Analytical uncertainty propagation in life cycle inventory and impact assessment: application to an automobile front panel

Background, aim, and scope

Uncertainty information is essential for the proper use of life cycle assessment (LCA) and environmental assessments in decision making. So far, parameter uncertainty propagation has mainly been studied using Monte Carlo techniques that are relatively computationally heavy to conduct, especially for the comparison of multiple scenarios, often limiting its use to research or to inventory only. Furthermore, Monte Carlo simulations do not automatically assess the sensitivity and contribution to overall uncertainty of individual parameters. The present paper aims to develop and apply to both inventory and impact assessment an explicit and transparent analytical approach to uncertainty. This approach applies Taylor series expansions to the uncertainty propagation of lognormally distributed parameters.

Materials and methods

We first apply the Taylor series expansion method to analyze the uncertainty propagation of a single scenario, in which case the squared geometric standard deviation of the final output is determined as a function of the model sensitivity to each input parameter and the squared geometric standard deviation of each parameter. We then extend this approach to the comparison of two or more LCA scenarios. Since in LCA it is crucial to account for both common inventory processes and common impact assessment characterization factors among the different scenarios, we further develop the approach to address this dependency. We provide a method to easily determine a range and a best estimate of (a) the squared geometric standard deviation on the ratio of the two scenario scores, “A/B”, and (b) the degree of confidence in the prediction that the impact of scenario A is lower than B (i.e., the probability that A/B<1). The approach is tested on an automobile case study and resulting probability distributions of climate change impacts are compared to classical Monte Carlo distributions.

Results

The probability distributions obtained with the Taylor series expansion lead to results similar to the classical Monte Carlo distributions, while being substantially simpler; the Taylor series method tends to underestimate the 2.5% confidence limit by 1-11% and the 97.5% limit by less than 5%. The analytical Taylor series expansion easily provides the explicit contributions of each parameter to the overall uncertainty. For the steel front end panel, the factor contributing most to the climate change score uncertainty is the gasoline consumption (>75%). For the aluminum panel, the electricity and aluminum primary production, as well as the light oil consumption, are the dominant contributors to the uncertainty. The developed approach for scenario comparisons, differentiating between common and independent parameters, leads to results similar to those of a Monte Carlo analysis; for all tested cases, we obtained a good concordance between the Monte Carlo and the Taylor series expansion methods regarding the probability that one scenario is better than the other.

Discussion

The Taylor series expansion method addresses the crucial need of accounting for dependencies in LCA, both for common LCI processes and common LCIA characterization factors. The developed approach in Eq. 8, which differentiates between common and independent parameters, estimates the degree of confidence in the prediction that scenario A is better than B, yielding results similar to those found with Monte Carlo simulations.

Conclusions

The probability distributions obtained with the Taylor series expansion are virtually equivalent to those from a classical Monte Carlo simulation, while being significantly easier to obtain. An automobile case study on an aluminum front end panel demonstrated the feasibility of this method and illustrated its simultaneous and consistent application to both inventory and impact assessment. The explicit and innovative analytical approach, based on Taylor series expansions of lognormal distributions, provides the contribution to the uncertainty from each parameter and strongly reduces calculation time.     

Error propagation methods for LCA—a comparison

Purpose

The analysis of uncertainty in life cycle assessment (LCA) studies has been a topic for more than 10 years, and many commercial LCA programs now feature a sampling approach called Monte Carlo analysis. Yet, a full Monte Carlo analysis of a large LCA system, for instance containing the 4,000 unit processes of ecoinvent v2.2, is rarely carried out by LCA practitioners. One reason for this is computation time. An alternative faster than Monte Carlo method is analytical error propagation by means of a Taylor series expansion; however, this approach suffers from being explained in the literature in conflicting ways, hampering implementation in most software packages for LCA. The purpose of this paper is to compare the two different approaches from a theoretical and practical perspective.

Methods

In this paper, we compare the analytical and sampling approaches in terms of their theoretical background and their mathematical formulation. Using three case studies—one stylized, one real-sized, and one input–output (IO)-based—we approach these techniques from a practical perspective and compare them in terms of speed and results.

Results

Depending on the precise question, a sampling or an analytical approach provides more useful information. Whenever they provide the same indicators, an analytical approach is much faster but less reliable when the uncertainties are large.

Conclusions

For a good analysis, analytical and sampling approaches are equally important, and we recommend practitioners to use both whenever available, and we recommend software suppliers to implement both.     

Uncertainty in LCA case study due to allocation approaches and life cycle impact assessment methods

Purpose

Uncertainty is present in many forms in life cycle assessment (LCA). However, little attention has been paid to analyze the variability that methodological choices have on LCA outcomes. To address this variability, common practice is to conduct a sensitivity analysis, which is sometimes treated only at a qualitative level. Hence, the purpose of this paper was to evaluate the uncertainty and the sensitivity in the LCA of swine production due to two methodological choices: the allocation approach and the life cycle impact assessment (LCIA) method.

Methods

We used a comparative case study of swine production to address uncertainty due to methodological choices. First, scenario variation through a sensitivity analysis of the approaches used to address the multi-functionality problem was conducted for the main processes of the system product, followed by an impact assessment using five LCIA methods at the midpoint level. The results from the sensitivity analysis were used to generate 10,000 independent simulations using the Monte Carlo method and then compared using comparison indicators in histogram graphics.

Results and discussion

Regardless of the differences between the absolute values of the LCA obtained due to the allocation approach and LCIA methods used, the overall ranking of scenarios did not change. The use of the substitution method to address the multi-functional processes in swine production showed the highest values for almost all of the impact categories, except for freshwater ecotoxicity; therefore, this method introduced the greater variations into our analysis. Regarding the variation of the LCIA method, for acidification, eutrophication, and freshwater ecotoxicity, the results were very sensitive. The uncertainty analysis with the Monte Carlo simulations showed a wide range of results and an almost equal probability of all the scenarios be the preferable option to decrease the impacts on acidification, eutrophication, and freshwater ecotoxicity. Considering the aggregate result variation across allocation approaches and LCIA methods, the uncertainty is too high to identify a statistically significant alternative.

Conclusions

The uncertainty analysis showed that performing only a sensitivity analysis could mislead the decision-maker with respect to LCA results; our analysis with the Monte Carlo simulation indicates no significant difference between the alternatives compared. Although the uncertainty in the LCA outcomes could not be decreased due to the wide range of possible results, to some extent, the uncertainty analysis can lead to a less uncertain decision-making by demonstrating the uncertainties between the compared alternatives.

     

A global approach for sparse representation of uncertainty in Life Cycle Assessments of waste management systems

Purpose

Identification of key inputs and their effect on results from Life Cycle Assessment (LCA) models is fundamental. Because parameter importance varies greatly between cases due to the interaction of sensitivity and uncertainty, these features should never be defined a priori. However, exhaustive parametrical uncertainty analyses may potentially be complicated and demanding, both with analytical and sampling methods. Therefore, we propose a systematic method for selection of critical parameters based on a simplified analytical formulation that unifies the concepts of sensitivity and uncertainty in a Global Sensitivity Analysis (GSA) framework.

Methods

The proposed analytical method based on the calculation of sensitivity coefficients (SC) is evaluated against Monte Carlo sampling on traditional uncertainty assessment procedures, both for individual parameters and for full parameter sets. Three full-scale waste management scenarios are modelled with the dedicated waste LCA model EASETECH and a full range of ILCD recommended impact categories. Common uncertainty ranges of 10 % are used for all parameters, which we assume to be normally distributed. The applicability of the concepts of additivity of variances and GSA is tested on results from both uncertainty propagation methods. Then, we examine the differences in discernibility analyses results carried out with varying numbers of sampling points and parameters.

Results and discussion

The proposed analytical method complies with the Monte Carlo results for all scenarios and impact categories, but offers substantially simpler mathematical formulation and shorter computation times. The coefficients of variation obtained with the analytical method and Monte Carlo differ only by 1 %, indicating that the analytical method provides a reliable representation of uncertainties and allows determination of whether a discernibility analysis is required. The additivity of variances and the GSA approach show that the uncertainty in results is determined by a limited set of important parameters. The results of the discernibility analysis based on these critical parameters vary only by 1 % from discernibility analyses based on the full set, but require significantly fewer Monte Carlo runs.

Conclusions

The proposed method and GSA framework provide a fast and valuable approximation for uncertainty quantification. Uncertainty can be represented sparsely by contextually identifying important parameters in a systematic manner. The proposed method integrates with existing step-wise approaches for uncertainty analysis by introducing a global importance analysis before uncertainty propagation.

     

Comparative lca of industrial objects part 1: lca data quality assurance — sensitivity analysis and pedigree matrix

Goal, Scope and Background  The main aim of this paper is to present some methodological considerations concerning existing methods used to assess quality of the LCA study. It relates mainly to the quality of data and the uncertainty of the LCA results. The first paper is strictly devoted to methodological aspects whereas, the second is presented in a separate article (Part II) and devoted mainly to a case study. Methods  The presented analysis is based on two well-known concepts: the Data Quality Indicators (DQIs) and the Pedigree Matrix. In the first phase, the Sensitivity Indicators are created on the basis of the sensitivity analysis and then linked with the DQIs and the Quality Classes. These parameters indicate the relative importance of input data and their theoretical quality levels. Next, the Weidema’s Pedigree Matrix (slightly modified) is used to establish the values of the new parameter called the Data Quality Distance (DQD) and to link them with the DQIs and Quality Classes. This way the information about the “real” quality levels is provided. Further analysis is performed using the probabilistic distributions and Monte Carlo simulations. Results and Discussion  Thanks to this approach it is possible to make a comparison between two types of the quality factors. On the one hand, the sensitivity analysis allows one to check the importance of input data and to determine their required quality. It is done according to the following relation: the higher the sensitivity indicator, the higher the importance of input data and the higher quality should be demanded. On the other hand the data have a certain real quality, not always in accord with the demanded one. To make possible a comparison between these two types of quality, it is necessary to find and develop a common denominator for them. Here, for this purpose the DQIs and Quality Classes are used. Conclusions  In the further stage of the assessment the DQIs are used to perform the uncertainty analysis of the LCA results. The results could be additionally analysed by using other techniques of interpretation: the sensitivity-, the contribution-, the comparative-, the discernability- and the uncertainty analysis. Recommendations and Outlook  The presented approach is put into practice to conduct the comparative LCA study for the industrial pumps by using the Ecoindicator99 method. Thanks to this, complex analysis of the credibility of the results is carried out. As a consequence, uncertainty ranges for the LCA results of every product system can be determined [1].     

Model uncertainty analysis using data analytics for life-cycle assessment (LCA) applications

Purpose

Objective uncertainty quantification (UQ) of a product life-cycle assessment (LCA) is a critical step for decision-making. Environmental impacts can be measured directly or by using models. Underlying mathematical functions describe a model that approximate the environmental impacts during various LCA stages. In this study, three possible uncertainty sources of a mathematical model, i.e., input variability, model parameter (differentiate from input in this study), and model-form uncertainties, were investigated. A simple and easy to implement method is proposed to quantify each source.

Methods

Various data analytics methods were used to conduct a thorough model uncertainty analysis; (1) Interval analysis was used for input uncertainty quantification. A direct sampling using Monte Carlo (MC) simulation was used for interval analysis, and results were compared to that of indirect nonlinear optimization as an alternative approach. A machine learning surrogate model was developed to perform direct MC sampling as well as indirect nonlinear optimization. (2) A Bayesian inference was adopted to quantify parameter uncertainty. (3) A recently introduced model correction method based on orthogonal polynomial basis functions was used to evaluate the model-form uncertainty. The methods are applied to a pavement LCA to propagate uncertainties throughout an energy and global warming potential (GWP) estimation model; a case of a pavement section in Chicago metropolitan area was used.

Results and discussion

Results indicate that each uncertainty source contributes to the overall energy and GWP output of the LCA. Input uncertainty was shown to have significant impact on overall GWP output; for the example case study, GWP interval was around 50%. Parameter uncertainty results showed that an assumption of ±?10% uniform variation in the model parameter priors resulted in 28% variation in the GWP output. Model-form uncertainty had the lowest impact (less than 10% variation in the GWP). This is because the original energy model is relatively accurate in estimating the energy. However, sensitivity of the model-form uncertainty showed that even up to 180% variation in the results can be achieved due to lower original model accuracies.

Conclusions

Investigating each uncertainty source of the model indicated the importance of the accurate characterization, propagation, and quantification of uncertainty. The outcome of this study proposed independent and relatively easy to implement methods that provide robust grounds for objective model uncertainty analysis for LCA applications. Assumptions on inputs, parameter distributions, and model form need to be justified. Input uncertainty plays a key role in overall pavement LCA output. The proposed model correction method as well as interval analysis were relatively easy to implement. Research is still needed to develop a more generic and simplified MCMC simulation procedure that is fast to implement.

     

Uncertainty calculation in life cycle assessments

Goal and Background  Uncertainty is commonly not taken into account in LCA studies, which downgrades their usability for decision support. One often stated reason is a lack of method. The aim of this paper is to develop a method for calculating the uncertainty propagation in LCAs in a fast and reliable manner. Approach  The method is developed in a model that reflects the calculation of an LCA. For calculating the uncertainty, the model combines approximation formulas and Monte Carlo Simulation. It is based on virtual data that distinguishes true values and random errors or uncertainty, and that hence allows one to compare the performance of error propagation formulas and simulation results. The model is developed for a linear chain of processes, but extensions for covering also branched and looped product systems are made and described. Results  The paper proposes a combined use of approximation formulas and Monte Carlo simulation for calculating uncertainty in LCAs, developed primarily for the sequential approach. During the calculation, a parameter observation controls the performance of the approximation formulas. Quantitative threshold values are given in the paper. The combination thus transcends drawbacks of simulation and approximation. Conclusions and Outlook  The uncertainty question is a true jigsaw puzzle for LCAs and the method presented in this paper may serve as one piece in solving it. It may thus foster a sound use of uncertainty assessment in LCAs. Analysing a proper management of the input uncertainty, taking into account suitable sampling and estimation techniques; using the approach for real case studies, implementing it in LCA software for automatically applying the proposed combined uncertainty model and, on the other hand, investigating about how people do decide, and should decide, when their decision relies on explicitly uncertain LCA outcomes-these all are neighbouring puzzle pieces inviting to further work.     

Quantifying system uncertainty of life cycle assessment based on Monte Carlo simulation

Background, aim, and scope

Many studies evaluate the results of applying different life cycle impact assessment (LCIA) methods to the same life cycle inventory (LCI) data and demonstrate that the assessment results would be different with different LICA methods used. Although the importance of uncertainty is recognized, most studies focus on individual stages of LCA, such as LCI and normalization and weighting stages of LCIA. However, an important question has not been answered in previous studies: Which part of the LCA processes will lead to the primary uncertainty? The understanding of the uncertainty contributions of each of the LCA components will facilitate the improvement of the credibility of LCA.

Methodology

A methodology is proposed to systematically analyze the uncertainties involved in the entire procedure of LCA. The Monte Carlo simulation is used to analyze the uncertainties associated with LCI, LCIA, and the normalization and weighting processes. Five LCIA methods are considered in this study, i.e., Eco-indicator 99, EDIP, EPS, IMPACT 2002+, and LIME. The uncertainty of the environmental performance for individual impact categories (e.g., global warming, ecotoxicity, acidification, eutrophication, photochemical smog, human health) is also calculated and compared. The LCA of municipal solid waste management strategies in Taiwan is used as a case study to illustrate the proposed methodology.

Results

The primary uncertainty source in the case study is the LCI stage under a given LCIA method. In comparison with various LCIA methods, EDIP has the highest uncertainty and Eco-indicator 99 the lowest uncertainty. Setting aside the uncertainty caused by LCI, the weighting step has higher uncertainty than the normalization step when Eco-indicator 99 is used. Comparing the uncertainty of various impact categories, the lowest is global warming, followed by eutrophication. Ecotoxicity, human health, and photochemical smog have higher uncertainty.

Discussion

In this case study of municipal waste management, it is confirmed that different LCIA methods would generate different assessment results. In other words, selection of LCIA methods is an important source of uncertainty. In this study, the impacts of human health, ecotoxicity, and photochemical smog can vary a lot when the uncertainties of LCI and LCIA procedures are considered. For the purpose of reducing the errors of impact estimation because of geographic differences, it is important to determine whether and which modifications of assessment of impact categories based on local conditions are necessary.

Conclusions

This study develops a methodology of systematically evaluating the uncertainties involved in the entire LCA procedure to identify the contributions of different assessment stages to the overall uncertainty. Which modifications of the assessment of impact categories are needed can be determined based on the comparison of uncertainty of impact categories.

Recommendations and perspectives

Such an assessment of the system uncertainty of LCA will facilitate the improvement of LCA. If the main source of uncertainty is the LCI stage, the researchers should focus on the data quality of the LCI data. If the primary source of uncertainty is the LCIA stage, direct application of LCIA to non-LCIA software developing nations should be avoided.     

A novel approach for uncertainty propagation applied to two different bio-waste management options

Purpose

A novel approach was used for quantifying uncertainty propagation in life cycle assessment (LCA). The approach was designed to be efficient and applicable in practice. The model was applied to a specific case study concerning alternative strategies for managing bio-waste: incineration versus anaerobic digestion followed by composting.

Methods

The uncertainty of each impact category was calculated starting from the variance (σ²) and geometric mean (μ) of the lognormal distribution of each input data. A procedure consisting of three mandatory steps and one facultative step was developed. Mandatory steps were calculation of the associated normal distribution for each input, calculation of the percentile curve for each input, and calculation of the percentile curve of the impact categories. The facultative step consisted in calculating the lognormal distribution of the impact categories if all the values of the percentile curve were >0.

Results and discussion

The uncertainty associated with the results of the anaerobic digestion and composting scenario was significantly higher than those associated with the incineration scenario. These results were confirmed by those obtained by Monte Carlo simulations. Environmental gains calculated for the scenario with incineration concerning acidification, global warming, terrestrial eutrophication, and photochemical ozone creation had a high level of probability (i.e., >90 %). On the contrary, the impact categories of the scenario with anaerobic digestion and composting had higher uncertainties.

Conclusions

The source of uncertainty in LCA analysis can be due to multiple factors. Among these, the variability of the values of the LCI can have a significant influence on the results of the study. LCA analysis based on the exploitation of geometric means and/or average values of inputs reported in LCI can lead to results affected by a low level of reliability. In particular, this aspect can play a relevant role for LCA-based decisions when different scenarios and options are compared. As in the case study reported in this work, neglecting the propagation of uncertainty can result in a relevant bias for obtaining a full informative impression of the problem analyzed.

     

Evaluation of Software for Propagating Uncertainty Through Risk Assessment Models

Quantitative uncertainty analysis has become a common component of risk assessments. In risk assessment models, the most robust method for propagating uncertainty is Monte Carlo simulation. Many software packages available today offer Monte Carlo capabilities while requiring minimal learning time, computational time, and/or computer memory. This paper presents an evalu ation of six software packages in the context of risk assessment: Crystal Ball, @Risk, Analytica, Stella II, PRISM, and Susa-PC. Crystal Ball and @Risk are spreadsheet based programs; Analytica and Stella II are multi-level, influence diagram based programs designed for the construction of complex models; PRISM and Susa-PC are both public-domain programs designed for incorpo rating uncertainty and sensitivity into any model written in Fortran. Each software package was evaluated on the basis of five criteria, with each criterion having several sub-criteria. A ‘User Preferences Table’ was also developed for an additional comparison of the software packages. The evaluations were based on nine weeks of experimentation with the software packages including use of the associated user manuals and test of the software through the use of example problems. The results of these evaluations indicate that Stella II has the most extensive modeling capabilities and can handle linear differential equations. Crystal Ball has the best input scheme for entering uncertain parameters and the best reference materials. @Risk offers a slightly better standard output scheme and requires a little less learning time. Susa-PC has the most options for detailed statistical analysis of the results, such as multiple options for a sensitivity analysis and sophisticated options for inputting correlations. Analytica is a versatile, menu- and graphics-driven package, while PRISM is a more specialized and less user friendly program. When choosing between software packages for uncertainty and sensitivity analysis, the choice largely depends on the specifics of the problem being modeled. However, for risk assessment problems that can be implemented on a spreadsheet, Crystal Ball is recommended because it offers the best input options, a good output scheme, adequate uncertainty and sensitivity analysis, superior reference materials, and an intuitive spreadsheet basis while requiring very little memory.     

Life cycle assessment of fuel cell vehicles a methodology example of input data treatment for future technologies

Life cycle assessment (LCA) will always involve some subjectivity and uncertainty. This reality is especially true when the analysis concerns new technologies. Dealing with uncertainty can generate richer information and minimize some of the result mismatches currently encountered in the literature. As a way of analyzing future fuel cell vehicles and their potential new fuels, the Fuel Upstream Energy and Emission Model (FUEEM) developed at the University of California—Davis, pioneered two different ways to incorporate uncertainty into the analysis. First, the model works with probabilistic curves as inputs and with Monte Carlo simulation techniques to propagate the uncertainties. Second, the project involved the interested parties in the entire process, not only in the critical review phase. The objective of this paper is to present, as a case study, the tools and the methodologies developed to acquire most of the knowledge held by interested parties and to deal with their — eventually conflicted—interests. The analysis calculation methodology, the scenarios, and all assumed probabilistic curves were derived from a consensus of an international expert network discussion, using existing data in the literature along with new information collected from companies. The main part of the expert discussion process uses a variant of the Delphi technique, focusing on the group learning process through the information feedback feature. A qualitative analysis indicates that a higher level of credibility and a higher quality of information can be achieved through a more participatory process. The FUEEM method works well within technical information and also in establishing a reasonable set of simple scenarios. However, for a complex combination of scenarios, it will require some improvement. The time spent in the process was the major drawback of the method and some alternatives to share this time cost are suggested.     

Uncertainty propagation of phase contrast-MRI derived inlet boundary conditions in computational hemodynamics models of thoracic aorta

This study investigates the impact that uncertainty in phase contrast-MRI derived inlet boundary conditions has on patient-specific computational hemodynamics models of the healthy human thoracic aorta. By means of Monte Carlo simulations, we provide advice on where, when and how, it is important to account for this source of uncertainty. The study shows that the uncertainty propagates not only to the intravascular flow, but also to the shear stress distribution at the vessel wall. More specifically, the results show an increase in the uncertainty of the predicted output variables, with respect to the input uncertainty, more marked for blood pressure and wall shear stress. The methodological approach proposed here can be easily extended to study uncertainty propagation in both healthy and pathological computational hemodynamic models.     

When the Background Matters: Using Scenarios from Integrated Assessment Models in Prospective Life Cycle Assessment

Prospective life cycle assessment (LCA) needs to deal with the large epistemological uncertainty about the future to support more robust future environmental impact assessments of technologies. This study proposes a novel approach that systematically changes the background processes in a prospective LCA based on scenarios of an integrated assessment model (IAM), the IMAGE model. Consistent worldwide scenarios from IMAGE are evaluated in the life cycle inventory using ecoinvent v3.3. To test the approach, only the electricity sector was changed in a prospective LCA of an internal combustion engine vehicle (ICEV) and an electric vehicle (EV) using six baseline and mitigation climate scenarios until 2050. This case study shows that changes in the electricity background can be very important for the environmental impacts of EV. Also, the approach demonstrates that the relative environmental performance of EV and ICEV over time is more complex and multifaceted than previously assumed. Uncertainty due to future developments manifests in different impacts depending on the product (EV or ICEV), the impact category, and the scenario and year considered. More robust prospective LCAs can be achieved, particularly for emerging technologies, by expanding this approach to other economic sectors beyond electricity background changes and mobility applications as well as by including uncertainty and changes in foreground parameters. A more systematic and structured composition of future inventory databases driven by IAM scenarios helps to acknowledge epistemological uncertainty and to increase the temporal consistency of foreground and background systems in LCAs of emerging technologies.     

Uncertainty analysis in LCA using precalculated aggregated datasets

Purpose

Some LCA software tools use precalculated aggregated datasets because they make LCA calculations much quicker. However, these datasets pose problems for uncertainty analysis. Even when aggregated dataset parameters are expressed as probability distributions, each dataset is sampled independently. This paper explores why independent sampling is incorrect and proposes two techniques to account for dependence in uncertainty analysis. The first is based on an analytical approach, while the other uses precalculated results sampled dependently.

Methods

The algorithm for generating arrays of dependently presampled aggregated inventories and their LCA scores is described. These arrays are used to calculate the correlation across all pairs of aggregated datasets in two ecoinvent LCI databases (2.2, 3.3 cutoff). The arrays are also used in the dependently presampled approach. The uncertainty of LCA results is calculated under different assumptions and using four different techniques and compared for two case studies: a simple water bottle LCA and an LCA of burger recipes.

Results and discussion

The meta-analysis of two LCI databases shows that there is no single correct approximation of correlation between aggregated datasets. The case studies show that the uncertainty of single-product LCA using aggregated datasets is usually underestimated when the correlation across datasets is ignored and that the magnitude of the underestimation is dependent on the system being analysed and the LCIA method chosen. Comparative LCA results show that independent sampling of aggregated datasets drastically overestimates the uncertainty of comparative metrics. The approach based on dependently presampled results yields results functionally identical to those obtained by Monte Carlo analysis using unit process datasets with a negligible computation time.

Conclusions

Independent sampling should not be used for comparative LCA. Moreover, the use of a one-size-fits-all correction factor to correct the calculated variability under independent sampling, as proposed elsewhere, is generally inadequate. The proposed approximate analytical approach is useful to estimate the importance of the covariance of aggregated datasets but not for comparative LCA. The approach based on dependently presampled results provides quick and correct results and has been implemented in EcodEX, a streamlined LCA software used by Nestlé. Dependently presampled results can be used for streamlined LCA software tools. Both presampling and analytical solutions require a preliminary one-time calculation of dependent samples for all aggregated datasets, which could be centrally done by database providers. The dependent presampling approach can be applied to other aspects of the LCA calculation chain.

     

Uncertainty Quantification in Life Cycle Assessments: Exploring Distribution Choice and Greater Data Granularity to Characterize Product Use

The life cycle environmental profile of energy‐consuming products is dominated by the products’ use stage. Variation in real‐world product use can therefore yield large differences in the results of life cycle assessment (LCA). Adequate characterization of input parameters is paramount for uncertainty quantification and has been a challenge to wider adoption of the LCA method. After emphasis in recent years on methodological development, data development has become the primary focus again. Pervasive sensing presents the opportunity to collect rich data sets and improve profiling of use‐stage parameters. Illustrating a data‐driven approach, we examine energy use in domestic cooling systems, focusing on climate change as the impact category. Specific objectives were to examine: (1) how characterization of the use stage by different probability distributions and (2) how characterizing data aggregated at successively higher granularity affects LCA modeling results and the uncertainty in output. Appliance‐level electricity data were sourced from domestic residences for 3 years. Use‐stage variables were propagated in a stochastic model and analyses simulated by Monte Carlo procedure. Although distribution choice did not necessarily significantly impact the estimated output, there were differences in the estimated uncertainty. Characterization of use‐stage power consumption in the model at successively higher data granularity reduced the output uncertainty with diminishing returns. Results therefore justify the collection of high granularity data sets representing the life cycle use stage of high‐energy products. The availability of such data through proliferation of pervasive sensing presents increasing opportunities to better characterize data and increase confidence in results of LCA.     

Good modeling practice for PAT applications: Propagation of input uncertainty and sensitivity analysis

The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model‐building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input uncertainty resulting from assumptions of the model was propagated using the Monte Carlo procedure to estimate the output uncertainty. The results showed that significant uncertainty exists in the model outputs. Moreover the uncertainty in the biomass, glucose, ammonium and base‐consumption were found low compared to the large uncertainty observed in the antibiotic and off‐gas CO₂ predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases ‐ meaning the model describes some periods better than others. To understand which input parameters are responsible for the output uncertainty, three sensitivity methods (Standardized Regression Coefficients, Morris and differential analysis) were evaluated and compared. The results from these methods were mostly in agreement with each other and revealed that only few parameters (about 10) out of a total 56 were mainly responsible for the output uncertainty. Among these significant parameters, one finds parameters related to fermentation characteristics such as biomass metabolism, chemical equilibria and mass‐transfer. Overall the uncertainty and sensitivity analysis are found promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Characterizing,Propagating, and Analyzing Uncertainty in Life‐Cycle Assessment: A Survey of Quantitative Approaches

Life‐cycle assessment (LCA) practitioners build models to quantify resource consumption, environmental releases, and potential environmental and human health impacts of product systems. Most often, practitioners define a model structure, assign a single value to each parameter, and build deterministic models to approximate environmental outcomes. This approach fails to capture the variability and uncertainty inherent in LCA. To make good decisions, decision makers need to understand the uncertainty in and divergence between LCA outcomes for different product systems. Several approaches for conducting LCA under uncertainty have been proposed and implemented. For example, Monte Carlo simulation and fuzzy set theory have been applied in a limited number of LCA studies. These approaches are well understood and are generally accepted in quantitative decision analysis. But they do not guarantee reliable outcomes. A survey of approaches used to incorporate quantitative uncertainty analysis into LCA is presented. The suitability of each approach for providing reliable outcomes and enabling better decisions is discussed. Approaches that may lead to overconfident or unreliable results are discussed and guidance for improving uncertainty analysis in LCA is provided.     

Hybrid Framework for Managing Uncertainty in Life Cycle Inventories

Life cycle assessment (LCA) is increasingly being used to inform decisions related to environmental technologies and polices, such as carbon footprinting and labeling, national emission inventories, and appliance standards. However, LCA studies of the same product or service often yield very different results, affecting the perception of LCA as a reliable decision tool. This does not imply that LCA is intrinsically unreliable; we argue instead that future development of LCA requires that much more attention be paid to assessing and managing uncertainties. In this article we review past efforts to manage uncertainty and propose a hybrid approach combining process and economic input–output (I‐O) approaches to uncertainty analysis of life cycle inventories (LCI). Different categories of uncertainty are sometimes not tractable to analysis within a given model framework but can be estimated from another perspective. For instance, cutoff or truncation error induced by some processes not being included in a bottom‐up process model can be estimated via a top‐down approach such as the economic I‐O model. A categorization of uncertainty types is presented (data, cutoff, aggregation, temporal, geographic) with a quantitative discussion of methods for evaluation, particularly for assessing temporal uncertainty. A long‐term vision for LCI is proposed in which hybrid methods are employed to quantitatively estimate different uncertainty types, which are then reduced through an iterative refinement of the hybrid LCI method.     

Combining industrial ecology tools to assess potential greenhouse gas reductions of a circular economy: Method development and application to Switzerland

Industrial ecology (IE) methodologies, such as input/output or material flow analysis and life cycle assessment (LCA), are often used for the environmental evaluation of circular economy strategies. Up to now, an approach that utilizes these methods in a systematic, integrated framework for a holistic assessment of a geographic region's sustainable circular economy potential has been lacking. The approach developed in this study (IE4CE approach) combines IE methodologies to determine the environmental impact mitigation potential of circular economy strategies for a defined geographic region. The approach foresees five steps. First, input/output analysis helps identify sectors with high environmental impacts. Second, a refined analysis is conducted using material flow and LCA. In step 3, circular strategies are used for scenario design and evaluated in step 4. In step 5, the assessment results are compiled and compared across sectors. The approach was applied to a case study of Switzerland, analyzing 8 sectors and more than 30 scenarios in depth. Carbon capture and storage (CCS) from waste incineration, biogas and cement production, food waste prevention in households, hospitality and production, and the increased recycling of plastics had the highest mitigation potential. Most of the scenarios do not influence each other. One exception is the CCS scenarios: waste avoidance scenarios decrease the reduction potential of CCS. A combination of scenarios from different sectors, including their impact on the CCS scenario potential, led to an environmental impact mitigation potential of 11.9 Mt CO₂-eq for 2050, which equals 14% of Switzerland's current consumption-based impacts.     

Data quality

A methodology is presented that enables incorporating expert judgment regarding the variability of input data for environmental life cycle assessment (LCA) modeling. The quality of input data in the life-cycle inventory (LCI) phase is evaluated by LCA practitioners using data quality indicators developed for this application. These indicators are incorporated into the traditional LCA inventory models that produce non-varying point estimate results (i.e., deterministic models) to develop LCA inventory models that produce results in the form of random variables that can be characterized by probability distributions (i.e., stochastic models). The outputs of these probabilistic LCA models are analyzed using classical statistical methods for better decision and policy making information. This methodology is applied to real-world beverage delivery system LCA inventory models. The inventory study results for five beverage delivery system alternatives are compared using statistical methods that account for the variance in the model output values for each alternative. Sensitivity analyses are also performed that indicate model output value variance increases as input data uncertainty increases (i.e., input data quality degrades). Concluding remarks point out the strengths of this approach as an alternative to providing the traditional qualitative assessment of LCA inventory study input data with no efficient means of examining the combined effects on the model results. Data quality assessments can now be captured quantitatively within the LCA inventory model structure. The approach produces inventory study results that are variables reflecting the uncertainty associated with the input data. These results can be analyzed using statistical methods that make efficient quantitative comparisons of inventory study alternatives possible. Recommendations for future research are also provided that include the screening of LCA inventory model inputs for significance and the application of selection and ranking techniques to the model outputs.