|Title||Coupling GIS and LCA for biodiversity assessments of land use: Part 1 Inventory modeling|
|Publication Type||Journal Article|
|Year of Publication||2010|
|Authors||Geyer, R, Stoms, DM, Lindner, JP, Davis, FW, Wittstock, B|
|Journal||International Journal of Life Cycle Assessment|
|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|
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.