Why Averages Fall Short in Emissions Accounting

By Dr Saulé Burkitbayeva

Measuring Agri-food products’ carbon footprint

A little while ago, it was considered impossible to measure emissions in agriculture, especially at the product level. With more knowledge and technological advancements, however, a lot of progress has been made in this space. Agriculture is finally coming to the forefront of discussions when it comes to climate change mitigation.

I would like to use this blog as a space to reflect on my recent work with OECD. Last year, with my colleagues Koen Deconinck and Hillena Thoms I wrote a report titled Measuring Carbon Footprints of Agri-Food Products: Eight Building Blocks. In this report, we provide a roadmap towards achieving reliable and widespread carbon footprint measurements in food systems using eight building blocks. These eight building blocks include:

• Reporting standards and guidelines for carbon footprint measurement.
• Science-based methods for measuring or estimating emissions.
• Farm-level calculation tools that allow farmers to use primary data to calculate their carbon footprint.
• Databases with secondary data, to be used when primary data is not available.
• A method for communicating carbon footprint data along the supply chain.
• A mechanism to ensure the integrity and quality of the data and calculations.
• Strategies to scale up carbon footprint calculations while keeping costs low, ensuring widespread adoption.
• A process to update these elements as new scientific insights and techniques become available.

The report has received welcoming reviews from various stakeholders, including policymakers, academia and the industry. But being more than 100 pages long, it’s a heavy read for those of us with limited attention spans and time at hand. Hence, this blog, in which I would like to summarise one key takeaway from our report: averages in emissions accounting are not enough. They often mask the very variation we need to see if we want to achieve real emissions reductions in agri-food chains. I also encourage you to check out Koen’s easily digestible version of our OECD report here.

Why can averages be misleading?

We often hear that beef has a higher carbon footprint than chicken or plant-based foods. It is true, but that’s just based on the average. What the average hides is that, even within the same product category, emissions vary a lot. Narratives based on averages, such as this one from the BBC, “[w]hat is the lowest-carbon protein?”, give us the impression that we have no other choice than to eat plant-based products instead of animal products. I will refer to a study by Joseph Poore and Thomas Nemecek (2018). This study comes up repeatedly in the context of food carbon footprints because it is one of the most valuable and most recent pieces of evidence we have on emissions across agri-food value chains. According to their data, on average, the carbon footprint for beef meat sits at around 60 kg CO₂eq, but this number glosses over an enormous spread. The footprint can range anywhere from below 20 kg CO₂eq to over 100 kg CO₂eq per 100 g protein. This clearly illustrates a significant variation in emissions, depending on the specific production methods. In other words, not all beef is created equally when it comes to climate unfriendliness. Some beef can be almost on par with pig meat, poultry meat, or tofu in terms of emissions intensity. This means that we do not necessarily have to become vegetarians but can, at the very least, choose more climate-friendly beef, provided we have that information. This idea, I think, is more comforting and realistic.

Here is how averages lead to ineffective targeting, a lack of consumer choice and producer incentive, and a lack of trust and comparability.

Ineffective targeting

If we rely only on averages, we are left with blunt policy tools such as lacklustre advice to just “eat less beef”.  Eating less beef helps, but it ignores the fact that we can also target relatively more climate-unfriendly producers and production systems. This could achieve bigger gains faster. Targeting does not necessarily mean punishing or penalising but rather incentivising reductions in emissions among producers. With farm-level data, policymakers could design interventions that reduce emissions more efficiently.

Lack of consumer choice and producer incentives

Consumers drove demand for better quality food in the past. Nowadays, consumers are becoming more informed and aware of climate change in general. They want to make climate-friendly choices. But if milk, for example, is all labelled with the same average footprint, buyers can’t reward producers who are doing better. Worse still, producers who invest in emissions-reducing practices have no way of signalling their efforts. Without this differentiation, there’s no incentive to innovate.

Lack of trust and comparability

Now, we seem to be calculating and accounting for emissions differently. Remember that I am talking about product- and farm-level calculations. Even for the same farm, different accounting tools can produce wildly different numbers. The results can be more than five or even ten times larger from one tool to another for the same farm! We mentioned this in our OECD report, but several other recent works come to the same conclusion. This undermines trust. If farmers, companies, or governments can shop around for the “lowest number”, it risks becoming a race to the bottom. Internationally, this makes disputes over “greenwashing” almost inevitable.

It is worth noting that agriculture is a special case. It is a lot harder to measure emissions for agricultural products as opposed to industrial products like steel or cement. I am currently writing a paper that delves into the challenges and importance of embedded emissions accounting in agriculture, and I will be happy to share it when it’s out.

A way beyond averages

So, what do we have to do to move beyond averages? We need farm-level data. This is because most of the emissions along agri-food value chains, according to Poore and Nemecek (2018), originate on the farm. Collecting farm-level data at scale is complex and resource-intensive, but it will help us design effective policy, foster innovation, and give consumers and producers the tools they need to make better choices.

The good news is that progress is being made towards standardised reporting methods that can get us there. Until then, we need to keep in mind: averages can be helpful and tell stories, but they are not enough. They can risk oversimplifying complex realities. Farms are different, each with their own practices, constraints, and opportunities when it comes to emissions mitigation. That is why bringing a farm-level perspective can be useful for driving more effective climate solutions.

More good news is that where I work, at The Australian National University (ANU), we continue to collaborate with the OECD on this front. We are currently building a global database of farm-level calculators. This is yet another important step towards understanding where we are in terms of harmonising our approaches and achieving reliable and widespread carbon footprints in agri-food chains.