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In environmental science and accounting, as in business, uncertainty is a fact of life.
Neither scientists nor business leaders can operate with 100% certainty. Sometimes the information you need to measure and communicate your corporate or product footprint just isn’t as clear, readily available, or precise as you’d like it to be. But uncertainty doesn’t mean the science is flawed – it simply signals an absence of certainty, which, in science, is not only normal, but also expected.
That said, uncertainty can create challenges for decision-making around impact reductions or tracking the progress of initiatives. This chapter explores what uncertainty means in the context of sustainability accounting, where it typically lies, how it creates risks, and what can be done to manage it.
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Uncertainty is inherent in sustainability accounting. It’s a normal part of life cycle assessments, footprinting, target-setting, and roadmapping exercises. It can stem from variability in the quality of data, methodological choices, or the fact that environmental science is still evolving.
Most of the uncertainty lies in scope 3 emissions because these are furthest away from a company’s direct operations and where the majority of secondary data is used. This makes them harder to measure precisely and more reliant on assumptions, methodological choices and proxies.
There are three main sources of uncertainty:
1. Sourcing-related uncertainty:
You don’t know exactly what you’re sourcing, how much or from where. Companies often rely on spend-based data or group raw materials together in ways that hinder the granularity needed.
2. Proxies and secondary emission factors:
When companies rely on generic databases, they often use secondary emission factors that may not reflect their actual sourcing or operations. These proxies are typically based on global or regional averages and can mask important differences across geographies, suppliers or practices.
3. Methodological and dataset-related uncertainty:
Even when using established datasets, results are influenced by methodological choices and by how those datasets are constructed. “Global averages” are often derived from limited samples and assumptions, rather than a true representation of all possible sources. Allocation methods, system boundaries and other modelling decisions can further affect the precision of results.
While uncertainty is normal, it can lead to risks if not acknowledged and addressed:
While uncertainty is normal, it can lead to risks if not acknowledged and addressed.
Dealing with uncertainty doesn’t mean eliminating it entirely (which isn’t possible) but rather deciding how to act given the circumstances. There are two main ways to reduce it:
Note that reducing uncertainty often entails rebaselining – revisiting and adjusting your original footprint to reflect improved data and identify progress. At the same time, it’s important to acknowledge that very few companies currently measure uncertainty in a formal way. We don’t expect our clients to be doing this today; what matters is recognizing where uncertainty lies and taking deliberate steps to reduce it where it’s most material.
A company wants to make a claim about how it has reduced its footprint. In 2020, the company’s footprint was estimated at 10 MtCO₂-eq, with an uncertainty range of 4 MtCO₂-eq (8–12 MtCO₂-eq). Because this range was both material and present in key areas identified for improvement, the company implemented levers to decrease its footprint and, at the same time, took the opportunity to improve data quality by collecting more precise data from key suppliers in 2025. By doing so, the footprint decreased and the uncertainty range narrowed to 2 MtCO₂-eq in 2025.
As this change was driven by data improvements, it required the company to rebaseline its corporate footprint from 2020 (see the figure below). Following rebaselining, the 2020 corporate footprint moved from 10 MtCO2-eq (8-12 MtCO2eq) to 9 MtCO2-eq (8-10 MtCO2-eq). In doing so, the company reduced not only its uncertainty but also its baseline emissions, making future progress easier to track with confidence. With this adjustment, the company could identify a “good confidence” impact reduction zone, showing that reductions linked to its climate roadmap were indeed real – even when uncertainty was taken into account.
When reduction is not feasible or impactful, managing expectations and transparent communication is key to secure buy-in by setting clear internal and external stakeholder expectations. To do so you should:
When considering whether to take action to reduce uncertainty or manage expectations, use the matrix below to help determine which course of action makes the most sense for your organization.
Note: The following represents a theoretical approach meant to spark initial thinking and exploration, rather than to indicate specific actions to be taken.
Ultimately, the best way to reduce uncertainty is to work with primary data. But the first step in that direction is to increase visibility on sourcing – knowing what you source, from where, and under what conditions. Greater visibility makes it possible to strengthen the quality of proxies in the near term and provides a practical pathway toward using more primary data over time.
Finally, we urge you to be careful in how you interpret your numbers. Uncertainty in your data can fundamentally change what they really entail. By understanding where uncertainty lies and taking deliberate steps to address it, you’ll strengthen both your environmental strategy and the credibility of your results.
Be transparent about uncertainty.
Acknowledge it in your analysis and communication. Clarity about what uncertainty means for your numbers builds credibility and avoids over-promising.
Prioritize where to act.
Reduce uncertainty where it’s most material – in hotspots, or in areas that affect decisions and claims. Don’t waste effort trying to eliminate uncertainty everywhere.
Don’t over-interpret small differences.
Avoid making claims based on changes that may fall within the margin of uncertainty. A 2–3% shift on a full assessment is unlikely to reflect a meaningful impact.
We have answers. Get in touch with our team today and let us guide you through the solutions that might help you on your journey toward a sustainable supply chain.
Charlotte Bande – Global Food + Beverage Lead
Pierre Collet – Global Footprint Lead, France
Alexi Ernstoff – Sustainability Principal, Land + Agriculture
Marcial Vargas-Gonzalez – Global Science + Innovation Lead
01
This chapter covers when to rebaseline, how to implement a clear base-year recalculation policy, and best practices for communication.
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02
This chapter considers data management tools and best practices for using primary data to estimate the impact reduction potential of interventions and track progress in the supply chain.
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03
This chapter covers how chain of custody connects verified sustainability results to the goods moving through supply chains, ensuring reductions are credible, traceable and countable toward climate targets.
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04
This chapter focuses on how to manage the double counting of emissions reductions when considering real progress toward a sustainable supply chain.
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05 – Currently reading
This chapter focuses on how uncertainty shapes sustainability accounting, where it typically arises in scope 3 data, and what companies can do to manage it.
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