals, like energy, transport, the change was already happening, like the cutting edge research wasn't needed, but more like it was already in the commercialization phase.

Whereas at the same time we had the food industry, which at the time was responsible for about a quarter of the problem and there was really nothing happening. So, I focused increasingly on that from a research point of view. And there also it became very clear to me that we know enough from the point of view of research to solve the problem and like from the, to get the measurement going and get the engineers working on finding the solutions.

But the industry was entirely lacking. tools and methods for, for working with it, to be data driven and to, to act on the business opportunity that I saw was there. And the business opportunity being the food that, the world has decided to solve climate change. The policies are coming. We can be very certain that people will eat also in the future.

We want food. And then if you're as a food producer, figure out a way to produce good food that people want without emissions, people will want to buy it. Basically, same thing as Tesla did for transport. And no one was doing that. And I was thinking why? Well, fundamentally, it had to do with a measurement problem.

So if you're in a food producer, you normally don't know what their emissions are. So to be able to go after a business opportunity like that, you need to have a strategy for how to decarbonize your production and still deliver good products. But as the food system looks, which is a very large network of long supply chains where they don't know a lot about each other.

No, no one really knew how to go about it or very few companies knew how to go about it. But I figured that I think I know how to solve that. So, I switched careers, left academia to fund this company to solve that problem, to develop a platform, a tool to enable, to put really, really good quality data in an understandable way, in the hands of the people who need the data to make this transition happen. And these people are then from everywhere in the supply chain, from the grocer or wholesaler, to the producer, to the ingredient supplier, to the farmer. And everyone will need to collaborate to collect data, to make innovations, to do changes in the production system to solve the problem.

And, and that's what we are working hard on now. So I ended up as like the reason for me to becoming then the software founder and building this company is that I see this as a very, very good tool to build a scalable solution to make this impact, to sort of take the learnings and insights that I've gotten and refine it and really sort of put it in the hands of the people who need it. And also it's awesome fun.

Keith Anderson: I'm sure it is. So just briefly from your academic background, you mentioned when you got into this area, food represented around a quarter of the problem. Can you just expand on that for folks that may not understand, why food is such a big source of emissions?

David Bryngelsson: Yeah, sure. All right. So, looking at climate change in total, I mean, you have emissions from many different sources where fossil fuels have been and still is the major part of it. Where you burn fossil fuels, you can get emissions. And that is a part of the problem that needs solving. But it's really not the only part of the problem.

You have other sectors as well, where food is a very large one, where, you have emissions from biological processes in soils, biological processes in livestock, in animals when they're eating, and then you also have energy used, in farming, you have energy in tractors, and then you have in sort of logistic systems and refinery processes.

But for food, it's primarily the vast majority of the emissions happen at the agricultural stage. Where some of the processes on the agricultural states are very easy to understand, like deforestation. You cut down trees, trees are made out of carbohydrates. You burn them or they rot or whatever. The carbon in the trees ends up in the atmosphere.

That is one large driver of emissions in agriculture, but then you have these nitrification processes in soil where bacteria, and stuff like have this, like, I don't know, biological processes where there are chemicals produced and you have nitrous oxide and methane and other very potent greenhouse gases getting emitted. And, yeah, globally, that is a big part of, big part of the problem. And also this goes back to when I talked about earlier about the measurement part of the food system, one that's tricky, like the, how to keep track of the emissions of food is much more difficult than other verticals. Because if you take a power plant to make electricity, you burn coal.

or natural gas, you get electricity out, and you get emissions. And the amount of emission is always proportional to the amount of coal you burn. So you burn a pound of coal, you know how much carbon dioxide you'd get. Same if you drive a car and you burn a liter of petrol, or a gallon of petrol or whatever, you always know how much carbon dioxide you get.

Whereas in food, you have the emissions happening somewhere else in the supply chain and also most of the emissions, as I mentioned, happen in the agricultural phase, so for most food producers and definitely for retailers, very far away and also very difficult to measure. So these microbiological processes are very, very tricky to measure because the flux, the amount of gas released from the field is low, but in aggregate, it's very large.

Keith Anderson: Well, and, and you've sort of anticipated and started to answer one of my big questions, which is, there's been a ton of venture capital invested in, I think, industry agnostic, sort of broad based carbon accounting and management platforms. Why does the food industry require something tailor made?

 It sounds like part of the, the question is, or part of the challenge is the complexity of measuring it. Are there other drivers?

David Bryngelsson: Yeah, yeah, I would say definitely are, and, and this goes back to which problem are you trying to solve? So if the problem you're trying to solve is figuring out roughly where are we at? So you have an operations, you want to figure out, so how much of the emissions come from our spend on food? How much come up on our spend on sort of housing properties, vehicles, whatever, the personnel then doing this industry agnostic where you have spend based data, rough estimate, it's, it's good enough. And, and for a lot of the reporting needs, it's, it's good enough, you're fine. But if you want to get to a solution of the problem, you go from, all right, so you figure out that, say you're a grocer, and you realize that 95 percent of our emissions come from the food that we sell.

And then like, all right, so what are we going to do about it? When you get to that, what are we going to do about it? You need to dig into the detail, because the details matter. And then suddenly knowing that a dollar spent on food causes roughly this amount of emission is not enough and also knowing that if it's spent on meat it's roughly this.

It's also not enough. You need to be able to differentiate between all your different options and this goes down to so as a grocer, if you're going to procure something to keep in, like, in your store, you need to know what's the difference between different suppliers and different options. Because if you were to say, stop selling meat, your business will probably not go very well.

So you need to figure out, you need to maintain sort of selling what people want. And this goes back to the business opportunity. People still want food, they want good food, but they will increasingly be prepared to do something to get the missions done, maybe pay a little bit of premium or whatever. And then you need to identify where that is.

And then you need to go down to the details.

What's the difference in getting an orange from Mexico, Florida, or Brazil? What's the difference, right? First on roughly level, and then eventually get down to, so you're getting your oranges from Mexico, and then someone supplying those realize that you want to pay premium for lower emissions, then they need to figure out what's, what's the out of our thousand farmers, say, that we procure from, how do they fare? Which ones are better? Which ones are worse? And then eventually figuring out how can we get the poorly performing ones to perform better? And that is definitely in the detail on how the nitty gritty in agriculture happens. And so the solution needs to be able to capture the data for how the production systems look like in the detail, to be able to tell the story, to be able to track it, to be able to track it over time and differentiate, and then create the visibility of the better performance towards those who care.

Because even say that you are in a country very far away where there is no climate policy whatsoever, but your market is the U. S. or the market, your market is Europe, where there is climate policy and there will be more policy coming, then if you can prove that your emissions are lower, you get access to that market and you get paid premium.

So then it becomes relevant to perform. But then you need To be able to measure on a very large scale in a very consistent manner, all the different producers to be able to tell that story. And there it needs to be very specific.

Keith Anderson: So I've got a good sense now for why food is so, different and, and more challenging than broad-based. You know, let's use averages to estimate based on spend. Let's unpack a little bit, what exactly is the data that you're able to provide, you know, at what level of granularity, where does it come from?

 And, and we can move from there, I think, too. So who's using it and how are they using it?

David Bryngelsson: Back to the measurement problem, right? So we say, we have agreed, our governments have agreed that by mid century. We are going to get down to zero emissions. So, at least not every country, but in the U. S. and Europe and some other places, we've agreed to this. So this is 26 years back, away.

Sorry. So in 26 years, we're going to get down to zero. And, the amount of data, like if you take a fairly complex product like, like this one, a soft drink here, with a lot of ingredients in it and, and each and every ingredient, like first, there's a lot of parameters to calculate the footprint. You need to know what the ingredients are, how much there are, where they're from, how it's produced, and so on and so forth.

And then for each ingredient, there is a value chain with several steps. And there are many, so it branches out. So for a sort of complex product, there might be something like 10, 000 parameters to calculate the footprint. And there will eventually we will need to collect all that data and not the data we're talking about production data.

So like yield levels, solid type levels or vehicle types, load factor types for transport, fuel types and so on. So a lot of different parameters need to be collected and then we need to make investments and innovations to sort of change how we operate to get the emissions down. So the totality of data that we'll need to track will be very similar to the amount of data we have in the financial accounting system. That's the scale of this measurement thing that we'll need to do and we'll need to build this up quickly. And compared to financial data, we've had something like 700 years to build that up. Right?

Keith Anderson: We don't have that much time, do we?

David Bryngelsson: We don't because we need to act on the data. So we need to build it up and we need to act on it to get it down in 26 years.

That is tricky. Fortunately for us, now we have built a lot of very good software tools that enables us to scale things extremely rapidly. But also there needs to be a bit of a pragmatism because if you take a food brand like this one that I showed, Tenzing here, that, they realize this, they want to act as your business opportunity and profiling themselves as we are working on climate change. They need to be able to start making decisions and use this before every single producer in along the value chain, including every single farmer has collected all the thousands of watts of data, because that would take forever and there wouldn't be an incentive really for compliance for most of them.

So, what we have done to solve this is that we have flipped the calculation process around by look, instead of collecting all the data and then making the calculation, we look for, we're basically built a framework. What are the equations that govern emissions in the food value system, in agriculture and everything after?

And then look for, what do we know about it? What has someone measured and collected somewhere? And there's a lot. And then we built this benchmark data set for every single parameter. So if you know that like you get a tea from Kenya that goes into this one, we have a pretty good idea how tea is produced in Kenya and how it gets here and what types of vehicles and so on and so forth for the entirety.

So we know that without knowing the specifics, we can actually tell a lot, which means that we can enable someone like that to, by only letting us know what goes into the product. More or less and where it's from tell with extremely high precision the performance of the product so that they can use that in their go to market and also in their future sort of thinking about how to develop the product. So long winded answer, but, but like you can start with very little data and through this pretty cool tech, we can enable you to say a lot. And then go from there, incentivize by sort of telling the supplier, hey, we're now quantifying our climate performance. We'd like buying from you. If you're going to go forward, we'd like you to collaborate.

We've already calculated your product. Could you verify it? And they just have to answer some very, very easy to answer questions and they're on board. And then they get access to the market and so on. And eventually, they collect a lot of data. But by the time they do that, they will see the value in it, because it's to prove performance that someone else values financially.

Keith Anderson: So there's sort of an assumption that over time, some of the sources and methods are going to evolve as more data is available and can be included in the model. But you're taking a really practical approach now, given the urgency to provide the best available data to make better decisions today.

David Bryngelsson: Exactly. So, I'd say, like, where will, Yes, it's improving. We are, like, our goal is to say At any given state of information that you have about your production processes or your assortment of products you're selling or whatever, we'll be able to give you the best possible quality of climate performance data for that. And that is typically good enough for you to not draw the wrong conclusions and to know how to navigate.

Keith Anderson: I know, I was digging into this general topic of, product level, climate footprinting and labeling. And, I didn't find a ton of examples of retailers that were doing it at scale. One of the interesting examples was Tesco, maybe 12 years ago had made an announcement that they were going to, label essentially the full range.

And, as I think we all know from walking their aisles, they didn't get there and it was largely due to the complexity and, cost of calculating product level emissions at that point in time, and even today, you know, Walmart was recently at the Goldman Sachs Global Sustainability Conference, and given the size of their assortment, was wringing their hands a little bit about this product level measurement challenge.

 So, I'd love to hear how the technology is really being used. Practically changing what's possible, and maybe we can help people understand, you know, what that cycle of input and output looks like in terms of, here's how much effort goes into, providing your current assortment, and here's the kind of questions you have to answer, and here's how long it takes to get out what you need to make better choices.

David Bryngelsson: Yeah. Yeah. So the Tesco example, I think is a good example of great ambition, but prematurely from a technology point of view attempt, right? So the years to be when you did product specific calculations, and for the most part, this is still how it's done. It's a very manual process where you have to redesign and figure out how to do it every time.

And there is a lot of back to the data problem, right? So you want to calculate the product, you have access to some data about the product, but most of the data you don't. And then if you're going to do the calculation, say you know you're getting tea from Kenya or you're getting oranges from Mexico or something like that into your product, and then you need to think about, all right, so how are they transported?

How do I calculate the transport? How do I, like a lot of different methodological things that need to be figured out. And then for all the stuff that you don't have data, where am I, where am I going to find that? Which makes it an excruciatingly sort of time consuming project to do for a product. And you can do like, and most of this has historically been done in Excel, basically, and it doesn't scale to, to the scale it needs to do, to do for a grocer or a retailer or whatever.

 And this is fundamentally one of the problems we have solved. So two of the problems we have solved. One is the automaticity of it. So that. Almost every step on this calculation is automatic and the data collection and the methodological choices have already been made. So there's nothing of that.

So we can do, and we are doing, full assortment for grocers. Like we did recently now we're doing with, Miniago, which is one of the biggest wholesalers here in the Nordics, a part of the Cisco group, where we've done 23, 000 SKUs, I believe it is. That soon will be made available to the customers on the website for every single one.

That's like where we can, by making this automatic, we can calculate this on a very large scale. And, we are about to do similar things for a lot of other large retailers, out there. And, and the second part, which is crucially important here, is the collaborative part of it. Where, as I mentioned, I think I mentioned the sort of network aspect of it.

Like the problem is a network problem. You need to collaborate with your supplier to know anything about your product. You can't tell the footprint of you, your product without knowing something about your supplier and they normally need to know something about their suppliers. So we built the platform like that so that we can model all these different steps.

And so that also we can get collaboration through the network. And that means that when we first, we pre calculate every product at the grocer or retailer wholesaler first. And then we can reach out to all the different suppliers and say, hey, so we did a life cycle assessment fundamentally of your product, the footprint calculation of your product.

Can you go in and take ownership of that, please? Verify the assumptions and then they can open up the box and they can look at what did the platform assume regarding ingredient contents, where it was produced, how it was shipped around and so on and so forth. So there are a lot of different parameters, but they need to first answer some very basic ones on the product like the bill of material, what goes in for making this? Where is it produced? Where do I get this stuff from? And that increases the level of quality tremendously. Then there are a lot of nitty gritty details you can also dive into over time if you want, but normally the predictive model gets those very close to the reality. And, and this process makes it possible to do, like, even if you are a producer, like on a scale, if you have a thousand products in your portfolio, that's easily done quickly.

So that you are then able to show whatever investments, whatever innovations, whatever good stuff you have been doing historically and are doing going forward becomes visible through your sales channel to your customer, which makes sort of one, the process is easy and quick and you can keep it up to date.

And secondly, it's visible to those who care. And yeah, this 12 years ago because we, of course, building this rely on a lot of tech that has been developed since, but now it is.

Keith Anderson: Yeah. And I keep coming back in this area to sort of two related, but distinct, rationales for doing it. You know, one is you're a retailer or a brand, I suppose, that's made a net zero commitment. Maybe you expanded from scopes one and two, and you're now doing scope three. And for all the reasons that you've articulated, that means you've got to understand all the inputs upstream.

You know, I see some retailers beginning to do these product level footprinting exercises, really for a couple, reasons, some of which are customer facing, but some of which are simply, hey, we need to understand and, both at a granular level and even, a category or department level, where are our emissions, coming from so that we can start to, score suppliers in the products they're offering us, through this lens when we're making buying decisions, full stop.

Then I think to your point, there's the, and so that alone may be influential to you. Regulators, investors, there's all kinds of stakeholders independent of the shopper that might care that you're engaging your supplier base and trying to reduce scope through emissions. Then there's the, again, related, value of communicating to your shopper.

How do you think about those, discreetly? Is, is one of those the clear winner in terms of where the demanded value is?

David Bryngelsson: Who's the clear winner? I don't know, but I mean, these things are very much connected in a really interesting way. Like, the policy makers will not introduce policies, effective policies, if they think that the industry will be fiercely opposed to them. That is impossible, because that, that politician will, like, their career will end.

So that doesn't work. But, and here, but here there's a nice cycle. I mean, eventually we can expect there to be policy, and the reason why I'm confident for that is that there is a business case to be made for the retailer, there's a business case to be made for the producer here. So there is, like, as long as the rules of the game are clear, there is like, you can play the game, you can win the games, you can earn more money, grow your business in an increasing policy environment.

And the problem, fundamentally, it's solvable. And here, the communication to the end customer is important because there is already a customer demand for doing more. There is a large cohort of people out there who are willing to pay premium for better performance; you see that. Why are people buying organic? Because they want to do something. Why are people buying the products that are labeled, like the one I showed here? They have Tenzing, they put climate labels or carbon labels on the pack as a way of differentiating, showing we are doing something. And that works. Like, the retailers like it, we have several customer examples where they have gotten climate labels and thus gotten shelf space at retailers, helped them get enlisted, getting shelf space, which is great.

Why? Because the retailers realize that we need to start doing something, like there is both the reporting requirements that are coming, where they need to report emissions, both scope 3, and the scope 3 is tricky for them. But then, even more importantly, like Back to then, again, the climate targets, so by 2050 we need to get to zero, which means that the retailers need to sell stuff without emissions, if they want to stay in business.

And then they need to figure out how to solve that. And the retailers are interested because it's not really their problem to solve, because they don't have the emissions that will make the production, but they can incentivize efforts. So by saying, hey, if you're transparent, we will sort of give you something.

We will highlight your product one way or another. And then there is a game that can start by, all right, yeah, sure. We're part of the transparency game. We show our emissions and we get the shelves space. When you're a competitor also, you need to get lower, but then very, very important, like lower. And then presumably you'll sort of gain the customer by, by being lower.

But very importantly in that is. That climate performance is not everything, like taste is king, you never get around that. So if you have a product they haven't tried before, your emissions are lower than the competing one next to it, you get the shot. But if they don't like the product, they're not going to keep on buying it anyway, right?

So, so that, that is, that needs to be in there, but you can also factor in the climate performance. and then once more and more food brands or producers start doing that, then of course from the policy point of view, it's quite safe for a politician to say, now transparency becomes mandatory, or else they see that they're competing on performance, they can implement whichever quantitative sort of policy you want, if you want it to be tax, a cap and trade system, a mission standards, whatever. I mean, there's a handful of different policies that work and as long then, if you are a producer, as long as you're better than the next guy, that policy will only make you get more revenue or profits.

Keith Anderson: Hey folks, this is the part of the show where we say thank you and see you soon to the general audience, plus and higher tier members of Decarbonize.co, stay tuned for the rest of the episode. 

Keith Anderson: Thanks for listening. I'm Keith Anderson, the executive producer and host of Decarbonizing Commerce. Sonic Futures handles audio, music, and video production. If you enjoyed the show, we'd really appreciate it if you took a moment to subscribe and leave a review or share it with a colleague. For the full episode and more member exclusive insight and analysis, join the Decarbonizing Commerce community at Decarbonize.co. Thanks for listening and we'll see you on the next episode of Decarbonizing Commerce.