Purpose Life cycle assessment (LCA) is intended as a quantitative decision support tool. However, the large amount of uncertainty characteristic of LCA studies reduces confidence in results. To date, little research has been reported regarding the comparative sources of uncertainty (and their relative importance) and how, or how commonly, they are quantified in attributional and consequential LCA. This paper answers these questions based on a review of recent LCA studies and methods papers, and advances recommendations for improved practice. Methods All relevant LCA methods papers as well as case studies (amounting to 2687 journal articles) published from 2014 to 2018 in the top seven journals publishing LCA studies were reviewed. Common sources and methods for analysis of uncertainty in both attributional and consequential LCA were described, and their frequency of application evaluated. Observed practices were compared to best practice recommendations from methods papers, and additional recommendations were advanced. Results and discussion Less than 20% of LCA studies published in the past five years reported any kind of uncertainty analysis. There are many different sources of uncertainty in LCA, which can be classified as parameter, scenario or model uncertainty. Parameter uncertainty is most often reported, although the other types are considered equally important. There are also sources of uncertainty specific to each kind of LCA-in particular related to the resolution of multi-functionality problems (i.e. allocation in attributional LCA versus the definition of market-mediated substitution scenarios in consequential LCA). However, there are currently no widely applied methods to specifically account for these sources of uncertainty other than sensitivity analysis. Monte Carlo sampling was the most popular method used for propagating uncertainty results, regardless of LCA type. Conclusions Data quality scores and inherent (i.e. stochastic) uncertainty data are widely available in LCI databases, and researchers should generally be able to define comparable uncertainty information for their primary data. Moreover, uncertainty propagation for parameter uncertainty is supported by LCA modelling software. There are hence no obvious barriers to quantifying parameter uncertainty in LCA studies. More standardized methods based upon context-specific data that strike the right balance between comprehensiveness and usability are, however, necessary in order to better account for both the shared and unique sources of uncertainty in attributional and consequential LCAs. More frequent and comprehensive reporting of uncertainty analysis is strongly recommended for published LCA studies. Improved practices should be encouraged and supported by peer-reviewers, editors, LCI databases and LCA software developers.

Deep eutectic solvent (DES) has recently been attracting great interest for its role in isolating nanocellulose owing to its distinct advantages of biodegradability, low toxicity, and recyclability. Lignin‐containing cellulose nanocrystals (LCNCs) obtained using DES pretreatment has led to an improvement in the production of nanomaterials. Understanding the potential environmental impacts of this novel technology at the laboratory scale provides important insights to improve its sustainability at full scale in the future. This study evaluates the environmental impacts of the production of LCNCs from thermomechanical pulp (TMP) following acidic DES pretreatment (using a binary system of ‘choline chloride – oxalic acid dihydrate’ or a ternary system of ‘choline chloride – oxalic acid dihydrate – p‐toluenesulfonic acid’) based on various laboratory trials. The evaluation was conducted through a cradle‐to‐gate life‐cycle assessment for global warming potential (GWP), acidification potential (AP) and the cumulative energy demand (MJ). The average GWP, AP, and energy use were 39 kg CO2‐eq, 0.17 kg SO2‐eq, and 995 MJ per kg LCNCs, respectively. The sensitivity analysis showed that different degrees of reduction in environmental impact could be achieved by varying the input volume and/or reuse frequency of DES solutions. The largest reductions in GWP, AP, and energy use were achieved by reducing the input volume of DES solutions to 20% of its default value. The results of this LCA study illustrate the direction for future research and development (R&D) to further improve the sustainability of this DES‐mediated LCNC production technology. Through comparisons with the existing literature, this study also confirms the predominant contribution of chemical manufacturing to the overall environmental impacts of nanocellulose isolation technologies in general. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd

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Missing data is the key challenge facing life cycle inventory (LCI) modeling. The collection of missing data can be cost-prohibitive and infeasible in many circumstances.Major strategies to address this issue include proxy selection (i.e., selecting a surrogate dataset to represent the missing data) and data creation (e.g., through empirical equations or mechanistic models). Within these two strategies, we identified three approaches that are widely used for LCI modeling: Data-driven, mechanistic, and future (e.g., 2050) inventory modeling. We critically reviewed the 12 common methods of these three approaches by focusing on their features, scope of application, underlying assumptions, and limitations. These methods were characterized based on the following criteria: "domain knowledge requirement" (both as a method developer and a user), "post-treatment requirement," "challenge in assessing data quality uncertainty," "challenge in generalizability," and "challenge in automation." These criteria can be used by LCA practitioners to select the suitable method(s) to bridge the data gap in LCI modeling, based on the goal and scope of the intended study. We also identified several aspects for future improvement for these reviewed methods.