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
2. Methods
Our approach consists on analysing statistical records on carbon emissions for agri-food products. For
this study to be successful, we needed a large number of secondary data that included many different sources
to ensure maximum heterogeneity.
This compilation is found in the database for the Carbonostics tool (Carbonostics, 2011), which is the
largest available built-in database for agri-food products (Verdantix, 2011). The database compiles more than
1,400 pre-recorded final LCIA results for CO2e emissions from data providers such as ADEME (2010),
CleanMetrics (2010), CLM (2010), the Danish LCA Food Database (Nielsen et al., 2003), DEFRA (2012),
ecoinvent (Frischknecht and Rebitzer, 2005) and ESU (2012). This number of data records is much higher
than any number mentioned in any of the calculators referred in Amani and Schiefer‘s (2011) survey of
tools. Each record in the database has been peer-reviewed and validated by the Swiss NGO MyClimate
(2012). Many assumptions built into the data records by different providers may be contradictory or inconsistent
with each other, which serves the purpose of our analysis by introducing even more variability. For
example, while some records include transportation steps, others do not. We did not remove these inconsistencies
since the objective is to maximize the variance and replicate the error a user would make when
choosing the wrong record from the database. The choice of food products is particularly suited to our objective,
since variability between specific products of the same time is reputedly high, e.g the amount of fertilisers
and yields change between farmers even in the same region and with the same general production
method.
The Carbonostics database hierarchizes records by grouping them in three levels:
1. General category (e.g, dairy, vegetables, oils, meat, crops). This is roughly equivalent to product
type. The number and distribution of these records is shown in Figure 1;
2. Product type within category (e.g., butter, buttermilk, milk – all within the category dairy);
3. Product variant within type (e.g, conventional plain butter in Europe, organic butter with herbs in
Europe – all within the type butter).
Figure 1. Number of records per type and CO2e emissions dispersion.
Our statistical method consisted on building one linear model at each of these three levels. We determined
the intra-level clustering, i.e, how many sub-divisions in sub-groups are needed, by running a cluster analysis,
using the Calínski and Harabasz pseudo-F index stop rule to determine the number of clusters, for each
level. In each model, CO2e emissions are the dependent variable and the independent variables are a group of
binary “dummy” variables that represent the category, type or variant. The model thus calculates averages
and standard errors for each group of records, and determines their statistical significance.
Since all variants are defined by geographical region and method of production, we included both as control
variables, i.e. we included them as binary variables in the model. In the results shown next, we cropped
data to show results only using agricultural records that were produced conventionally and in Europe (N=878
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