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
2.2. Uncertainty intervals due to scenario indeterminacy
An interval for an environmental impact of an agricultural production system, p, which expresses uncer-
L R
tainty due to scenario indeterminacy of an input, f, is written as[ e ( p), e ( p )] . Here, the lower bound (de-
f f
noted by superscript L) and the upper bound (denoted by superscript R) are defined as follows:
L L
ef ( p) f ( p) o( p) d( p)
, Eq. 1
R R
e ( p) f ( p) o( p) d( p)
, Eq. 2
f
LR LR where e ( p)
is the lower or upper bound of an environmental impact of p with respect to f, f ( p)
is
f
the lower and upper bounds of an environmental impact of p caused by the focused input f (e.g. fertilisers),
o(p) is an environmental impact of p caused by the other inputs and d(p) is an environmental impact of p
caused by direct emissions.
Although equations 1 and 2 express an interval with respect to an attribute of an input, we will cope with
multiple attributes of an input as illustrated in Table 1, which illustrates an example with three attributes. It is
supposed that there are t alternative scenarios in production technology, l alternative scenarios in production
location, and d alternative scenarios in description types. The lower and upper bounds for production technology,
for example, are determined with respect to i on the condition that j and k are set to the average (default)
scenarios ( j and k ).
Table 1. Derivation of uncertainty interval due to scenario indeterminacy: an example
Attribute Production technology Production location Description type a
Alternative scenario i T {1,2, , t}
j L {1,2, , l}
k D {1,2, , d}
L
f ( p )
min fi
j k
min i jk
min fijk
iT R
f ( p )
max fi
j k
iT j L f
max f j
a Information used to connect the foreground system and the background system
2.3. Scenario indeterminacy in practical situations
jL i k
kD max f k
We analyse the following three situations in which complete scenarios are difficult to construct. In the
first situation, the information on production technology is unavailable. For example, the technical details
about the production processes for fertilisers and pesticides are not publicised because they are the key to
successful business management. Although the full description of the production processes is difficult to
obtain, uncertainty due to unavailability of technological details is partly assessed by comparing the cases
with and without adaptation in life cycle inventory (LCI) data for agricultural inputs (Ossés de Eicker et al.,
2010).
The second situation concerns uncertainty due to the lack of knowledge of the production location of agricultural
inputs. A typical example is the information on the transportation of domestic and imported agricultural
inputs, such as fertilisers and pesticides. Uncertainty intervals are calculated by comparing different
scenarios, such as cases in which all chemical fertilisers are imported (import scenario), and those in which
all are made in Japan (domestic scenario). The average scenario was also estimated using national fertiliser
statistics.
The third situation we analyse is the lack of specifications for agricultural inputs. In this situation, the following
verbal expressions (description types) are used for the assessment: ‘40,000 JPY fertilisers were applied
per ha per year’ and ‘54 N-kg nitrogen fertilisers were applied per ha per year’. Uncertainty intervals
are obtained by comparing the scenario in which only the total sum of chemical fertiliser costs is known and
the scenario in which detailed information about the quantities of each chemical fertiliser is available.
2.4. Comparison at the level of agricultural production systems
The quantification of uncertainty is conducted at the level of agricultural production systems. Conventional,
environmentally friendly, and organic rice cultivation in the central part of Japan are compared; that
kD i j
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