As to the problem formulation, one major area of investigation for the future is
the consideration of dynamic evolution of the physical, economic, and social
environment. All current approaches are static, in the sense that they devise a
solution to be reached within a given time horizon (say, for instance, in 2020).
However, the system we want to control is non-stationary (e.g. the effect of the
current economic crisis) and it may therefore be more supportive for decision
makers to know where and when to currently invest with the highest priority in
order to follow a certain path to the target with the ability to adapt decisions with
time, in case the system evolution differs from the projected one. This involves the
necessity of flexibly adding into the plans the advent of new technologies and the
ability to determine the cost of scrapping old plants to substitute them with newer
ones. This essentially means designing a new generation of Decision Support
Systems to be intended more as control dashboards, than planning tools. Related to
the dynamic problem is also the issue of how to evaluate future benefits of air
quality investments. If economy has defined since long how to account for
investment costs lasting for a given period in the future, this is more difficult for
benefits that are not monetizable or last in the future for an unknown period. How
can we account for a 20 % improvement of an air quality index ten years from
now? What is the benefit from a reduction of PM10 today that will decrease
cardiovascular problems in a population sometime in the future?
A more synergic use of Source Apportionment and Optimization approaches
should also be fostered. SA could limit the degrees of freedom of cost-effectiveness
analysis, constraining the optimal solution to consider only a subset of the possible
measures previously identified applying SA. On the other hand, the optimization
approaches can automatically perform source apportionment establishing the most
cost-effective emission reductions and identifying the sources categories associated
to these reductions, without the need to monitor and chemically characterize air
pollutants.
4.4 Areas for Future Research of IAM Systems
A number of directions for future research have been identified by considering the
IAM as a whole, in particular related to the integration of IAM scales and the
uncertainty assessment.
One point is certainly the development of methodologies integrating widely used
source-apportionment and modelling approaches to quantify the effective potential
of regional-local policies and of European/national ones in a specific domain.
Different models are designed and implemented to approach different spatial
scales (from regional, to local, to street level). Future research should study how to
link these different scales and to build an IAM system able to connect different
“scale-dependent” approaches consistently, to model policy options from regional,
to local, to street scale.
4 Strengths and Weaknesses of the Current EU Situation
79
the consideration of dynamic evolution of the physical, economic, and social
environment. All current approaches are static, in the sense that they devise a
solution to be reached within a given time horizon (say, for instance, in 2020).
However, the system we want to control is non-stationary (e.g. the effect of the
current economic crisis) and it may therefore be more supportive for decision
makers to know where and when to currently invest with the highest priority in
order to follow a certain path to the target with the ability to adapt decisions with
time, in case the system evolution differs from the projected one. This involves the
necessity of flexibly adding into the plans the advent of new technologies and the
ability to determine the cost of scrapping old plants to substitute them with newer
ones. This essentially means designing a new generation of Decision Support
Systems to be intended more as control dashboards, than planning tools. Related to
the dynamic problem is also the issue of how to evaluate future benefits of air
quality investments. If economy has defined since long how to account for
investment costs lasting for a given period in the future, this is more difficult for
benefits that are not monetizable or last in the future for an unknown period. How
can we account for a 20 % improvement of an air quality index ten years from
now? What is the benefit from a reduction of PM10 today that will decrease
cardiovascular problems in a population sometime in the future?
A more synergic use of Source Apportionment and Optimization approaches
should also be fostered. SA could limit the degrees of freedom of cost-effectiveness
analysis, constraining the optimal solution to consider only a subset of the possible
measures previously identified applying SA. On the other hand, the optimization
approaches can automatically perform source apportionment establishing the most
cost-effective emission reductions and identifying the sources categories associated
to these reductions, without the need to monitor and chemically characterize air
pollutants.
4.4 Areas for Future Research of IAM Systems
A number of directions for future research have been identified by considering the
IAM as a whole, in particular related to the integration of IAM scales and the
uncertainty assessment.
One point is certainly the development of methodologies integrating widely used
source-apportionment and modelling approaches to quantify the effective potential
of regional-local policies and of European/national ones in a specific domain.
Different models are designed and implemented to approach different spatial
scales (from regional, to local, to street level). Future research should study how to
link these different scales and to build an IAM system able to connect different
“scale-dependent” approaches consistently, to model policy options from regional,
to local, to street scale.
4 Strengths and Weaknesses of the Current EU Situation
79