Decision Support for Developing Energy Strategies
Regional case in point
These lessons are evident in recent research in which several of us developed and tested a framework for crafting an energy strategy for Michigan State University (MSU). (For further information, see http://energytransition.msu.edu.) MSU has a cogeneration facility located on campus that converts the thermal energy from burning coal, natural gas, and biomass into electricity and steam. With a peak electrical output of 99.3 megawatts and a pressurized steam generation capacity of up to 1.3 million pounds per hour, it is the largest on-campus coal-burning power plant in the United States. The facility is the principal energy provider to the main campus and is capable of meeting approximately 97% of all electricity demand. Steam that is generated is distributed at high pressure to the campus to provide heating and cooling to a campus spread over approximately 5,000 acres.
In 2008, MSU commissioned development of a process for developing a new strategy for long-range energy generation on the campus. The goal was to transition away from a fossil fuel–based (coal and natural gas) energy strategy to one based entirely on renewables by approximately mid-century. A parallel goal was to help establish a multistakeholder decision support process that could serve as a template for similar energy strategy decisions in Michigan, elsewhere in the Unites States, and abroad.
The research team began by holding a series of meetings with university officials to define the decision problem (for example, the desire to transition from fossil fuels to renewables) and identify the boundary conditions for the decisionmaking process (for example, identifying stakeholders whose ideas would be critical to the process). We followed these meetings with several workshops and focus groups to identify the range of objectives that were important to key stakeholders on and off campus (for example, students, staff, faculty, and neighboring communities) and potential performance measures that would be useful for tracking their achievement. Through additional workshops and a lengthy engineering review process, we narrowed the objectives and their associated performance measures to a short list of critical considerations that would be used as part of a strategy development process cast widely across the community.
In a critical step at this stage, we created an energy system model capable of forecasting the anticipated outcomes of alternative energy strategies in terms of the key objectives and related performance measures. This model became the centerpiece of an online decision support platform that people—policymakers, experts, and the public—would use as a means of participating in the development of the energy strategy. The online platform built on recommendations from the National Research Council, issued in 2009, about how best to present information relevant to decisions about energy in a decision-focused environment. The platform was designed to engage people in the process of learning about energy systems, including their environmental, economic, and social considerations.
Beyond simply educating people, however, the decision support framework provided users with an opportunity to design their own alternative energy system. In constructing their energy system of the future, users could mix and match individual energy generation (and supporting) technologies for deployment at different times over the course of the energy strategy. The technologies for consideration included centralized power plant options (for example, coal, natural gas, biomass, or nuclear power), decentralized options (solar, natural gas, microturbines), energy from the national power grid (relying on either conventional fuels or renewables), carbon management techniques (for example, carbon capture and storage), and levels of effort expended on building efficiency. As users built their energy strategies, they were able to monitor their ability to meet future energy demand, and they could track, via the energy system model, the forecasted performance of their strategy, as measured against the agreed-on objectives and performance measures.
In addition to simply suggesting a desired energy strategy, this decision support framework also challenged users to evaluate their portfolios in comparison with a broad array of others representing markedly different priorities. In doing so, people were required to be explicit about the pros and cons of each of the energy strategy options under consideration; for example, how much additional cost were they willing to bear in exchange for reduced greenhouse gas emissions or the warm glow that comes with being at the leading edge of innovation? Conversely, to what extent were users willing to comprise on air quality or employment as a means of keeping costs near the status quo?
In order to inform these comparisons, the decision support platform included a module that helped users confront tradeoffs and make internally consistent choices (that is, choices that reflected objectives of greatest concern). We built this module, which uses tools from multicriteria decision analysis, on the notion that internally consistent choices begin by having a clear sense of how important individual objectives are to decisionmakers. With this information in hand, users could apply the energy system model and determine a rank order of energy strategy alternatives based on the degree to which each one best satisfied the most important objectives.
A scaled-down version of this decision support system is now on display at the Marian Koshland Science Museum of the National Academy of Sciences in Washington, DC. It can be used by museum visitors of all ages and all levels of education to simulate the creation of a national-level energy strategy in the United States. At the time that the MSU and Koshland frameworks were designed, they were intended for making discrete decisions required for the creation of an energy strategy. For making and revising decisions through time, users would need to revisit the decision support tool (and update the energy system model, if necessary) at various intervals during the rollout of an energy strategy. By doing so, decisionmakers could evaluate existing aspects of an energy strategy by the degree to which they still reflected the current state of the science around energy systems. And, importantly, they could evaluate an existing energy strategy by the degree to which it still reflected objectives of greatest, perhaps national, concern.
Approaching decisions about energy in this way may seem like a tall order and, worse, a recipe for making large investments (for example, in infrastructure) that cannot easily be reversed. It is true that energy strategies will require large investments of this type. But technically speaking, there are ways forward. In the case of our work with MSU, for example, energy alternatives that incorporated flexible infrastructure, such as swappable fuel powergeneration units, were favored over technologies that would lock decisionmakers into a particular fuel type for decades. Practically speaking, this meant that flexibility and reversibility became high-priority objectives (trumping others related to cost, for example) in the eyes of planners and policymakers.
Another example comes from the hydroelectric utility in British Columbia, where the provincial energy strategy was designed to include regular reviews of all decisions pertaining to water releases (and, therefore, electricity generation) at hydroelectric dams. These reviews are required to ensure that energy projects remain in line with the objectives of key stakeholders and the changing state of scientific knowledge about the broader social and environmental systems in which energy infrastructure resides. In both of these cases, and in others, policymakers are also beginning to recognize that following through on sunk costs, even if devoted to projects that cannot easily be reversed or retasked, is not a sensible strategy in many energy strategy decisions, because they are irrelevant when considering the outcome that ought to matter most—namely, future benefits.
Overall, an energy strategy needs to be flexible and adaptive so that it can incorporate what is learned over time. Admittedly, however, decisionmaking over time and the adaptive demands of making a sequence of choices add additional challenges to already difficult decisions. Fortunately, the kind of decisionmaking approach we are describing provides science-based guidance, and much-needed structure, to energy strategy development that will by necessity require multiyear (or multidecade) investments.
To this end, we are currently in the process of creating an upgraded version of the MSU decision support framework for use in developing a national energy strategy in Canada. This version of the framework includes an opportunity for decisionmakers to project decisions farther into the future, taking into account the changing tenor of the energy debate in the country. Such changes may include, for example, evolving assumptions about emerging technologies and the need for infrastructure, and the national and international demand for Canadian energy resources, which may be affected by concerns about climate change, adoption of policies that put a price on carbon, or changes in policies or behavior that may affect energy recovery, processing, or use. We are also using a similar approach to lend insight to decisions about hydraulic fracturing in oil and gas development (which has cumulative effects on environmental, economic, and social systems), pipeline-permitting processes, and carbon and climate management initiatives domestically (such as carbon capture and storage or geoengineering) and in the developing world (for example, through the United Nations Collaborative Initiative on Reducing Emissions from Deforestation and Forest Degradation).
In sum, the decision support framework outlined here encapsulates the five critical decision support elements: clarifying problems, thinking clearly about objectives, designing creative alternatives, modeling consequences, and confronting tradeoffs. It works by breaking what is a very complex decision—the creation of an energy strategy—into a series of smaller, more manageable parts that are less prone to error and bias. Research conducted to evaluate this framework has shown that it leads to higher-quality decisions (measured by the degree to which users’ choices are internally consistent), more-satisfied and better-educated decisionmakers, and, importantly, greater trust and transparency in the process.
The road ahead
Because of complexities associated with decisions of the type faced by policy makers and society around energy, we recommend strongly that policymakers (and researchers) turn their attention toward enhancing decision support capabilities around energy and related concerns, such as climate change. The Obama administration’s creation of a climate services portal within the National Oceanic and Atmospheric Administration, as well as other independent initiatives focused on energy, are important first steps toward this goal in that they place up-to-date information about problems and opportunities in the hands of decisionmakers. However, thoughtful and defensible decisions concerning the development of energy strategies will require more than high-quality scientific information. Energy strategies, whether local, regional, or national, will also require a process for incorporating the values and risk tolerances of stakeholders and for linking values and facts as part of a series of thoughtful decisions over time and space.
In this regard, energy strategies (and the decisions that underlie them) are not vastly different from strategies that many people are familiar with and support: those relating to national defense. Strategies for national defense require investments, reinvestments, and divestments across different branches of the military. Defense strategies must also recognize the need for different investment decisions on a geographic scale, understanding that there is no one-size-fitsall approach to securing the nation. And defense strategies must be nimble in the sense that they are flexible and can shift (sometimes quickly and sometimes more slowly) in response to existing and emerging national security threats.
Likewise, the development of energy strategies will require different levels of investment in different kinds of energy-generating technologies (and perhaps in technologies for managing carbon dioxide and other greenhouse gases). In a country as large as the United States, those decisions will need to be responsive to and respectful of different needs and constraints in different geographic locations. And as boundary conditions (policies, market demands, and environmental concerns, among others) change, so too will the need for investments in different energy technologies.
Even under the best circumstances, members of the public and policymakers alike will need help in making these kinds of complex and interlocking decisions. As we have argued, decision processes are often prone to shortcuts, error, and bias. In the case of choices as important as those concerning national energy strategies, failing to address these challenges in a credible way is as irresponsible as relying on out-of-date and substandard technologies. Failing to make strides in the science and application of decision support approaches for energy development choices would be as foolish as continuing to rely on kerosene to illuminate the nation’s streets and homes.
In the end, what will separate the successful actors from the unsuccessful ones in the new world energy order is the recognition that a focus on a single approach or even a bundle of approaches at a single point in time is not the answer. Moreover, successful nations will recognize that they need to go well beyond simply providing people with a menu of energy-related offerings. The real need is to provide people with a mechanism for making a series of difficult and interrelated choices among them over time. This is only way to avoid ideological stalemate. When viewed in this light, the real product of a national energy strategy is not a particular outcome. Instead, it is a sensible, credible, and defensible decisionmaking process.
J. L. Arvai, G. Bridge, N. Dolsak, R. Franzese, T. Koontz, A. Luginbuhl, P. Robbins, K. Richards, K. Smith Korfmacher, B. Sohngen, J. Tansey, and A. Thompson, “Adaptive Management of the Global Climate Problem: Bridging the Gap Between Climate Research and Climate Policy,” Climatic Change 78 (2006): 217–225.
B. Fischhoff, “Cognitive Processes in Stated Preference Methods,” in Handbook of Environmental Economics, eds. K. G. Maler and J. R. Vincent (London: Elsevier, 2005).
R. Gregory, L. Failing, M. Harstone, G. Long, T. McDaniels, and D. Ohlson, Structured Decision Making: A Practical Guide to Environmental Management Choices (Chichester, UK: Wiley-Blackwell, 2012).
R. Gregory, D. Ohlson, and J. L. Arvai, “Deconstructing Adaptive Management: Criteria for Applications to Environmental Management,” Ecological Applications 16 (2006): 2411–2425.
D. Kahneman, P. Slovic, and A. Tversky, Judgment Under Uncertainty: Heuristics and Biases (Cambridge, UK: Cambridge University Press, 1982).
R. L. Keeney, Value-focused Thinking. A Path to Creative Decision Making (Cambridge, MA: Harvard University Press, 1992).
S. Lichtenstein and P. Slovic, The Construction of Preference (Cambridge, UK: Cambridge University Press, 2006).
National Research Council, Informing Decisions in a Changing Climate (Washington, DC: National Academies Press, 2009).
R. S. Wilson, and J. L. Arvai, “When Less is More: How Affect Influences Preferences When Comparing Low and High-Risk Options,” Journal of Risk Research 9 (2006): 165–178.
Joseph Arvai ([email protected]) is the Svare Chair in Applied Decision Research at the Institute for Sustainable Energy, Environment, and Economy at the University of Calgary. He is also a senior researcher at Decision Research in Eugene, Oregon. Robin Gregory is a senior researcher at Decision Research and director of Value Scope Research, a consulting firm. Douglas Bessette is a Ph.D. student at the Institute for Sustainable Energy, Environment, and Economy. Victoria Campbell-Arvai is a research associate at the Institute for Sustainable Energy, Environment, and Economy.