Uncertainty Analysis
Uncertainty analysis is a quantitative assessment of the potential range of error in a greenhouse gas inventory, arising from measurement imprecision, emission factor variability, data gaps, and methodological assumptions. It helps stakeholders understand the confidence level of reported emissions figures.
What is Uncertainty Analysis?
No greenhouse gas inventory is perfectly precise. Every emissions figure contains uncertainty — from the measurement of activity data, the representativeness of emission factors, the treatment of data gaps, and the methodological choices made in boundary setting and scope classification. Uncertainty analysis quantifies this imprecision so that reported figures can be interpreted with appropriate confidence.
Sources of uncertainty in a GHG inventory include: measurement uncertainty (e.g., gas meters have a tolerance of ±2%, fuel pump meters ±0.5%); emission factor uncertainty (e.g., the DEFRA grid electricity factor is a national average — actual grid intensity varies hour by hour); model uncertainty (e.g., spend-based emission factors average across entire economic sectors); data gap uncertainty (e.g., estimating consumption for a site where meter data was unavailable); and temporal uncertainty (e.g., using last year's emission factor for the current period).
The GHG Protocol provides guidance on uncertainty analysis, recommending that organisations assess uncertainty at both the source level (each individual data point or calculation) and the inventory level (the aggregated total). Methods range from simple qualitative assessments (high/medium/low confidence) to quantitative techniques such as error propagation (combining percentage uncertainties across sources) and Monte Carlo simulation (running thousands of random samples from probability distributions to estimate the range of possible outcomes).
In practice, most corporate GHG inventories carry an overall uncertainty of ±5–15% for Scope 1 and 2 (where data is measured and factors are well-established) and ±30–60% for Scope 3 (where estimates and averages are common). These ranges are rarely reported explicitly but are acknowledged in methodology disclosures and data quality assessments.
Uncertainty analysis supports better decision-making. If a category's emissions are estimated at 1,000 tCO₂e ± 40%, it would be inappropriate to report a year-on-year reduction of 5% as a definitive achievement — the change falls within the margin of error. Understanding uncertainty helps organisations focus improvement efforts where they will have the most impact on accuracy.
Practical Examples
An organisation quantifies the uncertainty of its Scope 1 gas heating emissions at ±3% (based on meter accuracy and emission factor confidence), versus Scope 3 purchased goods at ±50% (based on spend-based estimation with sector-average factors).
A company uses error propagation to calculate overall inventory uncertainty: combining ±5% for Scope 1, ±8% for Scope 2, and ±40% for Scope 3 to arrive at a total inventory uncertainty of approximately ±25%.
A verification body assessing a company's GHG inventory applies materiality thresholds based on uncertainty analysis — accepting a 5% discrepancy in Scope 1 as within measurement tolerance while flagging a 60% discrepancy in a Scope 3 category for further investigation.
How Climatise Helps
Climatise tracks the data quality and methodology used for each emission source, providing transparency on where uncertainty is highest. The platform supports uncertainty reporting in verification-ready outputs and highlights the categories where improved data collection would most reduce overall inventory uncertainty.
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