Elsevier

Waste Management

Volume 32, Issue 12, December 2012, Pages 2482-2495
Waste Management

Quantifying uncertainty in LCA-modelling of waste management systems

https://doi.org/10.1016/j.wasman.2012.07.008Get rights and content

Abstract

Uncertainty analysis in LCA studies has been subject to major progress over the last years. In the context of waste management, various methods have been implemented but a systematic method for uncertainty analysis of waste-LCA studies is lacking. The objective of this paper is (1) to present the sources of uncertainty specifically inherent to waste-LCA studies, (2) to select and apply several methods for uncertainty analysis and (3) to develop a general framework for quantitative uncertainty assessment of LCA of waste management systems. The suggested method is a sequence of four steps combining the selected methods: (Step 1) a sensitivity analysis evaluating the sensitivities of the results with respect to the input uncertainties, (Step 2) an uncertainty propagation providing appropriate tools for representing uncertainties and calculating the overall uncertainty of the model results, (Step 3) an uncertainty contribution analysis quantifying the contribution of each parameter uncertainty to the final uncertainty and (Step 4) as a new approach, a combined sensitivity analysis providing a visualisation of the shift in the ranking of different options due to variations of selected key parameters. This tiered approach optimises the resources available to LCA practitioners by only propagating the most influential uncertainties.

Highlights

► Uncertainty in LCA-modelling of waste management is significant. ► Model, scenario and parameter uncertainties contribute. ► Sequential procedure for quantifying uncertainty is proposed. ► Application of procedure is illustrated by a case-study.

Introduction

Waste management has during the last decade been subject to a range of life cycle assessment (LCA; described in ISO (2006)) studies e.g. Damgaard et al., 2011, Finnveden et al., 2005, Lazarevic et al., 2010 and Pires et al. (2011). The purposes of these studies have been to help quantifying, for example, where in the waste management system the environmental loads and savings are taking place, which technologies are preferable under specific conditions, or the balance between material and energy recovery. LCA-models specifically focusing on waste management systems are available; see Gentil et al. (2010) for a review of the models.

As for any LCA study, results are subject to uncertainty due to the combined effects of data variability, erroneous measurements, wrong estimations, unrepresentative or missing data and modelling assumptions. Uncertainty is of two different natures: while epistemic uncertainty relates to an incomplete state of knowledge (Hoffman and Hammonds, 1994), stochastic uncertainty originates from the inherent variability of the natural world. Such uncertainty can be spatial (e.g. when the farming practice of land receiving compost varies spatially) or temporal (e.g. when the performance of a process varies with time). These two different natures of the uncertainty are usually treated together and referred to by the term “uncertainty”.

Several authors have suggested typologies to describe the different types of uncertainties in LCAs. A well established one was introduced by Huijbregts (1998) and divides uncertainties into three groups (Lloyd and Ries, 2007): (1) parameter uncertainties refer to the uncertainty in values due to e.g. inherent variability, measurement imprecision or paucity of data; (2) scenario uncertainties are due to the necessary choices made to build scenarios; and (3) model uncertainties are due to the mathematical models underlying LCA calculations. A first objective for this paper is to identify uncertainties in the particular context of waste-LCA studies.

Numerous methods have been developed to assess uncertainties and gathered under the term “uncertainty analysis”. Their common goal is to assess the robustness of results, but they employ different mathematical techniques to reach this goal. Sensitivity analysis evaluates the influence of input changes on a model’s results. The most common example is scenario analysis where assumptions are changed one-at-a-time. The procedure of calculating the uncertainty of a result due to all input uncertainties is referred to as “uncertainty propagation”. In LCA, uncertainty assessments are increasingly included in the interpretation phase, and life cycle inventory (LCI) databases include increasing amounts of information concerning uncertainty (Finnveden et al., 2009). Lloyd and Ries (2007) reviewed quantitative uncertainty analysis in 24 LCA studies performed on various products and services. They found that stochastic modelling was the most frequently-used method to propagate uncertainties in LCA. This method propagates probability distributions using random sampling like the Monte Carlo analysis. However, they noted that many of the studies using such modelling seemed to select uncertainty distributions somewhat arbitrarily. Other methods have been proposed to more faithfully depict epistemic uncertainties in LCA modelling e.g. by Benetto et al. (2008) and Heijungs and Tan (2010) using possibility theory (Dubois and Prade, 1988), or Chevalier and Le Téno (1996) using intervals.

In LCA-modelling of waste management the amount of data available is still limited for establishing the inventories of the waste management systems under study (including data on waste composition, collection systems, source separation systems, recovery and conversion technologies, landfilling, and technologies for utilising recovered materials). Few waste management LCAs have employed quantitative uncertainty assessment and these studies have often been limited to scenario analyses. Uncertainty propagation has been applied to specific waste management issues: for example Sonnemann et al. (2003) used stochastic modelling to evaluate emissions from an incinerator followed by a sensitivity analysis by means of correlation coefficients. Kaplan et al. (2009) used the same approach to evaluate and compare different waste management planning options, while Hung and Ma (2009) evaluated the relative contributions of the inventory, impact assessment, normalisation and weighting steps. Lo et al. (2004) applied a Bayesian Monte Carlo method to compare various waste treatment options. Each uncertainty analysis method has a specific goal and applicability depending on the nature of the model. Therefore the choice of the right tool may be difficult for LCA practitioners not familiar with uncertainty analysis.

Consequently we propose to select appropriate methods for waste-LCA models and show their benefits, complementarities and levels of complexity. The purpose of this paper is (1) to review the uncertainties commonly encountered in waste LCA-modelling, (2) to select and apply a range of methods for uncertainty analysis to waste LCA-modelling, (3) to develop a framework for the uncertainty analysis of waste management systems that combines various methods. The methods in this paper are applied to a case study that compares anaerobic digestion and incineration of organic kitchen waste in Denmark.

Section snippets

Uncertainties in LCA of waste management systems

This first section presents and discusses the sources of uncertainty typically encountered in LCA-modelling of waste management systems, based on the literature and experience acquired over the last decade. The characteristics and importance of an uncertainty analysis depend on the scope of the study and on the quality of the data available. The presentation and discussion below may provide valuable input for identifying sources of uncertainty in waste-LCAs, but each study should be associated

Selection of methods

Various methods for sensitivity and uncertainty analyses have been developed in scientific and engineering modelling; as presented by Saltelli et al. (2006). No single best method can be applied to all models: the choice depends on different criteria, namely the nature of the model, the requirements of the analysis and the resources available especially in terms of software (Morgan and Henrion, 1990). In this study, methods were selected that are adapted to different levels of available

Uncertainty modelling of a case study

A hypothetical case study was set up in order to implement the methods, illustrate their features, identify their complementarities and propose a procedure for uncertainty quantification in waste LCA-modelling. While the focus was not on intense data collection, processes were taken from the EASEWASTE database (Kirkeby et al., 2006). For the purpose of clarity, the study only presents results for the impact category global warming. Hence normalisation and weighting are excluded, although these

Discussion

Seven methods for quantifying the uncertainty of LCA results have been selected and applied in a comparative study of two waste management systems. This study was reduced to two scenarios and one impact category but it led to more general findings presented in this section. The presented study provides valuable insight into the possibilities offered by each method as well as its limitations and the difficulties of implementation. Based on the complementarities of these methods, as illustrated

Conclusions

LCA of waste management is subject to significant sources of uncertainty of diverse origins. In order to improve the reliability of the results, uncertainties must be addressed in a systematic and quantitative fashion. We described, based on a decade of experience, where the main uncertainties can be found within LCA – modelling of waste management systems. A systematic sequential method to evaluate uncertainty in LCA studies of waste management systems has been suggested and exemplified. It

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