This book presents forecasts for a range of innovative energy technologies that might help stabilize atmospheric concentrations of greenhouse gases during the course of the 21st century. These forecasts provide a wealth of important information for those who wish to inform their view of the climate-change problem and the actions governments or the private sector might take to address it. But these chapters nonetheless present a fundamental dilemma - for the one thing we know for sure about forecasts is that most of them are wrong. How then should we use the information in this book to shape policy?
The difficulty resides not so much in the forecasts themselves as in the methods that we commonly employ to bring the information they contain to bear on adjudicating among alternative policy choices. Generally, we argue about policy by first settling on our view of what will happen in the future and then by using this understanding to decide what actions we should take in response. For instance, if we came to believe, through arguments such as those in this book, that there were cost-effective technological means to stabilize greenhouse-gas emissions, we might be more likely to support policies that sought to achieve such a stabilization. We often make these arguments non-quantitatively, even if systematically. There are also a host of powerful mathematical tools, based on the mathematical techniques of optimization, that help us systematize and elaborate on this style of thinking about the future. These methods encapsulate our knowledge about the future in probability distributions that then allow us to rank the desirability of alternative strategies. These prediction-based analytic approaches work extraordinarily well in a wide variety of cases. So much so that they strongly affect our notions of the criteria we ought to use to compare our policy choices and the way we ought to use forecasts to support these choices. We often speak of choosing the optimum or best strategy based on our predictions of the future. In this approach then, the purpose of forecasts is to shape our views of what is likely to happen, as a means of shaping our decisions about how to act.
In this chapter we argue for a different approach to climate-change policy and thus a different use for forecasts. We argue that climate change presents a problem of decision-making under conditions of deep uncertainty. We begin with the premise that while we know a great deal about the potential threat of climate change and the actions we might take to prevent it, we cannot now, nor are we likely for the foreseeable future, answer the most basic questions, such as is climate change a serious problem and how much would it cost to prevent it? We argue that in the face of this uncertainty, we should seek robust strategies. Robust strategies are ones that will work reasonably well no matter what the future holds. Not only is this desirable in its own right, but a robust strategy may provide a firm basis for consensus among stakeholders with differing views about the climate-change problem, because all can agree on the strategy without agreeing on what is likely to happen in the future. Rather than using forecasts to specify a particular path into the future, in this alternative approach forecasts describe a range of plausible scenarios and sharpen our sense of what any particular future, if it comes to pass, might look like.
In this chapter we argue that robust strategies for climate change are possible by means of adaptive-decision strategies, that is, strategies that evolve over time in response to observations of changes in the climate and economic systems. Viewing climate policy as an adaptive process provides an important reconfiguration of the climate-change policy problem. The long-term goal of the Framework Convention on Climate Change calls for society to stabilize atmospheric levels of greenhouse gases at some, currently unknown, safe level, and the protocol negotiated in Kyoto in December 1997 commits the world's developed countries to specific, binding near-term emissions reductions. There is currently much debate as to how to best implement these reductions and whether or not they are justified. Viewed as the first steps of an adaptive-decision strategy, however, the Kyoto targets and timetables are but one step in a series of actions whose main purpose is to increase society's ability to implement large emissions reductions in the future. Contrary to most of today's assessments, the real measure of the Framework Convention's success a decade hence should not be any reductions in atmospheric concentrations of greenhouse gases, but rather the new potential for large-scale emissions reductions society has created for the years ahead.*
In this context, the potential for new technologies, the processes of innovation and diffusion by which these technologies come into widespread use, and
* See Lempert (2001) for an application of robust adaptive-decision strategies to the diplomatic situation that followed the United States withdrawal from the Kyoto Protocol.
the government policies needed to encourage such processes, play a central role in society's response to climate change. We will suggest in this chapter that the processes of technology diffusion might provide key indicators for monitoring the progress of an adaptive climate-change strategy, particularly in the face of significant climate variability that will make it difficult to observe a reliable signal about the extent of human-caused impacts to climate change. There is also much debate over the type of policy instrument - carbon taxes or tradable permits - that should be used to encourage emissions reductions. We find that one does not need to have particularly high expectations that these innovative technologies will achieve the potential described here in order to put in place an adaptive-decision strategy that also makes important use of a third type of policy instrument, technology incentives. Forecasts such as those in this book can provide a great deal of information that can help policy-makers design such robust adaptive strategies, but in order to be most useful, such forecasts need to provide a broader range and different types of information than is often provided.
We begin this chapter with a discussion of the importance of adaptive-decision strategies and how they differ from other views of the climate-change problem. We will then describe some first steps in employing an analytic method, based on a multi-scenario simulation technique called exploratory modeling, for designing and evaluating adaptive-decision strategies for climate change, and provide a simple example of its application. We will then turn to the design of adaptive-decision strategies, examining the conditions under which such strategies are appropriate, the tradeoffs between actions and observations in the design of such strategies, and some initial steps in examining climate-change policy as a process. We will present two example analyses - the impacts of climate variability on the design of such strategies and the choice of policy instruments in the presence of the potential for significant technology innovation. During the course of the discussion, we will show the role of innovation in emissions-reducing technologies in such adaptive-decision strategies, as well as the type of information from forecasts like those in this book that can prove most useful in designing them.
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