Because the spatial distribution of aircraft noise, local air quality,
and climate change damages differs, aggregate cost-benefit analyses
that examine tradeoffs in aviation environmental policy may not
indicate who bears the costs or who gains the benefits from aviation.
Wolfe et al. (in this issue) model both the net cost and distribution of
environmental damages from one year of aviation operations across
the three environmental domains. They find that populations living
at airport boundaries face damages of $100–400 per person per year
from aircraft noise and between $5–16 per person per year from
climate damages. Expected damages from air quality are dependent
on the number of operations at the airport and range from $20 to
over $400 per person per year with air quality damages approaching
those of noise at high volume airports. Mean expected noise and air
quality damages decay with distance from the airport, but for noise,
the range of expected damages at a given distance can be high and
depends on orientation with respect to runways and flight patterns.
Damages from aviation-induced climate change dominate those
from local air quality degradation and noise pollution further
away from the airport. However, air quality damages may exceed
those from climate when considering the impact of cruise emissions
on air quality.
Finally, Allaire and Willcox (in this issue) describe and conduct an
uncertainty analysis of an environmental simulation model so that
these uncertainties can be better understood to enable informed
improvements in the models, and to advise policymakers who may
be using the modeling results as input to decision-making. These
models typically have many factors of different character, such as
operational, design-based, technological, and economics-based. Such
factors generally contain uncertainty, which leads to uncertainty in
model outputs. For such models, it is critical to both the application
of model results and the future development of the model that a
formal approach to the assessment of uncertainty in the model be
established and carried out. In this paper, a comprehensive approach
to the uncertainty assessment of complex models intended to
support decision-making and policy-making processes is presented.
The approach consists of seven steps: establishing assessment goals,
documenting assumptions and limitations, documenting model
factors and outputs, classifying and characterizing factor uncertainty,
conducting uncertainty analysis, conducting sensitivity analysis, and
presenting results. Highlights of the approach are demonstrated on a
model that has been used to estimate the impacts of aviation on
climate change for use in informing policymakers