LCA Food 2012 in Saint Malo, France! - Manifestations et colloques ...

PARALLEL SESSION 2C: QUANTIFICATION AND REDUCTION OF UNCERTAINTY 8 th Int. Conference on LCA in the
Agri-Food Sector, 1-4 Oct 2012
bination of (1) and (2) shows that the main barrier to using secondary emissions records databases for LCA
identified in this paper is lack of data, not heterogeneity. Using emissions records at a macro scale was not
found to be problematic, . The study revealed that that even compiling most available databases is insufficient
to cluster data in most product types. This result is perfectly consistent with the replies to the agri-food
LCA practitioners survey that identified lack of data as one of the main obstacles to LCA (Teixeira and Pax,
2011).
After its completion, the main output of this work will be a table that, for each clustering group of agrifood
products produced with each method in each region, provides the average CO2e emissions and an error
estimate. The results in that table can fit several purposes in LCA, namely:
As a reference or benchmark in future LCA studies;
For the environmental assessment of large-scale agricultural policies;
In hybrid input-output analysis (IO-LCA), which uses data at this level of detail;
In simplified LCA methods and tools for screening calculations, as well as streamlined simple algorithms.
Although the data in this paper can hardly replace individual data records, it will be very useful
to suppress data gaps or make informed choices between available emission records;
To help developers of Product Category Rules set category-specific rules and hierarchies for data
quality.
To help LCA practitioners determine a product-specific hierarchy for data quality criteria. For example,
if records from a certain category are very sensitive to geographic region, then that attribute must
be privileged when choosing from secondary databases.
It should also be noted that it is not advised to use this type of data when users have objectives such as process
optimisations, labelling/external communication and comparative assertions (except for preliminary hotspot
analyses); because the data can only be used for generic macro assessment of static situations. Changes
in the baseline numbers will change the structure of the data itself and comparisons must be updated.
5. Conclusion
In this study, we tested whether it is possible to use “magic numbers” in secondary databases for LCA
studies and, if acceptable, we sought to determine the optimum level of detail. These results suggest that,
even though there is a high variability in the way agri-food products are produced, there are representative
averages that may be used when LCA practitioners, depending on their objective, provided sufficient statistical
work is done beforehand. Our work also exposes the dramatic lack of LCA data today. More than tools,
methodological developments or standards, practitioners identify lack of the data as the main problem in
LCA. This work shows analytically why.
6. References
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