Climate Economics with Arvid Viaene

#1 Dr. Koen Deconinck - Measuring Farm Emissions: The Fast, the Furious, and the Fixable

• Arvid Viaene • Episode 1

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In this episode, I sit down with Dr. Koen Deconinck, economist and policy analyst at the OECD, to explore a deeply technical but increasingly important piece of the climate puzzle: how we measure greenhouse gas emissions in the agricultural sector.

Direct and indirect emissions in Agriculture are responsible for up to 30% of global man-made emissions, yet it's often left out of the climate conversation. Why? Because measuring these emissions accurately is really hard. Dr. Deconinck walks us through the fascinating and complex world of emissions data—from cows burping methane to fertilizer releasing nitrous oxide—and why product-level averages are not sufficient.

We dive into:

  • Why farm-level variation in emissions matters way more than most people realize
  • The challenges (and importance) of moving beyond averages to granular, farm-specific data
  • The rapid rise of tools, databases, and labeling schemes—and the chaos that comes with them
  • How programs like Origin Green in Ireland succeeded in gathering farm-level data at scale
  • The two biggest gaps: harmonizing reporting standards and improving farm-level tools
  • Why getting the measurement right is critical—not just for better policies, but also for businesses and consumers who want to make more sustainable choices

Koen also shares behind-the-scenes insights from his work on the OECD report “Measuring Carbon Footprints of Agri-Food Products: Eight Building Blocks” and why the pace of development in this field has become—yes—fast and furious.

🔗 Link to the report:
Measuring Carbon Footprints of Agri-Food Products

🔬 Also cited:
Poore & Nemecek (2018) – Reducing food’s environmental impacts through producers and consumers, Science, DOI: 10.1126/science.aaq0216

If you’re interested in climate policy, sustainable food systems, or just curious about what it really means when we talk about a product’s "carbon footprint"—this one’s for you.

Thanks for listening,
— Arvid

#agriculture #emissions #carbonfootprint #sustainability #foodsystems #OECD #KoenDeconinck #farmdata #reportingstandards #climatepolicy #greenwashing #OriginGreen #methane #FastAndFurious #climateeconomics

For questions, comments or suggestions, you can contact me at arvid.viaene.ce@gmail.com

Arvid Viaene: Dr. Koen Deconinck is an economist and policy analyst at the OECD, and is the lead author of the OECD report, Making Better Policies for Food Systems in 2021, and of the concentration in seed markets.

Before joining the OECD, you were a management consultant with Bain & Company, and you hold a PhD in economics from the University of Liven in Belgium. And you work on sustainable food systems, an important lever to meet the sustainable development goals, and a challenging topic given its connections with the many different policy areas. So, Koen, welcome to the podcast.

Koen Deconinck: Thanks. Thanks for having me.

Arvid Viaene: So, to give a bit of background for the listener, I saw a few posts on LinkedIn that you posted about your report that really intrigued me. And as a framework, I think about climate economics in three areas. The first is measuring emissions, which I think a lot of people often miss and how important that is or how difficult it is. Second, how much harm do those emissions cause? And then third, what are the cost-effective policies? Now, I have a lot of experience with the second and the third, but not a lot of knowledge on the topic of how we measure emissions. So you had some posts on LinkedIn of basically measuring emissions in the agricultural sector. So would you maybe want to give an overview of what you did in the report and what drew you to the question?

Agriculture is related to 30% of emissions, so measuring it properly is important

Koen Deconinck: Sure, yeah. So when we think about climate change, of course, we immediately think about energy and fossil fuels. But of course, there's also a few other sectors, and agriculture is one of them, where there are other sources of emission that are not really fossil fuel related per se. And in agriculture, to give you just one example, if you have a cow standing in a field, as it's burping, it actually burps methane, which is a very powerful greenhouse gas as well.

And another example is when farmers use fertilizer or manure from the cows on their field that releases nitrous oxide, which is also a powerful greenhouse gas. So we know that agriculture is a large source of emissions as well.

And in addition, there's also sometimes places where people are still, for example, cutting down forests to make way for farmland, which also releases a lot of CO2 from cutting down those trees.

So if you add those things up, you're already at something like 20% of all the emissions. So that's actually, I think, larger than many people realize globally. And when you add in the entire supply chain, for example, the production of the fertilizers, the transport, the processing, et cetera, it actually ends up being something like a third of all man-made emissions that are linked to food one way or another.

Arvid Viaene: That's huge, because the number I have in my mind whenever I look at statistics is around 10% of EU emissions is due to agriculture.

Koen Deconinck: Right. Globally, it's also around 10%. That is the direct emissions from agriculture. So that's what happens on the farm itself. And then you add roughly another 10% for the land use change, the deforestation, and then roughly another 10% for everything that happens in the rest of the supply chain, from producing the fertilizer to what happens at the processing stage, refrigeration in the supermarkets, et cetera.

Arvid Viaene: So its measured in the statistics as transportation, but it's directly linked to the food in some sense, like creating the fertilizer.

Koen Deconinck: Exactly. So there's different lenses of looking at it. So, for example, the production of fertilizer that would normally be under industrial emissions. So there's a different lens of looking at it. If you think of it in terms of an entire supply chain from farm to fork or even before the farm stage, the production of the inputs, and then you get to a surprisingly large number.

And a big part of that is actually still happening on the farm or through land use change, which means that understanding what happens there is important because if you get those numbers wrong, then you're going to get all the other estimates such as the total impacts and the carbon footprint of specific products wrong.

A rapidly evolving landscape of tools for measurement

Arvid Viaene: So what drew you to the topic? Because this is, I mean, you're economist. I've looked at your research. This wasn't an immediate link. Like, how did you get to the topic of investigating this?

Koen Deconinck: As you mentioned, I was the lead author of a major OECD report called Making Better Policies for Food Systems. And food systems is a relatively new term that people are using. And what they mean by that is that you have to look at things like agriculture, environment, nutrition, health issues.

You have to look at these as connected issues and you need to have coherent policies. And we did a major report asking, what does that mean and how do you do that? And following up on that, we started asking, where are the evidence gaps?

And one of the ways that we wanted to explore that question was through some case studies. And I chose this case study on what do we know and not know about the environmental impacts along the food supply chain. It's something that had always intrigued me.

Because a lot of the data we have would be looking at agriculture. So we know the emissions of agriculture, but we don't necessarily know we're standing in the supermarket, what are the emissions of this product versus this other product? Or if we buy the products from one country versus another country, which one has the lower emissions?

And so we picked this because we thought this would be an area with large evidence gaps. And of course, there are evidence gaps. But we were mainly surprised at how quickly it's changing, how many new initiatives are popping up to try and quantify those environmental impacts.

And one expert told us that he had been in this space for more than 20 years and that things had never moved as fast as they are now in terms of new reporting standards, new databases, new approaches, new labeling initiatives, and so on.

And as we looked into that, we discovered that that was absolutely correct. And we ended up writing a paper called Fast and Furious, which was describing this rapid rise of initiatives to try quantify emissions in food systems.

Arvid Viaene: A very descriptive title. I liked it. It is not often you hear a title like that.

Koen Deconinck: Yeah, exactly, because things were moving very fast. So every few months or weeks, you would see some new initiatives coming out. But at the same time, it'sit's furious in the sense that there's so many different and initiatives competing for your attention. And it becomes difficult to know which one is serious and which one is greenwashing and which one is just adding to the noise.

And in that paper, we said, well, if we had better data on the environmental impacts in food systems, that would actually be great. Because it would build the data infrastructure or data foundation on which many other kinds of initiatives could build better policies, but also private initiatives. So for example, if you are running a restaurant chain, and you want you want to reduce your overall environmental impacts, at the moment it might be difficult to even know what those impacts are because the data isn't great. But if you had better data, you would be able to see that some choices, some differences in your menu might actually make a big impact in terms of lower environmental impacts.

So for example, if you run a restaurant chain, you might discover if there's better data that just by changing your menus a little bit, you can actually reduce your environmental impacts.

Important facts on the heterogeneity of emissions in agriculture: Product and farmer-level heterogeneity

Arvid Viaene: In the paper, you have a couple of really cool graphs, which you call background facts. And I just want to talk about these facts because I think it might be really interesting for people. Like just the estimated average carbon footprint. In the graph, beef herd has an emission of 60 kilo CO2 equivalents per kilogram of product, which is much lower than say rice or soybeans or other food items. So it is meat, but like specifically beef herd at the top.

See Figure 2.2 of the report

Arvid Viaene: And so you show these averages, but then not only that, there's also huge variation among producers of beef. So that’s one thing that really struck me is. So the first is that there are huge variations in products, but then there are also huge variations for the same product.

Koen Deconinck: Exactly. And when I first learned about that, it really drew my attention. As you know, in economics, there's this literature on international trades that also made this shift towards thinking in terms of what happens within industries and how which firms become exporters and which ones don't.

When I first heard people talk about this heterogeneity in carbon footprints, I immediately started thinking of that and thought, this is actually really interesting. we need to look into this a little bit more closely. And for example, the main study that people are still citing is a paper in Science from 2018 by Joseph Poore and Thomas Nemechek. And they did an incredible amount of work. They basically identified almost 600 studies on different types of food products or agricultural practices, and they reanalyzed all of them to make sure that everything was done using consistent, comparable methods.

And this way, they compiled a database with the environmental impacts of different types of food products over all over the world. And so they found that heterogeneity. And they describe that for most products and impacts, it's usually 25% of the production that accounts for half of the environmental impact. So that means that there's an enormous skew in that some types of production are more harmful than others, even for the same product. And what that also means is that actually, if you have a more targeted approach, It means that you could actually reduce those environmental impacts disproportionately fast if you focus on the right producers.

So rather than having something across the board where you try to reduce all of the consumption of a specific type of product, if you could target it to those producers that have the worst environmental impacts, it would be more efficient.

So the more I started thinking of this, the more I thought this is a story that really needs to be told more and needs to be investigated more. Because people have often talked about that first chart that you mentioned, where we show the carbon footprint of different types of food products: Beef is worse than chicken, chicken is worse than lentils, and so on.

So a first implication is if you want to reduce the environmental impacts of your diet is that you shift from beef to chicken or from chicken to lentils, sort of go down the list. But indeed, if we at the same time know that there's an enormous variation within each kind of products, it means that there's at least a second lever as well, right? So you could still be drinking milk but at least you now buy the milk from a producer that has a lower carbon footprint. And then automatically, if you're able to do that, if you have the kind of data, you basically create an incentive for a third lever, which is that all the producers then have a reason to start reducing their own carbon footprint. So you also trigger a process of innovation and producers trying to to find which kinds of management practices on the farm, which kinds of technological innovations can help them reduce their emissions.

You cannot do that if you only have rough averages. You really need to have farm level data for that.

Arvid Viaene: Yeah, I think that's such a key finding that that I learned from your paper is product averages are not enough.

Koen Deconinck: That's right.

Arvid Viaene: And like, there is so much heterogeneity among products. Like, I recommend everybody listening to actually go watch the figure. It's figure 2.3 and it's on page 21. And just to give a sense of the magnitude, like the beef herd goes from 20 kilograms CO2 equivalent per 100 grams of protein to 100 kilograms CO2.

So the average was around 60, it has like an 80 kilogram distribution. And the median is actually around 25 showing there's a huge skewness towards outliers, so I was a little shocked to see that because it's not something you think about that much. And I think it really provides such a strong foundation for the rest of what you're doing. Because in the ideal world, I would almost see the products and they have like a total CO2 measurement associated with them on this.

Because in the cap and trade, it's internalized in the price, you could say so to some extent. But here, I don't see it immediately happening. Maybe this is a bit off topic, but is there something in agriculture?

Koen Deconinck: So indeed, as you said earlier, industrial products are often covered by pricing schemes. In Europe, it's the the emissions trading scheme. Agriculture is not included in that. The only country that is now moving towards pricing agricultural emissions is Denmark. So they just introduced now a tax on agricultural emissions.

And they are the first country in the world to do that. New Zealand started working in that direction. So they did a lot of the background work, the analytical work for it. But then it turned out to be too sensitive. And so they didn't cancel the plans, but they put them on hold. So at the moment, it's only Denmark that actually has something.

And partly this reflects the fact that agriculture is politically a sensitive sector. and We've seen the farm protests a while ago. But it also partly reflects the difficulties of doing that.

Since if you think about the comparison with the steel industry, for example, there's globally something like a thousand steel factories. So if you wanted to, you could assemble a small team and you could go and visit every steel factory in the world in person if you had to. With farming, in the European Union alone, there's 9 million farms.

Arvid Viaene: Nine million.

Koen Deconinck: So the number is just so much higher. So that's one reason. And the second element is that unlike with steel or other industrial products, we're dealing with a biological process. And so biology is inherently much more variable.

So if you think about nitrous oxide emissions from soils, the slope of the soil can determine the rates of emission. So each variation you have in the slope of your soils on your farm can change the amount of emission that you would have.

The kind of breed of cow that you have would determine the emissions from enteric fermentation, from the burping of the cow, the methane emissions. The kind of animal feed that they're eating could determine that. So there's just so many factors that can influence that.

So those are two of the complexities that show that you just have a lot of different farms. Then we know that biologically those things can differ. So there are also different management practices, as different decisions farmers make can make a difference for the emissions. Then combine that with the fact that, of course, farmers are all small enterprises. You can't go to a farm and ask to see the vice president for sustainability reporting. It's the same person. So it is hard to impose really burdensome requirements in terms of reporting. So those reasons all make farming a bit more complicated than the industrial sectors.

Arvid Viaene: There are the eight building blocks, but some of those really go into giving farmers the tools to calculate it themselves, like the farm level tools so that the farmer can calculate their own emissions. And at the same time, you don't want to overburden farmers because that's the other thing, of course.

And I think in the paper, I'm kind of mixing up the building blocks now, but I get a sense because this is also an intensive process to start from a product average. And then to give the opportunity to make them more farm specific, depending on the tools and the products. And then there are tools being developed for that purpose.

Koen Deconinck: Yeah, that's right. So indeed, in our reports, the question we're asking is, despite all these challenges, if you wanted to have a system that was reliable and widespread, how would you do it? So what are the essential building blocks that you would need?

So we identified eight, and I'm not going to list them all. But indeed, it's not purely the technical ones. So there are a few obvious technical ones. Clearly, you need good reporting standards so that we are all agreed on what we're measuring.

We also need scientific methods. But then, a few of them have more to do with the implementation of it. And an important one is, given that you have a fragmented sector but a lot of small farmers, and not only farmers, actually, a lot of the other companies in the supply chain are SMEs as well. So you also don't want to crush them with reporting burdens.

Arvid Viaene: For the listeners, SMEs are the small and medium enterprises. For those who don't know the term.

Success-story of constructing farm-level emissions: Origin Green

Koen Deconinck: Yes, so there are literally tens of thousands of companies that are active in parts of the food supply chain in Europe. I think it's around 300,000. So you also cannot crush those enterprises with increased reporting burdens.And so we're asking, okay, if you wanted to do a pragmatic approach and you would that is still reliable, how would that look like?

And indeed, there are different ways of doing that. There are a few success stories. So in Ireland, there exists a scheme called Origin Green. And it's actually, it started as an export promotion initiative for Irish beef and dairy. And it was initially more about the quality of the product. So they would certify, for example, that you had not used any products that were forbidden. They would also certify things around animal welfare. And increasingly, because people in the export markets were increasingly asking about sustainability, they started adding information on sustainability.

And at some point, they realized that they had so much information already that it would not be that difficult to start calculating carbon footprints. So they worked with the Irish National Research Institute for Agriculture to develop a calculation tool, and then they used the data that they already had with some extra information to calculate that.

But because the scheme was actually so successful, they already already had something like 90 to 95% of the beef and dairy farmers that were part of the scheme. So from one day to the next, they suddenly had farm level data on basically all these beef and dairy farmers in Ireland, almost all of them.

So it's an incredible success story that if you are building on an existing initiative and you add it on top of something that people already know and trust, you don't need to duplicate a lot of things.

You know its all done in the same audit. It's done by the same people. And they achieved 90, 95% coverage. Exactly.

Building on existing regulations and databases

Arvid Viaene: I like that example because it exactly shows how fast it can go. Like from one day to the next, because the initiative was already there, they had the information because the farmers had partnered up already.

Koen Deconinck: Exactly. And this is something that I've been discussing with people in many countries. Because often farmers already need to report a lot of data to the government. Sometimes it's for subsidies in Europe for the common agricultural policy. Sometimes it might be for environmental regulations or for other purposes. So in many cases, the government actually already has a lot of data. So there might be possibilities to leverage that so that you don't need to go and ask the farmer again how many cows they have. The government probably already knows it through one database or another how many cows there are on the farm.

So there are different ways that you can do that. And people are now trying to figure out, okay, how can we access those databases? There are, of course, all the questions around data governance and data ownership and privacy of all that data as well.

So that's one thing that people are exploring right now is how can you make it as easy as possible for a farmer to do that?

Arvid Viaene: Yeah, that's another impression I got that, you know, maybe the Wild West is the wrong term, but there are like a lot of different things that are already in place, like reporting, databases, exchanges. You even have this whole sector in the report about reporting standards in the different levels. And then there are the different databases at the level that they're stored. So I'm just giving you my impression. Like it just seems like there is a lot out there.

Koen Deconinck: That's right. Yeah, absolutely. And when we started thinking about these building blocks, I really did this sort of conceptually working my way backwards from what would a good solution look like, what would be the necessary elements, and then looking around and asking what exists already. And so, I was pleasantly surprised to see that actually all of those elements already exist in one form or another. So nobody has really figured out all of it. So there is no country or initiative that ticks all eight of those boxes.

But you can find good examples everywhere around the world. So the Irish case is a fantastic example of how you can scale this up with farmers. Then there are other questions as well. As for example, if you want to be able to transfer data from one calculation tool to another calculation tool or from one database to another, there are technical questions around data formats and interoperability.

And even for that, people have been developing that. So people at Oxford have developed the standardized data formats. There are people in in the business world who have developed approaches and protocols for data sharing. So all these things are emerging.

So it's more a matter of further developing those different building blocks. But also, some of those building blocks, some of those initiatives have historically been developed for other reasons. So people might have thought about the methods for calculating farm-level emissions, but it was more done because they wanted to calculate national numbers for the United Nations.

And that means that the way that they have currently done it might not exactly fit how a farmer would look at his business. So you might need to make some modifications, some adjustments to make those things fit for purpose. But we're not starting from zero. Actually, there is a lot of work that has been done already. And there is indeed this fast and furious rise of different initiatives. We really see that across those eight building blocks.

Area for improvement I: Reporting Standards

Arvid Viaene: It seems there is a lot out there. So where would you see the biggest gap? I don't know if this is the right term. Is it connecting the different steps of the building blocks? Or is there still like one block that you think is lagging.

Koen Deconinck: I think you you what you mentioned is actually correct. So aligning those different building blocks with each other is an important step. People are working on that. But yeah, those things are often developed independently from each other. So there are often some alignments and adjustments that need to be made. But then looking across all the building blocks, I think there are two main areas.

One has to do with reporting standards. And so this can get really tricky and technical, but there are different kinds of standards. So there are corporate standards for how companies should report their emissions, including what people nowadays call scope three emissions.

And this can get pretty technical, but there are different kinds of reporting standards. So for example, there exists standards for how companies should report emissions at the company level. And that means not only their own emissions of their own activities and installations, what people call scope one emissions, and the emissions of the energy that they're purchasing, which is the scope two, but also increasingly people are reporting scope three emissions. So that means the emissions in your supply chain upstream and downstream.

So for example, if you are a fertilizer company, your product, the fertilizer, will get used on the farm. And then on the farm, it will actually generate emissions. So those emissions get counted as part of the downstream scope three emissions of the fertilizer company. So they have to report on that as well.

But also on the other end of the supply chain, the supermarket, if they're selling you milk or yogurts, their scope three emissions include the emissions of the cows that created the milk. So there are a whole set of standards around how companies should calculate those things and report those things. And then there exists a separate family of standards on product-level accounting, product-level carbon footprints.

And again, those things kind of developed independently from each other, so there are all kinds of questions about how you align those things. Because ideally, you would be able to just capture upstream emissions when you're buying the product, right? So you buy fertilizer, and then the fertilizer company should be able to tell you this is how much the carbon footprint was of producing this fertilizer. And so you could do that, but at the moment, those things are not completely aligned yet.

And there are especially for agriculture a lot of open questions. So here's just one. If you want to calculate emissions at the product level, you could calculate how much a cow is emitting in terms of methane, for example. But that cow will give you both milk and eventually meat. So how do you divide the total emissions between the milk and the meat? So there are no one right way of doing that, but you do need to make a decision about what kind of allocation key are you going to use for that. So there are a lot of questions like that that need to be resolved that at the moment are a bit unclear.

Area for improvement II: Farm-level tools

Arvid Viaene

Yeah. Yeah. Cause if, if I'm in a supermarket and I buy the milk, but I get assigned all the methane emissions, but like, you know, some of that might be proportioned to the meat instead of just being assigned all to the product.

Koen Deconinck

Exactly. Yeah. And If you play around with different kinds of allocation keys, you could make your own product look better than the competition just because you've chosen a different allocation key. And so you manage to put a lot of the emissions on a product that where people don't really care about the carbon footprint, for example. So it's really important that we have good rules on how to do that and especially that everybody's on the same page.

So that's one big area. It's all these reporting standards. And this is, it's a little bit like when financial reporting standards were developed. And this was a process of centuries to figure out how we should do all these different things. Except now with climate, we don't have the luxury of waiting centuries to do it. So it all has to happen a little bit on a compressed timeline to figure out all these things. And the other big area has to do with actually farm level methods and tools. So it's this question, if you have a a cow standing in a field and it's burping methane, how much methane did it burp? Given that we know that the type of breed of the cow can play a role, the kind of animal feed could play a role, how do we quantify that?

There are different ways of doing that. And at the moment, if you look around, there are different tools. where a farmer can put in numbers on about their farm, and those tools would give you an estimate. And it's not always clear what methods those tools are using, what kind of equations and parameters you're using. So sometimes you can get very different results depending on the tool you're using. So that's definitely one area where we need to to do more work to standardize those methods.

Arvid Viaene: Yeah, because I could even imagine that some of those tools are created by private sector agents and then some of them are created by institutions.

Koen Deconinck: That's right, yes. So there are several countries that have created the public tool. Ireland is one of them. The United States is another one. Spain is another one. And then in many countries, you also have private sector tools. And then some of those tools would be very serious about what they do. They would publish their methodology. Some of them are open source. You can actually look at the equations that they have. You would have an independent scientific advisory board. And others would just be a black box and you would not really know what they're doing. Or they might have a methodology document, but they're not really precise enough about which kinds of parameters they have.

And sometimes they might claim to be using the same methods, but you still somehow end up with different numbers because they might have made sometimes even just good faith choices, right? They might have made other simplifications behind the scenes that end up in different numbers.

So it's really important that we get this right because otherwise it's garbage in garbage out. So if the farm level numbers are wrong or if they are in doubt, then everything else that happens downstream, the numbers you calculate downstream from that, are going to be in doubt as well. So [the measurement of emissions] is very important to get right. It's very technical. It's very nerdy stuff, but it's really important to get it right.

Arvid Viaene: I would agree, because, you know, going back to we actually want farm level emissions to get away from the product averages. Like what are some ways that our people are trying to harmonize it, say in the EU? Is there, because, you know, every country, like you say, probably countries have their own independent tool and then there are some, I don't know if there are EU tool or a guideline or it's like, how would you see that being done? Because it it seems like such to me a hard problem if you have all these different tools and assumptions.

Koen Deconinck: Actually at the moment, so we will be working on that in the OECD. So we just started a program of work on that because we saw that nobody else was really working on this. And the way we think about it is that there are actually two different problems at the moment. So one is that within a single country for the same farm, you could get very different numbers depending on the tool that you're using. And there was one study in the United Kingdom which found that we're not talking about a 5% difference. We're talking about a 50% difference, a 100% difference, really, really big differences.

Arvid Viaene: Those are big differences.

Koen Deconinck: Purely based on which tool you chose.

Arvid Viaene: So there is an incentive to then choose the tool, you know.

Koen Deconinck: Yes, and and you want to avoid that what you end up with is a race to the bottom, where the tools start competing on who can give you the lowest carbon footprint number. That's not what you want. You want them to compete on all kinds of other things like user friendliness and what kind of advice they can give the farmer about you know the most cost-effective way of reducing those emissions, et etc. But you don't want them to compete purely on who can playing around with the equations to see who can give you the lowest number.

Arvid Viaene: Which I think would happen

Koen Deconinck: It definitely might. Some tools are serious and some are less so. And of course...

Arvid Viaene: Yeah, I should be more careful. I should say there is probably an incentive for some to use some tool that might give you a better estimate and not all would do it. And of course, and like, you know, but um so then what you're developing that, that sounds really exciting that you are going to tackle this incredibly challenging.

Harmonizing farm-level measurement tools internationally

Koen Deconinck: And as I said, there are two reasons why we want to look at this. So one is this purely this domestic problem. So if you're in the UK and you're a farmer and you see that you can get a number that's completely different depending on the tool, this clearly undermines trust. So clearly that's there's a domestic reason to fix that.

But there's also an international trade aspect to that. So if you are, for example, an exporter from New Zealand and you're exporting dairy products around the world, and you calculate your carbon footprint, and then suddenly your competitors claim that your numbers are wrong because you used the wrong methods, how will we decide on that? So how will we deal with those kinds of issues? If you have different companies accusing each other of greenwashing their carbon footprint claims, And the difficulty is, because agriculture is so different from one country to the next, it would not make sense to force everyone to use the exact same equations. Because agriculture in New Zealand is quite different from agriculture in Denmark, for example. So it would make sense that they actually are using different equations, different parameters. So there has to be some heterogeneity in the methods people are using. But then how can you reconcile that with still having numbers that are somehow comparable? So that's the difficult question. And there is probably a path forward. So this is what we're working on right now, which has to do with the way that countries are already reporting their numbers to the United Nations. Because many countries are reporting their annual total emissions to United Nations, including their agricultural emissions. So they already have researchers who are trying to quantify their agricultural emissions. So the researchers already had to ask themselves, what are the right methods for doing that? And IPCC has actually developed guidelines for them.

There's a sort of a methodological baseline. If you don't have good data or good information, use this simple method. If you have more sophisticated data, you can use these other methods. But they also give countries a lot of leeway in thinking about what the best method would be for their country. But in the UN system, there's already some kind of peer review mechanism as well. So if you claim that your kind of your cows don't emit any methane, somebody will call bullshit. So there is some kind of quality control already in there.

And so what we want to explore is whether we could use that as a basis for discussion. Because eventually you might need to end up in a situation where we discuss which methods the different countries are using and we have a discussion and we say, okay, we accept that these methods make sense for Denmark and this other set of methods make sense for New Zealand, make sense for Greece, make sense for Chile and so on.

So that you do end up working with different methods because everyone has a different agricultural sector. But we have some kind of agreement that we agree that these methods make sense for you, these methods make sense for us. And so as long as you stick to those methods, we will not call into question your calculations if there's ever a dispute on that.

So that's what we're currently working on. So we're currently first trying to understand what countries are already doing, what kind of methods they're currently using, and what kind of methods are used in those different tools. And this will be a long process, of course. So we're just starting those conversations now.

Arvid Viaene: Awesome. And I think it also illustrates the fast and the furious again. It's like, how can we move fast? Well, there's this already existing process of reporting emissions. Can we build on that to use a similar process or given that it's already in place, you know, to then tackle this on or use something similar, you know, still accounting for the heterogeneity in information.

Koen Deconinck: Exactly. It's again the same issue that that process is a really interesting one. But of course, it was d to be used in international trade contexts to decide on a claim about carbon footprints of a product. It was to help countries give good numbers to get good total emission numbers. So there's something there. It's again the same story. There's already something there. We're not starting from zero, but we do need to think carefully about how to adjust, how to build on that. So we can get widespread and reliable carbon footprints for the agri-food sector.

Arvid Viaene: Well, I really enjoyed this. I think this this is probably in a good way to to end it. I would i would also just say I was thinking, the Fast and the Furious movies was a very successful franchise with like two, three, and four. So, you know, so I hope that's an inspiration. So I look forward to more reports.

But thanks so much because i have to say when I saw your post, I was very excited to talk about this. And I'm pleasantly surprised how exciting it can be to talk about the technicalities of emissions reporting. And thanks, gracious to to come on, I really appreciate it.

Koen Deconinck: When you first start thinking about this, you think, well, this is a really technical topic. But then once you get into it and you start to see the importance of it, it is a big intellectual puzzle, right? So it is interesting to try and figure out how we can solve this. It's important that we solve it. So that gives us a motivation to try and figure it out.

Arvid Viaene: Awesome. Thank you very much, Koen.

Koen Deconinck: Thank you so much.