Functionality
The functionality of this block must suggest responses to the decision maker, to
reduce precursor emissions (PRESSURES), or modify the DRIVERS, or directly
act to improve the STATE (Vlachokostas et al. 2009).
The main components of this block are:
– Control variables: these represent the measures that can be applied by the
regional/local Authority. They can be related to a macrosector or a pollutant
level reduction (aggregated approach), or to a single technology acting on one or
more pollutants (detailed approach). A further classification distinguishes
between “end-of-pipe measures” (applied to reduce emissions at the “pipe” of an
emitting activity) and “efficiency measures” (often called “non-technical measures”, that reduce activity levels, e.g. acting on people behaviour, etc.).
– Objectives: these represent what a Decision Maker would like to
improve/optimize. For instance, an objective could be to reach a given level of
an AQI at minimum cost, or to use a predefined budget to minimize an AQI.
More than one objective can be considered within the same problem (e.g.
reducing two pollutants with a given budget).
– Constraints: these can be of different types, as legislative (i.e. new obligations
on emission sources), economic (i.e. limited budget to be spent), physical (i.e.
due to domain features), etc. Constraints can be mathematically formalized, if
using a formal approach to take decisions; or they can be taken into account
when making decisions, but without explicitly modelling them.
– Implementation technique: this represent, from an operational point of view,
how all the ingredients already described (control variables, objectives, constraints) are put together and processed, to suggest one or more solution(s) to the
problem. In some cases, the implementation would simply mean an expert
advice, in other cases, the use of some piece of software running a suitable
optimization procedure.
The RESPONSES block can again be described by three levels of complexity:
– LEVEL 1: Expert judgment and Scenario analysis. In this case the selection of
measures to be adopted is based on expert opinion, with/without modelling
support to test the consequences of a predefined emission reduction scenario. In
this context, the costs of the emission reduction actions can be evaluated as an
output of the procedure (even if in many cases they are not considered).
– LEVEL 2: Source Apportionment and Scenario analysis. In this case, the most
significant sources of emissions are derived through a formal approach; this then
allows to select the measures that should be applied. Again, emission reduction
costs, if any, are usually evaluated as a model output.
– LEVEL 3: Optimization. In this case the whole decision framework is described
through a mathematical approach (Carlson et al. 2004), and costs are usually
taken into account. Different approaches (both in discrete and continuous setting) are available, as:
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N. Blond et al.
The functionality of this block must suggest responses to the decision maker, to
reduce precursor emissions (PRESSURES), or modify the DRIVERS, or directly
act to improve the STATE (Vlachokostas et al. 2009).
The main components of this block are:
– Control variables: these represent the measures that can be applied by the
regional/local Authority. They can be related to a macrosector or a pollutant
level reduction (aggregated approach), or to a single technology acting on one or
more pollutants (detailed approach). A further classification distinguishes
between “end-of-pipe measures” (applied to reduce emissions at the “pipe” of an
emitting activity) and “efficiency measures” (often called “non-technical measures”, that reduce activity levels, e.g. acting on people behaviour, etc.).
– Objectives: these represent what a Decision Maker would like to
improve/optimize. For instance, an objective could be to reach a given level of
an AQI at minimum cost, or to use a predefined budget to minimize an AQI.
More than one objective can be considered within the same problem (e.g.
reducing two pollutants with a given budget).
– Constraints: these can be of different types, as legislative (i.e. new obligations
on emission sources), economic (i.e. limited budget to be spent), physical (i.e.
due to domain features), etc. Constraints can be mathematically formalized, if
using a formal approach to take decisions; or they can be taken into account
when making decisions, but without explicitly modelling them.
– Implementation technique: this represent, from an operational point of view,
how all the ingredients already described (control variables, objectives, constraints) are put together and processed, to suggest one or more solution(s) to the
problem. In some cases, the implementation would simply mean an expert
advice, in other cases, the use of some piece of software running a suitable
optimization procedure.
The RESPONSES block can again be described by three levels of complexity:
– LEVEL 1: Expert judgment and Scenario analysis. In this case the selection of
measures to be adopted is based on expert opinion, with/without modelling
support to test the consequences of a predefined emission reduction scenario. In
this context, the costs of the emission reduction actions can be evaluated as an
output of the procedure (even if in many cases they are not considered).
– LEVEL 2: Source Apportionment and Scenario analysis. In this case, the most
significant sources of emissions are derived through a formal approach; this then
allows to select the measures that should be applied. Again, emission reduction
costs, if any, are usually evaluated as a model output.
– LEVEL 3: Optimization. In this case the whole decision framework is described
through a mathematical approach (Carlson et al. 2004), and costs are usually
taken into account. Different approaches (both in discrete and continuous setting) are available, as:
32
N. Blond et al.