Optimized measurement uncertainty and decision-making in the metering of energy, fuel, and exhaust gases

Risks of incorrect decision-making in conformity assessment associated both with sampling and measurement uncertainties are tackled with a decision theory approach, earlier used in analytical measurement, where the costs of testing are balanced against the costs associated with the consequences of incorrect decision-making. Examples cover the metering of energy, fuel, and environmental emissions; all areas of increasing societal importance where costs are rising at the same time as more stringent accuracy requirements are successfully introduced.