Environmental Engineering Reference
In-Depth Information
The SEAMLESS-IF Outputs as Science-Policy Interface
A SEAMLESS-IF integrated assessment can be used as input in an Impact
Assessment which provides policy-experts with information about the likely
impacts of a specific policy option. This information is provided by indicators and
indices, which are tested against a baseline scenario and a selected policy scenario.
The baseline scenario can e.g. be constituted by the likely impacts of the CAP
reform as it is anticipated to be implemented in 2013 (or any other defined
time horizon), including already decided future legislative changes. The scenario is
the point of reference for the interpretation of policy effects, or various shocks
coming from the 'market level' or the 'farm level' This makes that the resulting
SEAMLESS-IF foresights can be understood as interfaces between 'system
science' (procedures and computer tools for supporting impact assessment) and
'social significance' (the tested policy options and the selected indicators).
The analysis made by SEAMLESS-IF can be regarded as 'social foresights'
(economic, social and ecological needs of society, and means to satisfy these
needs - amongst which are technological means and institutional considerations)
and consequently the foresights can be understood as science-policy interfaces
for the construction of 'strategic knowledge'. Drawing on Grunwald's
(2004)
definition there are three reasons to regard the SEAMLESS-IF foresights as strategic
knowledge as they are:
-
Context-sensitive combinations
of explanatory knowledge about the tested
policies/innovative technologies;
-
Provisional and incomplete
in their descriptive aspects due to the degree of
uncertainty in given models and data;
-
Non-verifiable in nature
since they do not give a representation of an empirical
reality.
The foresights are an explorative type of knowledge. Their aim is not to forecast
the future - which is assumed to be plural - but rather on bounding the uncertainty.
By offering a basis for discussing policy impacts they can play as communication
devices.
Dealing with strategic knowledge for societal application requires a thorough
reflection on the premises that are inherent in the integrated assessment models and
on the sensitivity of the results to the hidden values and other assumptions of the
used models (Morgan and Dowlatabadi
1996
; Brugnach et al.
2007)
. It has been
suggested (Van Ittersum et al.
2008b)
that uncertainty shall be addressed in
SEAMLESS-IF in four steps: identifying the sources of uncertainty in the selected
model chain; communication of that uncertainty to the user; establish insight into
the effects and/or relative importance of sources of uncertainty relevant to the users;
communication of these effects to the users.
Problems and challenges for the SEAMLESS-IF science-policy interfaces can
be summarized as follows (Table
12.1
).
During the
pre-modelling phase
, two challenges must be addressed. As a result
of making different selections during the process of problem definition some
