@article {424,
	title = {Coupling GIS and LCA for biodiversity assessments of land use: Part 1 Inventory modeling},
	journal = {International Journal of Life Cycle Assessment},
	volume = {15},
	number = {5},
	year = {2010},
	pages = {454-467},
	abstract = {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: GIS modeling was used to generate crop production scenarios for corn and sugar beets that met a range of ethanol production targets. The selected study area was a four county region in the southern San Joaquin Valley of California, USA. The resulting land use maps were translated into maps of habitat types. From these maps, vectors were created that contained the total areas for each habitat type in the study region. These habitat composition vec-tors are treated as elementary input flows and used to calculate different biodiversity impact indicators in a second paper (Geyer et al. this volume). Results and discussion: Ten ethanol production scenarios were developed with GIS modeling. Current land use is added as baseline scenario. The parcels selected for corn and sugar beet production were generally in different loca-tions. Moreover, corn and sugar beets are classified as different habitat types. Consequently the scenarios differed in both the habitat types converted and in the habitat types expanded. Importantly, land use increased non-linearly with increasing ethanol production targets. The GIS modeling for this study used spatial data that are commonly available in most developed countries and only required functions that are provided in virtually any commercial or open-source GIS software package. Conclusions: This study has demonstrated that GIS-based inventory modeling of land use allows important refine-ments in LCA theory and practice. Using GIS, land use can be modeled as a geospatial and non-linear function of output. For each spatially explicit process, land use can be expressed within the conventional structure of LCA methodology as a set of elementary input flows of habitat types.},
	keywords = {Biodiversity habitats land use geographic information systems GIS spatially-explicit inventory modeling bioethanol biofuel LCA life cycle assessment crop production model spatially-explicit LCI consequential LCA geographic variability},
	author = {Geyer, R. and Stoms, D. M. and Lindner, J. P. and Davis, F. W. and Wittstock, B.}
}
@article {421,
	title = {Coupling GIS and LCA for biodiversity assessments of land use: Part 2 Impact assessment},
	journal = {International Journal of Life Cycle Assessment},
	volume = {15},
	number = {7},
	year = {2010},
	pages = {692-703},
	abstract = {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.},
	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},
	author = {Geyer, R. and Lindner, J. P. and Stoms, D. M. and Davis, F. W. and Wittstock, B.}
}
