Environmental Engineering Reference
In-Depth Information
changes whilst sustaining or increasing food, water and
energy production. There is thus considerable demand
particularly from international development organiza-
tions, for policy support in the area of climate impacts
assessment to test the long-term sustainability of pro-
posed investments and also to hotspot areas where climate
change may have negative consequences for international
development goals.
COMMUNICATION - science is difficult to commu-
nicate (even between scientists). Communications
between scientists and policy advisors (who may
or may not be scientists themselves) can be very
difficult - in both directions. To design models, the
problem being modelled needs to be very clearly
specified. Modelling can therefore act as a means of
producing a very clear specification for the problem
being addressed and thus help to clarify ongoing con-
ceptual uncertainties. Moreover, modelling outputs
can be highly graphical either as charts or as maps and
these can be very effective tools for communicating
outcomes of scenarios for change or impacts of policy
interventions.
20.1.4 The roleofmodelling inpolicysupport
Modelling is an important basis for policy support since
it makes explicit our understanding of processes (repre-
sented as model equations or rules; see Chapter 2), and
couples these with spatio-temporal datasets representing
state . Modelling is thus potentially a robust, negotiable
and explicit abstraction and representation of the system
under study and hence a potentially excellent framework
for communication, analysis, comparison and trade-off
of policy options. Modelling can help bridge the gap
between science and policy through summary of complex
processes and communication of their outcomes as maps
or charts. It can also reach beyond simple mapping and
GIS-overlay to provide dynamic scenario analysis indicat-
ing system change over time and space in response to the
operation of processes over a landscape and population.
Modelling offers at least the following benefits to under-
standing the environment in policy-relevant situations.
Unfortunately, despite these positive characteristics,
most models seem to be regarded by the policy com-
munity as 'black boxes' that, in fact, hide the basis for
their results in mathematical mystery and technological
magic(seeOreskes et al ., 1994). This perception is in part
because most models were originally developed for use by
modellers and have often been rather crudely interfaced
for use by others than the modeller in order to attract
funding for real-world applications. In part, this view of
models may also result from the highly specialist nature
of both mathematical modelling and computer program-
ming, coupled with poor or vague documentation and a
lack of opportunities for training in many provided mod-
els. In many cases models are not 'user friendly', are very
data demanding, do not tackle the problems of interest
to potential users or are not validated (see Oreskes et al .,
1994; Chapter 2) nor trusted by users. Taken together
these factors have led to a slow and limited uptake of
simulation models in policy support, despite its clear
potential.
Research at the WEEF nexus is a case in point. Models
have been developed to help understand elements of
this policy-relevant issue since at least the 1960s with
the advent of the first crop models, but there is still
relatively little uptake of them in policy support today.
This lack reflects the fact that from the 1960s the models
built in this area were intended as science models not
as policy models (see discussion on the difference in
Mulligan, 2009 and Table 20.1, but note that these types
represent the opposite ends of a continuum of from
research-focused to policy-focused models) and were
thus not meant for application by others than the model
developers or other scientists and modellers. Rather,
most models were originally considered as tools for the
scientists themselves to better understand the systems
SIMPLIFICATION - models by their nature are simpli-
fications of complex systems. Careful abstraction and
conceptualization can capture the important elements
of a dynamic system whilst ignoring the less relevant
details. Making the right assumptions here can simplify
a policy problem without ignoring important elements.
QUANTIFICATION - models produce numbers. The
precision and capability for objectivity in modelling
can be of great value in complex policy problems
where expert opinion disagrees or where understand-
ing the magnitudes is critical (for example the potential
of check dams to reduce sedimentation into a reser-
voir and the cost: benefit of these versus sediment
dredging). Uncertainties can be rendered explicit and
communicated as such.
INTEGRATION - models can integrate across hetero-
geneous spaces, across variable time horizons, across
processes, between disciplines and institutions. They
are thus an important tool for bringing together highly
reductionist science into a more holistic realm that can
be used for better understanding social-environmental
systems in all their complexity as shown in Chapter 18.
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