A hybrid life cycle assessment model for comparison with conventional methodologies in Australia

Background, aim, and scope  

One barrier to the further implementation of LCA as a quantitative decision-support tool is the uncertainty created by the diversity of available analytical approaches. This paper compares conventional (‘process analysis’) and alternative (‘input–output analysis’) approaches to LCA, and presents a hybrid LCA model for Australia that overcomes the methodological limitations of process and input–output analysis and enables a comparison between the results achieved using each method. A case study from the water industry illustrates this comparison.     

Tool for the environmental assessment of cranes based on parameterization

Purpose  

Information constitutes one of the main barriers for applying life cycle assessment (LCA) due to complexity and need for great amounts of it. However, most of the parameters that determine the data are defined early in the product development process. Knuckle boom cranes constitute a complex product which poses a particularly pressing need for simplification. This paper models the LCA inventory information out of design parameters. The paper also presents a tool implementing this.     

Uncertainties in the application of the species area relationship for characterisation factors of land occupation in life cycle assessment

Purpose  

Uncertainties in land use damage modeling are recognized, but hardly quantified in life cycle assessment (LCA). The objective of this study is to analyze the influence of various key assumptions and uncertainties within the development of characterisation factors (CFs) for land use in LCA. We assessed the influence on land use CFs of (1) parameter uncertainty and (2) the choice for a constant or land use-specific species accumulation factor z and including or excluding regional effects.     

Uncertainty of global warming potential for milk production on a New Zealand farm and implications for decision making

Background, aim and scope  

As a food exporting nation, New Zealand recognises that the Global Warming Potential (GWP) impact of agriculture has become important to food customers. Food production policy and industry analysts make GWP decisions based on greenhouse gas inventory and life cycle assessment (LCA) results. For decision making, the level of confidence associated with information is important. However, treatment of uncertainty has been problematic in LCA, especially in agricultural systems. In this paper, the GWP of 1 kg of milk was used as a case study to test the feasibility of quantifying uncertainties by Monte Carlo simulation in an LCA applied to an agriculture product. The study also contributes to the development of good practice and has implications for the incorporation of uncertainties into decision making.     

Post-consumer waste wood in attributive product LCA

Background  

In product life cycle assessment (LCA), the attribution of environmental interventions to a product under study is an ambiguous task. This is due to a) the simplistic modeling characteristics in the life cycle inventory step (LCI) of LCA in view of the complexity of our techno-economic system, and b) to the nontangible theoretical nature of the product system as a representation of the processes ‘causally’ linked to a product. Ambiguous methodological decisions during the setup of an LCI include the modeling of end-of-life scenarios or the choice of an allocation factor for the allocation of joint co-production processes. An important criterion for methodological decisions — besides the conformity with the relevant series of standards ISO 14 040 — is if the improvement options, which can be deduced from the LCI, are perceived by the decision-maker as to redirect the material flows at stake into more sustainable paths.     

Application of robust optimization in matrix-based LCI for decision making under uncertainty

Purpose

When product systems are optimized to minimize environmental impacts, uncertainty in the process data may impact optimal decisions. The purpose of this article is to propose a mathematical method for life cycle assessment (LCA) optimization that protects decisions against uncertainty at the life cycle inventory (LCI) stage.

Methods

A robust optimization approach is proposed for decision making under uncertainty in the LCI stage. The proposed approach incorporates data uncertainty into an optimization problem in which the matrix-based LCI model appears as a constraint. The level of protection against data uncertainty in the technology and intervention matrices can be controlled to reflect varying degrees of conservatism.

Results and discussion

A simple numerical example on an electricity generation product system is used to illustrate the main features of this methodology. A comparison is made between a robust optimization approach, and decision making using a Monte Carlo analysis. Challenges to implement the robust optimization approach on common uncertainty distributions found in LCA and on large product systems are discussed. Supporting source code is available for download at https://github.com/renwang/Robust_Optimization_LCI_Uncertainty.

Conclusions

A robust optimization approach for matrix-based LCI is proposed. The approach incorporates data uncertainties into an optimization framework for LCI and provides a mechanism to control the level of protection against uncertainty. The tool computes optimal decisions that protects against worst-case realizations of data uncertainty. The robust optimal solution is conservative and is able to avoid the negative consequences of uncertainty in decision making.     

Impact of lifetime on US residential building LCA results

Purpose  

Many life cycle assessment (LCA) studies do not adequately address the actual lifetime of buildings and building products, but rather assume a typical value. The goal of this study was to determine the impact of lifetime on residential building LCA results. Including accurate lifetime data into LCA allows a better understanding of a product’s environmental impact that would ultimately enhance the accuracy of LCA results.     

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.

     

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.     

Pesticide ecotoxicological effect factors and their uncertainties for freshwater ecosystems

Background, aim, and scope  Characterization factors for ecotoxicity in the Life Cycle Impact Assessment (LCIA) are used to convert emissions into ecotoxicological impacts. Deriving them involves a fate and an effect analysis step. The fate factor quantifies the change in environmental concentration per unit of emission, while the effect factor quantifies the change in impact on the ecosystem per unit of environmental concentration. This paper calculates freshwater ecotoxicological effect factors for 397 pesticides belonging to 11 pesticide-specific toxic modes of action (TMoA), such as acetylcholinesterase inhibition and photosynthesis inhibition. Moreover, uncertainties in the effect factors due to uncertain background concentrations and due to limited toxicity data are quantified. Methods  To calculate median ecotoxicological effect factors (EEFs), toxic pressure assessments were made, based on the species sensitivity distribution—and the multisubstance potentially affected fraction—concept. The EEF quantifies an estimate of the fraction of species that is probably affected due to a marginal change in concentration of a pesticide. EEFs were divided into a TMoA-specific and a chemical-specific part, which were calculated on the basis of physicochemical properties, emissions, and toxicity data. Propagation of parameter uncertainty in the EEFs and the TMoA- and chemical-specific parts was quantified by Monte Carlo simulation and results were reported as 90% confidence intervals. Results  Median EEFs range from 2·10⁻³ to 7·10⁶ l/g. Uncertainty in the TMoA-specific part is dominated by uncertainty in the TMoA-specific spread in species sensitivity and by uncertainty in the effective toxicity of a TMoA. Uncertainty in the chemical-specific part of the EEFs depends on the number of species for which toxicity data are available to calculate average toxicity (n _s) and ranges from a median uncertainty of 2.6 orders of magnitude for n _s = 2 to one order of magnitude for n _s ≥ 4. The TMoA-specific effect factor for systemic fungicides shows the largest uncertainty range. For seven TMoAs, uncertainty ranges of the TMoA-specific effect factor are less than two orders of magnitude. For the other four TMoAs, the EEF uncertainty range is between two and eight orders of magnitude. For the chemical-specific part of the EEFs, we found that variation in uncertainty readily decreases for pesticides for which toxicity data are available for at least three species. Discussion  The same parameters that contributed most to uncertainty were found for pesticides as were found before for high-production-volume chemicals. However, uncertainty in concentrations of pesticides was lower. TMoA-specific factors obtained with the applied nonlinear method differ up to nine orders of magnitude from the factor of 0.5, which is used in the linear method. With the applied method, a distinction in EEFs can be made among different TMoAs. Conclusions   Ecotoxicological effect factors are presented, including overviews of their uncertainty ranges and the main contributors to uncertainty. The applied nonlinear method provides the possibility to quantify parameter uncertainty in the TMoA-specific part of the ecotoxicological effect factor, which is helpful to get more insight in how uncertainty in ecotoxicological characterization factors can be reduced. Recommendations and perspectives  The calculated uncertainty ranges can be included in life cycle assessment (LCA) case studies, which allows for better interpretation of LCA results obtained with the EEFs. To put the uncertainty in effect factors into perspective within LCIA, more information on the uncertainty in fate factors should be derived. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

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].     

Statistical analysis for the development of national average weighting factors—visualization of the variability between each individual’s environmental thoughts

Purpose  

Weighting is one of the steps involved in life cycle impact assessment (LCIA). This enables us to integrate various environmental impacts and facilitates the interpretation of environmental information. Many different weighting methodologies have already been proposed, and the results of many case studies with a single index have been published. However, a number of problems still remain. Weighting factors should be based on the preferences of society as a whole so that the life cycle assessment (LCA) practitioner can successfully apply them to every product and service. However, most existing studies do not really measure national averages but only the average of the responses obtained from the people actually sampled. Measuring the degree of uncertainty in LCIA factors is, therefore, one of the most important issues in current LCIA research, and some advanced LCIA methods have tried to deal with the problem of uncertainty. However, few weighting methods take into account the variability between each individual’s environmental thoughts. LIME2, the updated version of life cycle impact assessment method based on endpoint modeling (LIME), has been developed as part of the second LCA national project of Japan. One of the aims of LIME2 is to develop new weighting factors which fulfill the following requirements: (1) to accurately represent the environmental attitudes of the Japanese public, (2) to measure the variability between each individual’s environmental thoughts and reflect them in the choice of suitable weighting factors.     

Energy decision making in a pulp and paper mill: selection of LCA system boundary

Background, aim, and scope  

North American pulp and paper mills are facing tremendous challenges, which may necessitate major mill modernizations. An example is process modification to reduce dependency on purchased power, which is an expensive resource. Such modifications may have environmental implications at the mills’ sites, on their product life cycle, and on other interconnected systems, and therefore, systematic tools such as life cycle assessment (LCA) need to be applied. Different LCA system boundary approaches can be used for such process design applications, and these approaches need to be compared to determine their respective benefits and limitations in this context. This study compares setting the system boundary according to a cradle-to-gate approach [attributional LCA (ALCA)] and a system expansion [consequential LCA (CLCA)] approach using a case study, which deals with implementing cogeneration and increased de-inked pulp production at an integrated newsprint mill.     

Quantifying uncertainty,variability and likelihood for ordinary differential equation models

Background  

In many applications, ordinary differential equation (ODE) models are subject to uncertainty or variability in initial conditions and parameters. Both, uncertainty and variability can be quantified in terms of a probability density function on the state and parameter space.     

Pavement Life Cycle Assessment Workshop,May 5–7, 2010 in Davis,California, USA

Purpose  

A workshop was convened on life cycle assessment (LCA) applied to pavement. The workshop’s primary goals were to establish common practices for conducting LCAs for pavements. In general, pavement LCA has been implemented without clear guidelines for modeling assumptions and reporting. This shortcoming has led to challenges in interpreting and comparing pavement LCA outcomes.     

Guidelines for consistent reporting of exchanges/to nature within life cycle inventories (LCI)

Data availability and data quality are still critical factors for successful LCA work. The SETAC-Europe LCA Working Group ‘Data Availability and Data Quality’ has therefore focused on ongoing developments toward a common data exchange format, public databases and accepted quality measures to find science-based solutions than can be widely accepted. A necessary prerequisite for the free flow and exchange of life cycle inventory (LCI) data and the comparability of LCIs is the consistent definition, nomenclature, and use of inventory parameters. This is the main subject of the subgroup ‘Recommended List of Exchanges’ that presents its results and findings here:

PIQET: the design and development of an online ‘streamlined’ LCA tool for sustainable packaging design decision support

Background, aim and scope  

‘Streamlined’ life cycle assessment (LCA) tools hold out the possibility of providing LCA information quickly and easily in order to support a variety of decision-making environments and situations. The utility of such tools is closely related to the accuracy needs and possibilities, and the particular decisions to be supported. In order to facilitate the provision and application of LCA information in decision making during packaging design, development and utilisation, there is a prima facia case for a ‘streamlined’ LCA tool, provided it meets a set of requirements, including functionality, accuracy, validity, reliability and usability.     

Consequential life cycle assessment: a review

Purpose  

Over the past two decades, consequential life cycle assessment (CLCA) has emerged as a modeling approach for capturing environmental impacts of product systems beyond physical relationships accounted for in attributional LCA (ALCA). Put simply, CLCA represents the convergence of LCA and economic modeling approaches.     

Analyzing uncertainty in a comparative life cycle assessment of hand drying systems

Purpose

The goal of this study is to evaluate and compare the environmental impact (with a focus on global warming potential) of five hand drying systems: hands-under (HU) dryers, high-speed hands-under (HSHU) dryers, high-speed hands-in (HSHI) dryers, cotton roll towels, and paper towels. Another objective is to incorporate uncertainty into this comparative life cycle assessment (LCA) as a means of understanding the statistical robustness of the difference between the environmental impacts of the hand drying systems.

Methods

We conducted a life cycle assessment in accordance with the ISO 14040/14044 standards using data primarily from publicly available reports. As part of the study, we performed a parameter uncertainty analysis for multiple scenarios to evaluate the impact of uncertainty in input data on the relative performance of products. In addition, we conducted a probabilistic scenario analysis of key drying system parameters in order to understand the implications of changing assumptions on the outcomes of the analyses.

Results and discussion

The scope of the analyses enabled us to draw robust conclusions about the relative environmental performance of the products. We can say with a high degree of confidence that the high-speed dryers have a lower impact than paper towels and cotton roll towels. Differentiating the performance of the hand dryers requires being more specific about framing assumptions. Under certain conditions, the HSHI dryer is expected to have a lower impact than the HU and HSHU dryers. However, under other conditions, one cannot say that the HSHI dryer is clearly better than the other dryers. We cannot differentiate the performance between the HU dryer, cotton roll towels, and paper towels.

Conclusions

This work demonstrates the importance of going beyond traditional uncertainty analyses for comparative LCAs that are used for assertions of relative product environmental impact. Indeed, we found instances where the conclusions changed as a result of using the probabilistic scenario analysis. We outline important elements that should be included in future guidance on uncertainty analyses in comparative LCAs, including conducting parameter and scenario uncertainty analyses together and then using the outcomes to guide selection of parameters and/or choices to analyze further.     

Evaluating alternative life-cycle strategies for electrical appliances by the waste input-output model

Goal, Scope and Background  

In 2001, a new law on the recycling of end-of-life electric home appliances (EL-EHA) was put into effect in Japan; it was the first legislation of its sort in the world, and deserves to be called the ‘Japan model.’ This article is concerned with the LCA of alternative life-cycle strategies for EL-EHA, which consist of recycling as prescribed by the law, ‘ecodesign’ strategies such as the implementation of design for disassembly (DfD) and the extension of product life (EPL), with and without ex-post functional upgradability, and the once-dominant treatment methods such as landfilling and simple shredding.