The Power of Change Innovation for Development and Deployment of Increasingly Clean Electric Power Technologies (2016) / Chapter Skim
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Appendix C: The Role of Research, Learning, and Technology Deployment in Clean Energy Innovation
Appendix C: The Role of Research, Learning, and Technology Deployment in Clean Energy Innovation
Pages 265-288
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From page 265... ...
. SCALE OF THE CHALLENGE AND NEED FOR CORRESPONDINGLY SCALED INNOVATION EFFORT The scale of the challenge to develop and deploy clean energy technology underscores the importance of investment in research and development (R&D)
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For instance, the policy for internalizing the cost of carbon emissions has a major impact on the scope and design of policies for supporting technology innovation and deployment of clean energy technologies that address the problem of global warming. A particular focus is on the benefits of early deployment.
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These pathways touch the innovation stages in different ways and have different policy implications. Figure C-1 shows the stages of the innovation process and key obstacles to accelerating innovation at each stage that are important policy targets.
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268 FIGURE C-1 Stages of the innovation process and key obstacles to acceleration. SOURCE: Adapted from Lester and Hart, 2012, Figure 2.1, p.33.
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A related but different question addresses the degree to which there should be public policy support, and the form of policy most likely to produce results, at the later stage of large-scale market penetration, where clean energy is the principal product, and innovation through cost reduction is a by-product. How large and of what types are the aggregate social returns for diffusion of innovative clean energy technologies?
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in the sense that it is treated as a free by-product of deployment rather than an explicit costly product of the deployment. There can be significant spillover effects.
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, a representative version of an experience or learning curve model for a given technology, for a firm or an industry, illustrates the central components and key parameters. New production of clean energy in period t is Qt.
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LR = PR = 2−b. 1− 1− Hence, with an LR of 20 percent, a doubling of cumulative production results in a 20 percent reduction in future production cost, or a PR of 80 percent.
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LRs can appear to be negative at certain stages. And the relative cost of a prominent competing fossil fuel technology creates a receding target for clean energy technologies.
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The purpose is to understand the primary elements of an estimate of any learning premium. The basic model employed by Nordhaus starts with an exogenous forecast of future clean technology production, Qt.
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Subsidies set high enough to make clean technologies competitive with dirty technologies that cost half as much or less would require a premium of more than 50 percent of the clean technology cost. Variable Clean Deployment The Nordhaus model provides an important set of insights, but raises immediate issues that appear to be relevant to an evaluation of the future role of LBD.
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. The treatment of the benefits of substitution for dirty technologies could be incorporated by assuming an exogenous growth rate for the total new technologies and allowing introduction of the clean technology when it is competitive, including accounting for the effect of the negative dirty technology externality and the positive clean technology learning externality.
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, for simplicity the dirty technology represented here has no learning and is described by an exogenous price Pt and an externality cost Et. Again, for simplicity, assume that there is no emission externality cost for the clean technology.
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A "fossil externality policy" case assumes an initial dirty externality cost of 20 percent of the price of the dirty technology that grows at the discount rate. The results appear in Figure C-3.
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increases the value of the clean technology and therefore increases deployment and makes future cost savings more valuable. With the exception of the important dirty externality impact, most of the elements of the learning premium interact in offsetting ways, and this helps explain why the value of the learning premium is relatively low.
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Surprisingly, changing the discount rate over the range 3 percent to 10 percent has only a modest effect on the size of the learning premium or the waiting time to deployment. The implementation here assumes the dirty technology externality grows at the rate of discount.
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The Fischer and Newell (2008) technology learning model is a singlecomponent model, i.e., Cmin = 0, and treatment of future production and learning benefits includes output from both new and existing capacity, which should increase the implied learning premium.
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. A natural and relatively simple extension of current applications would be to modify the cost model to include a representation of a time trend, cumulative production, and knowledge (t,Yt,Kt)
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A policy needs to focus on the interactions of the market externalities and market failures. The learning premium is material, but cannot carry much of the burden of supporting large-scale deployment of expensive clean energy technologies.
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Compared with the direct effect of a carbon price, indirect deployment subsidies for clean energy technology tend to be ineffective in addition to being unsupported by the small LBD premium: Subsidies for green power (or mandated utility offer prices for power generated in this way, known as "feed-in tariffs") have been portrayed as nearly equivalent to pricing externalities, but more politically acceptable.
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Assuming that the marginal generation displaced is equal to the average generation mix in the system can be a poor approximation.…The problem arises because subsidizing green power is an indirect approach to the pollution problem, and the relationship between green power and emissions avoided is not uniform. It would not arise with a direct tax (or pricing through tradable permits)
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In fact, we find that an optimal portfolio of policies can achieve emissions reductions at a significantly lower cost than any single policy, although the emissions reductions continue to be attributed primarily with the emissions price. Together, these results illuminate some of the arguments in Montgomery and Smith that R&D is the key for dealing with climate change and that an emissions price high enough to induce the needed innovation cannot be credibly implemented.
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For example, an investment tax credit can affect the economics of wind without the perverse collateral effects of a production tax credit in lowering the perceived variable cost of wind from zero to minus the value of the credit. Given the low value of the LBD premium and the high value of reducing costs before largescale deployment, a direct expansion of government support for upstream transformational R&D would be better than a broad subsidy for deploying existing clean energy technology.
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