It is perhaps not surprising that an adaptive-decision strategy which can evolve over time in response to new information is more robust against a wide range of uncertainty than strategies which do not adapt. In order to shape policy choices, however, we need to determine the best adaptive-decision strategies from among the options available. In the above example, we examined only a single, threshold-based, adaptive-decision strategy, which started with slow near-term emissions reductions and used particular trigger levels for its observations of damage and abatement costs. In order to design and use adaptive-decision strategies, we need to answer question such as: should such strategies begin with fast or slow emission reductions? What observations should suggest a change in the initial emissions-reduction rate?
Several academic literatures offer insights into the design of adaptive-decision strategies. Economics and financial theory suggest that a successful strategy will employ a portfolio of different policy instruments and the mix and intensity of these instruments may change over time. Control theory suggests that there is an intimate relation between our choice of instruments and the types and accuracy of the observations we can make. Scenario planning offers a useful language for the components of an adaptive-decision strategy: shaping actions intended to influence the future that comes to pass, hedging actions intended to reduce vulnerability if adverse futures come to pass, and signposts which are observations that warn of the need to change strategies (Dewar et al., 1993; van der Heijden, 1996).
We can use our exploratory-modeling methods to systematically combine these seemingly disparate elements and find robust adaptive-decision strategies. Generalizing on the application described above, we compare the performance of a large number of alternative adaptive-decision strategies across the landscape of plausible futures. In order to make sense of this information, we calculate the regret of each strategy in each of many plausible states of the world, defined as the difference between the performance, for that state of the world, of the strategy and of the optimal strategy. We then search for robust strategies, defined as those with relatively small regret over a wide range of states of the world. In some cases we can find strategies that are robust across the landscape of plausible futures. In other cases, we find particular states of the world that strongly influence the performance of robust strategies. We then might report tradeoff curves which show, for instance, the choice of robust strategy as a function of the probabilistic weight a decision-maker might assign to some critical state of the world.
In this section we will use these methods to address two important questions in the design of adaptive-decision strategies. First, we will examine the impacts of climate variability on the choice of near-term policy choices (Lempert, Schlesinger, Bankes, and Andronova, 2000 - henceforth LSBA). Variability, one of the most salient features of the earth's climate, can strongly affect the success of adaptive-decision strategies by masking adverse trends or fooling society into taking too strong an action. Interestingly for this book, our preliminary analysis suggests that the most robust adaptive strategies in the face of climate variability begin with moderate near-term emission reductions which are more likely to shift to more rapid abatement based on observations of innovation-reduced abatement costs than of increased climate damages. Second, we examine the mix of policy instruments an adaptive-decision strategy ought to use to encourage the diffusion of emissions-reducing technologies (Robalino and Lempert, 2000 - henceforth RL). We find that in many circumstances a mix of price-based mechanisms (e.g., carbon taxes or tradable permits) and direct technology incentives is more robust than a single price-based mechanism alone. This analysis provides some direct lessons for the type of information that technology forecasts can most usefully provide.
3.4.1 Impact of variability on adaptive-decision strategies
In addressing the impacts of climate variability on adaptive-decision strategies, it is important to first note that an adaptive strategy is not always the best strategy to follow if you take into account the costs of adapting. For instance, there might be expensive monitoring equipment needed to gather information, adjustment costs every time a strategy is changed, and/or the observations used to inform the adaptive strategy might be ambiguous so that mistakes are possible. If the costs of adapting are greater than the expected benefits, it is best to just ignore any signposts and make a permanent, for the foreseeable future at least, choice of shaping and hedging actions. This is the message in the lower-left and upper-right hand corners of Figure 3.1. In these regions the costs of waiting to observe new information outweigh the expected benefits of acting on that information.
The risk of adapting incorrectly is a key cost of an adaptive-decision strategy. Observations are often ambiguous, especially if the observed system has noise and other fluctuations. Decision-makers must balance between waiting until the information becomes more clear and the risk of acting on erroneous information (often called Type I and Type II errors). Given the degree of variability in the climate system, these dangers may be acute for climate-change policy. We need to ask if adaptive-decision strategies can still be robust in the face of climate variability, and if so, what these strategies are.
Our work in LSBA is among the first studies of the impacts of variability on climate-policy choices. While a simple, preliminary treatment, it nonetheless provides some important insight into the design of adaptive-decision strategies. We begin by making two changes to the models used in LSB in order to represent climate variability and its impacts. First, we treat climate variability as a white-noise component to the radiative forcing, so that the change in forcing is given by
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