title: Application of hybrid life cycle approaches to emerging energy technologies--the case of wind power in the UK.
abstract: Future energy technologies will be key for a successful reduction of man-made greenhouse gas emissions. With demand for electricity projected to increase significantly in the future, climate policy goals of limiting the effects of global atmospheric warming can only be achieved if power generation processes are profoundly decarbonized. Energy models, however, have ignored the fact that upstream emissions are associated with any energy technology. In this work we explore methodological options for hybrid life cycle assessment (hybrid LCA) to account for the indirect greenhouse gas (GHG) emissions of energy technologies using wind power generation in the UK as a case study. We develop and compare two different approaches using a multiregion input-output modeling framework - Input-Output-based Hybrid LCA and Integrated Hybrid LCA. The latter utilizes the full-sized Ecoinvent process database. We discuss significance and reliability of the results and suggest ways to improve the accuracy of the calculations. The comparison of hybrid LCA methodologies provides valuable insight into the availability and robustness of approaches for informing energy and environmental policy.
authors.affiliation: CSIRO Ecosystem Sciences, GPO Box 1700, Canberra, ACT 2601, Australia. tommy.wiedmann@csiro.au
authors.forename: Thomas O
authors.initials: TO
authors.lastname: Wiedmann
authors.forename: Sangwon
authors.initials: S
authors.lastname: Suh
authors.forename: Kuishuang
authors.initials: K
authors.lastname: Feng
authors.forename: Manfred
authors.initials: M
authors.lastname: Lenzen
authors.forename: Adolf
authors.initials: A
authors.lastname: Acquaye
authors.forename: Kate
authors.initials: K
authors.lastname: Scott
authors.forename: John R
authors.initials: JR
authors.lastname: Barrett
mesh_terms: D003246:Conservation of Energy Resources / Q000379:methods* / Q000706:statistics & numerical data; D017598:Efficiency, Organizational; D004784:Environmental Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D057232:Global Warming / Q000517:prevention & control*; D017752:Greenhouse Effect; D008962:Models, Theoretical; D011210:Power Plants / Q000706:statistics & numerical data*; D006113:United Kingdom; D014919:Wind