quired and consent identification. One main characteristic of the database is its transparency in reporting to enable individual assessment of data appropriateness and to support the plurality in methodological approaches. Outlook. Further work on the ecoinvent database may comprise work on the database content (new or more detailed datasets covering existing or new economic sectors), LCI (modelling) methodology, the structure and features of the database system (e.g. extension of Monte Carlo simulation to the impact assessment phase) or improvements in ecoinvent data supply and data query. Furthermore, the deepening and building up of international co-operations in LCI data collection and supply is in the focus of future activities.

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depth analysis of the future development of each single unit process, while still accounting for the requirements of the final scenario integration. Due to its high transparency, the procedure supports the validation of LCI results. Furthermore, it is wellsuited for incorporation into participatory methods so as to increase their credibility. Outlook and Future Work. Thus far, the proposed approach is only applied on a vehicle level not taking into account alterations in demand and use of different transport modes. Future projects will enhance the approach by tackling uncertainties in technology assessment of future transport systems. For instance, environmental interventions involving future maglev technology will be assessed so as to account for induced traffic generated by the introduction of a new transport system.

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This paper provides an overview on the content of the ecoinvent database and of selected metholodogical issues applied on the life cycle inventories implemented in the ecoinvent database. In the year 2000, several Swiss Federal Offices and research institutes of the ETH domain agreed on a joint effort to harmonise and update life cycle inventory (LCI) data for its use in life cycle assessment (LCA). With the ecoinvent data base and its actual data v1.1 a consistent set of more than 2'700 product and service LCIs covering the energy, transport, building materials, chemicals, pulp and paper, waste management and agricultural sectors is now available. Datasets are valid for European and Swiss conditions but partly also for other regions in the world (e.g., gas and oil extraction, metals mining). Nearly all process datasets are trans parently documented on the level of unit process inputs and outputs. Methodological approaches have been applied consistently throughout the entire database content and thus guarantee for a coherent set of LCI data. This is particularly true for market and trade modelling, and for the treatment of multi out put and of recycling processes. Most multi-output processes are implemented as such, i.e., in their unallocated form with several co-product outputs and related allocation factors. With the help of these allocation factors, unit process raw data are derived and additionally stored in the ecoinvent database. This approach guarantees that 100 % of all inputs to and outputs from the multi-output process are allocated to the co-products. Transparency in reporting on a unit process level helps to adjust allocation factors applied on a multi-output process to one's own needs. With limited own efforts one can even change the allocation approach from allocation to system expansion, in case this is considered more appropriate. Although measures have been taken to minimise errors in the database, they cannot be excluded. A pro-active information policy on data errors is followed. It helps the users to judge whether they may still work with the present version of ecoinvent data or they better correct selected errors that might otherwise influence the outcome of their current LCA case studies. The existence of the ecoinvent database proves that it is possible and feasible to build up a large interlinked system of LCI unit processes. The project work proved to be demanding in terms of coordination efforts and consent identification. One main characteristic of the database is its transparency in reporting to enable individual assessment of data appropriateness, to support the plurality in methodological approaches when using an ecoinvent dataset in another context and to allow for efficient and individual error corrections.