Greenhouse gas uncertainty is not a formal note appended beside a result. The causes and magnitude of carbon-emissions calculation error affect what data should be improved and what decisions can be made from current results. Chapter 7 of Technology and Solutions for Demonstrating Carbon Reduction presents an approach that considers uncertainty in measurement, sampling, and models together.
Define the concept and scope of application first
The sources of uncertainty can differ. Equipment performance, spatial and temporal representativeness, activity data and emission factors, and model assumptions should be examined separately. Improving precision in one area does not reduce total error by the same proportion. When submitting results, retain the method and assumptions used to calculate the range. Sensitivity analysis, statistical uncertainty, and conservative treatment required by a program should not be used as if they were the same concept. The criterion for improvement investment is whether a proposed enhancement would actually change the decision.
An emission-factor-based baseline is also an option. IPCC Tier 1 uses default factors, while Tier 2 for enteric fermentation reflects feeding characteristics, gross energy intake, methane conversion rates, and other factors in greater detail. Average factors may not capture the effect of a technology at an individual farm, but using factors is not inherently unsuitable for compensation. The deciding questions are whether the factors and activity data are allowed under the applicable methodology, and whether its conditions of application, uncertainty treatment, and verification requirements have been met.
Which source of uncertainty dominates depends on measurement conditions. In naturally ventilated barns, ventilation rates and spatial representativeness may contribute substantially; when reading small concentration differences, equipment repeatability, drift, and background correction may be important. Start by preparing an uncertainty budget and comparing the cost of reducing each component with the resulting improvement in the final decision. More expensive sensors or a larger sample are not always the best investment.
A reduction estimate needs both metrological traceability and calculation traceability. Measurement values are linked to a reference through a documented calibration chain and uncertainty, while activity data, emission factors, and models are traced to the final result through their sources, versions, and conditions of application. Not every emissions reduction comes from a single direct measurement. Reviewers should check not only whether the calculation can be reproduced, but also whether the data and methods used are appropriate for the claim.
Concentration is the proportion of a gas in the air, while an emission rate is the mass emitted per unit of time. Measuring or estimating emissions requires linking concentration and flow rate with a measurement model suited to the purpose. Even if concentration falls, the mass emission rate cannot be assumed to move in the same direction if airflow has increased. When converting an emission rate over time into emissions, the measurement period and treatment of missing intervals must also be addressed.
A practical path forward is to accumulate concentration observations together with reference emission-rate measurements, and link these to ventilation, weather, animal counts, and feed information. Prediction error should be assessed on farms and time periods not used to develop the model, and the scope of application should be stated. Even a large volume of simultaneous observations does not automatically increase model reliability if they lack representativeness or share a common bias.
Conditions and limitations of field application
Suppose a project has precise sensors but weak evidence for its ventilation-rate estimates. Assess how much adding more sensors would improve the reliability of the results. If the main source of error lies elsewhere, simply increasing the number of devices may do little to improve the decision. Improvement investments should target the main contributing factors.
Non-dispersive infrared (NDIR) uses methane’s absorption of infrared light. It does not have oxygen dependence or catalyst poisoning of the kind associated with catalytic combustion, but it is not free from optical contamination or the effects of temperature, pressure, and moisture. Do not assume it is suitable for long-term farm monitoring based on its operating principle alone. For each product, check whether its detection limit, repeatability, and drift are adequate in relation to the expected concentration and the difference between indoor and outdoor concentrations.
When comparing sensors, check measurement range and resolution, cross-sensitivity to other gases, response time, and drift together. Environmental conditions such as temperature, humidity, and dust, as well as calibration requirements, should also be reviewed. If the purpose is to distinguish small concentration differences, examine by product and operating mode whether the device’s display units and uncertainty fit that purpose. The name of a sensor principle cannot stand in for these conditions.
AI can be used to flag candidate anomalies, estimate short gaps under defined conditions, and explore relationships among multiple variables. It can also help organize data, conduct quality checks, and prepare report drafts. Because the purpose and cost of failure differ for each function, they should not all be evaluated using the same performance metric. State what inputs and assumptions produced a result, and distinguish estimated values from directly observed values.
Evidence needed for investment review
Investors should distinguish whether a reported range represents statistical uncertainty, a business scenario, or a program’s conservative adjustment. Treating different concepts as one discount rate obscures their meaning. Precise decimal places do not make up for poor-quality input data.
AI evaluation data should be separated according to the intended use. To predict future operations, consider chronological ordering; to deploy at other farms, consider separating data by farm. Also distinguish the data used to select the model from those reserved for the final performance check. Compare with a simple baseline model, and examine not only average error but also bias by farm and season and the conditions under which the model fails. This is necessary to explain its actual scope of use.
Specify what happens when the system fails. If calibration has expired, communications are missing, or the feed, season, or ventilation conditions have not been evaluated, limit or suspend the output and refer the case to a responsible person. Do not resume use immediately after retraining. Validate the changed model using independent evaluation data and record its deployment date and approver. A model’s role in supporting decisions and external verification of emissions reductions are separate functions.
Criteria to keep in view when making a decision
Managing uncertainty is not a task of collecting as many numbers as possible. It is a process of identifying inputs that are sensitive to the conclusion and comparing the value of improving data with its cost. Reviewers can make more concrete judgments when the range of a result and its limits of use can be explained.
About the author
Bongjun Lee graduated from the Graduate School of Management of Technology (MOT) at Sogang University. He spent seven years at Infitron Co., Ltd. and 20 years at Honeywell, working for a total of 27 years in gas measurement in service engineering, technical sales, and channel management. He is now the founder of AI Safety Korea Co., Ltd., where he is researching livestock methane monitoring and measurement, reporting, and verification (MRV) systems using sensors, AI, and data analytics.