Methods for handling uncertainty This repository contains: · Guide on the appraisal of uncertainty in the LCA of bio-based products This document presents practical approaches to handle uncertainty in the environmental assessments of bio-based products within the ALIGNED project. Includes both theoretical elements, such as definitions and examples of uncertainty types and description of different approaches for uncertainty and sensitivity, as well as tiered recommendations for practitioners applying these approaches in case studies. · Calculator of uncertainty using pedigree matrix (.xlsx file) Spreadsheets to calculate uncertainty estimates using the pedigree matrix approach, with example. · Example of calculation of uncertainty estimates from measurement data (.xlsx file) Spreadsheet to calculate uncertainty estimates for the exchanges of a unit process, with example. · Example of calculation of sensitivity ration (.xlsx file) Spreadsheet showing an example of how to calculate the sensitivity ration. · Calculator sheet for analytical error propagation (.xlsx file) Spreadsheet showing an example of how to perform analytical error propagation using a simple case and a more complex case using sensitivity ratios and uncertainty estimates as starting point. · README instructions for running the notebooks (.txt file) A text file with all details needed to set up a virtual environment with the right requirements to reproduce the notebooks, including versions of databases used. · README instructions for running the parametrized notebooks (.md file) A text file with all details needed to run the parametrized analysis. · Data for a dummy biobased product (.xlsx and .csv files) Data about a fictional product system for a biobased product that is imported in the notebooks (below) and used as example for applying the various techniques of sensitivity and uncertainty analysis. · Tutorial for assessment of model uncertainty (.ipynb and .html files) Tutorial in python code using the brightway open source LCA software to implement a specific case of assessment of modelling uncertainty due to the modelling choice of energy mix. Can be adapted to design and perform similar types of analysis on other modelling choices. · Tutorial for comparative Monte Carlo simulation (.ipynb and .html files) Tutorial in python code using the brightway open source LCA software to implement the uncertainty analysis in the case of a comparative assessment of two alternative products, using stochastic simulation. Can be adapted to design and perform a similar analysis on another case. · Tutorial for One at Time (OAT) sensitivity analysis (.ipynb and .html files) Tutorial in python code using the brightway open source LCA software to implement a one at the time sensitivity analysis on several parameters. Can be adapted to design and perform a similar analysis on another case. · Tutorial for Global Sensitivity Analysis (GSA) using correlation (.ipynb and .html files) Tutorial in python code using the brightway open source LCA software to implement a global sensitivity analysis where the values of multiple parameters are changed simultaneously. Correlation analysis is used to estimate the sensitivity associated to each parameter. Can be adapted to design and perform a similar analysis on another case. · Tutorial for Global Sensitivity Analysis (GSA) with FAST method (.ipynb and .html files) Tutorial in python code using the brightway open source LCA software to implement a global sensitivity analysis where the values of multiple parameters are changed simultaneously. A specific sampling strategy is used and a specific sensitivity index is calculated. Can be adapted to design and perform a similar analysis on another case. · Tutorial for Parametrized uncertainty and sensitivity analysis (.ipynb and .html files) Tutorial in python code using the brightway open source LCA software to implement local sensitivity, global sensitivity, and uncertainty analysis for inventories that are parametrized, i.e. inventories that are generated starting from variables that are outside the LCA model. · Importers for life cycle inventory data and for parameters (.py file) Specific python script used to import the data · A change log (.txt file) List of changes compared to previous version.