A Learning Collaboratory: Improving Federal Climate Change Adaptation Planning

I. INTRODUCTION Human history becomes more and more a race between education and catastrophe.1 Adapt or perish, now as ever, is Nature's inexorable imperative2 Though composed many decades ago, these observations by H. G. Wells hold as true today - in the context of global anthropogenic climate change - as ever. The regularly dynamic global climate is currently shifting precipitously, caused at least in part by increases in greenhouse gas concentrations due to continuing development and industrialization.3 Evidence confirms that widespread4 harmful effects to ecological and human systems have already occurred.5 Amidst projections of a wide range of risks to both biota6 and humans7 from future warming in the United States over the next several decades,8 some have even suggested treating such change as not only an agent or catalyst of other catastrophic environmental events,9 but as a natural disaster in itself.10 Perhaps more importantly, global anthropogenic climate change magnifies the uncertainty that exists for private parties, resource managers, and regulatory institutions in planning for or responding to environmental problems. As a result, the continuing health of natural resources - and indeed the effectiveness of environmental governance - hinges on the capacity of regulatory institutions to inform, to learn, and to adapt. Unfortunately, American environmental and natural resources law and its institutions are poorly suited to cultivate successful adaptations to climate change because they are not designed to reduce uncertainty and foster learning by both regulators and the public.11 This Article proposes the development of a revised regulatory infrastructure that requires and promotes systematic monitoring, assessment and adjustment of management decisions, and also establishes an interactive information-sharing network.12 Drawing on emerging cyberinfrastructure research initiatives, the paper asserts that an adaptive "collaboratory" dedicated to climate change adaptation can facilitate not only information dissemination but also collaborative learning among resource managers, research scientists, and the public. The Article then describes how recent attempts to manage the effects of climate change, while encouraging, have insufficiently improved existing regulatory institutions' efforts to promoting agency learning.13 It details two of the most advanced climate change adaptation initiatives by the federal government to date - the Environmental Protection Agency's Climate Ready Estuaries program and the Council on Environmental Quality's Federal Agency Adaptation Planning Implementing Instructions. Though better than the existing management framework, these initiatives largely fall well short of requiring and otherwise promoting the necessary framework that will help agencies and the private sector manage uncertainty. The Article concludes that instilling continued assessment and an adaptation collaboratory as a part of these new initiatives would enable sharing among authorities, help reduce uncertainty, foster more accountable and adaptive resource management, and thus help natural resources governance adapt. II. THE LIMITATIONS OF AMERICAN NATURAL RESOURCES MANAGEMENT As I have argued elsewhere, as significant as the physical and ecological effects of climate change already are and are likely to be in the foreseeable future, uncertainty is the greatest challenge raised by climate change.14 Climate involves more complex and potentially confounding variables than most environmental issues, and the localized modeling needed to aid adaptation decisions is especially difficult.15 However, deficiencies in knowledge are certainly not only limited to the effects of climate change; there also is substantial uncertainty regarding the efficacy of potential adaptation strategies. This lack of information is attributable in part to the fact that information about the performance of adopted resource management strategies is rarely, if ever, systematically generated. …