• The planning indicators for human, ecosystems and materials exposure. The
decision problem determines the abatement measures or other actions that
optimize the objectives, and that have to comply with the physical, economic
and environmental constraints. Objectives and environmental constraints are
typically indicators of human, ecosystems and material exposure. How do different sets of indicators impact on policies design?
• The source/receptor relationships. What is the uncertainty of source/receptor
relationships? Which is the sensitivity of the decision problem solutions to
different source/receptor relationships?
• The emission and climatic conditions. Such source/receptor relationships are
identified processing CTM simulations for different reference years, meaning for
some specific emission and meteorological scenarios. The overall results of IAM
application are indeed variations with respect to these conditions that probably
will not be exactly replicated in the future, when decision will be implemented.
How do the assumption about these reference years impact the design of
policies?
In general, all these points highlight 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, for IAMs this is not
possible, since an absolute “optimal” policy is not known and most of the times
it 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. Indeed, since long ago, the “UNECE workshop on
uncertainty treatment in integrated assessment modelling” (UNECE 2002), concluded that policy makers are mainly interested in robust strategies. Robustness
implies that optimal policies do not significantly change due to changes in the
uncertain model elements. Robust strategies should avoid regret investments
(no-regret approach) and/or the risk of serious damage (precautionary approach)
(Amann et al. 2011).
Acknowledgments This chapter is partly taken from APPRAISAL Deliverable D2.7 (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: modelling and policy
applications. Environ Model Softw 26:1489–1501
Borrego C, Sá E, Carvalho A, Sousa J, Miranda AI (2012) Plans and Programmes to improve air
quality over Portugal: a numerical modelling approach. Int J Environ Pollut 48(1/2/3/4): 60–68
4 Strengths and Weaknesses of the Current EU Situation
81
decision problem determines the abatement measures or other actions that
optimize the objectives, and that have to comply with the physical, economic
and environmental constraints. Objectives and environmental constraints are
typically indicators of human, ecosystems and material exposure. How do different sets of indicators impact on policies design?
• The source/receptor relationships. What is the uncertainty of source/receptor
relationships? Which is the sensitivity of the decision problem solutions to
different source/receptor relationships?
• The emission and climatic conditions. Such source/receptor relationships are
identified processing CTM simulations for different reference years, meaning for
some specific emission and meteorological scenarios. The overall results of IAM
application are indeed variations with respect to these conditions that probably
will not be exactly replicated in the future, when decision will be implemented.
How do the assumption about these reference years impact the design of
policies?
In general, all these points highlight 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, for IAMs this is not
possible, since an absolute “optimal” policy is not known and most of the times
it 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. Indeed, since long ago, the “UNECE workshop on
uncertainty treatment in integrated assessment modelling” (UNECE 2002), concluded that policy makers are mainly interested in robust strategies. Robustness
implies that optimal policies do not significantly change due to changes in the
uncertain model elements. Robust strategies should avoid regret investments
(no-regret approach) and/or the risk of serious damage (precautionary approach)
(Amann et al. 2011).
Acknowledgments This chapter is partly taken from APPRAISAL Deliverable D2.7 (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: modelling and policy
applications. Environ Model Softw 26:1489–1501
Borrego C, Sá E, Carvalho A, Sousa J, Miranda AI (2012) Plans and Programmes to improve air
quality over Portugal: a numerical modelling approach. Int J Environ Pollut 48(1/2/3/4): 60–68
4 Strengths and Weaknesses of the Current EU Situation
81