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Regionalized LCI Modeling: A Framework for the Integration of Spatial Data in Life Cycle Assessment

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Advances and New Trends in Environmental Informatics

Part of the book series: Progress in IS ((PROIS))


Life Cycle Assessment (LCA), the most prominent technique for the assessment of environmental impacts of products, typically operates on the basis of average meteorological and ecological conditions of whole countries or large regions. This limits the representativeness and accuracy of LCA, particularly in the field of agriculture. The production processes associated with agricultural commodities are characterized by high spatial sensitivity as both inputs (e.g. mineral and organic fertilizers) and the accompanying release of emissions into soil, air and water (e.g. nitrate, dinitrogen monoxide, or phosphate emissions) are largely determined by micro-spatial environmental parameters (precipitation, soil properties, slope, etc.) and therefore highly context dependent. This spatial variability is vastly ignored under the “unit world” assumption inherent to LCA. In this paper, we present a new calculation framework for regionalized life cycle inventory modeling that aims to overcome this inherent limitation. The framework allows an automated, site-specific generation and assessment of regionalized unit process datasets. We demonstrate the framework in a case study on rapeseed cultivation in Germany. The results from the research are (i) a framework for generating regionalized data structures, and (ii) a first examination of the significance of further use cases.

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  1. 1.

    Site-generic values represent an average over large geographic regions, such as continents or the globe (Mutel et al. 2012).

  2. 2.

    Site-dependent values follow country or state boundaries (Mutel et al. 2012).

  3. 3.

    Site-specific values are usually used only for individual locations, such as a particular plot, factory or landfill (Mutel et al. 2012).

  4. 4.

    The framework is currently still under development and therefore not publicly available.

  5. 5.

    To date, the merging of the data is the most time consuming step (see discussion section).

  6. 6.

    That is, the LCIA vector can be generated for 692 LCIA indicators.

  7. 7.

    This means that due to the difference in resolution between the crop specific raster datasets (see no. 5 and no. 4 in Table 1) and the soil property raster datasets (see no. 4, 10 and 11 in Table 1) the 100 rapeseed UPDs generated for a ~10 × 10 km grid cell will always have the same yield and fertilizer input. That is, within a ~10 × 10 km grid cell, the only varying parameters are the soil properties and the therefrom computed emissions. Although this computation strategy comes at higher computational costs—for one parameter, we have to extract the values of roughly one million grid cells—it pays off by enabling us to assess the influence of changing soil properties on emissions in a ceteris paribus examination.

  8. 8.

    That is, all upstream interventions are included. The further usage of the rapeseed (e.g. as feed or biofuel) is not considered.

  9. 9.

    Roughly 6 % of the UPDs (or 40’000 UPDs) were excluded due to data gaps. The extrapolation of missing data was not in the scope of this article.

  10. 10.

    For Brazil, we have to merge about roughly 25 × 20 million data entries. With the current merging approach, this would require more than a month on a modern laptop computer.

  11. 11.

    To date, exchange flow specific variability in LCI databases (used for Monte-Carlo analysis) is largely based on rough estimates.


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We thank our colleague Mireille Faist-Emmenegger for her support with WFLDB emission models and the three anonymous reviewers for their helpful comments.

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Correspondence to Juergen Reinhard .

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Reinhard, J., Zah, R., Hilty, L.M. (2017). Regionalized LCI Modeling: A Framework for the Integration of Spatial Data in Life Cycle Assessment. In: Wohlgemuth, V., Fuchs-Kittowski, F., Wittmann, J. (eds) Advances and New Trends in Environmental Informatics. Progress in IS. Springer, Cham.

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