Uncertainty assessment by a Monte Carlo simulation in a life cycle inventory of electricity produced by a waste incinerator |
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| Institution: | 1. Luxembourg Institute of Science and Technology (LIST), Department of Environmental Research & Innovation (ERIN), 41 Rue du Brill, 4422 Belvaux, Luxembourg;2. Vrije Universiteit Amsterdam, School of Business and Economics, 1105 De Boelelaan, 1081 HV Amsterdam, the Netherlands;3. Leiden University, Department of Industrial Ecology, P.O. Box 9500, 2300 RA Leiden, the Netherlands;4. National Institute for Public Health and the Environment (RIVM), 9 Antonie van Leeuwenhoeklaan, 3721 MA Bilthoven, the Netherlands;5. University of Amsterdam, Institute for Biodiversity and Ecosystem Dynamics (IBED), P.O. Box 94240, 1090 GE Amsterdam, the Netherlands;1. French Environment and Energy Management Agency (ADEME), Energy Networks and Renewable Energy Department, 27 rue Louis Vicat, 75737 Paris Cedex 15, France;2. MINES ParisTech, PSL Research University, Centre Observation, Impacts, Energy (O.I.E.), CS 10207, F-06904 Sophia-Antipolis, France;3. MINES ParisTech, PSL Research University, Centre for Processes, Renewable Energies and Energy Systems (PERSEE), CS 10207, F-06904 Sophia-Antipolis, France |
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| Abstract: | The existence of uncertainties is often mentioned as a crucial limitation for a clear interpretation of LCA results. Due to this problem, slowly the uncertainty analysis is gaining importance in the realisation of LCAs, but its use is not common practise. As an example of application for a typical process chain of many LCAs, the uncertainties in the Life Cycle Inventory (LCI) of a life cycle study on waste incineration in Tarragona/Spain has been analysed. The procedure applied consists of selection of essential parameters, determination of probability distributions, Monte Carlo simulation, significance analysis and interpretation of the results. By the use of the obtained probability distributions for the essential factors in a Monte Carlo Simulation, the inventory results were transformed from a concrete value into a probability distribution around a mean value. The probability distributions obtained correspond to a better understanding of the magnitude of the uncertainties in LCA results. |
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