(North, 1968), but in practice it is difficult to do, especially with complex decisions affecting the environment. Furthermore, environmental decisions also involve considerations of fairness and learning from experience that are not explicit parts of most formal decision frameworks (Dietz, 2003).
All of these criteria for good decisions can be met only if scientific analysis is used effectively. Uncertainties in the best available knowledge (element 4 above), regardless of whether this knowledge comes from data collected using scientific methods, from the judgments of scientific experts, or from observations made without the use of formal methodologies, must be considered as a part of scientific analysis, using appropriate qualitative and quantitative methods. Of course, the interested and affected parties to a decision are generally the best judges of what they want and of their values—but without scientific analysis, they may not know when or how environmental decisions affect those values. For example, as science showed that climate change will affect not only average temperature, but also the frequency and intensity of coastal storms, floods, droughts, wildfires, and so forth, some people who had considered themselves at little risk reconsidered their positions.
Scientists are usually in the best position to identify and systematically consider the effects of environmental processes and actions. However, good scientific analysis often requires information about local context that is most likely to come from people with close experience with local conditions. In a well-known example, British authorities advised sheep farmers after the Chernobyl nuclear accident that they could avoid radioactive contamination of their flocks by simply keeping the lambs out of the valleys. But the farmers knew that the fields were unfenced, so the solution was not practical and the risk was greater than the government scientists thought (Wynne, 1989).
These examples are among many that could be cited to show that integrating scientific analysis and public input requires more than a handoff of tasks from one group to another. The public cannot make good value judgments without good science, and scientists cannot do good decision-oriented analysis without public input. Recognizing the latter point, many policy reviews have advocated integration of public input into environmental assessment processes that have traditionally been dominated by science (e.g., National Research Council, 1996, 1999a, 1999b, 2005a, 2007a; Presidential/Congressional Commission on Risk Assessment and Risk Management, 1997a,b). They recognize that past nonintegrated assessment efforts have suffered in terms of both quality and legitimacy because they did not fully incorporate information (including appropriate consideration of uncertainties) and concerns coming from various affected parties.
These studies represent an important departure from previous thinking about how to conduct environmental assessments for informing practical