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Application of Hybrid Life Cycle Approaches to Emerging Energy Technologies - The Case of Wind Power in the UK

Wiedmann, Thomas O., Suh, Sangwon, Feng, Kuishuang, Lenzen, Manfred, Acquaye, Adolf, Scott, Kate, Barrett, John R. (2011) Application of Hybrid Life Cycle Approaches to Emerging Energy Technologies - The Case of Wind Power in the UK. Environmental Science and Technology, 45 (13). pp. 5900-5907. ISSN 0013-936X. (doi:10.1021/es2007287) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:35218)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1021/es2007287

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.

Item Type: Article
DOI/Identification number: 10.1021/es2007287
Subjects: H Social Sciences
H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Faculties > Social Sciences > Kent Business School
Faculties > Social Sciences > Kent Business School > Management Science
Faculties > Social Sciences > Kent Business School > Centre for Logistics and Heuristic Organisation (CLHO)
Depositing User: Catherine Norman
Date Deposited: 16 Sep 2013 13:52 UTC
Last Modified: 06 Feb 2020 04:08 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/35218 (The current URI for this page, for reference purposes)
Acquaye, Adolf: https://orcid.org/0000-0003-0430-2498
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