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If climate change is indeed a serious problem, society will have to make significant reductions in greenhouse-gas emissions, on the order of 80% below extrapolations of current trends, by the end of the 21st century. Technology innovation will likely play a major role in any changes of this scale. A number of modeling studies have made this point simply by treating innovation as an exogenous influence on key parameters, such as the rate of autonomous energy efficiency improvements, the cost of low-carbon emitting technologies, or the rate of technology spillovers from developed to developing countries (see for instance, Dowlatabadi, 1998 and Edmonds and Wise, 1998). By considering a range of assumptions about these parameters, these studies confirm that over the long term, sustained rates of either fast or slow innovation can make virtually any emissions-reduction target either inexpensive or impossibly costly to meet.

This fact, and our argument that the most robust, adaptive-decision strategies may emphasize technology innovation, raises a key question - what should policy-makers do to encourage such innovation? In practice, governments pursue a wide range of technology policies designed to improve the technology options for emissions reductions in the future. Such policies include supporting research and development, training individuals, funding demonstration projects, building infrastructure, disseminating information, and implementing a variety of tax credits and subsidies to encourage the use of new technologies. These programs often appear attractive both on substantive and political grounds, but their record in practice is mixed. In addition, economic theory suggests that in the absence of market failures, such policies are inefficient compared to policies designed to "get the prices right", such as carbon taxes or tradable permits. While there is wide agreement that governments ought to fund research and development (though less agreement on the extent to which this funding ought to focus on specific areas such as emissions-reducing technologies), it is not clear the extent to which climate-change policy ought to focus on getting the price of carbon right, developing technology policies designed to improve future options for emissions reductions, or some combination of both.

Our judgements about this question will depend on our expectations about the ability of policy to change the dynamics of technology diffusion. Many recent studies have begun to address these issues (Grübler, Nakicenovic, and Victor, 1999; Azar and Dowlatabadi, 1999), focusing on both the effects of learning-by-doing on technology diffusion (Gritsevskyi and Nakicenovic, 2000; Mattsson and Wene, 1997; Anderson and Bird, 1992) and on the accumulation of knowledge that can lead to new innovation (Goulder and Schneider, 1998, 1999; Goulder and Mathai, 2000). In addition, much empirical work (e.g., Newell, Jaffe, and Stavins, 1999; Grübler, Nakicenovic, and Victor, 1999) is becoming available to inform these modeling efforts. The modeling efforts to date, however, have largely focused on creating scenarios of the future and showing their sensitivity to a variety of assumptions about technology innovation. Few have used this information to adjudicate among alternative policies and, in particular, examine the desirability of technology policies. In our recent work, we have examined the conditions under which technology incentives should be a key building block of a robust, adaptive-decision approach to climate change. This is an interesting question in its own right, but it also sheds important light on the design of adaptive design strategies and on the types of information about technology futures that, in the absence of an ability to forecast, should prove most useful to policy analysis and policymakers.

We have addressed these questions using our exploratory modeling methods and a model of technology diffusion which focuses on the social and economic

AGENTS MACROECONOMY POLICY

Differ by:

Differ by:

AGENTS MACROECONOMY POLICY

Figure 3.6 Agent-based model of technology diffusion used to compare alternative climate-change abatement strategies. Economic agents choose among alternative technologies on the basis of forecasts of cost and performance, which in turn are influenced by learning among the agents and potential price decreases due to increasing returns to scale. The agents have heterogeneous initial expectations about technology performance and heterogeneous preferences for technology cost/performance tradeoffs. The agents' choices influence the level of energy prices and of greenhouse-gas emissions, which both influence the rate of economic growth. Policy decisions about the level of carbon taxes and technology incentives, which depend on observations of economic growth, damages and technology diffusion, also influence the agents' technology choices.

Figure 3.6 Agent-based model of technology diffusion used to compare alternative climate-change abatement strategies. Economic agents choose among alternative technologies on the basis of forecasts of cost and performance, which in turn are influenced by learning among the agents and potential price decreases due to increasing returns to scale. The agents have heterogeneous initial expectations about technology performance and heterogeneous preferences for technology cost/performance tradeoffs. The agents' choices influence the level of energy prices and of greenhouse-gas emissions, which both influence the rate of economic growth. Policy decisions about the level of carbon taxes and technology incentives, which depend on observations of economic growth, damages and technology diffusion, also influence the agents' technology choices.

factors that influence how economic actors choose to adopt, or not to adopt, new emissions-reducing technologies. As shown in Figure 3.6, we use an agent-based model of technology diffusion coupled to a simple macro model of economic growth. Used in this fashion, an agent-based model is merely a stochastic mathematical function representing the dynamics of factors in the macro-model, such as energy intensity. Like any time series model, its parameters can be calibrated to reproduce real-world data. Such agent-based models are particularly useful, however, because they conveniently represent key factors influencing technology diffusion, and thus policy choices, such as the heterogeneity of economic actors and the flows of imperfect information that influence their decisions. Each agent in our model represents a producer of a composite good that is aggregated as total GDP, using energy as one key input. In each time period the agents first choose among several energy-generation technologies and second, given their chosen technology, choose how much energy to consume. (That is, agents choose a production function and where to operate on that production function.)

We assume that agents pick a technology in order to maximize their utility, which depends on each agent's expectations about the cost and performance of each technology. The agents have imperfect information about the current performance of new technologies, but can improve their information based on their own past experience, if any, with the technology, and by querying other agents who have used it (Ellison and Fudenberg, 1995). The agents are also uncertain about the future costs of new technologies, which may or may not decline significantly due to increasing returns to scale. Agents estimate these future costs based on observations of the past rates of adoption and cost declines. Thus, the diffusion rate can depend reflexively on itself, since each user generates new information that can influence the adoption decisions of other potential users.

We write each agent's utility function using a risk-adjusted, Cobb-Douglas form

(UigJ (t)) = (Perf ¿j (t)a) (Cost^ (t)1 — a)— [Varf t) + Var ^(t)], (3.3)

where the first term, (Perf .(t)a), is the expectation of agent i, in region g, at time t of the performance it will get from technology j. This term depends on information flows among the agents, which we model crudely as a random sampling process, where each agent queries some number of the other agents each time period. The second term, (Costi .(t)1 — a), is the expected cost of using the technology over its lifetime, derived from observations of past trends in usage and cost of the technology. This term depends on the potential for cost reductions due to increasing returns to scale. The third term represents the agent's risk aversion, taken as a function of the variance of the estimates of technology performance and future costs.

Equation (3.3) focuses on the heterogeneity of the agents' preferences, which is important in creating the possibility for early adopters and niche markets that can strongly influence the course of technology diffusion. First, different agents obtain different levels of performance from the same technology because technologies differ according to characteristics such as size of the equipment and ease of maintenance that matter to some users more than others (Davies, 1979). We represent this in our model with a distribution of performance factors for each technology across the population of agents. Economic actors also have different cost/performance preferences for new technologies. Potential early adopters are generally much more sensitive to performance than price, while late-adopters are often more price sensitive. We represent this in our model by allowing agents to have different values for the exponents a, representing the cost/performance tradeoffs, and in the risk aver sion coefficient Finally, in a world of imperfect information, different economic actors will have different expectations about the performance and cost of each technology. At the beginning of each of our simulations, each agent is assigned an expectation about the performance of each technology, which it can update as time goes by. Each agent's expectations of performance are private; that is, they apply only to that agent, since in fact each agent will gain a unique performance from each technology. The cost forecasts are public, that is, shared in common by all the agents, but each agent in general will have different planning horizons, determined by the remaining lifetime of the technology they are already using. Each type of heterogeneity considered in our model can significantly affect market shares and the dynamics of the diffusion process (Bassanini and Dosi, 1998).

This model requires data on the social and economic context of technology diffusion, quite different from that generally demanded by other climate-change-policy models and often supplied by technology forecasts. Each of the technologies in our study is represented by three factors: the cost (which can drop over time due to increasing returns), the carbon intensity (the quantity of CO2 emitted when generating one unit of energy), and the performance. The cost and the carbon intensity are intrinsic to the technology, and are treated in our model similarly to other models. However, the performance, as noted above, depends on the agents using it. In addition, we need information on the different preferences and current expectations of the users and potential users of the new technologies. Thus, we are interested in data which show how the performance of a technology varies across many different types of users and, in particular, how different key segments of the market along the technology adoption life cycle (Moore and McKenna, 1995) - the early adopters, early and late majority, and laggards - may perceive, judge, and use the technology. In our model we treat these factors crudely, assuming in each case that preferences, expectations, and performance are distributed normally across our population of agents. Using these simple assumptions we can draw what seem to be important, but general, policy conclusions. More detailed policy recommendations would probably require better information about such factors.

In addition to considering the agents' individual technology and energy consumption choices, we also need to consider the aggregate impact of the agents' actions. We capture these effects with a very simple representation of economic growth. Assuming a world economy in a steady state, we write the Gross Domestic Product (GDP) in each of two world regions, the OECD and the rest of the world (ROW), with the difference equation, where output per capita grows at some exogenous rate that can be modified by changes in the price of energy and any damages due to climate change. Thus,

GDPg(t) = GDPg(t -1)[1 + 7g - $xg• ACeng(t) - $sg• Csub(t)][1 - impact (t)], (3.4)

where yg represents the exogenous growth rate in the regions g = OECD and ROW; Ceng is the growth rate of the average cost of energy per unit of output in region g, including the costs of energy-producing technologies, any carbon tax imposed in order to reduce CO2 emissions, and any subsidies on new low-emitting technologies; Csub is the per unit of output cost of the subsidy; 4>xg and $sg are the corresponding elasticities in energy prices, including the costs of energy-producing technologies, any carbon tax imposed in order to reduce CO2 emissions, and the cost of the subsidy to changes in economic growth; and impacts due to climate change are given by a simple polynomial function of the atmospheric concentrations of greenhouse gases, impact (t) = ko [Conc (t)IConc (1765)]K1, using a simple difference equation to relate concentrations to emissions (Cline, 1992; Nordhaus and Yang, 1996). The aggregate decisions of the population of agents affect the cost of energy, the emissions, and thus, the climate impacts. In turn, the rate of GDP growth affects the number of new agents with new expectations and no sunk capital cost and, thus, the decisions of each individual agent.

Having defined our model, we can use it to create the landscape of plausible futures by defining ranges for the model inputs and constraints on the model outputs. On the micro (bottom-up) level, we confine ourselves to parameters describing only three generic types of technologies: high-emission-intensity systems, such as coal-fired power plants, which at present provide the bulk of the world's energy; medium-emissions-intensity systems, such as natural-gas-powered combustion, which provide a significant minority of the world's energy; and low-emissions-intensity systems, such as renewable, biomass, andIor new nuclear power facilities, which at present are not in widespread use, but may be significant energy sources in the future. This is clearly a very coarse grouping, similar to that used by Grübler, Nakicenovic, and Victor (1999), but it is sufficient to draw policy conclusions about the appropriate mix of carbon taxes and technology subsidies. We choose a range of cost and performance parameters for these three generic energy technologies, as well as parameters describing the behavior of the population of consumers of these energy technologies, based on averages of data describing technology systems currently with significant market penetration and standard forecasts of technologies with potential significant future market penetration (Tester et al., 1991; Manne and Richels, 1992).

On the macro (top-down) level, we use data for a variety of parameters describing the growth of the economy and its response to changes in the cost of energy and damages due to climate change (World Bank, 1996; Dean and

Hoeller, 1992). These parameters include the exogenous rate of economic growth, the elasticity of economic growth with respect to the cost of energy, the elasticity of economic growth with respect to the cost of the subsidy, the parameters characterizing damages, and the concentration of carbon in the atmosphere, the parameters defining the energy-demand functions, and those used to simulate exogenous improvements in energy efficiency.

We can now search across the thirty-dimensional space of model inputs that plausibly represent the micro-level data, looking for those combinations that give plausible, macroscopic model outputs. Given the simplicity of the model and the types of data readily available, we choose three constraints on model outputs: current (1995) market shares for energy technologies; current levels of carbon emissions, energy intensities, and carbon intensities; and diffusion rates no faster than 20 years (from 1% to 50% penetration). The first two constraints guarantee that our model is consistent with current data. The third, limiting diffusion to a rate no faster than the rates historically observed for energy technologies (Grübler, 1990), forces the model to be consistent with one of the historically observed patterns of technology diffusion.

We next generate the most expansive ensemble we can produce of model input parameters consistent with these constraints. Using a genetic search algorithm, we generated 1611 such sets of input parameters, covering a wide variety of assumptions about key parameters such as the level of increasing returns to scale, agents' heterogeneity - represented by the distribution of values for the coefficients in Eq. (3.3) across the population of agents, uncertainty regarding new technologies, and future damages due to climate change. This set does not include points with very small levels of uncertainty and heterogeneity regarding expectations about the performance of new technologies (such points do not satisfy the constraint on initial market shares), sets of points where the agents' utility functions are largely insensitive to costs, and sets of points with both very high learning rates and levels of increasing returns to scale.

We can now ask the question as to whether an adaptive-decision strategy ought to use technology incentives and carbon taxes, or carbon taxes alone, in order to address the threat of climate change. We posit two strategies, "Taxes Only" and "Combined Strategy", whose performance we compare across the landscape of plausible futures. (We also considered no response and a strategy using only technology subsidies. Neither of these were very attractive compared to the other two.)

As the name implies, the "Taxes Only" strategy employs only a carbon tax, whose level can change over time in response to observations of the rate of economic growth and the damages due to climate change, as shown in Figure 3.7.

Figure 3.7 Adaptive decision strategy for adjusting carbon taxes and technology incentives over time.

Figure 3.7 Adaptive decision strategy for adjusting carbon taxes and technology incentives over time.

The tax begins at some initial level per ton of emitted carbon and grows at a fixed annual rate. However, if in any year the cost of the tax is greater than the marginal cost of emissions of carbon dioxide (expressed as a percentage of GDP), the tax remains constant. Similarly, if in any year the global economy growth rate is below some minimum rate, the tax returns to its initial level, from which it can begin to grow again. This description of a steadily growing tax is consistent with the optimum taxes described in the literature (Goulder and Schneider, 1999), and the stopping condition represents a way in which political conditions may force a tax to terminate.

The "Combined Strategy" uses both the carbon tax and a technology subsidy, which can change over time in response to observations of the market share of low-emitting technologies, as shown in Figure 3.7. The subsidy begins at some initial level, expressed as percent of the cost of the subsidized technology. This subsidy stays at a constant level over time until either the market share for low-emitting technologies goes above a threshold value or the market share fails to reach a minimum level after a certain number of years. If either of these conditions is met, the subsidy is permanently terminated. Thus, we assume the subsidy is terminated once policy-makers observe that the technology succeeds or that it fails to diffuse by some deadline.

These tax and incentive policies are together characterized by a total of seven parameters - the beginning levels of the tax and subsidy, the annual increase in the tax, the minimum level of economic growth needed to maintain the tax, the maximum market share which terminates the subsidy, and the minimum market share over what time period that terminates the subsidy. We choose the particular value of these parameters used to define our "Taxes Only" and our "Combined Strategy" by searching for the best tax and the best subsidy strategy at the point in uncertainty space characterized by the average value for each of the model input parameters. The tax starts at an initial value of $100/ton carbon and grows at 5% per year. The initial subsidy is 40% of the cost of the low-emitting technology. The subsidy terminates if the low-emitting technology reaches 50% of market share, or fails to reach 20% of market share after 15 years. This process is a crude approximation to the procedure used in LSBA, in which we found the best strategy for many different states of the world. We are currently applying the LSBA procedure to a comparison of trad-able permits and technology incentives. The general conclusions presented here seem to hold.

In order to compare the performance of the alternative strategies, we calculate their performance for each of a large number of different states of the world, looking for those that distinguish one policy choice from another. There are too many dimensions of uncertainty (30) in this model for an exhaustive search, so we used knowledge about a system and the goals of the analysis to summarize a very large space with a small number of key scenarios. We performed a Monte Carlo sample over the thirty-dimensional space, used econometrics (Aoki, 1995) to look for those input parameters most strongly correlated with a key model output of interest (GHG emissions after fifty years), and used variations across these key parameters to define scenarios. (This is a version of critical factors analysis, as in Hope et al. (1993).) After making hypotheses about policy recommendations based on this reduced set of scenarios, we then tested our results by launching a genetic search algorithm (Miller, 1998) across the previously unexamined dimensions looking for counter examples to our conclusions.

Figure 3.8, a typical result of such comparisons, shows the regret of the Taxes-Only strategy (dashed line) with the regret of the Combined strategy (solid line), as a function of the heterogeneity of the agent population. For this figure we have assumed moderately increasing returns to scale, a moderate level of social interactions, and moderate damages due to climate change. The figure shows that the Taxes-Only strategy is preferable in a world where the agents are homogeneous. As the heterogeneity of the agents' preferences increases, the Combined tax and subsidy strategy quickly become more attractive.

Taxes-Only ,__Strategy

Combined Strategy

Figure 3.8 Expected regret of the taxes only (dashed line) and combined tax and technology incentives (solid line) adaptive-decision strategies. The lines are displayed as a function of heterogeneity of the agents' population. All other input parameters are held constant at their mean values.

Taxes-Only ,__Strategy

Combined Strategy j_I_I_I—

Number of potential early adopters (heterogeneity measured by % deviation from the mean)

Figure 3.8 Expected regret of the taxes only (dashed line) and combined tax and technology incentives (solid line) adaptive-decision strategies. The lines are displayed as a function of heterogeneity of the agents' population. All other input parameters are held constant at their mean values.

The Combined strategy becomes less costly than Taxes-Only due to the competition between two effects. Carbon taxes reduce emissions by slowing economic growth and inducing agents to switch to low-emitting technologies. Subsidies slow growth slightly, and may or may not induce agents to switch to low-emitting technologies. When the agent population has low heterogeneity, there are few potential early adopters and the tax is more efficient than the subsidy. Increasing heterogeneity favors the Combined strategy, because it creates a number of potential early adopters that the subsidy will encourage to use the new, low-emitting technology which, in turn, generates learning and cost reductions that benefit society as a whole. But the Subsidy-Only strategy never dominates in our simulations, because when the heterogeneity is large, there are always a substantial number of late-adopters who will reduce emissions only when faced with a tax.

Figure 3.9 Regions in probability space where the expected gross world product resulting from the Taxes-Only strategy is greater than that for the Combined tax and technology incentives strategy as a function of the probability of high climate damages and of the potential for significant cost reductions for new technologies due to increasing returns to scale, reduction of uncertainty about the performance of new technologies due to learning among agents, and heterogeneity in the cost/performance preferences for new technologies among agents ("non-classical world").

Figure 3.9 Regions in probability space where the expected gross world product resulting from the Taxes-Only strategy is greater than that for the Combined tax and technology incentives strategy as a function of the probability of high climate damages and of the potential for significant cost reductions for new technologies due to increasing returns to scale, reduction of uncertainty about the performance of new technologies due to learning among agents, and heterogeneity in the cost/performance preferences for new technologies among agents ("non-classical world").

We have considered a large number of results such as those in Figure 3.8, which we summarize in Figure 3.9. The figure shows the expectations about the future that should cause a decision-maker to prefer the Taxes-Only strategy to the Combined strategy. The horizontal axis represents the range of expectations a decision-maker might have for how likely it is - from very unlikely on the left to very likely on the right - that factors such as the potential number of early adopters and the amount of increasing returns to scale will significantly influence the diffusion of new technologies.4 The vertical axis represents the range of expectations a decision-maker might have that there will be significant impacts due to climate change (greater than 0.3% of the global economic

4 The horizontal axis shows a linear scaling factor applied to four key model inputs: the level of increasing returns for low-emitting technologies, the rate at which agents learn from one another about the performance of new technologies, the agents' risk aversion in Eq. (3.3), and the heterogeneity of the agents' preferences defined by the ai parameter in Eq. (3.3). On the far left (a value of zero on the horizontal axis), the four input parameters take on their minimum plausible values. On the far right (a value of 1 on the horizontal axis), the four input parameters take on their maximum plausible values.

product). The figure shows that the Combined strategy dominates even if decision-makers have only modest expectations that impacts from climate change will be significant and that information exchange and heterogeneity among economic actors will be important to the diffusion of new, emissions-reducing technologies.

Our results are consistent with those of other models of induced technology change introduced in the climate-change literature in recent years. Gritsevskyi and Nakicenovic (1999) use a stochastic optimization technique to examine the effects of induced technology learning, uncertainty, and increasing returns, and technology clusters, but without heterogeneous preferences. They find a wide, bimodal range of "basecase" emissions scenarios and argue for near-term investment in emissions-reducing technologies focused on clusters of such technologies. Goulder and Schneider (1998) examine greenhouse-gas-abatement policies using a general equilibrium model for the United States that takes into account incentives to invest in research and development, knowledge spillovers, and the functioning of research and development markets. They find that the tax should be accompanied by a subsidy only when there are spillover benefits from research and development. The work presented here shows that this result can be more general. Many types of spillovers, such as those resulting from increasing returns to scale, network externalities, or non-R&D knowledge spillovers from users to non-users of new technologies may justify a subsidy. However, we find that the level of spillovers that justify the subsidy depends on the degree of heterogeneity of agents' preferences and their attitude towards risk, and that, all other things being equal, the critical level to justify the subsidy decreases as heterogeneity and risk aversion increase.

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