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Willingness to Pay for a Climate Backstop: Liquid Fuel Producers and Direct CO2 Air Capture

Gregory F. Nemet and Adam R. Brandt

Year: 2012
Volume: Volume 33
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol33-No1-3
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Abstract:
We conduct a sensitivity analysis to describe conditions under which liquid fuel producers would fund the development of a climate backstop. We estimate (1) the cost to develop competitively priced direct CO2 air capture technology, a possible climate backstop and (2) the effect of this technology on the value of liquid fuel reserves by country and fuel. Under most assumptions, development costs exceed individual benefits. A particularly robust result is that carbon prices generate large benefits for conventional oil producers--making a climate backstop unappealing for them. Unilateral investment does become more likely under: stringent carbon policy, social discount rates, improved technical outcomes, and high price elasticity of demand for liquid fuels. Early stage investment is inexpensive and could provide a hedge against such developments, particularly for fuels on the margin, such as tar sands and gas-to-liquids. Since only a few entities benefit, free riding is not an important disincentive to investment, although uncertainty about who benefits probably is.

Keywords: Air capture, Backstop technology, Climate policy, Learning by doing, R&D, Unconventional oil



Learning by Doing with Constrained Growth Rates:An Application to Energy Technology Policy

Karsten Neuhoff

Year: 2008
Volume: Volume 29
Number: Special Issue #2
DOI: 10.5547/ISSN0195-6574-EJ-Vol29-NoSI2-9
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Abstract:
Learning by doing methodology attributes cost reductions of a technology to cumulative investment and experience. This paper argues that in addition market growth rates must also be considered. Historically growth rates have been limited in most sectors, thus allowing for feedback in the learning process. When market growth is below the optimal rate, the marginal value of additional investment could be a multiple of the direct learning benefit. Analytic and numeric models quantify this impact emphasizing the need for tailored technology policy in addition to carbon pricing. Implications for the modeling of endogenous technological change are discussed.



The Perils of the Learning Model for Modeling Endogenous Technological Change

William D. Nordhaus

Year: 2014
Volume: Volume 35
Number: Number 1
DOI: 10.5547/01956574.35.1.1
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Abstract:
Modeling of technological change has been a major empirical and analytical obstacle for many years. One approach to modeling technology is learning or experience curves, which originated in techniques used to estimate cost functions in manufacturing. These have recently been introduced in policy models of energy and climate-change economics to make the process of technological change endogenous - that is, allow technologies to vary with economic conditions. It is not widely appreciated that using learning in modeling raises major potential problems. The present note has three points. First, it shows that there is a fundamental statistical identification problem in trying to separate learning from exogenous technological change and that the estimated learning coefficient will generally be biased upwards. Second, we present two empirical tests that illustrate the potential bias in practice and show that learning parameters are not robust to alternative specifications. Finally, we show that an overestimate of the learning coefficient will provide incorrect estimates of the total marginal cost of output and will therefore bias optimization models to tilt toward technologies that are incorrectly specified as having high learning coefficients. Keywords: Learning by doing, Climate change, Technological change





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