Synergies among scales
The main issue of this type is the fact that regional authorities have to decide actions
constrained by “higher levels” decisions, i.e. coming from national or EU scale. In
practical terms, this means that regional scale policies are constrained to consider
the national/EU legislation as a starting point for their choices. In the effort to “go
beyond CLE” within their regional domain, some “higher level” constraints cannot
be disregarded or modified. This issue has to be considered for both Air Quality and
Climate Change fields. In both cases, in fact, there are a lot of agreement/protocols
that are already in force.
Uncertainty
As stated in UNECE (2002), it is important that the decisions focus on robust
strategies, that is to say on “policies that do not significantly change due to changes
in the uncertain model elements”. This issue is linked to the need of defining a set
of indexes and a methodology to measure the sensitivity of the decision problem
solutions. It is in fact worth underlining that, while for air quality models the
sensitivity can be measured by referring in one way or the other to field data
(Thunis et al. 2012), for IAMs this is not possible, since an absolute “optimal”
policy is not known and most of the times does not even exist. The traditional
concept of model accuracy must thus be replaced by notions such as risk of a
certain decision or regret of choosing one policy instead of another.
Acknowledgments This chapter is partly taken from APPRAISAL Deliverable D3.2 (downloadable from the project website http://www.appraisal-fp7.eu/site/documentation/deliverables.
html).
References
Amann M, Bertok I, Borken-Kleefeld J, Cofala J, Heyes C, Höglund-Isaksson L, Klimont Z,
Nguyen B, Posch M, Rafaj P, Sandler R, Schöpp W, Wagner F, Winiwarter W (2011)
Cost-effective control of air quality and greenhouse gases in Europe: Modeling and policy
applications. Environ Model Softw 26:1489–150
Carnevale C, Finzi G, Pisoni E, Volta M, Wagner F (2012a) Defining a nonlinear control problem
to reduce particulate matter population exposure. Atmos Environ 55:410–416
Carnevale C, Finzi G, Guariso G, Pisoni E, Volta M (2012b) Surrogate models to compute optimal
air quality planning policies at a regional scale. Environ Model Softw 34:44–50
Carlson DA, Haurie A, Vial J-P, Zachary DS (2004) Large-scale convex optimization methods for
air quality policy assessment. Automatica 40:385–395
EEA (2012) Europe’s environment: an assessment of assessments. EEA, Copenhagen. Available
from http://www.eea.europa.eu/publications/europes-environment-aoa. Last accessed March
2016
EMEP/EEA (2009a) Air pollutant emission inventory guidebook—2009, EMEP/EEA. Available
from http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009.
Last accessed March 2016
EMEP/EEA (2009b) Air pollutant emission inventory guidebook
34
N. Blond et al.
The main issue of this type is the fact that regional authorities have to decide actions
constrained by “higher levels” decisions, i.e. coming from national or EU scale. In
practical terms, this means that regional scale policies are constrained to consider
the national/EU legislation as a starting point for their choices. In the effort to “go
beyond CLE” within their regional domain, some “higher level” constraints cannot
be disregarded or modified. This issue has to be considered for both Air Quality and
Climate Change fields. In both cases, in fact, there are a lot of agreement/protocols
that are already in force.
Uncertainty
As stated in UNECE (2002), it is important that the decisions focus on robust
strategies, that is to say on “policies that do not significantly change due to changes
in the uncertain model elements”. This issue is linked to the need of defining a set
of indexes and a methodology to measure the sensitivity of the decision problem
solutions. It is in fact worth underlining that, while for air quality models the
sensitivity can be measured by referring in one way or the other to field data
(Thunis et al. 2012), for IAMs this is not possible, since an absolute “optimal”
policy is not known and most of the times does not even exist. The traditional
concept of model accuracy must thus be replaced by notions such as risk of a
certain decision or regret of choosing one policy instead of another.
Acknowledgments This chapter is partly taken from APPRAISAL Deliverable D3.2 (downloadable from the project website http://www.appraisal-fp7.eu/site/documentation/deliverables.
html).
References
Amann M, Bertok I, Borken-Kleefeld J, Cofala J, Heyes C, Höglund-Isaksson L, Klimont Z,
Nguyen B, Posch M, Rafaj P, Sandler R, Schöpp W, Wagner F, Winiwarter W (2011)
Cost-effective control of air quality and greenhouse gases in Europe: Modeling and policy
applications. Environ Model Softw 26:1489–150
Carnevale C, Finzi G, Pisoni E, Volta M, Wagner F (2012a) Defining a nonlinear control problem
to reduce particulate matter population exposure. Atmos Environ 55:410–416
Carnevale C, Finzi G, Guariso G, Pisoni E, Volta M (2012b) Surrogate models to compute optimal
air quality planning policies at a regional scale. Environ Model Softw 34:44–50
Carlson DA, Haurie A, Vial J-P, Zachary DS (2004) Large-scale convex optimization methods for
air quality policy assessment. Automatica 40:385–395
EEA (2012) Europe’s environment: an assessment of assessments. EEA, Copenhagen. Available
from http://www.eea.europa.eu/publications/europes-environment-aoa. Last accessed March
2016
EMEP/EEA (2009a) Air pollutant emission inventory guidebook—2009, EMEP/EEA. Available
from http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009.
Last accessed March 2016
EMEP/EEA (2009b) Air pollutant emission inventory guidebook
34
N. Blond et al.