Precautionary Principle

the upshot OF precaution is that it is better to be safe than sorry when there are severe or irreversible consequences. It has been a very important notion in environmental and public health policy. It has been advocated in several issues ranging from climate change to genetic engineering to phase-out of persistent organic pollutants. The invocation of precaution has been particularly controversial when there are significant business interests at stake. The problem with simply asserting precaution whenever a technology, policy, or action involves possible negative outcomes is that it often poses significant challenges in evaluating the public versus private trade-offs involved.

This use of precaution is often invoked when outcomes are uncertain. The notion of uncertainty is used to characterize how well future events or scientific truths can be predicted or known. It is used in both social and natural science disciplines, from mathematics to philosophy, to risk assessment and public policy. If probability is a measure of likelihood, then uncertainty is a measure of how well the probability is known. Uncertainty can be classified into known and unknown probabilities. Events with known probabilities are referred to as events with statistical uncertainties. Events with unknown probabilities are often called events with true uncertainty.

Uncertainty in the context of the environment mainly refers to scientific uncertainty. Here, science generates truths through the testing of hypotheses. But often, the affirmation of hypotheses involves a certain degree of uncertainty due to the method or research design. Scientists often use the benchmark of 95 percent certainty when deciding whether or not cause and effect have been correctly identified. Scientists often report confidence limits based on research design and sampling error in their studies to account for uncertainty.

The precautionary principle is often invoked under uncertain circumstances, particularly when the consequences are irreversible or permanent. This differs from the choice that scientists make when deciding what to do under conditions of uncertainty. Typically, scientists are interested in avoiding false negatives, because science is epistemologically conservative. Scientists do not want to suggest something as truth when in fact it may not be. In public or environmental policy, however, because the consequences are not epistemological but are ethical, there is desire to avoid false positives and be ethically conservative.

In public and environmental policy, it is important to understand how to make decisions in the absence of perfect information. Knowing the degree of uncertainty is particularly important when questions about risk arise. Risk assessment is a policy approach that deals with uncertainty. Risk assessment is widely used by the Environmental Protection Agency (EPA), but mainly focuses on known probabilities. Because of difficulties with codifying the precautionary principle into policy, the EPA has yet to include true uncertainty in environmental policy.

classes of scientific uncertainty

Scientist Schrader-Frechette describes four classes of scientific uncertainty dealt with by scientists and policymakers: framing uncertainty, modeling uncertainty, statistical uncertainty, and decision-theoretic uncertainty. In framing uncertainty, scientists often use a two-value frame to accept or reject a hypothesis. Frechette argues that in public policy, it is more appropriate to adopt a three-value frame that creates a category to deal with situations where significant uncertainty and serious consequences suggest adopting the precautionary principle. Modeling uncertainties involve those involved in the prediction of future scenarios. These are highly speculative, despite claims to be verified and validated models.

In public and environmental policy, statistical uncertainty should be dealt with in such a way that highlights the difference between epistemological consequences and ethical ones. When faced with decision-theoretic uncertainty, scientists are forced to distinguish between using expected value rules and the minimax rule. The former argues that decisions should be based on the expected value, while the latter seeks to prevent the worst-case scenario. More recently, Bayesian statistics has been used to help evaluate data under conditions of uncertainty by updating the probabilities as new data come to view. A Bayesian approach involves the introduction of prior knowledge into statistical models.

There are many environmental policy debates where questions about uncertainty are raised. In debates about global climate change, for example, scientists typically agree that there is significant uncertainty in the projection of future climate change models. Climate change skeptics, to discredit climate change science, often highlight uncertainty. In debates about genetic engineering, uncertainty about the prediction of how transgenic organisms will behave in the environment, or uncertainty about how markets will react to the adoption of transgenic organisms, is cited as a reason to invoke the precautionary principle. In debates about nuclear waste disposal at Yucca Mountain, uncertainty about how the storage facility will perform in the long term is cited as reason to question the suitability of nuclear waste repository.

sEE ALsO: Climatic Data, Nature of the Data; Environmental Protection Agency; Measurement and Assessment.

bibliography. Daniel Bodansky, "Scientific Uncertainty and the Precautionary Principle," Environment (v.33/7, 1991); John Lemons, ed., Scientific Uncertainty and Environmental Problem Solving (Blackwell Science Press,

1996); Allison MacFarlane and R.C. Ewing, Uncertainty Underground: Yucca Mountain and the Nation' High-Level Nuclear Waste (MIT Press, 2006).

DUSTIN MULVANEY University of California, Santa Cruz

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