Regionalized lifecycle inventory (LCI) modeling generates and links process datasets to the location in which they occur[i]. Given agricultural lifecycle assessments (LCA) are particularly spatially-sensitive, Quantis’s Rainer Zah was involved in developing a framework streamlining the generation of high-resolution crop production datasets in any region of the world. Our experience with applying the framework to the footprint assessment of food, feed, fiber or biofuel and material production shows that it allows decision-makers to consider micro-spatial variations otherwise overlooked in LCA.
Regionalized lifecycle inventory (LCI) modeling generates and links process datasets to the location in which they occur[i]. Given agricultural lifecycle assessments (LCA) are particularly spatially-sensitive, Quantis’s Rainer Zah was involved in developing a framework streamlining the generation of high-resolution crop production datasets in any region of the world. Our experience with applying the framework to the footprint assessment of food, feed, fiber or biofuel and material production shows that it allows decision-makers to consider micro-spatial variations otherwise overlooked in LCA.
Understanding the high spatial variability of agricultural products
Agricultural datasets often operate on meteorological and ecological estimates. Yet the type and amount of resources used (water, land…), the intermediate flows required (the application of mineral and organic fertilizers, the use of machinery…) and the corresponding emissions into soil, air and water (carbon dioxide, nitrate, di-nitrogen monoxide, phosphate…) are determined by micro-spatial parameters such as precipitation, soil properties and slope[ii].
Due to the likelihood of their underpinning data being incomplete, agricultural LCA studies are subject to major uncertainties. As a result, country-level spatial resolutions might not allow decision-makers to consider site-specific conditions the likes of water scarcity or resource planning in the context of their supply chain management and/or sourcing decisions. A challenge has arisen in merging spatial data and Geographic Information Systems capabilities in agricultural LCI models to generate site-specific unit process datasets.
A framework to refine regionalized lifecycle inventories
Regionalized LCI modeling had already been recognized as a driver in improving unit process datasets[iii] and scientific literature stressed how both the location of a given emission and its surroundings influence agricultural impact assessments[iv]. Our work proves that spatial data strengthens the overall quality of agricultural LCA when supplemented with a computerized method for regionalized LCI modeling.
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Eventually, the contextual data contained in each cell was automatically transformed into site-specific agricultural process datasets, based on version 3.2 of the Ecoinvent database and the emission models from the World Food LCA Database (WFLDB) Guidelines[vi]. The end result is exemplified in figure 2, where the climate change impact and marine eutrophication potential of 580,000 rape seed cultivation systems in Germany are pinpointed down to a square kilometer.
The framework paves the way for a refined management of spatially-sensitive projects, as is the case with the environmental impact assessment of food, feed, fiber or biofuel and material production. Decision-making grounded in regionalized LCI advantageously includes micro-spatial variations; a level of nuance likely to be valuable to large-scale bioenergy producers as they set out to compare the environmental performance of alternative bioenergy-cropping systems in a given spatial setting.
Thanks to its automated extraction of information ranging from precipitation to soil type, climate zone, yield and such, Quantis only requires the relevant site coordinates be entered. This simplifies the data-entry process in using web-based footprint assessment tools such as the RSB-tool[vii] (a web-based tool that could benefit from the implementation of regionalized LCI modeling simply because the data entry effort would be cut by half), with substantive gains in user-friendliness and accuracy for agricultural production managers.
From there, users of the Quantis framework might consider integrating increasingly sophisticated emission models. To date, the emission models included in agricultural LCI are often adjusted to fit the generic nature of LCA and therefore lack granularity in spatial and temporal orientation. The nitrates analyses they entail typically omit losses due to waterborne and wind borne sediments, where the Quantis framework offers a modular testbed capable of harboring more advanced models.
From a methodological perspective, the framework will eventually guide the enhancement of today’s mostly site-generic agricultural LCI databases, fitting them with dovetailed estimates. Instead of being constrained by geopolitical borders, the spatial scale of an average LCI dataset could be determined – amongst other indicators – by the spatial variation in exchange flow ranges. In a welcome side-effect, Quantis also supports spatial uncertainty modeling. Combined with spatially-explicit computation, results gain in representativeness, rendering agricultural LCA altogether more reliable.
Moving forward
Regionalizing LCA presents an engaging approach to streamlining data management towards assessing the footprint of agricultural products. By introducing Geographic Information Systems capabilities, Quantis enhanced the generation of high resolution agricultural process datasets for major crops in all regions of the world, gaining in representativeness, accuracy and reproducibility. The framework operationalized, a new frontier lies in helping agribusinesses effectively transpose the resulting analytics into value added sustainable practices.
For more information, please contact Quantis experts Jürgen Reinhard or Xun Liao.
References
[i] Mutel, C. L., Pfister, S. & Hellweg, S. GIS-based regionalized life cycle assessment: how big is small enough? Methodology and case study of electricity generation. Environ. Sci. Technol. 46, 1096–1103 (2012).
[ii] Reinhard, J., Zah, R. & Hilty, L. M. Regionalized LCI Modeling: A Framework for the Integration of Spatial Data in Life Cycle Assessment. in Advances and New Trends in Environmental Informatics: Stability, Continuity, Innovation (eds. Wohlgemuth, V., Fuchs-Kittowski, F. & Wittmann, J.) 223–235 (Springer International Publishing, 2017).
[iii] Hellweg, S. & Milà i Canals, L. Emerging approaches, challenges and opportunities in life cycle assessment. Science 344, 1109–1113 (2014).
- Mutel, C. Framework and Tools for Regionlization in Life Cycle Assessment. (ETH, 2012).
- Mutel, C. L. & Hellweg, S. Regionalized Life Cycle Assessment: Computational Methodology and Application to Inventory Databases. Environ. Sci. Technol. 43, 5797–5803 (2009).
- Seto, K. C. et al. Urban land teleconnections and sustainability. Proc. Natl. Acad. Sci. (2012).
[iv] Hauschild, M. Spatial Differentiation in Life Cycle Impact Assessment: A decade of method development to increase the environmental realism of LCIA. Int. J. Life Cycle Assess. 11, 11–13 (2006).
[v] ESRI: What is raster data? Webpage ArcGIS for Desktop. http://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/what-is-raster-data.htm (2017)
[vi] Nemecek, T. et al. Methodological Guidelines for the Life Cycle Inventory of Agricultural Products. World Food LCA Database (WFLDB), Version 3.0. (2015).
[vii] Reinhard, J., Emmenegger, M. F., Widok, A. H. & Wohlgemuth, V. RSB tool: a LCA tool for the assessment of biofuels sustainability. in Proceedings of the Winter Simulation Conference 1048–1059 (Winter Simulation Conference, 2011).
Published Jul 19, 2017 11am EDT / 8am PDT / 4pm BST / 5pm CEST
Sustainable Brands Staff