The shale gas boom by the numbers
C&EN Global Enterprise
The energy sector relies on analytical results to inform decision-making-from policy to investment. Over the last decade the United States has undergone a "revolution" in its energy landscape, due primarily to natural gas production from shale plays, as well as other factors. Despite the enormity of this change, it was hardly, or not at all, predicted or projected by forecasters, analysts, or industry experts even a year or two before its emergence. We consider what the projections looked like,
... ctions looked like, how changeable they still remain, and implications for refining the interaction between analysis and decision-making in the energy sector. More broadly, we use the shale gas boom to illuminate the more universal challenges that energy forecasters face-and the solutions they employ-in managing and explaining two significant types of uncertainty: epistemic (unknown unknowns) and stochastic (known unknowns). Epistemic and stochastic uncertainties affect both the production of forecasts as abstractions of reality and our meta-considerations of how accurately such abstractions represent reality. Compounding these difficulties, these two domains of prediction-the world of the model and the world the model attempts to simulateare often unconsciously confused or conflated, especially by the consumers of energy forecasts who do not themselves deal directly with forecast intricacies: industry analysts, scientists, advocates, and policymakers, among others. We thus attempt to elucidate a simple typology of energy forecast uncertainties and delineate the domains of prediction for decision-makers in the private, public, and research sectors who may benefit from a better understanding of how modelers themselves conceptualize and manage uncertainty. We conclude with a call for new and innovative discourse modes for discussing uncertainty in energy forecasting, both within the modeling community itself and in its engagements with decision-makers.