Abstract

Bioenergy and biofuels are key to meeting renewable energy and carbon reduction targets. Life Cycle Assessment (LCA) techniques are being used, with varying success and consistency, to help determine the sustainability of the current fuels and pathways selected. In order to meet our longer term targets and pursue long term sustainability emerging processes and systems need to be examined, as well as existing processes. Designers recognise that a large percentage of impacts and costs are pre-ordained within the design stage; so it makes sense to use LCA at the start of the research process in order to minimise these. Determining impacts at this stage could also help select the most promising options with maximum sustainability/GHG reduction potential. At the same time policy makers are beginning to use LCA as a tool to help inform policy choices for future energy pathways. Never the less, there are various uncertainties involved with its use at early stage research level, and also the expansion of LCA to look at wider consequences of the use of a particular product or system. LCA is changing from a traditional, retrospective tool to a more dynamic, forward thinking tool. Whilst this brings a multitude of benefits in terms of ability to predict impacts and minimise these in advance, this method of LCA use is not without uncertainties and difficulties. This paper explores why LCA is important within the bioenergy context and highlights some of the benefits, disadvantages, and changes that are seen through its use.

LanguageEnglish
Title of host publicationModeling, Dynamics, Optimization and Bioeconomics II - DGS III, and Bioeconomy VII, Selected Contributions
PublisherSpringer New York
Pages363-370
Number of pages8
ISBN (Print)9783319552354
DOIs
StatusPublished - 2017
Event3rd International Conference on Dynamics, Games and Science, DGS 2014 - Porto, Portugal
Duration: 17 Feb 201421 Feb 2014

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume195

Conference

Conference3rd International Conference on Dynamics, Games and Science, DGS 2014
CountryPortugal
CityPorto
Period17/02/1421/02/14

Fingerprint

Life Cycle Assessment
Pathway
Sustainability
Minimise
Uncertainty
Target
Renewable Energy
Term
Percentage
Carbon
Predict
Costs
Energy

Keywords

  • Anticipatory
  • Bioenergy
  • Emerging LCA

ASJC Scopus subject areas

  • Mathematics(all)

Cite this

McManus, M. C. (2017). The use of LCA for the development of bioenergy pathways. In Modeling, Dynamics, Optimization and Bioeconomics II - DGS III, and Bioeconomy VII, Selected Contributions (pp. 363-370). (Springer Proceedings in Mathematics and Statistics; Vol. 195). Springer New York. https://doi.org/10.1007/978-3-319-55236-1_17

The use of LCA for the development of bioenergy pathways. / McManus, Marcelle C.

Modeling, Dynamics, Optimization and Bioeconomics II - DGS III, and Bioeconomy VII, Selected Contributions. Springer New York, 2017. p. 363-370 (Springer Proceedings in Mathematics and Statistics; Vol. 195).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

McManus, MC 2017, The use of LCA for the development of bioenergy pathways. in Modeling, Dynamics, Optimization and Bioeconomics II - DGS III, and Bioeconomy VII, Selected Contributions. Springer Proceedings in Mathematics and Statistics, vol. 195, Springer New York, pp. 363-370, 3rd International Conference on Dynamics, Games and Science, DGS 2014, Porto, Portugal, 17/02/14. https://doi.org/10.1007/978-3-319-55236-1_17
McManus MC. The use of LCA for the development of bioenergy pathways. In Modeling, Dynamics, Optimization and Bioeconomics II - DGS III, and Bioeconomy VII, Selected Contributions. Springer New York. 2017. p. 363-370. (Springer Proceedings in Mathematics and Statistics). https://doi.org/10.1007/978-3-319-55236-1_17
McManus, Marcelle C. / The use of LCA for the development of bioenergy pathways. Modeling, Dynamics, Optimization and Bioeconomics II - DGS III, and Bioeconomy VII, Selected Contributions. Springer New York, 2017. pp. 363-370 (Springer Proceedings in Mathematics and Statistics).
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