Modelling data uncertainty is not common practice in life cycle inventories (LCI), although different techniques are available for estimating and expressing uncertainties, and for propagating the uncertainties to the final model results. To clarify and stimulate the use of data uncertainty assessments in common LCI practice, the SETAC working group ‘Data Availability and Quality’ presents a framework for data uncertainty assessment in LCI. Data uncertainty is divided in two categories: (1) lack of data, further specified as complete lack of data (data gaps) and a lack of representative data, and (2) data inaccuracy. Filling data gaps can be done by input-output modelling, using information for similar products or the main ingredients of a product, and applying the law of mass conservation. Lack of temporal, geographical and further technological correlation between the data used and needed may be accounted for by applying uncertainty factors to the non-representative data. Stochastic modelling, which can be performed by Monte Carlo simulation, is a promising technique to deal with data inaccuracy in LCIs.
The framework separates uncertainty caused by missing or insufficiently representative data from inaccuracy in the data itself. A data gap means that information is unavailable; non-representative data exist, but differ from the time, geography or technology needed for the study. These situations call for different responses. The authors describe ways to fill data gaps, including input-output modelling, using information from similar products or a product’s main ingredients, and applying the law of mass conservation. When data are available but do not match the required context, uncertainty factors can be used to account for the lack of temporal, geographical or technological correlation.
Uncertainty in inventory data can also be carried through the model to the final results. The paper identifies stochastic modelling, including Monte Carlo simulation, as a promising way to address data inaccuracy in life cycle inventories. Instead of reporting only one fixed inventory value, a stochastic analysis represents uncertain inputs through distributions and propagates them through the calculations. The framework therefore connects data assessment with interpretation: practitioners consider what information is missing or inaccurate, choose a method suited to that limitation, and communicate how the uncertainty may affect the results.
