Abstract
Regionalization of land use (LU) impact in life cycle assessment (LCA) has gained relevance in recent years. Most regionalized models are statistical, using highly aggregated spatial units and LU classes (e.g. one unique LU class for cropland). Process-based modelling is a powerful characterization tool but so far has never been applied globally for all LU classes. Here, we propose a new set of spatially detailed characterization factors (CFs) for soil organic carbon (SOC) depletion. We used SOC dynamic curves and attainable SOC stocks from a process-based model for more than 17,000 world regions and 81 LU classes. Those classes include 63 agricultural (depending on 4 types of management/production), and 16 forest sub-classes, and 1 grassland and 1 urban class. We matched the CFs to LU elementary flows used by LCA databases at country-level. Results show that CFs are highly dependent on the LU sub-class and management practices. For example, transformation into cropland in general leads to the highest SOC depletion but SOC gains are possible with specific crops.
Highlights
Background & SummaryLand use (LU) and land use (LU) change are important drivers of change in the state of ecosystems globally[1]
Soil organic carbon (SOC) depletion has been one of the most used indicators related to LU and LU change because it is a good proxy for LU damages to the biotic primary production potential of soils[7] and other ecosystem services[8,9]
If a certain region is divided in 50% cropland and 50% forest, ImpactLU1 is the average impact of the individual crops feasible in that region multiplied by 50% plus the average impact of the individual forest types feasible in the region multiplied by 50%
Summary
Land use (LU) and LU change are important drivers of change in the state of ecosystems globally[1]. All published methods that used SOC depletion indicator are proxy-based and are based on a combination of statistical analysis and geographical information systems They have varying levels of regionalization (i.e. spatial differentiation) and LU class differentiation. They consider temporal and spatial scales based on scenarios that characterize intra and inter-annual dynamics They generally require more data than proxy-based models, but allow higher level of detail and have the possibly of reducing uncertainties because they are based on processes and not on statistics[16]. We considered 81 foreground LU classes (63 individual cropland classes, 16 forest classes and 1 grassland class, plus an urban LU class) and 17,203 regions This is a new paradigm for how global CFs in LCIA can be calculated that combines PBM with LCA. We define foreground CFs as those that have two known LU classes, i.e. when both initial and final LU classes are known (e.g. “transformation from irrigated tomato to irrigated cabbage”)
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