An applicability assessment and sensitivity analysis of land use impact models: application of the LANCA model in site-specific conditions

Purpose In this work, we study a land use impact model with the aim of obtaining spatially differentiated as opposed to default average characterisation factors. In particular, we study the application of LANCA®, a multi-indicator model with available country average characterisation factors expressing the alteration of the soil quality level of the current land use of one kind with respect to a reference situation. Method To this purpose, we use the LANCA® method documentation at a higher spatial resolution and apply all the required elemental steps. From a user perspective, we score the transparency of the method down to the basic methodological references and single out the source of errors that the user may incur when: (i) collecting the input data, (ii) selecting the appropriate soil/land classes and (iii) applying the individual calculation steps. For a greater insight, we couple the source of errors with a sensitivity analysis. Results In the comparison between a site-specific test area and the related country default values, we obtained relevant discrepancies regarding the erosion resistance and the physicochemical filtration of the soil. For example, we find that the erosion resistance potential is −1.06 * 10−3 kg m2 a−1 locally while the country default value is 13.1. We explain differences through the sensitivity analysis and having analysed in depth the underpinned soil erosion equation and the critical steps for its calibration. Together with systematic errors, we find that the method generally implies 9 scarcely guided steps out of 42, and one-third of the basic methodologies are not fully explained or accessible. These factors make the results related to Biotic Production, Mechanical Filtration, Physicochemical Filtration and Groundwater Regeneration user dependent and — in this sense — difficult to replicate. Conclusions From the analysis, we distil 7 main directions for improvement addressed to LANCA® and soil models especially in sight of a broader application of a regionalised life cycle impact assessment.

The Influence of Database Selection on Environmental Impact Results. Life Cycle Assessment of Packaging Using GaBi, Ecoinvent 3.6, and the Environmental Footprint Database

This research analyses the differences in impact assessment results depending on the choice of a certain software-database combination. Six packaging systems were modelled in three software-database combinations (GaBi database in GaBi software, ecoinvent 3.6 database in openLCA, Environmental Footprint database in openLCA). The chosen Life Cycle Impact Assessment (LCIA) method is EF 2.0. Differences and errors in the implementation of the LCIA method are a possible source of deviations. We compared the published characterisation factors with the factors implemented in the software-database combinations. While results for the climate change category are similar between the different databases, this is not the case for the other impact categories. In most cases, the use of the ecoinvent 3.6 database leads to higher results compared to GaBi. This is partly due to the fact, that ecoinvent datasets often include more background processes than the corresponding GaBi datasets. We found striking discrepancies in LCIA implementation, including the lack of regionalisation for water use in ecoinvent. A meaningful communication of LCIA results requires an excellent knowledge of the analysed product system, as well as of database quality issues and LCIA methodology. We fully acknowledge the constant efforts of database providers to improve their databases.

Where that emissions went? Characterising the uncertainty of characterisation factors of underspecified emissions.

Life cycle impact assessment (LCIA) methods use unspecified archetypes to model situations of imperfect knowledge. There is an inherent uncertainty in characterisation factors to unspecified archetypes, and this study proposes an estimation of this uncertainty using continuous and discrete probability distributions. The extent of “archetype uncertainty” is analysed for several methods and the impact on LCIA scores is quantified for all activities in the database ecoinvent. Results indicate that this source of uncertainty can be very large, introducing systematic as well as random errors in LCIA scores. Based on this research we recommend using undefined archetypes only when needed and quantify this source of uncertainty by default. The continuous and discrete approaches to model uncertainty give similar results, but the continuous approach is easier to implement.