It requires a good knowledge of the distributions of all input parameters and much more computing power. Global sensitivity analysis (GSA), on the contrary, is based on simultaneous variations of all parameters. It is simple and fast to carry out, but shows several limitations: It does not provide a quantitative assessment of the variance of the impacts and may hide the combined effect of several parameters.
Local analysis, which consists in varying one parameter at a time (OAT). Several approaches are available for sensitivity analysis, varying in complexity and accuracy: They allow sensitivity analyses that quantify the effects of parameter variation on the LCA result (Pianosi et al.
Reference LCA models provide such a parametrized description of a system. Stochastic uncertainty can be accounted for by describing the system using well-defined parameters (Wei et al. It corresponds to various configurations and technical choices within a single sector. Stochastic uncertainty is inherent to the variability of the system and cannot be reduced with a single static model. It can be reduced by acquiring more data and refining mathematical models (Clavreul et al. ( 2003) distinguishes two types of uncertainty, influencing the spread in LCA results:Įpistemic uncertainty is due to a lack of knowledge of the system or bad accuracy of the model. For example, for mono crystalline PV system, the Intergovernmental Panel on Climate Change (IPCC) reports a carbon footprint ranging from 30 to 215 g CO 2eq/kWh (Eldenhofer et al. However, the important spread of LCA results complicates decision making and may affect the confidence in this analytical method. Such assessments typically rely on life cycle assessment (LCA), a methodology estimating the potential environmental impacts of a system over all its life cycle stages (ISO 14040, 2006). In that context, authorities and industrial stakeholders increasingly request accurate assessments of the potential environmental impacts of energy systems to avoid any burden-shifting when replacing existing energy systems by renewable ones and provide decision support (Parliament 2014). Renewable energies are expected to develop rapidly in the next decades to help reduce greenhouse gas emissions and therefore limit global climate change (Eldenhofer et al. Further work could also integrate the uncertainty of background activities, described, for example, by pedigree matrices. The library mainly explores the uncertainties of the foreground activities. This work brings powerful and practical tools to the LCA community to better understand, identify, and quantify the sources of variation of environmental impacts and produce simplified models to spread the use of LCA among non-experts. The resulting models are both compact and aligned with the reference parametric LCA model of crystalline silicon PV systems. Based on these key parameters, we generated simplified arithmetic models for quick and simple multi-criteria environmental assessments to be used by non-expert LCA users. The proposed tools helped building a detailed parametric reference LCA model of the PV system to identify the range of variation of multi-criterion LCA results and the key foreground-related parameters explaining these variations. A comprehensive sensitivity analysis was performed based on the protocol and the complementary functions provided by lca_algebraic. The protocol and library were validated through their application to the assessment of impacts of mono crystalline photovoltaic (PV) systems. An additional algorithm uses the key parameters, identified from their high Sobol indices, to generate simplified arithmetic models for fast estimates of LCA results. Thanks to this processing speed, a large number of Monte Carlo simulations can be generated to evaluate the variation of the impacts and apply advanced statistic tools such as Sobol indices to quantify the contribution of each parameter to the final variance (Sobol in Math Comput Simul 55(1–3):271–280, 2001). The use of symbolic calculus eases the definition of parametric inventories and enables a very fast evaluation of impacts by factorizing the background activities. This library, written in Python and based on the framework Brightway2 (Mutel in J Open Source Softw 2(12):236, 2017) provides functions to support sensitivity analysis by bringing symbolic calculus to LCA. The tools consist of a standard protocol and an open-source library: lca_algebraic. In this paper, we present new tools to ease the analysis of the effect of variability and uncertainty on life cycle assessment (LCA) results.