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


Reinhard, Jürgen; Zah, Rainer; Hilty, Lorenz (2017). Regionalized LCI Modeling: Framework for the Integration of Spatial Data in Life Cycle Assessment. In: Wohlgemuth, Volker; Fuchs-Kittowski, Frank; Wittmann, Jochen. Advances and New Trends in Environmental Informatics. Cham: Springer (Bücher), 223-235.

Abstract

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 prop-erties, slope, etc.) and therefore highly context dependent. This spatial variability is vastly ignored under the “unit world” assumption inherent to LCA. In this pa-per, 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 better understanding of the char-acteristics and application of spatial data in the context of LCI modeling, (ii) a framework for generating regionalized data structures, and (iii) a first examination of the characteristics and significance of further use cases.

Abstract

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 prop-erties, slope, etc.) and therefore highly context dependent. This spatial variability is vastly ignored under the “unit world” assumption inherent to LCA. In this pa-per, 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 better understanding of the char-acteristics and application of spatial data in the context of LCI modeling, (ii) a framework for generating regionalized data structures, and (iii) a first examination of the characteristics and significance of further use cases.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:2017
Deposited On:30 Aug 2017 13:21
Last Modified:03 Sep 2017 05:08
Publisher:Springer (Bücher)
Series Name:Progress in IS
ISSN:2196-8705
ISBN:978-3-319-44710-0
Publisher DOI:https://doi.org/10.1007/978-3-319-44711-7
Official URL:http://www.springer.com/series/10440
Other Identification Number:merlin-id:13648

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