GIS-Based Regionalized Life Cycle Assessment: How Big Is Small Enough? Methodology and Case Study of Electricity Generation

We describe a new methodology for performing regionalized
life
cycle assessment and systematically choosing the spatial scale of
regionalized impact assessment methods. We extend standard matrix-based
calculations to include matrices that describe the mapping from inventory
to impact assessment spatial supports. Uncertainty in inventory spatial
data is modeled using a discrete spatial distribution function, which
in a case study is derived from empirical data. The minimization of
global spatial autocorrelation is used to choose the optimal spatial
scale of impact assessment methods. We demonstrate these techniques
on electricity production in the United States, using regionalized
impact assessment methods for air emissions and freshwater consumption.
Case study results show important differences between site-generic
and regionalized calculations, and provide specific guidance for future
improvements of inventory data sets and impact assessment
methods.