Forecasts
We begin by revisiting some past predictions and the assumptions underlying them. This exercise requires an understanding of forecasting in the energy sector: why, how, and to what end predictions are made, and how we evaluate their successes or failures. Forecasts serve a range of purposes. They provide a context for organizing and making sense of large data sets, help define possible future scenarios, and make clear current assumptions. Many forecasts, however, exist to further political or social agendas, and these motivations are not necessarily explicit in their construction. For example, projections of rapidly growing demand may be used to push for the construction of new power plants, while worst-case climate change scenarios may be used to spur action on emissions reduction.
The intended use of a forecast, whether explicit or hidden, shapes the process by which it is made. Many forecasting techniques have been applied to the energy sector with varying degrees of success. A simple but problematic way to predict the future is to extrapolate from present conditions; this method can yield some insight but obscures the motivating forces behind current trends. More sophisticated models may incorporate several variables, but these are sensitive to parameter choice and to assumptions about how variables interact with the wider environment. There is always, of course, the option of taking stated targets at face value. Governments and industries often make public pronouncements regarding their future plans, but these often obscure intent as well as reality. Still, these targets can help provide important insight into the motivations of policy-makers and the factors they consider most important.
Considering these forecasting methods leads to an uncomfortable truth: many past predictions have been not merely inaccurate, but spectacularly wrong. Perhaps most notorious is the 1954 prediction by Lewis Strauss, then head of the Atomic Energy Commission, that “our children will enjoy in their homes electrical energy too cheap to meter.” Such energy was to be provided by a fleet of nuclear fission reactors that were presumably efficient, safe, and secure as well as inexpensive to license, site, and build.7 Strauss, however, was not the only one who proved overconfident about the potential of nuclear power. The Energy Information Administration of the Department of Energy (DOE) anticipated that the United States would have 1,200 GWe8 of installed nuclear capacity by 2000; the actual capacity was 98 GWe. The forecasts failed not only to predict the magnitude of nuclear energy but also to capture the prevailing trend. While the DOE anticipated a growth in nuclear capacity of almost 700 GWe between 1990 and 2000, in reality the industry saw a slight decline as reactors were taken out of commission.
Such problems are not limited to the nuclear industry but are found in many long-range energy models. Figure 1 shows the total U.S. energy demand in the year 2000 as predicted by several models developed in the early 1970s.9 Notably, all the models drastically overestimate the actual 2000 figure, having failed to take into account the oil price shocks of the late 1970s and subsequent efficiency measures. They extrapolate trends from the relatively profligate late 1960s and early 1970s, when readily available cheap oil made efficiency and conservation unnecessary. Paul Craig, Ashok Gadgil, and Jonathan Koomey note that only one forecast,10 designed to show the possibility of a future powered by renewables (rather than attempt a reasonable forecast from contemporary trends), comes close to approximating the actual energy consumption.
Figure 1: Predicted versus Actual U.S. Primary Energy Use, 1975 to 2005
The figure suppresses the zero baseline. Each line represents a different model used to make a prediction. Source: Paul P. Craig, Ashok Gadgil, and Jonathan G. Koomey, “What Can History Teach Us? A Retrospective Examination of Long-Term Energy Forecasts for the United States,” Annual Review of Energy and the Environment 27 (November 2002): 83–118. Figure reprinted here with permission.
These problems remain endemic to energy forecasts. Long-term energy models that aim to track greenhouse gas emissions similarly failed to anticipate the success of shale gas drilling technologies, which have helped increase known U.S. natural gas reserves by 35 percent.11 Because gas-fired power plants produce during combustion roughly half the greenhouse gas emissions of traditional coal-fired generation, many estimates of U.S. emissions growth have had to be revised downward. Further discoveries may lead to the widespread use of natural gas as a transition fuel, altering the picture for international climate agreements and domestic policy.
It may seem that these failures are insignificant; after all, the inability of energy planners to foresee the oil shocks of the 1970s did not lead to catastrophic energy shortages, nor did the United States’ underestimation of its natural gas reserves significantly affect national security. In both cases, the market was able to handle the unforeseen changes and devise solutions that did not lead to economic or socio-political catastrophe. The system, it seems, has proven to be relatively resilient against prediction failure. It does not follow, however, that the solutions devised were the best of all possible solutions, nor that mistakes were consequence-free. The fortunes of individual companies rise and fall with changing market conditions, and reliable predictions are important for them to increase their competitiveness under changed conditions.
Predictions are important at the state and international levels as well. Particularly when prediction mistakes involve a common good, such as environmental protection, market solutions may be inadequate to address the failure. Regulation or some form of externality pricing may be required and can be imposed only by governments or outside bodies. Trusting market solutions to materialize in the absence of reliable predictions means governments may be ill-prepared to provide for the common good of their citizens. Additionally, preparation may be a question of scale: terrorist attacks, accidents, or proliferation concerns are best dealt with at the state or international level, not at the vendor or utility level. Accurate predictions, or at least a thorough understanding of their limitations, are therefore crucially important, for both the players that must compete in a changing market and the governments and international institutions that must prepare for a changing world.
The question remains, why do forecasts so often fail to anticipate future events? It may be that they are simply wrong; their initial assumptions may give an inaccurate or incomplete picture of the present. Physical or economic processes may be poorly understood or modeled so that even correct inputs lead to incorrect conclusions. More often than not, however, models fail because they do not anticipate events outside these initial assumptions: they do not consider game changers. In this paper, we focus on this latter category of events as applied to future projections for nuclear power. This requires us both to explain what we believe to be the current state of forecasting for nuclear energy over the next fifty years and to make clear the scope of the assumptions and initial conditions that enter into these predictions. We call this assumed reference case the “no-surprise scenario.”
ENDNOTES
10. Amory B. Lovins, Soft Energy Paths: Toward a Durable Peace (New York: Harper Colo phon, 1979).