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Forecasting impact scenarios based on energy trajectories is key to effective energy policy
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Forecasting impact scenarios based on energy trajectories is key to effective energy policyPaul P. Craig 2, Professor of Engineering Emeritus at the University of California, Davis, What Can History Teach Us? A Retrospective Examination of Long-Term Energy Forecasts for the United States, Annu. Rev. Energy Environ. 2002. 27:83–118 The applicable measure of success here is the degree to which the forecast can prompt learning and induce desired changes in behavior. The Limits to Growth model (discussed below) has been widely used to help students understand the counterintuitive nature of dynamical systems (11). Simulations and role-playing games have also been used to teach executives in the utility industry how new markets for SO2 emissions permits or electric power might behave. Experience with exercising these types of models can improve intuition for the behavior of complex systems (12–14).¶ 2.4. Use 4: In Automatic Management Systems Whose Efficacy Does Not Require the Model to be a True Representation¶ Hodges & Dewar use the example of the Kalman filter, which can be used to control (for example) the traffic on freeway on-ramps. These filters can model traffic flow, but only in a stochastic representation that does not pretend to be exact and validated, just useful. Similar filters can also be embedded in management systems controlling power systems or factory processes. As long as the model cost-effectively controls the process in question, the issue of whether it is an exact representation of reality is not of concern. Neural networks fall into this category (15).¶ 2.5. Use 5: As Aids in Communication and Education¶ By forcing analysts to discuss data and analysis results in a systematic way, forecasting models can facilitate communication between various stakeholders. The measure of success for this use is the degree to which the model improves understanding and communication, both for individuals and between groups with different mindsets and vocabularies.¶ For example, the population of a developing country at some future time might depend on childhood survival rates, longevity, female literacy, affluence, income distribution, health care, and nutrition. Modeling these influences could permit better understanding of interlinkages between them and improve communication between expert groups with diverse backgrounds. Such a model could inform, for instance, a government’s long-term plans. Another example is the U.S. DOE’s Energy Information Administration (EIA) Annual Energy Outlook forecast (16). This widely used forecast, based on the EIA’s latest analysis of the current data and industry expectations, provides a baseline that others can and do use for their own explorations of the future.¶ When a problem is being analyzed, word leaks out and leads to suggestions, ideas, and information from outside parties. This can add to the analysis directly, or stimulate helpful complementary work by others. A politician facing a thorny problem might commission a study to locate knowledgeable people. Thus, studies can identify talent as a by-product. The National Academy of Sciences Committee on Nuclear and Alternative Energy Systems (CONAES) study, one of those assessed in the DOE review of forecasts from the 1970s (Figure 1) (5), was directly or indirectly responsible for many career shifts. The American Physical Society “Princeton Study” held during the summer of 1973 was explicitly designed with this intent (17). The oil embargos of the 1970s had led many physicists to think about making career shifts. The study gave them an opportunity to learn about energy issues, to meet and get to know experts, and to find jobs.¶ 2.6. Use 6: To Understand the Bounds or Limits on the Range of Possible Outcomes¶ Models can enhance confidence through limiting or bounding cases. The Princeton Study referred to in Use 5 includes many examples (17). This study emphasized energy efficiency, with a focus on physical constraints to energy use. The cornerstone of the analysis was the concept of fundamental physical limits such as the first and second laws of thermodynamics. This work showed that great potential existed for improving efficiency by engineering change. Energy efficiency became a major theme of energy policy and remains so to this day.¶ 2.7. Use 7: As Aids to Thinking and Hypothesizing¶ Forecasts can help people and institutions think through the consequences of their actions. Researchers often begin their exercises with baseline or “business-as usual” forecasts, which attempt to predict how the world will evolve assuming current trends continue. Alternative forecasts are then created to assess the potential effects of changes in key factors on the results. For example, an economic forecaster might use such an analysis to assess the likely effects of a change in property taxes on economic growth in a particular state.¶ Computer forecasting is an excellent tool to teach people the dynamics of complex systems (12, 13). The behavior of these systems is often counterintuitive, so such forecasting games can help people learn to manage them better. For example, systems dynamics models (described below) were used in the 1960s to explain why building premium housing in urban areas can under some plausible circumstances accelerate, rather than slow, migration to suburbs (14, p. 5)2.¶ Some forecasts are generated as part of scenario exploration exercises, which can be helpful any time a person or institution faces a critical choice. Oil companies, for example, are well aware that at some point the transportation sector may have to switch to some other fuel. Even though this switch may be a long time in the future, the prospect needs to be part of current contingency planning. Considering a wide range of scenarios can help institutions prepare for the many different ways the future can evolve. Institutions use forecasts to allocate physical and personnel resources. 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