Wind Power as a Case Study
Improving Life Cycle Assessment Reporting to Better Enable Meta-analyses
Version of Record online: 19 MAR 2012
© 2012 by Yale University
Journal of Industrial Ecology
Special Issue: Meta-Analysis of Life Cycle Assessments
Volume 16, Issue Supplement s1, pages S22–S27, April 2012
How to Cite
Price, L. and Kendall, A. (2012), Wind Power as a Case Study. Journal of Industrial Ecology, 16: S22–S27. doi: 10.1111/j.1530-9290.2011.00458.x
- Issue online: 3 MAY 2012
- Version of Record online: 19 MAR 2012
- carbon intensity;
- energy intensity;
- industrial ecology;
- renewable energy;
Meta-analyses of life cycle assessments (LCAs) have become increasingly important in the context of renewable energy technologies and the decisions and policies that influence their adoption. However, a lack of transparency in reporting modeling assumptions, data, and results precludes normalizing across incommensurate system boundaries or key assumptions. This normalization step is critical for conducting valid meta-analyses.
Thus it is necessary to establish clear methods for assessing transparency and to develop conventions for LCA reporting that promote future comparisons. While concerns over transparency in LCA have long been discussed in the literature, the methods proposed to address these concerns have not focused on the transparency and reporting characteristics required for performing meta-analyses. In this study we identify guidelines for assessing reporting transparency that anticipate the needs of meta-analyses of LCA applied to renewable energy technologies.
These guidelines were developed after an attempt to perform a meta-analysis on wind turbine LCAs of 1 megawatt and larger, with the goal of determining how life cycle performance, as measured by global warming intensity, might trend with turbine size. The objective was to normalize system boundaries and environmental conditions, and reinterpret global warming potential with new impact assessment methods. Previous wind LCAs were reviewed and assessed for reporting transparency. Only a small subset of studies proved to be sufficiently transparent for the normalization of system boundaries and modeling assumptions required for meta-analyses.