Learning curves for energy technologies:
Toward sound policy analysis and design
Richard Newell
Resources for the Future
Workshop on Learning-by-Doing in Energy Technologies
Washington, DC
June 17, 2003
What do learning curves represent?
• Relationship between a product’s cost (price) and the
“knowledge stock” associated with its production,
typically measured by cumulative output
• Reduced form of a very complex process of
technological change, including the following forces
− Labor-based learning
− Returns from R&D investments
− Economies of scale
− Technological improvements (e.g., in capital equipment
and materials) occurring over time, independent of
production
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What is the cost of learning?
• Direct costs (investment costs) of producing at a loss
in the short run to lower future production costs
− difficult to predict in advance and very sensitive to
assumed learning rate
• Opportunity cost of not learning in areas that receive
less investment due to the shifting of resources
− depends on relative rate of learning (and learning
spillovers) in the competing technologies
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Will the private market get it right?
• If environmental policy does not fully “price” the
environment, there will be under-production of green
energy, and suboptimal learning
• If firms cannot capture an adequate return on
learning investments (due to spillovers or
competition) then firms may underinvest in learning
• Key questions therefore include:
− What is the assumed environmental policy?
− What is the rate of learning and learning spillovers?
− How do we avoid devolving to arguments for subsidizing
all technology?
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Learning curve empirics
• LCs are a pervasive reduced-form phenomena
• Convenient and simple, but masks important
underlying dynamics
• Difficult to forecast learning rates for new
technologies (and the rate is endogenous to policy
and management decisions)
• Important to control for other forces, including R&D,
scale economies, exogenous technological change,
capital investment, global production, quality of
output, demand responses
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Sound policy analysis
• Do not assume learning only occurs in green
technologies; fossil competition is a moving target
• Do not ignore the opportunity costs of learning
• Avoid double counting technological change through
inclusion of both learning effects and other sources
• Hard to conclude anything about the value of
technology production subsidies without comparing
the learning costs to the benefits (avoided
environmental harm)
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Sound policy design
• Best place to start is by fully pricing environmental
externalities
− clarifies policy question considerably, because it
minimizes the difference between green energy and other
technologies
• If a technology subsidy approach is to be used…
− is it flexible with regard to targets and technologies?
− is there a clear exit strategy if the technology becomes
competitive on its own, or looks like it may always be
expensive?
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Bottom line
• Learning curves may be useful reduced-form tools for
summarizing past technology cost paths
• But at this point they have limited ability to predict
the full costs and effectiveness of policies
• Further research is needed to:
− disentangle forces associated with cost reduction
− estimate relative learning rates and spillovers for
competing technologies
− develop conceptual and empirical approaches for
incorporating learning into aggregate (CGE) models
− and thus support the design of smart policies
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