To say that something has decreased, we need a starting point for comparison. Is it last month, or another farm in the same season? Chapter 7 of Technology and Solutions for Demonstrating Carbon Reduction looks at carbon-reduction baselines together with greenhouse gas system boundaries. To understand what a reduction figure means, we need to know what it is compared with and what is included in the calculation.
A distinction to make first as you read the book
Once you have chosen a comparator, you also need a procedure for changing it. New data may justify updating a baseline, but that is different from looking at the results and choosing only conditions that make performance look favorable. Record the reason, effective date, and scope affected. You can examine items omitted from the calculation the same way: distinguish items excluded under the rules from those that could not be quantified because data were unavailable. Having both explanations makes it easier to judge what the results support saying.
Even accurate equipment cannot make a conclusion reliable if the system boundary or baseline is inappropriate. And even if the calculation is correct, a lack of source data and change histories limits what can be verified. Accuracy and traceability cannot substitute for each other. Check the quality of the measurements and the process by which they lead to a conclusion.
Before signing a contract, agree on the claim, period, boundaries, and applicable criteria; the assurance level and materiality; access to source data; conflict-of-interest management; and how the deliverables may be used. Where needed, also check the organization’s accreditation or registration scope. Read a verification opinion within that scope, and do not expand it into a blanket certification of the entire company, product performance, or future emissions reductions.
A system boundary defines which emission sources, gases, periods, and organizations are included in a calculation. If the boundary is unclear, parties may expect different results from the same reported reduction. Check the inclusion and exclusion rules and materiality criteria in the applicable methodology, and state at the beginning of the report whether the result covers the whole farm or a specific emissions source.
One particular boundary-setting error to watch for is cherry-picking: including only the sources where reductions look favorable and excluding the rest. This is often unintentional. It can happen naturally when only the items with available data are included.
A simple before-and-after comparison without controls can be a starting point for identifying changes in the field. However, that difference alone cannot isolate the causal effect of a technology. Add control data or a valid quasi-experimental design and assess the remaining confounding. Distinguishing what was observed from what was inferred is the basic principle for using the matrix that follows.
Calibration. Record when and under which standard and procedure it was performed, and distinguish the condition before calibration from the condition after adjustment. The difference between two check points alone cannot establish the path of drift during the intervening period. Review interim checks, environmental logs, and recalibration data together; if the affected period or basis for correction is uncertain, reflect that in the uncertainty or use limitations of the result.
What to look for in the field
Imagine a year when the number of animals on a farm fell and total farm emissions also declined. If you explain that change only as an effect of the feed, you will miss the effect of production scale. You need to check which emissions sources and periods were included and how the comparison conditions were aligned. Choosing a favorable baseline can make the effect look larger even when the underlying data are the same.
Concentration is the proportion of a gas in air, while an emissions rate is the mass emitted per unit of time. To measure or estimate emissions, connect concentration with flow rate and a measurement model suited to the purpose. Even if concentration falls, an increase in air movement means you cannot conclude that the mass emissions rate changed in the same direction. When converting emissions rates over time into total emissions, you also need to account for the measurement period and how missing intervals are handled.
When comparing sensors, examine measurement range and resolution, cross-sensitivity to other gases, response time, and drift together. Also review environmental conditions such as temperature, humidity, and dust, along with calibration requirements. If the purpose requires distinguishing small concentration differences, check whether the device’s display units and uncertainty are fit for that purpose for each product and operating mode. The name of a sensor principle alone cannot substitute for these conditions.
There are details of sensor placement that must be documented: the precise position of each sensor (coordinates and height on a drawing), installation orientation, nearby structures, distances to supply and exhaust vents, and any changes after installation. Barns keep changing. Partitions are added, fans are replaced, and feed troughs are moved. If these changes are not recorded, data discontinuities remain unexplained.
Questions to ask in an investment review
In an investment review, ask where the baseline came from and what procedure governs changes to it. Also ask whether the comparator was changed after results were observed. If items were omitted, explain whether the rules allowed their exclusion or whether they could not be calculated because data were unavailable. Those two cases mean different things.
Evaluate AI materials according to their intended use. To predict future operation, consider preserving time order; to deploy at other farms, consider separating data by farm. Also separate the data used to select the model from the data reserved for final performance checks. Compare against a simple baseline model, and examine not only average error but also bias by farm and season and conditions under which the model fails. This is necessary to explain the model’s real scope of use.
When making a change, document the reason, timing, scope covered by the results, and impact assessment. A plan may need to change because of a safety issue or data error. But do not present an analysis chosen after seeing results as if it had been specified in advance. Distinguish the results under the original plan from those after the change, and test any new hypothesis using separate data.
A question to leave with before closing the book
Setting a baseline and boundary is preparation before calculation and a standard for interpreting the results. Try describing the comparator and included scope in one paragraph. Once that paragraph is clear, it becomes easier to see the conditions under which an emissions-reduction claim can be made.
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.