Geoscience Reference
In-Depth Information
the legal and political system in developed countries requires greater clarity than
normal or post-normal science can deliver.
One solution to the inevitably value laden character of IA has been to open up
technical models to greater public involvement and stakeholder scrutiny. The justi-
fi cation is that this allows policymakers and stakeholders to be explicit about the
impacts of value judgements embedded in models. More recently, demands for
stakeholder and public input to model development have emerged, in parallel to
similar calls in environmental assessment and risk assessment. This participatory
turn in IA is discussed below.
The Participatory Turn in IA
The late 20th century witnessed a decline in the public's perception of the credibility
and authority of science in society. A range of explanations have emerged, including
science's culpability in the creation of many of the problems (made explicitly in
Beck's
Risk Society
), a general decline in deference for authority and rising educa-
tional levels and access to information. In response, the environmental and risk
assessments underpinning public policy often now require signifi cant public input
for substantive as well as procedural reasons. Substantive justifi cations recognise
that stakeholders may be able to provide useful input to scientifi c assessments.
Procedural justifi cations recognise that participation may improve the legitimacy of
decision-making processes. Participatory IA has been shaped by both demands.
Substantive justifi cations go beyond the usual claims that stakeholders have useful
information to bring to bear on modelling exercises. Once we admit that IAMs are
often driven by socio-economic scenarios, then human choice has a direct infl uence
over the future. To take a trivial example, public acceptance of alternative energy
systems such as wind power will have an impact on development pathways. Broadly
speaking, the argument runs that non-experts should be engaged in making choices
about the range of social futures represented in the models. In the strongest cases,
described below, the
desirability
of modelled futures is one of the key performance
criteria.
As IA has evolved in this direction it has come to share more in common with
scenario based approaches made famous by Shell over the last three decades (Van
Heijden, 2005). Recognising that their business operates under conditions of great
uncertainty, Shell began to challenge their own assumptions about the business
environment and then used these variations in input assumptions to create different
scenarios, informed in many cases by quantitative data. Shell utilises narrative-based
approaches that develop clear storylines for their scenarios and these make the
outputs accessible to a wider audience. This tradition informed the backcasting
approach described below. Narrative driven approaches have also emerged under
the banner of qualitative IA, which undertakes only limited modelling and focuses
effort instead on creating compelling visions of the future. Narrative based
approaches have the advantage of telling the story of the future in an attractive and
accessible manner; they create a coherent storyline that can hold infl uence over
policymakers who may struggle with numerical outputs.
Some of the most important contributions to this domain have sought to combine
qualitative and quantitative methods, using focus group discussions and other quali-
tative social scientifi c methods to characterise the input variables for models that
generate outputs and provide feedback to participants. These approaches seek to
