As it is already done with CTMs, a research direction could be devoted to
developing IAMs nesting capabilities (both one-way and two-way nesting) to easily
manage EU/national constraints at regional level, and at the same time to provide
feedbacks from the regional to the EU/national scale.
At the moment, national climate change policies simply dictate some constraints
to local air quality plans, but it is well known that also local air quality policies (e.g.
the reduction of aerosols) can have consequences in terms of climate change. In a
“resource limited” world, the aspect of maximizing the efficiency of the actions (to
get win-win solutions for AQ and CC) will become of extreme importance and will
require guidelines to integrate climate change policies (normally established at
national or even international levels) with air quality plans developed at
regional/local level.
Uncertainty estimates are an essential element of integrated assessment, as a
whole. Uncertainty information is not intended to directly dispute the validity of the
assessment estimates, but to help prioritize efforts to improve the accuracy of those
assessments in the future, guiding decisions on methodological choices with respect
to the tools that are being used.
In order to assess the total uncertainty and evaluate the performance of an IAM
system, the uncertainty related to the different modelling components of the system
(meteorological modelling, air quality modelling, exposure modelling, cost-benefit
modelling) has to be quantified separately. In literature, there are very few works
concerning the application of uncertainty/sensitivity analysis in the IAM considered
as a whole system. The most complete works in this frame are due to Uusitalo et al.
(2015), who present a quite complete methodological review concerning possible
application of uncertainty and sensitivity analysis in IAM, and to Oxley and
ApSimon (2007), who reviewed the issues related to uncertainty in IAM, particularly focusing on space and time resolution and on the problem of uncertainty
propagation in integrated system. More in general, all works, possibly with the only
exception of Freeman et al. (1986), use a numerical approach based on Monte Carlo
simulation at different levels of complexity. This is probably due to the increasing
computational capacity and to the relatively newness of the problem treatment in
the context, causing scientist to directly start the study from the numerical
approaches both for uncertainty and sensitivity analysis.
As the chemical and physical processes involved are not linear, and some
uncertainties may compensate each other (Carnevale et al. 2016), the interconnection of all IAMs individual uncertainties remains a scientific challenge.
Combining all uncertainties to calculate a total uncertainty would require a great
number of simulations, accounting for all possible combinations. This complexity
does not allow for setting straightforward quality criteria in terms of IAMs, even
though IAM is considered an important policy tool.
In more detail, some of the issues still to be investigated on IAM concern:
• The optimization algorithms. The decision problem is solved by means of
optimization algorithms. How does the optimization algorithm bias the determination of effective policies?
80
C. Belis et al.
developing IAMs nesting capabilities (both one-way and two-way nesting) to easily
manage EU/national constraints at regional level, and at the same time to provide
feedbacks from the regional to the EU/national scale.
At the moment, national climate change policies simply dictate some constraints
to local air quality plans, but it is well known that also local air quality policies (e.g.
the reduction of aerosols) can have consequences in terms of climate change. In a
“resource limited” world, the aspect of maximizing the efficiency of the actions (to
get win-win solutions for AQ and CC) will become of extreme importance and will
require guidelines to integrate climate change policies (normally established at
national or even international levels) with air quality plans developed at
regional/local level.
Uncertainty estimates are an essential element of integrated assessment, as a
whole. Uncertainty information is not intended to directly dispute the validity of the
assessment estimates, but to help prioritize efforts to improve the accuracy of those
assessments in the future, guiding decisions on methodological choices with respect
to the tools that are being used.
In order to assess the total uncertainty and evaluate the performance of an IAM
system, the uncertainty related to the different modelling components of the system
(meteorological modelling, air quality modelling, exposure modelling, cost-benefit
modelling) has to be quantified separately. In literature, there are very few works
concerning the application of uncertainty/sensitivity analysis in the IAM considered
as a whole system. The most complete works in this frame are due to Uusitalo et al.
(2015), who present a quite complete methodological review concerning possible
application of uncertainty and sensitivity analysis in IAM, and to Oxley and
ApSimon (2007), who reviewed the issues related to uncertainty in IAM, particularly focusing on space and time resolution and on the problem of uncertainty
propagation in integrated system. More in general, all works, possibly with the only
exception of Freeman et al. (1986), use a numerical approach based on Monte Carlo
simulation at different levels of complexity. This is probably due to the increasing
computational capacity and to the relatively newness of the problem treatment in
the context, causing scientist to directly start the study from the numerical
approaches both for uncertainty and sensitivity analysis.
As the chemical and physical processes involved are not linear, and some
uncertainties may compensate each other (Carnevale et al. 2016), the interconnection of all IAMs individual uncertainties remains a scientific challenge.
Combining all uncertainties to calculate a total uncertainty would require a great
number of simulations, accounting for all possible combinations. This complexity
does not allow for setting straightforward quality criteria in terms of IAMs, even
though IAM is considered an important policy tool.
In more detail, some of the issues still to be investigated on IAM concern:
• The optimization algorithms. The decision problem is solved by means of
optimization algorithms. How does the optimization algorithm bias the determination of effective policies?
80
C. Belis et al.