Global sensitivity analysis of correlated uncertainties in life cycle assessment - Aleksandra Kim; Christopher Mutel; Stefanie Hellweg (Journal of Industrial Ecology, 2025)
Recent advances in research have made global sensitivity analysis of very large and highly linear life cycle assessment systems feasible. In this paper, we build on these developments to include sensitivity analysis of correlated parameters and nonlinear models. We augment numerical uncertainty propagation with Monte Carlo simulations (i) to include propagation of uncertainty from uncertain variables in parameterized inventory datasets; (ii) to account for correlations between process inputs and outputs and in particular incorporate the carbon balance of combustion activities; (iii) to employ published time‐series data instead of static values for electricity generation market mixes in Europe; (iv) to ensure that inputs which are supposed to reach a fixed total (e.g., the percentage contributions of power sources to an electricity mix) actually do so consistently by using the Dirichlet distribution. We then iterate on existing global sensitivity analysis protocols for high‐dimensional systems to improve their computational performance. To correctly calculate sensitivity rankings for correlated inputs, we use SHapley Additive exPlanations as feature importance metrics with gradient boosted trees. Our results for a case study of climate change impacts of an average Swiss household confirm that neglecting correlations limits the validity of uncertainty and sensitivity analysis. Our methodology and correlated sampling modules are given as open source code.

A stepwise approach for Scenario-based Inventory Modelling for Prospective LCA (SIMPL) - Sabine Langkau; Bernhard Steubing; Christopher Mutel; Maulana Permana Ajie; Lorenz Erdmann; Ariane Voglhuber-Slavinsky; Matty Janssen (The International Journal of Life Cycle Assessment, 2023)
Purpose In prospective life cycle assessment (pLCA), inventory models represent a future state of a production system and therefore contain assumptions about future developments. Scientific quality should be ensured by using foresight methods for handling these future assumptions during inventory modelling. We present a stepwise approach for integrating future scenario development into inventory modelling for pLCA studies. Methods A transdisciplinary research method was used to develop the SIMPL approach for scenario-based inventory modelling for pLCA. Our interdisciplinary team of LCA and future scenario experts developed a first draft of the approach. Afterwards, 112 LCA practitioners tested the approach on prospective case studies in group work projects in three courses on pLCA. Lessons learned from application difficulties, misunderstandings and feedback were used to adapt the approach after each course. After the third course, reflection, discussion and in-depth application to case studies were used to solve the remaining problems of the approach. Ongoing courses and this article are intended to bring the approach into a broader application. Results and discussion The SIMPL approach comprises adaptations and additions to the LCA goal and scope phase necessary for prospective inventory modelling, particularly the prospective definition of scope items in reference to a time horizon. Moreover, three iterative steps for combined inventory modelling and scenario development are incorporated into the inventory phase. Step A covers the identification of relevant inventory parameters and key factors, as well as their interrelations. In step B, future assumptions are made, by either adopting them from existing scenarios or deriving them from the available information, in particular by integrating expert and stakeholder knowledge. Step C addresses the combination of assumptions into consistent scenarios using cross-consistency assessment and distinctness-based selection. Several iterations of steps A–C deliver the final inventory models. Conclusion The presented approach enables pLCA practitioners to systematically integrate future scenario development into inventory modelling. It helps organize possible future developments of a technology, product or service system, also with regard to future developments in the social, economic and technical environment of the technology. Its application helps to overcome implicit bias and ensures that the resulting assessments are consistent, transparently documented and useful for drawing practically relevant conclusions. The approach is also readily applicable by LCA practitioners and covers all steps of prospective inventory modelling.

LC‐IMPACT: A regionalized life cycle damage assessment method - Francesca Verones; Stefanie Hellweg; Assumpció Antón; Ligia B. Azevedo; Abhishek Chaudhary; Nuno Cosme; Stefano Cucurachi; Laura de Baan; Yan Dong; Peter Fantke; Laura Golsteijn; Michael Hauschild; et al. (Journal of Industrial Ecology, 2020)

Regionalization in LCA: current status in concepts, software and databases—69th LCA forum, Swiss Federal Institute of Technology, Zurich, 13 September, 2018 - Rolf Frischknecht; Stephan Pfister; Jonas Bunsen; Adrian Haas; Josef Känzig; Martin Kilga; Jens Lansche; Manuele Margni; Christopher Mutel; Jürgen Reinhard; Philippe Stolz; Rosalie van Zelm; et al. (The International Journal of Life Cycle Assessment, 2019)

Effects of Distribution Choice on the Modeling of Life Cycle Inventory Uncertainty: An Assessment on the Ecoinvent v2.2 Database - Stéphanie Muller; Christopher Mutel; Pascal Lesage; Réjean Samson (Journal of Industrial Ecology, 2018)
Summary Life cycle inventory data have multiple sources of uncertainty. These data uncertainties are often modeled using probability density functions, and in the ecoinvent database the lognormal distribution is used by default to model exchange uncertainty values. The aim of this article is to systematically measure the effect of this default distribution by changing from the lognormal to several other distribution functions and examining how this change affects the uncertainty of life cycle assessment results. Using the ecoinvent 2.2 inventory database, data uncertainty distributions are switched from the lognormal distribution to the normal, triangular, and gamma distributions. The effect of the distribution switching is assessed for both impact assessment results of individual products system, as well as comparisons between product systems. Impact assessment results are generated using 5,000 Monte Carlo iterations for each product system, using the Intergovernmental Panel on Climate Change (IPCC) 2001 (100‐year time frame) method. When comparing the lognormal distribution to the alternative default distributions, the difference in the resulting median and standard deviation values range from slight to significant, depending on the distributions used by default. However, the switch shows practically no effect on product system comparisons. Yet, impact assessment results are sensitive to how the data uncertainties are defined. In this article, we followed what we believe to be ecoinvent standard practice and preserved the “most representative” value. Practitioners should recognize that the most representative value can depart from the average of a probability distribution. Consistent default distribution choices are necessary when performing product system comparisons.

The application of the pedigree approach to the distributions foreseen in ecoinvent v3 - Stéphanie Muller; Pascal Lesage; Andreas Ciroth; Christopher Mutel; Bo P. Weidema; Réjean Samson (The International Journal of Life Cycle Assessment, 2016)

Streamlining scenario analysis and optimization of key choices in value chains using a modular LCA approach - Bernhard Steubing; Christopher Mutel; Florian Suter; Stefanie Hellweg (The International Journal of Life Cycle Assessment, 2016)

Lifting Industrial Ecology Modeling to a New Level of Quality and Transparency: A Call for More Transparent Publications and a Collaborative Open Source Software Framework - Stefan Pauliuk; Guillaume Majeau‐Bettez; Christopher L. Mutel; Bernhard Steubing; Konstantin Stadler (Journal of Industrial Ecology, 2015)
Summary Industrial ecology (IE) is a maturing scientific discipline. The field is becoming more data and computation intensive, which requires IE researchers to develop scientific software to tackle novel research questions. We review the current state of software programming and use in our field and find challenges regarding transparency, reproducibility, reusability, and ease of collaboration. Our response to that problem is fourfold: First, we propose how existing general principles for the development of good scientific software could be implemented in IE and related fields. Second, we argue that collaborating on open source software could make IE research more productive and increase its quality, and we present guidelines for the development and distribution of such software. Third, we call for stricter requirements regarding general access to the source code used to produce research results and scientific claims published in the IE literature. Fourth, we describe a set of open source modules for standard IE modeling tasks that represent our first attempt at turning our recommendations into practice. We introduce a Python toolbox for IE that includes the life cycle assessment (LCA) framework Brightway2, the ecospold2matrix module that parses unallocated data in ecospold format, the pySUT and pymrio modules for building and analyzing multiregion input‐output models and supply and use tables, and the dynamic_stock_model class for dynamic stock modeling. Widespread use of open access software can, at the same time, increase quality, transparency, and reproducibility of IE research.