regions and cities and to the complex systems dynamics of societal and technological changes, as well as by a working set of tools that may help at different stages
of the transition.
Indeed, TM includes and extends the concept Integrated Assessment Modelling,
a methodology that, as seen in the preceding chapters, has been developed over the
last two decades to select “optimised” policies aiming at reducing the negative
impacts of air pollution and climate change.
Central to the concept of TM is a multi-level perspective on long-term change
processes (Rotmans et al. 2001) that may take a long time, sometimes decades, to
be realised. Although there may be periods of slower and faster development, in
general, there are no major jumps due to the manifold inter-dependencies in
socio-technical systems. TM is therefore a goal-oriented process of continuous
learning and adjustment among a broad range of actors and stakeholders.
The application of TM to air quality and at climate change mitigation actions
will need the design of innovative strategies based on an in-depth analysis of the
scientific findings (e.g. atmospheric composition dynamics, new modelling
approaches, technological innovation, social analysis) and on technical and economic challenges (e.g. implementation of cleaner technologies or urban planning).
It will also need the definition of the right level of actions to find efficient synergies
and good compromises between European/national/local policies and air quality
and climate issues.
While consensus can often be reached on the overarching transition goals (e.g.
cleaner air in cities, fighting climate change), conflicts of interest may easily arise
once those objectives and targets become more specific, and when policies in one
area have negative impacts on another. Specific policies thus need to be carefully
designed in order to disentangle the mechanisms behind acceptability for the different actors and stakeholders concerned and to avoid disruptive conflicts.
Negotiating and moderating debates about conflict-prone policies is crucial to the
success of TM (Smith et al. 2005). In order to underpin these debates and
decision-making processes with information and knowledge as accurate as possible,
suitable tools and approaches for data collection, analysis and assessment are
needed. They are also essential to enable monitoring, learning, and adjustment
during the relatively long time periods of the TM processes.
A key issue in the development of a transition process is the assessment of the
social acceptability of political decisions. Different techniques are already available
for this purpose.
Discrete Choice Models, for instance, present the advantages of stressing the
trade-offs among different choice alternatives and have been used for the first time
within the SEFIRA FP7 coordination action (http://www.sefira-project.eu) to assess
the acceptability of Air Quality and Climate Policies. Different degrees of acceptability depend on citizens’ preferences and their awareness of the drivers/pressures/
impact in AQ and LC. These perspectives may be investigated using a discrete
choice analysis performed asking citizens to fill a traditional questionnaire on their
choices in relation to AQ and LC policies and/or developing CAWIs (Computer
Assisted Web Interviewing). When conducted in selected regions where specific
6 Conclusions: A Way Forward
107
of the transition.
Indeed, TM includes and extends the concept Integrated Assessment Modelling,
a methodology that, as seen in the preceding chapters, has been developed over the
last two decades to select “optimised” policies aiming at reducing the negative
impacts of air pollution and climate change.
Central to the concept of TM is a multi-level perspective on long-term change
processes (Rotmans et al. 2001) that may take a long time, sometimes decades, to
be realised. Although there may be periods of slower and faster development, in
general, there are no major jumps due to the manifold inter-dependencies in
socio-technical systems. TM is therefore a goal-oriented process of continuous
learning and adjustment among a broad range of actors and stakeholders.
The application of TM to air quality and at climate change mitigation actions
will need the design of innovative strategies based on an in-depth analysis of the
scientific findings (e.g. atmospheric composition dynamics, new modelling
approaches, technological innovation, social analysis) and on technical and economic challenges (e.g. implementation of cleaner technologies or urban planning).
It will also need the definition of the right level of actions to find efficient synergies
and good compromises between European/national/local policies and air quality
and climate issues.
While consensus can often be reached on the overarching transition goals (e.g.
cleaner air in cities, fighting climate change), conflicts of interest may easily arise
once those objectives and targets become more specific, and when policies in one
area have negative impacts on another. Specific policies thus need to be carefully
designed in order to disentangle the mechanisms behind acceptability for the different actors and stakeholders concerned and to avoid disruptive conflicts.
Negotiating and moderating debates about conflict-prone policies is crucial to the
success of TM (Smith et al. 2005). In order to underpin these debates and
decision-making processes with information and knowledge as accurate as possible,
suitable tools and approaches for data collection, analysis and assessment are
needed. They are also essential to enable monitoring, learning, and adjustment
during the relatively long time periods of the TM processes.
A key issue in the development of a transition process is the assessment of the
social acceptability of political decisions. Different techniques are already available
for this purpose.
Discrete Choice Models, for instance, present the advantages of stressing the
trade-offs among different choice alternatives and have been used for the first time
within the SEFIRA FP7 coordination action (http://www.sefira-project.eu) to assess
the acceptability of Air Quality and Climate Policies. Different degrees of acceptability depend on citizens’ preferences and their awareness of the drivers/pressures/
impact in AQ and LC. These perspectives may be investigated using a discrete
choice analysis performed asking citizens to fill a traditional questionnaire on their
choices in relation to AQ and LC policies and/or developing CAWIs (Computer
Assisted Web Interviewing). When conducted in selected regions where specific
6 Conclusions: A Way Forward
107