Abstracts (PDF file, 1.8MB) - Society for Risk Analysis

SRA 2013 Annual Meeting Abstracts
P.106 Orozco, G; Universidad del Norte;
gorozcorestrepo@gmail.com
Risk Management in Colombia: The Challenge of
Development
This article addressed both the vulnerability of the biodiversity
in the Colombian territory and the priority of the government to
improve the mining activities in favor of the economic growth.
The main purpose is to explain how risk management is not
appropriate to the major environmental problems in Colombia.
T4-K.3 Ortwin Renn, O; State University of Stuttgart;
ortwin.renn@sowi.uni-stuttgart.de
Socioeconomic Dimensions of Geo-engineering and
Carbon Sequestration: Requirements for Sustainable Risk
Governance
In recent times, attempts to improve adaptation to climate
change by means of large-scale CO2 absorption, sequestration
and storage options have been discussed in scientific and
political communities. These options range from increasing the
absorption capability of the oceans, over weather changing
patterns by inserting chemicals into the atmosphere to
separating and storing carbon from fossil power stations. All
these options can only be effective in global terms when they
are applied either in large quantities or extended over vast
areas. So far the discussion has been focused on technical
feasibility, effectiveness, efficiency and to a lesser degree
environmental implications. However, projects of such size will
trigger major social, cultural and psychological impacts that
need to be anticipated and taken into account before making
any far-reaching decisions. The model of risk governance
provides a suitable framework to include such impacts in the
assessment, evaluation and management of the anticipated
benefits and risks of these projects. The paper will address the
risk governance framework and explore the methodologies for
including the socioeconomic dimensions in an integrated effort
to balance the pros and cons of large-scale geo-engineering.
Based on empirical studies on public perception and evaluation
of carbon sequestration in Germany, the emphasis will be on
public acceptability, environmental justice issues, and social
costs.
M4-D.4 Oryang, D*; Fanaselle, F; Anyamba, A; Small, J; Food
and Drug Administration, Center for Food Safety and Applied
Nutrition; NASA Goddard Space Flight Center;
David.Oryang@fda.hhs.gov
Using Geospatial risk assessment to forecast produce
contamination potential
New responsibilities and challenges require FDA to develop
innovative tools and approaches to protect the food safety and
public health. Risk assessment is increasingly used to support
the science basis of, and inform decision making. In a novel
approach, geographic information systems (GIS) and remote
sensing are being used by FDA, in collaboration with other
agencies, to enhance the capabilities of risk assessment to take
into account spatial and temporal dimensions, and thereby
forecast where and when produce contamination events are
more likely to occur. The GIS model requires an assessment of:
hazards found in fresh produce; contamination modes;
production practices (such as soil amendment, and irrigation
water sources and systems), factors that impact growth and
spread of pathogens; environmental impacts of urbanization
and industrialization; livestock and wildlife population and
proximity to crops; topography and soil types; microbial status
of surface water, wells, and reservoirs proximate to crops, and
used for irrigation; and impacts of temperature, rainfall,
irradiation, fog, and extreme events on contamination likelihood
and amounts. These factors vary spatially and temporally, and
acquiring geospatial and time series data on these factors, is
crucial to the development of this novel system. This paper
presents a synopsis of the effort by FDA, NASA, USDA-ARS, and
USDA-APHIS to compile the data necessary to develop this
geospatial risk assessment forecasting tool to provide early
warning to industry and government about future potential
locations, modes, and dates of produce contamination. This
approach will enable industry and government to take the
necessary pre-emptive measures to prevent contaminated
produce from entering the food supply and causing
illness/death.
M3-D.3 O'Rawe, J; Ferson, S*; Sugeno, M; Shoemaker, K;
Balch, M; Goode, J; Applied Biomathematics;
sandp8@gmail.com
Specifying input distributions: No method solves all
problems
A fundamental task in probabilistic risk analysis is selecting an
appropriate distribution or other characterization with which to
model each input variable within the risk calculation. Currently,
many different and often incompatible approaches for selecting
input distributions are commonly used, including the method of
matching moments and similar distribution fitting strategies,
maximum likelihood estimation, Bayesian methods, maximum
entropy criterion, among others. We compare and contrast six
traditional methods and six recently proposed methods for their
usefulness in risk analysis in specifying the marginal inputs to
be used in probabilistic assessments. We apply each method to
a series of challenge problems involving synthetic data, taking
care to compare only analogous outputs from each method. We
contrast the use of constraint analysis and conditionalization as
alternative techniques to account for relevant information, and
we compare criteria based on either optimization or
performance to interpret empirical evidence in selecting input
distributions. Despite the wide variety of available approaches
for addressing this problem, none of the methods seems to
suffice to handle all four kinds of uncertainty that risk analysts
must routinely face: sampling uncertainty arising because the
entire relevant population cannot be measured, mensurational
uncertainty arising from the inability to measure quantities
with infinite precision, demographic uncertainty arising when
continuous parameters must be estimated from discrete data,
and model structure uncertainty arising from doubt about the
prior or the underlying data-generating process.
December 8-11, 2013 - Baltimore, MD