Building Science for Environmental Protection in the 21st Century - Science For Environmental Protection: The Road Ahead - page 136

innovation, or change in ecology or human health. The preference index then
leads to a partial ranking of the policy options under consideration and recom-
mendation of an “optimal” set of choices or competitive choices (Brans and
Vincke 1985). MCDM has been applied successfully in environmental decision-
making (Moffett and Sarkar 2006; Hajkowicz and Collins 2007); however, crite-
rion-specific constituents of the preference index for each policy option are af-
fected by the quality of the science and evidence, scaling, and other factors that
can limit validity ( Hajkowicz and Collins 2007) .
An alternative to single-objective formulations is to provide decision-
makers with the Pareto optimal set of nondominated candidate solutions. Essen-
tially, the Pareto optimal set is constructed by identifying decisions that can im-
prove one or more objectives without harming any other. Use of the Pareto op-
timal set does not determine a single preferred approach but presents decision-
makers with a smaller set of options from which to choose. The concept of
Pareto optimal sets is not new, but the capacity to apply it in decision-making
has been greatly expanded by recent methodologic advances in optimization
techniques (most notably multiobjective evolutionary algorithms) and computa-
tion of Pareto sets for large complex problems, and this has increased the scope
of environmental and other applications (Coello et al. 2007; Nicklow et al.
2010). Rabotyagov et al. (2010) give an example of evolutionary computation
for the analysis of tradeoffs between pollution-control costs and nutrient-
pollution reductions. Optimal sets of air pollution control measures have been
developed that consider aggregate health benefits and inequality in the distribu-
tion of those benefits as separate dimensions (Levy et al. 2007). Kasprzyk et al.
(2009) demonstrate how multiobjective methods can be used to inform policies
for the management of urban water-supply risks that are caused by growing
population demands and droughts. Multiobjective optimization in support of
environmental-management decisions is especially compelling given the emerg-
ing paradigm of managing for multiple ecosystem services and consideration of
cumulative risks for human health. Tradeoffs and complementarities can exist
between alternative services and between other relevant performance metrics
(for example, public and private costs and distribution outcomes by location or
income class). Applications of multiobjective optimization methods would pro-
mote the explicit specification of preference indices relevant to environmental
decision-making and science to quantify outcomes and evaluate tradeoffs; all
this would serve to improve the transparency and scientific soundness of deci-
sions.
Addressing Uncertainty in Complex Systems
With any of the solutions-oriented approaches delineated above, regard-
less of which analytic tools or indicators are used by EPA to support decisions in
the future, uncertainty will be an overriding concern. With increasingly complex
multifactorial problems and a push for tools that are sufficiently timely and