|Title||Coupling GIS and LCA for biodiversity assessments of land use: Part 2 Impact assessment|
|Publication Type||Journal Article|
|Year of Publication||2010|
|Authors||Geyer, R, Lindner, JP, Stoms, DM, Davis, FW, Wittstock, B|
|Journal||International Journal of Life Cycle Assessment|
|Keywords||GIS-based inventory modeling land use habitats hemeroby species richness abundance evenness biodiversity impacts bioethanol Biodiversity geographic variability life cycle impact assessment bioethanol biofuel LCA|
Purpose: 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. Methods: 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. this volume). The concepts used to develop characterization models are hemeroby, species richness, species abun-dance, and species evenness. The biodiversity assessments based on species richness, abundance, and evenness use a habitat-species suitability matrix, which relates 443 terrestrial vertebrate species native to California to the 29 habi-tat types that occur in the study area. Results and discussion: 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 scenar-ios, the indicator results are in fairly good agreement. For the corn production scenarios, however, they come to fun-damentally different results. The overall approach of using GIS-based inventory data on land use together with in-formation on habitat-species relationships is not only feasible, but also grounded in ecological science and well con-nected with existing LCIA efforts. Conclusions: 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 is a sound basis for biodiversity impact assessments, in particular if coupled with information on habitat-species rela-tionships. However, much more case studies and structural analysis of indicators is required, together with an evaluation framework that enables comparisons and ranking of indicators.