Resource depletion potentials from bottom-up models: Population dynamics and the Hubbert peak theory

Arnaud Hélias, Reinout Heijungs

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Life cycle impact assessment uses so-called characterization factors to address different types of environmental impact (e.g. climate change, particulate matter, land use…). For the topic of resource depletion, a series of proposals was based on heuristic and formal arguments, but without the use of expert-based models from relevant research areas. A recent study in using fish population models has confirmed the original proposal for characterization factors for biotic resources of the nineties. Here we trace the milestones of the arguments and the designs of resource depletion, delivering an ecological-based foundation for the biotic case, and extend it by a novel analysis of the Hubbert peak theory for the abiotic case. We show that the original abiotic depletion potential, used for two decades in life cycle assessment, estimates accurately a marginal depletion characterization factor obtained from a dynamic model of the available reserve. This is illustrated for 29 metal resources using published data.

LanguageEnglish
Pages1303-1308
Number of pages6
JournalScience of the Total Environment
Volume650
DOIs
StatePublished - 10 Feb 2019

Fingerprint

resource depletion
Population dynamics
population dynamics
Life cycle
life cycle
Particulate Matter
resource
heuristics
Land use
Climate change
Fish
Environmental impact
particulate matter
Dynamic models
environmental impact
Metals
land use
climate change
metal
fish

Keywords

  • Abiotic resource
  • Biotic resource
  • Characterization factor
  • Life cycle impact assessment (LCIA)
  • Marginal approach

Cite this

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Resource depletion potentials from bottom-up models : Population dynamics and the Hubbert peak theory. / Hélias, Arnaud; Heijungs, Reinout.

In: Science of the Total Environment, Vol. 650, 10.02.2019, p. 1303-1308.

Research output: Contribution to JournalArticleAcademicpeer-review

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