Prediction and Yucca Mountain

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Prediction and Yucca Mountain

• Jeremy Van Cleve
• Global Climate Change and Society Summer Program
• August 8th, 2002

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Predictionwho me?

• GoalThe summer project will focus on creating
  a WWW interface to allow for the documentation of
  predictions as they are made in a diverse set of
  policy-relevant settings, evaluation of accuracy
  of predictions, grades of predictive performance,
  and assessment of the role of predictions in the
  decision making process.
• Where to start? RESEARCH
• Yucca Mountain seemed like a worthwhile topic.

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What is Yucca Mountain?

• Yucca Mountain is the chosen site in the U.S. for
  the disposal of spent nuclear fuel (SNF) and
  high-level nuclear waste (HLW).
• It is a deep, geologic repository located at a
  depth of 300 meters in the unsaturated zone, 300
  meters above the water table.

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Background

• By the late 1970s, it became apparent that the
  federal government rarely stuck to any specific
  policy or schedule. Essentially, stateshad
  developed strong doubts that the Federal
  Government could be counted on to keep its word
  on waste management matters. (OTA 1985)
• Nuclear Waste Policy Act of 1982 establishes a
  schedule and framework for siting and building
  one or more geologic repositories.
• 1987, the Nuclear Waste Policy Act Amendments
  obligated DOE to investigate only one site, Yucca
  Mountain.

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Background

• 1992 Energy Policy Act (EnPA) mandated EPA to
  set health and safety standards for the proposed
  repository (specifically to prescribe the
  maximum annual effective dose equivalent to
  individual members of the public. (EnPA section
  801(a)(1)).
• Dose limit is 15 mrem/yr, which corresponds to
  8.5 chances in 1,000,000 of contracting a fatal
  cancer per year.

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TSPA Prediction in Fine Form

• Total System Performance Assessment (TSPA) is a
  way to provide a defensible analysis of system
  behavior incorporating models and parameters that
  are based on scientific observations in order
  that decision-makers can assess the ability of
  the repository system to comply with proposed
  regulations. (TSPA-SR, 2000)
• The TSPA can be conceptualized as a series of
  levels on a pyramid

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TSPA Prediction in Fine Form

• Three main elements
• The primary element is an estimate of the most
  likely behavior of the repository system.
• The next element is a quantification of the
  probability of natural events that might cause a
  loss of waste containment.
• Finally, a human intrusion component. This is
  the likelihood that human beings will
  inadvertently drill into a waste repository.
• In the TSPA, the final models are probabilistic
  in nature

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Critiques of TSPA

• The DOE should improve its understanding of the
  repository system behavior as a whole. While the
  TSPA-SR demonstrates regulatory compliance, it
  places little emphasis on contextualizing model
  conceptualizations and results within the body of
  knowledge acquired during the site investigation
  and repository design.
• DOE has often mixed conservative assumptions and
  models with optimistic or realistic ones. This
  has left unclear the level of conservatism and
  uncertainty in the models
• ACNW believes that motivation of the TSPA-SR is
  to demonstrate regulatory compliance rather than
  provide a risk informed assessment of repository
  performance.

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Critiques of Disposal Approach

• U.S. approach to waste disposal is rife with
  technical optimism and as a result, DOE feels
  confident in setting rigid timelines and
  regulatory specifications. Any future problems
  can be solved through further engineering the
  solution.
• TSPA methodology is heavily technical in nature.
  To rely on the results from TSPA model is to put
  a great deal of faith in the underlying
  scientific data and the conceptualizations of
  repository processes and features. Thus, the use
  of TSPA is contemporary instantiation of DOEs
  technically optimistic approach.

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Critiques of Disposal Approach

• NRC suggests that the biggest challenges to
  successful waste disposal programs are societal.
  In contrast, the focus of the U.S. program has
  been on technical issues to the exclusion of
  social ones.
• Erosion of public trust has been and continues to
  be a central problem as there are few avenues for
  public discourse and input into decision making.
  
• Lack of transparency in the waste disposal and
  management program.
• Is site selection process is equitable? NWPA
  Amendments of 1987 limited study to Yucca
  Mountain and residents of Nevada felt that they
  had been left out of the site selection process.

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Alternative Adaptive Staging

• Adaptive staging is defined as a process where
  the development of geologic repository for
  high-level waste is divided into stages that are
  separated by explicit decision points. (National
  Research Council, 2002)
• At each decision point, the results of scientific
  studies, site investigations, and computer models
  can be evaluated. The goals and work of each
  stage are predicated upon the outcomes of
  previous stages.
• Key advantages to adaptive management when
  compared with a linear approach are that it
  provides greater flexibility, transparency,
  integrity, and responsiveness to stakeholders.

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The Role of Prediction

• In the linear methodology, prediction is heavily
  used and often becomes the goal of some public
  policies instead of a means to study repository
  performance. (goal substitutionremember?)
• Adaptive management uses prediction where it is
  applicable but acknowledges that prediction has
  limits and that social forces play a primary role
  in making policy decisions.
• Bomb-pulse chlorine-36 example.

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Conclusions and Opinion

• Over the next 4 years, the DOE and USNRC will
  have an opportunity to design the policy
  framework that will work towards permanently
  disposing of the glut of SNF and HLW.
• If a linear methodology is continued and
  prediction is the only barometer for repository
  viability, public opposition is inevitable.
• An adaptive staging approach can be adopted,
  which focuses on an evolving repository design
  and performance criteria informed through
  incremental learning. This could generate more
  trust between the DOE, the state of Nevada and
  other stakeholders.

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Reproducible?

• http//sciencepolicy.colorado.edu/jvc/