Brightcon 2025, hackathon & courses in Grenoble and online

Correctly modelling the relationships between correlated, uncertain input data is crucial for producing accurate uncertainty estimates of model results. This requires both uncertainty propagation that accounts for correlations and the appropriate communication of the results, so that other analysts can correctly interpret the reported uncertainties. A common case in Industrial Ecology, in which correlations are important but often ignored, is data disaggregation. One reason for this is that dealing with correlated uncertainties is a complex undertaking, particularly when data is only partially available. MaxEnt_disaggregation is a Python (and R) package that makes it a breeze to propagate correlated uncertainties when disaggregating data points. It is based on the Maximum Entropy Principle, ensuring maximally unbiased results. It aims to help improve the accuracy of uncertainty quantification in Material Flow Analysis (e.g. where allocation coefficients split flows to sectors), Input Output Analysis (e.g. where aggregated environmental impact data has to be disaggregated to detailed economic sectors), and some instances in Life Cycle Assessment (e.g. where market shares are uncertain).