Environmental assessment of solid waste systems and technologies: EASEWASTE

Kirkeby, Janus; Birgisdóttir, Harpa; Hansen, Tina Beck; Christensen, Thomas Højlund; Bhander, Gurbakhash Singh; Hauschild, Michael Zwicky

doi:10.1177/0734242x06062580

Cited by 165 publications

(95 citation statements)

References 8 publications

(5 reference statements)

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“…Water consumption in the CT facility (0.56 m 3 Mg OFMSW -1 ) should be considered as an P r e -p r i n t extreme value, since in this particular plant the scrubber used in the gas treatment process…”

Section: Watermentioning

confidence: 99%

“…Regarding Municipal Solid Wastes (MSW) some literature can be found on waste management systems modeling: for example: EASEWASTE, 3 ORWARE 4 and WASTED, 5 are simulation tools that include the environmental burdens associated to waste management. Also, Life Cycle Assessment (LCA) has been applied to generic waste management systems 6 and to MSW management systems of different cities or regions such as Wales, 7 Ankara, 8 Phuket, 9 Corfu 10 or Delaware 11 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Determination of the energy and environmental burdens associated with the biological treatment of source-separated Municipal Solid Wastes

Colón

Cadena

Pognani

et al. 2012

Energy Environ. Sci.

143

104

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Section: Watermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of the energy and environmental burdens associated with the biological treatment of source-separated Municipal Solid Wastes

Colón

Cadena

Pognani

et al. 2012

Energy Environ. Sci.

143

104

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“…Thus, the relative contribution of the uncertainty in x to the uncertainty in z is: 388 The two systems were modelled in the waste-LCA tool EASEWASTE (Kirkeby et al, 2006) and global 405 warming factors (GWF) of all sub-processes were calculated. GWF are defined as the impact on global warming of 406 the waste management system and expressed in kg CO 2 -eq per tonne of waste treated.…”

Section: Uncertainty Contribution Analysis 378mentioning

confidence: 99%

Quantifying uncertainty in LCA-modelling of waste management systems

Clavreul¹,

Guyonnet²,

Christensen³

2012

Waste Management

Self Cite

281

158

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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...

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“…In addition to the problems associated with the resources flows in the system, the prioritisation order of the WMO can sometimes lead to erroneous, even inadequate, decision-making [4]. Presently, numerous tools exist to assist the waste hierarchy assessment for the WMO in a system and to designate which options must be prioritised.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Waste Management (DWM): A new step towards industrial ecology

Rojo

Laforest

Glaus

et al. 2008

WIT Transactions on Ecology and the Environment

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The load caused by growing volumes of collected residues produces a pressure both on the environment and on the population. Presently, wastes are no longer considered just simple residues, but also resources which can be reused or recovered. Various tools are now available to help compare the various options in waste management. These tools lead to a linear management of the systems. In addition, they generate a division of flow streams and do not allow an optimal use of the available materials. An industrial ecology perspective requires one to move towards a systemic approach.Based on the law of conservation of energy, the Dynamic Waste Management approach (DWM) studies the behaviour of systems to insure a constant supply of favourable waste management options (WMO) and to reduce the global load within these systems. The flow distribution in a global system rests on the principle that the available volumes and the transportation influence the validity of the hierarchical organisation of WMO. Adhering to the philosophy of industrial ecology, the DWM offers a dynamic and evolutionary alternative to reduce the impacts generated by inadequate waste management. The present article exposes the characteristics of the proposed approach, and presents an example of its application in order to demonstrate the advantages of the DWM.

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Environmental assessment of solid waste systems and technologies: EASEWASTE

Cited by 165 publications

References 8 publications

Determination of the energy and environmental burdens associated with the biological treatment of source-separated Municipal Solid Wastes

Determination of the energy and environmental burdens associated with the biological treatment of source-separated Municipal Solid Wastes

Quantifying uncertainty in LCA-modelling of waste management systems

Dynamic Waste Management (DWM): A new step towards industrial ecology

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