Many of today's challenging environmental problems, to the point of manifesting themselves on a scale that has global, political consequences, likely result from long-term evolutionary developmental processes. The degree to which humans can manage the evolution of these problems remains an open question. Over the past several decades, the dominant management approach in environmental policy has been standard setting. Managers relied primarily on command-and-control approaches for environmental management. This approach relies on regulations and policies, and it assumes a tightly coupled response between human intervention and environmental improvement. While standard setting has been successful for certain environmental problems, some of the more complicated ones, however, remain unresponsive to amelioration through command-and-control approaches because of large or unsurmountable uncertainties.
Examples of intractable environmental problems include cases of natural resource mismanagement, for instance, fishery and forest depletion, anthropogenic climate change, nitrate contamination of coastal waters, and the management of nuclear waste. Faced with such challenges, since the mid 1980s, an increasing number of scholars have developed other forms of management, in particular, adaptive management. Although a promising approach, adaptive management programs have performed unevenly. The reasons for their success or failure are the subject of increasing investigation.
Scholars suggest that improving the performance of adaptive management requires that environmental problems be understood as ‘complex’, open, evolving systems of interacting social and environmental subsystems. As such, complexity science postulates that system development may not be random. A few key system components, such as adaptive capacities, may be particularly sensitive to changes and hence drive the system's response on the whole over time. The task is then twofold: to identify key drivers of the system in question (or project), and to use these drivers for ‘steering’ the system toward some set management goal.
To date, narrative has been the primary approach used by researchers to study complex social-environmental systems, including their developmental patterns and underlying factors driving the system. Although useful as an initial step, narratives may be biased, misleading, or incomplete. Other methods are needed to draw complementary inferences between data and theory. The absence of a more systematic method—for example, one that combines qualitative and quantitative analyses—points to a critical gap in the adaptive management literature. In view of this gap, I undertook comparative research that combines computer-assisted content analysis of national policy documents with statistical exploratory multivariate analyses.
As a case study, I examine the development of national nuclear waste management policies as a complex social-environmental system. The development of nuclear waste management policies in twenty-three (23) countries—from North America, Europe, Asia, Africa, and South America—is compared through a multiple-case, retrospective, and archival study. First, I assess whether general patterns of broad system development, namely, linear, periodic, or chaotic, exist. I also examine how external or internal factors influence the general development of the system. Second, I assess the relative importance of potential key system drivers—here, stakeholder adaptive capacities (SACs)—when sustainable development is a specific management goal. Six SACs were selected: learning by managers, social responsibility of managers, public participation in decision-making, government oversight, formal project collaboration, and emergency preparedness.
Study findings suggest that the development of national nuclear waste management policies follows a non-random, possibly chaotic, pattern of development where networking and learning SACs are key system drivers. Once drivers are identified by managers, they can be used to optimize system complexity under a strategy adaptive management framework to maximize system adaptation over the long term. Managers would thus be able to 'steer' complex environmental problems toward a set goal.
The research design can open robust lines of inquiry on the development of a wide range of complex environmental problems. Both private and public managers are likely to welcome the study findings, because they can to more effective allocation of resources to adaptive management programs. Governments, as influenced by the public, are also likely to find the findings useful for the development of sound environmental management policies in the interest of present and future generations.