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A complete decision-support infrastructure for food waste valorisation

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journal contribution
posted on 2019-12-10, 09:46 authored by Phil Sheppard, Guillermo Garcia-Garcia, Jamie Stone, Shahin RahimifardShahin Rahimifard
The quantity of energy and materials embodied in food means that wasting a third of it, which is the level of inefficiency reached according to studies in recent years, impacts negatively on living standards at whatever level they are around the world. An increased level of consciousness about the issue has stimulated initiatives to address it, leading, sensibly, to the development of decision-making systems to support proper management of the materials. Here, we present the first review and evaluation of four recently developed systems targeting food waste. These tools broadly embody a logical model which identifies and quantifies food waste flows at different scales, characterises them, identifies appropriate conversion technologies, and enables assessment of the economic, environmental and social effects of different pathway options, along with other factors to provide a final fit with the circumstances of each owner of the food waste. Our review concludes that these tools are necessary but not sufficient to lift the management of food waste from a grossly sub-optimal level to a system which would be recognised by pre-and emerging-industrial generations but with valorisations of much higher value. Specifically, we identify knowledge-based elements of a management system which would be free of specific supply chain context and therefore have much greater power to direct resources affordably for maximum economic, environmental and social value.

Funding

Engineering and Physical Sciences Research Council [grant reference EP/P008771/1].

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Journal of Cleaner Production

Volume

247

Publisher

Elsevier

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

2019-12-07

Publication date

2019-12-09

Copyright date

2019

ISSN

0959-6526

Language

  • en

Depositor

Mr Phil Sheppard Deposit date: 9 December 2019

Article number

119608

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