UC Santa Barbara

Geospatial details about land use are necessary to assess its potential impacts on biodiversity. Geographic information systems (GIS) are adept at modeling land use in a spatially explicit manner, while life cycle assessment (LCA) does not conventionally utilize geospatial information. This study presents a proof-of-concept approach for coupling GIS and LCA for biodiversity assessments of land use and applies it to a case study of ethanol production from agricultural crops in California. In Part 2 of this paper series, four biodiversity impact indicators are presented and discussed, which use the inventory data on habitat composition and sizes from the GIS-based inventory modeling in Part 1 (Geyer et al. 2010). The concepts used to develop characterization models are hemeroby, species richness, species abundance, and species evenness. The biodiversity assessments based on species richness, abundance, and evenness use a species–habitat suitability matrix which relates 443 terrestrial vertebrate species native to California to the 29 habitat types that occur in the study area. The structural similarities and differences of all four characterization models are discussed in some detail. Characterization factors and indicator results are calculated for each of the four characterization models and the 11 different land use scenarios from Part 1 of this paper series. For the sugar beet production scenarios, the indicator results are in fairly good agreement. For the corn production scenarios, however, they come to fundamentally different results. The overall approach of using GIS-based inventory data on land use together with information on species–habitat relationships is not only feasible but also grounded in ecological science and well connected with existing life cycle impact assessment efforts. Excluding biodiversity impacts from land use significantly limits the scope of LCA. Accounting for land use in inventory modeling is dramatically enhanced if LCA is coupled with GIS. The resulting inventory data are a sound basis for biodiversity impact assessments, in particular if coupled with information on species–habitat relationships. However, much more case studies and structural analysis of indicators is required, together with an evaluation framework that enables comparisons and ranking of indicators.