Life Cycle Assessment under Uncertainty: A Scoping Review

Abstract

Today, life cycle assessment (LCA) is the most widely used approach to model and calculate the environmental impacts of products and processes. The results of LCAs are often said to be deterministic, even though the real-life applications are uncertain and vague. The uncertainty, which may be simply ignored, is one of the key factors influencing the reliability of LCA outcomes. Numerous sources of uncertainty in LCA are classified in various ways, such as parameter and model uncertainty, choices, spatial variability, temporal variability, variability between sources and objects, etc. Through a scoping review, the present study aims to identify and assess the frequency with which LCA studies reflect the uncertainty and what are the tools to cope with the uncertainty to map the knowledge gaps in the field to reveal the challenges and opportunities to have a robust LCA model. It is also investigated which database, methodology, software, etc., have been used in the life cycle assessment process. The results indicate that the most significant sources of uncertainty were in the model and process parameters, data variability, and the use of different methodologies and databases. The probabilistic approach or stochastic modeling, using numerical methods such as Monte Carlo simulation, was the dominating tool to cope with the uncertainty. There were four dominant LCA methodologies: CML, ReCiPe, IMPACT 2002+, and TRACI. The most commonly used LCA software and databases were SimaPro® and Ecoinvent®, respectively.

Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models

“…The difference in classification scores between multi-label and multi-class highlights an interesting conclusion that the LCI data from the PRTR transfer database would make it possible to construct QSTR models that predict potential outcomes for an industrial and hazardous chemical over an annual time horizon. Due to assumptions and uncertainty propagation, it would not be a good idea to attempt to adjust the time-scale for the PRTR transfer data . Environmental government agencies typically use annual data to evaluate chemical exposure and risk; therefore, applying and integrating the QSTR models into these types of evaluations will not present any hindrance .…”

“…Due to assumptions and uncertainty propagation, it would not be a good idea to attempt to adjust the time-scale for the PRTR transfer data. 60 Environmental government agencies typically use annual data to evaluate chemical exposure and risk; therefore, applying and integrating the QSTR models into these types of evaluations will not present any hindrance. 61 Except for the landfill QSTR, all of the QSTR models created using the one-vs-all technique have f 1 -scores that are higher than the targeted cutoff of 70%.…”

Anaerobic Digestion Process Modeling Under Uncertainty: A Narrative Review

“…Among the most common methods for estimating uncertainty is the Monte Carlo technique [16]. Statistical modeling and random sampling are used in Monte Carlo simulations to estimate mathematical functions and simulate the behavior of complex systems [17].…”

“…Statistical modeling and random sampling are used in Monte Carlo simulations to estimate mathematical functions and simulate the behavior of complex systems [17]. The purpose of this technique is to develop probabilistic models for real-world processes to estimate specific average properties, such as mathematical expectations, variances, and correlations [16]. A Monte Carlo simulation involves generating random numbers, simulating the random values with a more complicated distribution and performing calculations [18].…”

“…The SA technique is used in numerical models to estimate the effect of uncertainty on one or more input variables on output variables [20]. Additionally, SA can serve as a guide in conducting experimental analyses, reducing models, and estimating parameters [16]. SAs can be either local or global.…”