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Environment Magazine September/October 2008

 

July-August 2015

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Preparing for Sustainability Through Messy Predicting

Each of the three articles in this issue (by Wilbanks, Szabo, and Huntington along with their colleagues) addresses the difficulties of predicting how human capabilities for responding to climate change risks can be converted into more sustainable outcomes. In terms of providing reliable predictions, the case studies of the Arctic fisheries in the face of sea ice melt-back (Huntington); lacustrine lagoons in the exposure of higher tides, more frequent storms, and increased pollution (Szabo); and appropriately resilient infrastructures to cope with fires, storms, floods, and droughts (Wilbanks) reveal that at best such predictions are “messy.” By “messy” we can mean confusing and complicated and challenging. Messiness is an admission of scientific strength, not weakness. The timelines of historical changes to the connected geographies of any given location are not sufficiently robust to forecast outcomes that are the consequences of never-before-experienced combinations of future weather patterns. That is problematic enough. But predicting the social circumstances and the spread of economic opportunities, successes, and failures in such locations, irrespective of future weather risks, is also likely to be messy. Messiness is duplicated by trying to bring the two broad patterns of forecasting together.

What emerges from these fascinating reports is the sheer incomprehensibility of possible outcomes for regions where existing levels of social and ecological well-being are almost unknown and even unknowable. On the Alaskan northern shore, communities are forced to relocate as their coastlines become marshy as a consequence of heating seas. Yet there could be profitable whaling and fishing as species migrate north from the warming currents. Similar mixes of threat and opportunities abound in estuaries, where fishing could flourish or atrophy, or where pollution, propelled by unusual water or sediment warmth, could wipe out whole livelihoods within a few frightening years.

Messy predicting offers fascinating new ways of bringing changing ecological processes into the histories of cultural memories. Clever combinations of interpreting the social settings of the past with the possibilities offered by the geographical modulations of the future, coupled to ingenious uses of big data management, could offer the basis for recasting sustainability in regions of social and environmental flux.

Sylvia Szabo and her co-authors offer the vision of deploying sustainable development goals for this process. This would be a wonderful use of such goals. It would be most interesting to hear how far Henry Huntington and Tom Wilbanks believe that the clever application of sustainable development goals might offer a way forward to their arenas of shifting sea ice and inappropriately prepared infrastructure. My hunch is that these much-vaunted goals are simply not yet designed for messy predicting. Indeed, they are “messy” in a more conventional way. They are too many, too convoluted, and too nonspecific for the kinds of tasks expected of them by Szabo. Certainly the scenarios offered by Huntington and Wilbanks do not seem suited for the application of sustainable development goals as they are currently being formulated.

This is no reason to despair. It is precisely because predicting climate change risks are “messy” that it should be possible to create ways of bringing together credible forecasts with ingenious deployment of data and imaginative forms of interest-group participation to shape sustainable futures through not only conscious choice, but also much more attention to social and ecological justice. Bending sustainable development goals to this purpose (they are sufficiently flexible to permit such interpretation) could well be the lifesaving of their concept and purpose. Indeed, such an approach could help shape the betterment of regions currently experiencing the prospect of perilous patterns of messy adjustment.

—Tim O'Riordan

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