When viewed as an adaptive-decision strategy, one that can evolve over time in response to observations of the climate and economic systems, climate-change policy has important implications for new energy technologies and the role of forecasts of these technologies. In the work described here, we examined several facets of this broad question. First, we argued that an adaptive-decision approach to climate change will be significantly more robust in the face of deep uncertainty than an approach that does not adapt. We found that the near-term steps in such a robust, adaptive-decision response to climate change should likely include actions explicitly designed to encourage the early adoption of new, emissions-reducing technologies. Such actions are justified if there are significant social benefits of early adoption beyond those gained by the adopters themselves, as is the case when there is heterogeneity in economic actors'
cost/performance preferences for new technologies, when new technologies have increasing returns to scale, or when potential adopters can learn from others about the uncertain performance of new technologies. These results are consistent with most other climate-policy analyses which include both significant uncertainty and technological change that responds to policy choices and economic signals.
In addition, we examined the impacts of climate variability on near-term policy choices and found that the future course of emissions-reducing technologies may be one of the key indicators that should shape the evolution of an adaptive-decision strategy. The ability of future innovation to radically reduce emissions of greenhouse gases during the 21st century is one of the key uncertainties facing climate-change policy, perhaps more so than our uncertainty in the response of the climate system to those emissions. Particularly in the presence of climate variability, over time, policy-makers may gain more definitive information about the potential of emissions-reducing technology than they will about the damages due to anthropogenic influences on the climate. Over the next decades then, the most reliable information policy-makers can use to adjust emissions constraints on greenhouse gases and other policy actions may come from indicators of society's ability to reduce such emissions at low cost.
Combining these findings supports the broad view that in the near-term, climate-policy ought to be more focused on improving society's long-term ability to reduce greenhouse-gas emissions than on any particular level of emissions reductions. As a long-term goal, the Framework Convention on Climate Change aims to stabilize atmospheric concentrations of greenhouse gases at some safe, currently unknown and unspecified, level. As a first step, the Kyoto Protocol requires the developed countries to reduce their greenhouse-gas emissions to specific targets by the end of the next decade. Both advocates and opponents usually see these emissions caps as hedging actions, designed to decrease the severity of any future climate change by reducing the concentration of greenhouse gases in the atmosphere. Thus, much of the climate-change debate, particularly in the United States, takes the particular level of these goals very seriously, revolves around whether or not such targets are justified, and seeks to determine the least expensive means to meet them.
An adaptive-decision approach, combined with the uncertain, but large potential of the various technologies described in this volume, suggests that the Kyoto targets are in fact shaping actions, whose most significant impact may be making future emissions reductions, if necessary, less costly and easier to implement, rather than reducing atmospheric greenhouse-gas concentrations. Goals and forecasts often serve this type of shaping function. For instance, the leadership of organizations, such as private firms, often set goals for sales or profits intended to motivate their employees. The leaders would be disappointed if the goals were consistently met, for that would be an indication they had not asked their employees to strive hard enough. Conversely, the firm's financial planners will likely produce conservative sales and profit forecasts and be disappointed if reality does not exceed their expectations. Similarly, the Kyoto targets may be most important as a signal to numerous actors throughout society that they should take the climate problem seriously and plan their actions and investments accordingly.
To some extent, Kyoto has already been effective in this regard. For instance, many firms are beginning to reduce their own emissions, factor potential climate change into their internal planning, and invest in research that would help them reduce emissions more in the future. Traders have begun to implement markets for trading carbon-emissions rights. Governments have begun to construct the institutions necessary to implement international regulation of greenhouse-gas emissions. It is unclear and, for an adaptive-decision strategy, perhaps irrelevant, whether or not these actions will result in Kyoto emissions-reductions targets being met. Rather, the question becomes what are the means to induce technological change that best balance between the possibility that such innovations will soon be needed to address severe climate change and the possibilities that they will not be needed or that the technologies will not meet their promise.
In this context, technology forecasts, to be most useful for informing the design of adaptive-decision strategies, should provide a broader range and different types of information than is often the case. Typically, an energy-technology forecast describes the future costs of the power produced by the system, estimates the maximum amount of society's energy needs the technology might satisfy, and reviews the technology's pros and cons. Often these predictions stem from an analysis of fundamental physical or engineering constraints and/or from analogies from similar systems. For instance, forecasts of the potential generation capability of future photovoltaic energy systems might be based on comparisons of the current and theoretical efficiencies of various types of cells and a survey of the solar insolation and land available for photovoltaic installations. Forecasts of the future costs of these systems might compare past rates of cost reductions to limits imposed by the basic costs of materials (that is, setting the design, production, and installation costs to zero). Sometimes these technology forecasts are given as best estimates; sometimes as a range of scenarios. Generally they are conceived as an answer to the question: how important to society will this technology be in the future?
Such information is useful to policy analyses of climate change. But it is also contrary to much of what we know about processes of technology change, especially over the timescales relevant to the climate problem. In the long term, we know costs and market shares of particular technologies are impossible to predict with any accuracy. We also know that social and economic factors, generally under-emphasized in technology forecasts, are among the primary determinants of the fate of new technologies. The unpredictability of such social and economic factors is a reason that technological change itself is impossible to predict. As one example, a recent study found that cost projections for renewable-energy systems had been reasonably accurate over the last twenty years, while predictions of market share have proved significantly too optimistic. The reasons are not entirely clear, but cost reductions may have been helped by rapid advances in materials and information technologies occurring outside of renewable-energy development, and the market shares have been hindered by the fossil-energy prices being much lower than past forecasters imagined possible.
Nonetheless, these social and economic factors impose common patterns on the processes of technology diffusion and can play an important role in constraining future scenarios, thus helping us choose the best near-term steps in an adaptive-decision strategy. For instance, we know that new technologies generally experience lifecycles that begin with periods of rapid improvement in cost and performance, continue with a period of mature incremental improvements, and end with a period of replacement by newer technologies. A technology's early stages are characterized by many firms experimenting with alternative designs, followed by a period of consolidation over a single design and competition of better means to produce and distribute that design (Utterbeck, 1994). We know there are common patterns of entry and exit as technologies compete against one another in the market place, although radical new technologies sometimes replace established competitors, the old technologies can sometimes crush the new technologies with a burst of incremental improvements. We know there are network effects in which improvements, or the lack thereof, in one technology area can impact improvements in other areas.
Our analysis gives one example of how such patterns, and information about the social and economic factors that influence them, can be used to help design adaptive-decision strategies. We find that expectations about factors such as the heterogeneity of the potential adopters of a new technology - the availability of niche markets - and the speed with which information about experience with a new technology travels through a society - the networks and industry structure among these niche markets - are important in determining the best policy choices. In our analysis we used only crude estimates for such processes. Technology forecasts that provide information on factors such as the size of niche markets, the types of networks in which these potential early adopters reside, and the types of cost/performance preferences they display, could greatly enhance analysis of this type. Forecasts that examine how these factors might play out in a variety of diffusion scenarios would be even more useful. Such information might be seen as less rigorous than the hard, physical constraints on predicted cost and total deployment generally offered in technology forecasts. However, good descriptions of early markets and potential diffusion paths are a more realistic goal than predicting the long-term course of new technologies and, as described in this chapter, provide a more solid basis on which to design responses to climate change that adapt over time and, thus, are robust across a wide range of plausible futures.
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