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 global sensitivity analysis quantifies how much the uncertainty around each input parameter contributes to the output variance. To perform a global sensitivity analysis, regression-based methods may be used, but other variance decomposition methods, such as the Sobol' method, can be used as well.
The code for performing a global sensitivity analysis using the squared standardised regression coefficients (SSRC) in matrix-based life cycle assessment can be found here:
Matlab/Octave: MatLab code SSRC LCA
IPython notebook: IPython code SSRC LCA
Python: Python code SSRC LCA
The code for performing a global sensitivity analysis using the squared Spearman correlation coefficients (SSCC) in matrix-based life cycle assessment can be found here:
Matlab/Octave: MatLab code SSCC LCA
IPython notebook: IPython code SSCC LCA
Python: Python code SSCC LCA
The code for performing a global sensitivity analysis using a first order Taylor expansion, also knowns as key issue analysis (KIA) in matrix-based life cycle assessment can be found here:
Matlab/Octave: MatLab code KIA LCA
IPython notebook: IPython code KIA LCA
Python: Python code KIA LCA
The MatLab code for performing a global sensitivity analysis using the Sobol' indices in matrix-based life cycle assessment can be found here: MatLab code Sobol' method LCA
The MatLab code for performing a global sensitivity analysis using a random balance design (RBD) in matrix-based life cycle assessment can be found here: MatLab code RBD LCA
A global sensitivity analysis quantifies how much the uncertainty around each input parameter contributes to the output variance. To perform a global sensitivity analysis when input parameters are correlated, regression-based methods may still be used, but need to be adapted. An alternative to the regression-based method is to use the analytical approach described below.
The MatLab code for performing a global sensitivity analysis using the regression coefficients in matrix-based life cycle assessment with correlated input parameters can be found here:
Matlab/Octave: MatLab code correlated LCA (regression)
The MatLab code for performing a global sensitivity analysis using the key issue analysis extended for correlated input parameters in matrix-based life cycle assessment with correlated input parameters can be found here:
Matlab/Octave: MatLab code correlated LCA (analytic)
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 SSRC, SSCC, KIA, Sobol' indices and RBD was used in Methods for global sensitivity analysis in life cycle assessment, Internation Journal of Life Cycle Assessment, July, 2017.
The MatLab code for performing global sensitivity analysis with correlated input parameters (both the analytic and the sampling approach) was used in Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk?, Environmental Impact Assessment Review, January, 2017.
The MatLab code for performing global sensitivity analysis for a model with correlated input parameters 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.
The MatLab code for performing global sensitivity analysis using SSRC was used in Benchmarking nutrient losses of dairy farms: the effect of epistemic uncertainty, Agricultural Systems, September, 2017.
Last updated: September 7, 2017