Environmental Engineering Reference
In-Depth Information
and indicators are selected. The choices and selections made during this phase will
express the interests, values and motivations of the organisation that has initiated
the assessment of a policy or a technological change.
The following example shows how policy options can steer an assessment in
different directions. The objective of the policy to be tested could be to increase
oil-producing crops. In order to achieve this goal a specific group wants to support
biofuel. To promote this, the group needs arguments in favour of biofuel. So, the
group suggests that new cropping systems should be tested and assessed on an
indicator of biofuel energy balance. The group could also be interested in assessing
the global performances of cropping systems that include oil-producing crops in
comparison to cropping systems without such crops. The group might further like
to get ideas about how oil crops could be introduced in new regions, for example at
which price level these crops would be accepted among producers. More thought
would be needed to suggest changes in policies or practices to assess this question.
The main concern for the group is the location of oil-producing crops and the political
and economic elements that would favour or disadvantage their introduction as
these two factors would have implications on the whole supply chain.
During the next step, the modelling phase , the appropriate model chain is used
with the relevant data. A consistent micro-macro analysis involves a bio-economic
farm model (FSSIM) and an agricultural sector model (CAPRI). The FSSIM farm
models are run for the major farm types in a sample of regions within the EU. These
farm models are run for a range of prices of the commodities modelled with FSSIM
and CAPRI. This results in modelled supplies of commodities for these farm
types and regions at a certain price level. The price-supply relationships are then
extrapolated to the farm types in regions for which no FSSIM model was run, using
the econometric model EXPAMOD. Then the supply models in CAPRI are
calibrated to these supply responses allowing derivation of commodity market
prices consistent with the farm level behaviour in CAPRI. The prices simulated with
CAPRI will then be used for a final run of FSSIM, to simulate the farm behaviour
(Van Ittersum et al. 2008 a).
During the post-modelling phase the impacts of tested policy options and techno-
logical changes are analysed and explored, including their institutional compatibility.
The post-modelling phase may also include the transformation of the indicators into
indices for the comparison between various policy options. Finally, the various results,
newly constructed indicators and models are stored for future retrieval and reuse, in
a knowledge base, together with annotations, and other process related documents.
Both the pre- and post-modelling phases require comprehensive interaction
between the two types of actors: policy experts and integrative modellers . Policy
experts express the problem to be assessed according to their interest and the political
role of their organisation, and thus influence the assessment (definition of problem,
policy options, indicators) in a way that supports their stake. Integrative modellers
run the SEAMLESS framework. As providers of expert knowledge, data and analysis,
they feed scientific knowledge into the policy process, based on their values.
This makes that the SEAMLESS-IF procedures can be regarded as a plan for
institutionalization of 'deliberation'.
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