Note to the user: all MatLab code is written in MatLab R2014, and some require additional toolboxes (e.g. the statistics toolbox, which is mentioned in the scripts). In case you don’t have access to MatLab, there is a free alternative called Octave available. Both the ipython notebook and the python scripts are written in Python 3.
A local sensitivity analysis quantifies the effect on the output when an input parameter is changed.
The code for performing a local sensitivity analysis using the multiplier method (MPM) in matrix-based life cycle assessment can be found here:
MatLab/Octave: MatLab code MPM LCA.
IPython notebook: IPhyton code MPM LCA
Python notebook: Python code MPM LCA
A screening analysis quantifies the effect on the output when an input parameter is changed according the to uncertainty range of an input parameter.
The MATLAB code for performing a screening analysis using the methods of elementary effects (MEE) in matrix-based life cycle assessment can be found here: MATLAB code MEE LCA.
Source: PhD thesis Evelyne Groen, An uncertain climate: the value of uncertainty and sensitivity analysis in environmental impact assessment of food, 2016
ISBN: 978-94-6257-755-8; DOI: 10.18174/375497
The MatLab code for performing MPM and MEE was used in Sensitivity analysis of greenhouse gas emissions from a pork production chain, Journal of Cleaner Production, August 2016 (Volume 129, Pages 202–211).
The MatLab code for performing MPM was used in Assessing greenhouse gas emissions of milk prodution: which parameters are essential?, The international Journal of Life Cycle Assessment, First online: 31 July, 2016.