No abstract

No abstract

26 27Uncertainty analysis in LCA studies has been subject to major progress over the last years. In the context of waste 28 management, various methods have been implemented but a systematic method for uncertainty analysis of waste-29 LCA studies is lacking. The objective of this paper is (1) to present the sources of uncertainty specifically inherent to 30 waste-LCA studies, (2) to select and apply several methods for uncertainty analysis and (3) to develop a general 31 framework for quantitative uncertainty assessment of LCA of waste management systems. The suggested method is a 32 sequence of four steps combining the selected methods: (Step 1) a sensitivity analysis evaluating the sensitivities of 33 the results with respect to the input uncertainties, (Step 2) an uncertainty propagation providing appropriate tools for 34 representing uncertainties and calculating the overall uncertainty of the model results, (Step 3) an uncertainty 35 contribution analysis quantifying the contribution of each parameter uncertainty to the final uncertainty and (Step 4) 36 as a new approach, a combined sensitivity analysis providing a visualization of the shift in the ranking of different 37 options due to variations of selected key parameters. This tiered approach optimizes the resources available to LCA 38 practitioners by only propagating the most influential uncertainties. Waste management has during the last decade been subject to a range of life cycle assessment (LCA; described in 48 ISO, 2006) studies e.g. Damgaard et al. (2011, Lazarevic et al. (2010) and Pires et al. 49 (2011). The purposes of these studies have been to help quantifying, for example, where in the waste management 50 system the environmental loads and savings are taking place, which technologies are preferable under specific 51 conditions, or the balance between material and energy recovery. LCA-models specifically focusing on waste 52 management systems are available; see Gentil et al. (2010) for a review of the models. 53As for any LCA study, results are subject to uncertainty due to the combined effects of data variability, 54 erroneous measurements, wrong estimations, unrepresentative or missing data and modelling assumptions. 55Uncertainty is of two different natures: while epistemic uncertainty relates to an incomplete state of knowledge 56 (Hoffman and Hammonds, 1994), stochastic uncertainty originates from the inherent variability of the natural world. 57Such uncertainty can be spatial (e.g. when the farming practice of land receiving compost varies spatially) or 58 temporal (e.g. when the performance of a process varies with time). These two different natures of the uncertainty are 59 usually treated together and referred to by the term "uncertainty". 60 They found that stochastic modelling was the most frequently-used method to propagate uncertainties in LCA. This 75 method propagates probability distributions using random sampling like the Monte Carlo analysis. However, they 76 noted that many of the studies using such modelling...

The Planetary Boundaries concept has emerged as a framework for articulating environmental limits, gaining traction as a basis for considering sustainability in business settings, government policy and international guidelines. There is emerging interest in using the Planetary Boundaries concept as part of life cycle assessment (LCA) for gauging absolute environmental sustainability. We tested the applicability of a novel Planetary Boundaries-based life cycle impact assessment methodology on a hypothetical laundry washing case study at the EU level. We express the impacts corresponding to the control variables of the individual Planetary Boundaries together with a measure of their respective uncertainties. We tested four sharing principles for assigning a share of the safe operating space (SoSOS) to laundry washing and assessed if the impacts were within the assigned SoSOS. The choice of sharing principle had the greatest influence on the outcome. We therefore highlight the need for more research on the development and choice of sharing principles. Although further work is required to operationalize Planetary Boundaries in LCA, this study shows the potential to relate impacts of human activities to environmental boundaries using LCA, offering company and policy decision-makers information needed to promote environmental sustainability.

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Abstract:The Planetary Boundaries (PB) framework represents a significant advance in specifying the ecological constraints on human development. However, to enable decision-makers in business and public policy to respect these constraints in strategic planning, the PB framework needs to be developed to generate practical tools. With this objective in mind, we analyse the recent literature and highlight three major scientific and technical challenges in operationalizing the PB approach in decision-making: first, identification of thresholds or boundaries with associated metrics for different geographical scales; second, the need to frame approaches to allocate fair shares in the 'safe operating space' bounded by the PBs across the value chain and; third, the need for international bodies to co-ordinate the implementation of the measures needed to respect the Planetary Boundaries. For the first two of these challenges, we consider how they might be addressed for four PBs: climate change, freshwater use, biosphere integrity and chemical pollution and other novel entities. Four key opportunities are identified: (1) development of a common system of metrics that can be applied consistently at and across different scales; (2) setting 'distance from boundary' measures that can be applied at different scales; (3) development of global, preferably open-source, databases and models; and (4) advancing understanding of the interactions between the different PBs. Addressing the scientific and technical challenges in operationalizing the planetary boundaries needs be complemented with progress in addressing the equity and ethical issues in allocating the safe operating space between companies and sectors.

International corporations in an increasingly globalized economy exert a major influence on the planet's land use and resources through their product design and material sourcing decisions. Many companies use life cycle assessment (LCA) to evaluate their sustainability, yet commonly-used LCA methodologies lack the spatial resolution and predictive ecological information to reveal key impacts on climate, water and biodiversity. We present advances for LCA that integrate spatially explicit modelling of land change and ecosystem services in a Land-Use Change Improved (LUCI)-LCA. Comparing increased demand for bioplastics derived from two alternative feedstock-location scenarios for maize and sugarcane, we find that the LUCI-LCA approach yields results opposite to those of standard LCA for greenhouse gas emissions and water consumption, and of different magnitudes for soil erosion and biodiversity. This approach highlights the importance of including information about where and how land-use change and related impacts will occur in supply chain and innovation decisions.

Purpose: When performing uncertainty propagation, most LCA practitioners choose to represent uncertainties by single probability distributions and to propagate them using stochastic methods. However the selection of single probability distributions appears often arbitrary when faced with scarce information or expert judgement (epistemic uncertainty). Possibility theory has been developed over the last decades to address this problem. The objective of this study is to present a methodology that combines probability and possibility theories to represent stochastic and epistemic uncertainties in a consistent manner and apply it to LCA. A case study is used to show the uncertainty propagation performed with the proposed method and compare it to propagation performed using probability and possibility theories alone.Methods: Basic knowledge on the probability theory is first recalled, followed by a detailed description of epistemic uncertainty representation using fuzzy intervals. The propagation methods used are the Monte Carlo analysis for probability distribution and an optimisation on alpha-cuts for fuzzy intervals. The proposed method (noted IRS) generalizes the process of random sampling to probability distributions as well as fuzzy intervals, thus making the simultaneous use of both representations possible.Results and discussion: The results highlight the fundamental difference between the probabilistic and possibilistic representations: while the Monte Carlo analysis generates a single probability distribution, the IRS method yields a family of probability distributions bounded by an upper and a lower distribution.The distance between these two bounds is the consequence of the incomplete character of information pertaining to certain parameters. In a real situation, an excessive distance between these two bounds might motivate the decision-maker to increase the information base regarding certain critical parameters, in order to reduce the uncertainty. Such a decision could not ensue from a purely probabilistic calculation based on subjective (postulated) distributions (despite lack of information), because there is no way of distinguishing, in the variability of the calculated result, what comes from true randomness and what comes from incomplete information.Conclusions: The method presented offers the advantage of putting the focus on the information rather than deciding a priori of how to represent it. If the information is rich, then a purely statistical representation mode is adequate, but if the information is scarce, then it may be better conveyed by possibility distributions.3