Application of uncertainty and variability in LCA

As yet, the application of an uncertainty and variability analysis is not common practice in LCAs. A proper analysis will be facilitated when it is clear which types of uncertainties and variabilities exist in LCAs and which tools are available to deal with them. Therefore, a framework is developed to classify types of uncertainty and variability in LCAs. Uncertainty is divided in (1) parameter uncertainty, (2) model uncertainty, and (3) uncertainty due to choices, while variability covers (4) spatial variability, (5) temporal variability, and (6) variability between objects and sources. A tool to deal with parameter uncertainty and variability between objects and sources in both the inventory and the impact assessment is probabilistic simulation. Uncertainty due to choices can be dealt with in a scenario analysis or reduced by standardisation and peer review. The feasibility of dealing with temporal and spatial variability is limited, implying model uncertainty in LCAs. Other model uncertainties can be reduced partly by more sophisticated modelling, such as the use of non-linear inventory models in the inventory and multi media models in the characterisation phase.     

How LCA studies deal with uncertainty

In recent years many workers have examined the implications of various sources of uncertainty for the reliability of Life Cycle Assessment (LCA). Indeed, the International Standardization Organization (ISO) has recognised the relevance of this work by including several cautionary statements in the ISO 14040 series of standards. However, in practice, there is a risk that the significance of these uncertainties for the results of an LCA could be overlooked as practitioners strive to complete studies on time and within budget. This paper presents the findings of a survey of LCA studies we made to determine the extent to which the problem of uncertainty had been dealt with in practice. This survey revealed that the significance of the limitations on the reliability of LCA results given in the standards has not been fully appreciated by practitioners. We conclude that the standards need to be revised to ensure that LCA studies include at least a qualitative discussion on all relevant aspects of uncertainty.     

Sensitivity coefficient-based uncertainty analysis for multi-functionality in LCA

Purpose

In LCA, a multi-functionality problem exists whenever the environmental impacts of a multi-functional process have to be allocated between its multiple functions. Methods for fixing this multi-functionality problem are controversially discussed because the methods include ambiguous choices. To study the influence of these choices, the ISO standard requires a sensitivity analysis. This work presents an analytical method for analyzing sensitivities and uncertainties of LCA results with respect to the choices made when a multi-functionality problem is fixed.

Methods

The existing matrix algebra for LCA is expanded by explicit equations for methods that fix multi-functionality problems: allocation and avoided burden. For allocation, choices exist between alternative allocation factors. The expanded equations allow calculating LCA results as a function of allocation factors. For avoided burden, choices exist in selecting an avoided burden process from multiple candidates. This choice is represented by so-called aggregation factors. For avoided burden, the expanded equations calculate LCA results as a function of aggregation factors. The expanded equations are used to derive sensitivity coefficients for LCA results with respect to allocation factors and aggregation factors. Based on the sensitivity coefficients, uncertainties due to fixing a multi-functionality problem by allocation or avoided burden are analytically propagated. The method is illustrated using a virtual numerical example.

Results and discussion

The presented approach rigorously quantifies sensitivities of LCA results with respect to the choices made when multi-functionality problems are fixed with allocation and avoided burden. The uncertainties due to fixing multi-functionality problems are analytically propagated to uncertainties in LCA results using a first-order approximation. For uncertainties in allocation factors, the first-order approximation is exact if no loops of the allocated functional flows exist. The contribution of uncertainties due to fixing multi-functionality problems can be directly compared to the uncertainty contributions induced by uncertain process data or characterization factors. The presented method allows the computationally efficient study of uncertainties due to fixing multi-functionality problems and could be automated in software tools.

Conclusions

This work provides a systematic method for the sensitivity analysis required by the ISO standard in case choices between alternative allocation procedures exist. The resulting analytical approach includes contributions of uncertainties in process data, characterization factors, and—in extension to existing methods—uncertainties due to fixing multi-functionality problems in a unifying rigorous framework. Based on the uncertainty contributions, LCA practitioners can select fields for data refinement to decrease the overall uncertainty in LCA results.     

Propagating aleatory and epistemic uncertainty in land cover change prediction process

An objective of satellite remote sensing is to predict or characterize the land cover change (LCC) over time. Sometimes we are capable of describing the changes of land cover with a probability distribution. However, we need sufficient knowledge about the natural variability of these changes, which is not always possible. In general, uncertainties can be subdivided into aleatory and epistemic. The main problem is that classical probability theory does not make a clear distinction between aleatory and epistemic uncertainties in the way they are represented, i.e., both of them are described with a probability distribution. The aim of this paper is to propagate the aleatory and epistemic uncertainty associated with both input parameters (features extracted from satellite image object) and model structure of LCC prediction process using belief function theory. This will help reducing in a significant way the uncertainty about future changes of land cover. In this study, the changes prediction of land cover in Cairo region, Egypt for next 16 years (2030) is anticipated using multi-temporal Landsat TM5 satellite images in 1987 and 2014. The LCC prediction model results indicated that 15% of the agriculture and 6.5% of the desert will be urbanized in 2030. We conclude that our method based on belief function theory has a potential to reduce uncertainty and improve the prediction accuracy and is applicable in LCC analysis.     

Parameter uncertainty in LCA: stochastic sampling under correlation

Purpose  

At the parameter level, data inaccuracy, data gaps, and the use of unrepresentative data have been recognized as sources of uncertainty in life cycle assessment (LCA). In many LCA uncertainty studies, parameter distributions were created based on the measured variability or on “rules of thumb,” but the possible existence of correlation was not explored. The correlation between parameters may alter the sampling space and, thus, yield unrepresentative results. The objective of this article is to describe the effect of correlation between input parameters (and the final product) on the outcome of an uncertainty analysis, carried out for an LCA of an agricultural product.     

Inclusion of uncertainty in the LCA comparison of different cherry tomato production scenarios

Purpose

Knowledge regarding environmental impacts of agricultural systems is required. Consideration of uncertainty in life cycle assessment (LCA) provides additional scientific information for decision making. The aims of this study were to compare the environmental impacts of different growing cherry tomato cultivation scenarios under Mediterranean conditions and to assess the uncertainty associated to the different agricultural production scenarios.

Materials and methods

The burdens associated to cherry tomato production were calculated and evaluated by the LCA methodology. The functional unit (FU) chosen for this study was the mass unit of 1 t of commercial loose cherry tomatoes. This study included the quantitative uncertainty analysis through Monte Carlo simulation. Three scenarios were considered: greenhouse (GH), screenhouse (SH), and open field (OF). The flows and processes of the product scenario were structured in several sections: structure, auxiliary equipment, fertilizers, crop management, pesticides, and waste management. Six midpoint impact categories were selected for their relevance: climate change, terrestrial acidification, marine eutrophication, metal depletion, and fossil depletion using the impact evaluation method Recipe Midpoint and ecotoxicity using USEtox.

Results and discussion

The structure, auxiliary equipment, and fertilizers produced the largest environmental impacts in cherry tomato production. The greatest impact in these stages was found in the manufacture and drawing of the steel structures, manufacture of perlite, the amount of HDPE plastics used, and the electricity consumed by the irrigation system and the manufacture and application of fertilizers. GH was the cropping scenario with the largest environmental impact in most categories (varying from 18 and 37% higher than SH and OF, respectively, in metal depletion, to 96% higher than SH and OF, in eutrophication). OF showed the highest uncertainty in ecotoxicity, with a bandwidth of 60 CTUe and a probability of 100 and 99.4% to be higher than GH and SH, respectively.

Conclusions

The LCA was used to improve the identification and evaluation of the environmental burdens for cherry tomato production in the Mediterranean area. This study demonstrates the significance of conducting uncertainty analyses for comparative LCAs used in comparative relative product environmental impacts.

     

Assessing the individual and combined effects of uncertainty and variability sources in comparative LCA of pavements

Purpose

Several efforts have attempted to incorporate the sources of uncertainty and variability into the life cycle assessment (LCA) of pavements. However, no method has been proposed that can simultaneously consider data quality, methodological choices, and variability in inputs and outputs without the need for complementary software. This study aims to develop and implement a flexible method that can be used in the LCA software to assess the effects of these sources on the conclusions.

Methods

A Monte Carlo analysis was conducted and applied in a comparative LCA of pavements to assess the preferred scenario. The uncertainty of the results was first estimated by considering the data quality using the ecoinvent database. Moreover, the variabilities of the materials, construction methods, and repair stages of the pavement life cycle were included in the analysis by assigning continuous uniform probability distributions to each variable. Ultimately, the probability of methodological choices was modeled using uniform distributions and assigning a portion of the area of the distribution to each scenario. The individual and combined effects of these uncertainty and variability sources were assessed in a comparative LCA of asphalt and concrete pavements in a cold region such as Quebec (Canada).

Results and discussion

The results of the Monte Carlo analysis show that the allocation choices can change the environmentally preferred scenario in four midpoint categories. These categories are significantly dominated by the crude oil supply chain. The variability in construction materials and methods can change the preferred scenario in the damage categories, namely, human health and global warming. Additionally, parameter uncertainty has a significant effect on the conclusion of the preferred scenario in ecosystem quality. The worst qualitative scores are given to the geographical uncertainty of the elementary flow that primarily contributes to this category (i.e., zinc). The simultaneous effect of the uncertainty and variability sources prevents the decision-maker from reaching a less uncertain conclusion about ecosystem quality, human health, and global warming effects.

Conclusions

This study demonstrates that it is feasible to assess the cumulative effects of common uncertainty and variability sources using commercial LCA software, including Monte Carlo simulation. Based on the variability and uncertainty of the results, the identification of a certain conclusion is case specific at both the midpoint and endpoint levels. Increasing the quality of the inventory is one solution to decreasing the uncertainties related to human health, ecosystem quality, and global warming regarding pavement LCA. This improvement can be achieved by avoiding the adaptation of a similar process to match the considered process and using practical construction efficiencies and realistic construction materials. The effectiveness of these tasks must be assessed in future studies. It should be noted that these conclusions were determined regardless of the uncertainty in the characterization factors of the impact assessment method.

     

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.

     

A stochastic collocation method for uncertainty quantification and propagation in cardiovascular simulations

Simulations of blood flow in both healthy and diseased vascular models can be used to compute a range of hemodynamic parameters including velocities, time varying wall shear stress, pressure drops, and energy losses. The confidence in the data output from cardiovascular simulations depends directly on our level of certainty in simulation input parameters. In this work, we develop a general set of tools to evaluate the sensitivity of output parameters to input uncertainties in cardiovascular simulations. Uncertainties can arise from boundary conditions, geometrical parameters, or clinical data. These uncertainties result in a range of possible outputs which are quantified using probability density functions (PDFs). The objective is to systemically model the input uncertainties and quantify the confidence in the output of hemodynamic simulations. Input uncertainties are quantified and mapped to the stochastic space using the stochastic collocation technique. We develop an adaptive collocation algorithm for Gauss-Lobatto-Chebyshev grid points that significantly reduces computational cost. This analysis is performed on two idealized problems--an abdominal aortic aneurysm and a carotid artery bifurcation, and one patient specific problem--a Fontan procedure for congenital heart defects. In each case, relevant hemodynamic features are extracted and their uncertainty is quantified. Uncertainty quantification of the hemodynamic simulations is done using (a) stochastic space representations, (b) PDFs, and (c) the confidence intervals for a specified level of confidence in each problem.     

The propagation of uncertainty in human mortality processes operating in stochastic environments

This paper presents a model describing how the uncertainty due to influential exogenous processes combines with stochasticity intrinsic to physiological aging processes and propagates through time to generate uncertainty about the future physiological state of the population. Variance expressions are derived for (a) the future values of the physiological variables under the assumption that external factors evolve under a linear stochastic diffusion process, and (b) the cohort survival functions and cohort life expectancies which reflect the uncertainty in the future values of the physiological variables. The model implies that a major component of uncertainty in forecasts of the physiological characteristics of a closed cohort is due to differential rates of survival associated with different realizations of the external process. This suggests that the limits to forecasting may be different in physiological systems subject to systematic mortality than in physical systems such as weather where the concepts of closed cohorts and of mortality selection have no simple analog.     

Transportation in LCA

The purpose of this paper is to investigate whether transport and logistics substantially contribute to the environmental interventions and impacts identified in LCAs. Four LCAs, encompassing very different products in different countries, were screened for the relative contribution of transport to the overall environmental interventions and impacts. Aside from this, the contribution of transport within individual life cycle phases was investigated. In none of the LCAs did transport contribute to less than 5% of the energy related interventions or impacts, whereas contributions with more than ten percent occurred regularly, especially in events involving NO_x related impact. The importance of transport strongly depends on the kind of product studied. It seems to be especially important for agricultural products. With respect to individual phases of the life cycle, the study indicates that special attention is required for the transport of raw materials, for use phase of electronics and for the disposal phase of recyclable products.     

Quantifying the propagation of parametric uncertainty on flux balance analysis

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g mmol⁻¹, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.     

Stochastic simulations on the reliability of action potential propagation in thin axons

It is generally assumed that axons use action potentials (APs) to transmit information fast and reliably to synapses. Yet, the reliability of transmission along fibers below 0.5 μm diameter, such as cortical and cerebellar axons, is unknown. Using detailed models of rodent cortical and squid axons and stochastic simulations, we show how conduction along such thin axons is affected by the probabilistic nature of voltage-gated ion channels (channel noise). We identify four distinct effects that corrupt propagating spike trains in thin axons: spikes were added, deleted, jittered, or split into groups depending upon the temporal pattern of spikes. Additional APs may appear spontaneously; however, APs in general seldom fail (<1%). Spike timing is jittered on the order of milliseconds over distances of millimeters, as conduction velocity fluctuates in two ways. First, variability in the number of Na channels opening in the early rising phase of the AP cause propagation speed to fluctuate gradually. Second, a novel mode of AP propagation (stochastic microsaltatory conduction), where the AP leaps ahead toward spontaneously formed clusters of open Na channels, produces random discrete jumps in spike time reliability. The combined effect of these two mechanisms depends on the pattern of spikes. Our results show that axonal variability is a general problem and should be taken into account when considering both neural coding and the reliability of synaptic transmission in densely connected cortical networks, where small synapses are typically innervated by thin axons. In contrast we find that thicker axons above 0.5 μm diameter are reliable.     

Traffic noise in LCA

Background, aim, and scope  

According to some recent studies, noise from road transport is estimated to cause human health effects of the same order of magnitude as the sum of all other emissions from the transport life cycle. Thus, ISO 14′040 implies that traffic noise effects should be considered in life cycle assessment (LCA) studies where transports might play an important role. So far, five methods for the inclusion of noise in LCA have been proposed. However, at present, none of them is implemented in any of the major life cycle inventory (LCI) databases and commonly used in LCA studies. The goal of the present paper is to define a requirement profile for a method to include traffic noise in LCA and to assess the compliance of the five existing methods with this profile. It concludes by identifying necessary cornerstones for a model for noise effects of generic road transports that meets all requirements.     

Incorporating costs in LCA

The goal of LCA is to identify the environmental impacts resulting from a product, process, or activity. While LCA is useful for evaluating environmental attributes, it stops short of providing information that business managers routinely utilize for decision-making — i.e., dollars. Thus, decisions regarding the processes used for manufacturing products and the materials comprising those products can be enhanced by weaving cost and environmental information into the decision-making process. Various approaches have been used during the past decade to supplement environmental information with cost information. One of these tools is environmental accounting, the identification, analysis, reporting, and use of environmental information, including environmental cost data. Environmental cost accounting provides information necessary for identifying the true costs of products and processes and for evaluating opportunities to minimize those costs. As demonstrated through two case studies, many companies are incorporating environmental cost information into their accounting systems to prioritize investments in new technologies and products.