In the discourse on climate change, a central metric of importance is the carbon footprint which tells us how much greenhouse gas (GHG) emissions are emitted and is primarily expressed in units of carbon dioxide (CO₂) or its equivalents (CO₂-eq), which aggregates different greenhouse gases based on their global warming potential relative to CO₂. This quantification helps us in understanding the anthropogenic impacts on the environment which will subsequently help in framing policy decisions aimed at reducing these emissions. Like many other sectors, estimating carbon footprints in agriculture is crucial, as it is one of the major sources of GHG emissions. But how accurate are these carbon footprint estimates?

How are carbon footprints estimated?

When it comes to estimating carbon footprints in agriculture, there are several carbon balance tools, varying in complexity of their calculations and input data used for this purpose. They range from simple empirical models like Cool Farm Tool or the Farm Carbon Calculator, which provide a wholistic farm-level assessments, to more complex process-based models like ROTHC which focus on specific aspects like Soil Organic Carbon.

While there are many methods used by the tools to estimate GHG emissions, the simple and commonly used method in the tools is by using an emission factor for certain management activities. Tools like Cool Farm Tool require such activity data from different areas of the farm as inputs, while having the emission factors built in the tool. Multiplying activity data by emission factors accounts for how much GHG is released per unit of activity:

Activity data * Emission factor = GHG Emission

For example,

100 kg N fertiliser/ha * 0.01 = 1 kg N₂O-N/ha = 265 kg CO₂ eq./ha

— where 0.01 is the emission factor for the activity fertiliser application (from IPCC).

Similarly, different GHG emissions (e.g., Methane, Carbon dioxide) from different sources are estimated and aggregated into CO₂ equivalents for a unified measure and to arrive at a single value for the farm.

The single value problem

In the above example, the emission factor of 0.01 is derived from statistical models. Models, approximating reality, usually come with an uncertainty associated with them. The uncertainty of the emission factor used in this example ranges from 0.002 – 0.018, where the true emission factor falls within this range, with a mean value of 0.01. The mean value represents the best estimate or the most likely value that we can expect based on the data we have. This means that, instead of a fixed value for total CO₂ emissions from fertiliser application (265 kg CO₂ eq. per ha), the true emissions could realistically be anywhere in between 53 to 477 kg CO₂ eq. per ha. Consequently, the overall carbon footprint of the farm also has an uncertainty range associated with it. This uncertainty reflects both the variability in the data and the limitations of our models. One significant issue with many carbon balance tools is their tendency to output this single value for carbon emissions without an accompanying range of uncertainty. This practice can be misleading in certain instances like:

Lack of Context: A single number can suggest precision which may or may not be true, often making us overlook factors like data quality or assumptions in calculations.
Policymaking: Policymakers might base decisions on these numbers without fully realising the potential errors or variances.

Implications on Policy

When policy is shaped and developed based on carbon footprint estimates without any knowledge of the uncertainty associated with them, there will be:

Risk of Misallocation: Resources could be incorrectly allocated when the estimates are off from the real-world values.
Regulatory Challenges: Rules and regulations built on such data might not account for potential variability, which will lead to many mitigation and climate friendly policies being implemented but still not getting the desired results in some cases.

Public Perception: There is a potential risk of greenwashing if companies present overly precise but uncertain data. This can influence public trust in climate policies and corporate sustainability claims.

What can be done to address this issue?

To provide a complete picture and have a clearer understanding of carbon footprints:

Incorporate Uncertainty: Tools, by default, should report an uncertainty range in addition to the single estimate. This means the farm’s total carbon emissions could range from X to Y kg CO₂ eq., giving a more realistic scope of impact.
Educating stakeholders: Educating stakeholders and policymakers about uncertainty can lead to better decision-making.
Policy Recommendations: Policies should mandate or at least encourage the reporting of uncertainty in carbon assessments to ensure more informed and effective climate strategies.

Understanding carbon footprints goes beyond just looking at a single number. By acknowledging and addressing uncertainty, we can move towards more transparent and reliable carbon footprints and consequently, an effective climate action.

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