Combined material flow analysis and life cycle assessment as a support tool for solid waste management decision making

Published

Journal Article

© 2016 Elsevier Ltd. All rights reserved. Material flow analysis (MFA) and life cycle assessment (LCA) have both widely been applied to support solid waste management (SWM) decision making. However, they are often applied independently rather than conjointly. This paper presents an approach that combines the MFA and LCA methodologies to evaluate large and complex SWM systems from an environmental perspective. The approach was applied to evaluate the environmental performance, focusing on greenhouse gas (GHG) emissions, of a local authority SWM system and to compare it with alternative systems to assess the potential effectiveness of different waste policy measures. The MFA results suggest that national recycling targets are unlikely to be met even if the assessed policies are implemented optimally. It is likely that for the targets to be met, investigated policies would need to be combined with additional policies that target reductions in waste arisings. The LCA results found landfilling of residual waste to be the dominant source of GHG burdens for the existing system, whilst material reprocessing was found to result in GHG benefits. Overall, each of the alternative systems investigated were found to result in lower GHG impacts compared to the existing system, with the diversion of food waste from the residual waste stream found to be potentially the most effective strategy to reduce GHG emissions. The results of this study demonstrate that the complementary methodologies of MFA and LCA can be used in combination to provide policy and decision makers with valuable information about the environmental performance of SWM systems.

Full Text

Duke Authors

Cited Authors

  • Turner, DA; Williams, ID; Kemp, S

Published Date

  • August 15, 2016

Published In

Volume / Issue

  • 129 /

Start / End Page

  • 234 - 248

International Standard Serial Number (ISSN)

  • 0959-6526

Digital Object Identifier (DOI)

  • 10.1016/j.jclepro.2016.04.077

Citation Source

  • Scopus