Framework for modelling data uncertainty in life cycle inventories

Modelling data uncertainty is not common practice in life cycle inventories (LCI), although different techniques are available for estimating and expr essing uncertainties, and for propagating the uncertainties to the final mo del results. To clarify and stimulate the use of data uncertainty assessmen ts in common LCI practice, the SETAC working group 'Data Availability and Q uality' presents a framework for data uncertainty assessment in LCI Data un certainty is divided in two categories: (1) lack of data, further specified as complete lack of data (data gaps) and a lack of representative data, an d (2) data inaccuracy. Filling data gaps can be done by input-output modell ing, using information for similar products or the main ingredients of a pr oduct, and applying the law of mass conservation. Lack of temporal, geograp hical and further technological correlation between the data used and neede d may be accounted for by applying uncertainty factors to the non-represent ative data. Stochastic modelling, which can be performed by Monte Carlo sim ulation, is a promising technique to deal with data inaccuracy in LCIs.