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On backstops and boomerangs: Environmental R&D under technological uncertainty

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  • Goeschl, Timo
  • Perino, Grischa

Abstract

In areas such as climate change, the recent economic literature has been emphasizing and addressing the pervasive presence of uncertainty. This paper considers a new and salient form of uncertainty, namely uncertainty regarding the environmental characteristics of 'green' innovations. Here, R&D may generate both backstop technologies and technologies that turn out to involve a new pollution problem ('boomerangs'). In the optimum, R&D will therefore typically be undertaken more than once. Extending results from multi-stage optimal control theory, we present a tractable model with a full characterization of the optimal pollution and R&D policies and the role of uncertainty. In this setting, (i) the optimal R&D program is defined by a research trigger condition in which the decision-maker's belief about the probability of finding a backstop enters in an intuitive way; (ii) a decreasing probability of finding a backstop leads to the toleration of higher pollution levels, slower R&D, a slower turnover of technologies, and an ambiguous effect on the expected number of innovations; (iii) learning about the probability of a backstop is driven by failures only and leads to decreasing research incentives; and (iv) small to moderate delays in the resolution of technological uncertainty do not affect the optimal policy.

Suggested Citation

  • Goeschl, Timo & Perino, Grischa, 2009. "On backstops and boomerangs: Environmental R&D under technological uncertainty," Energy Economics, Elsevier, vol. 31(5), pages 800-809, September.
  • Handle: RePEc:eee:eneeco:v:31:y:2009:i:5:p:800-809
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    References listed on IDEAS

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    1. Goeschl, Timo & Perino, Grischa, 2007. "Innovation without magic bullets: Stock pollution and R&D sequences," Journal of Environmental Economics and Management, Elsevier, vol. 54(2), pages 146-161, September.
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    Cited by:

    1. Baker, Erin & Solak, Senay, 2011. "Climate change and optimal energy technology R&D policy," European Journal of Operational Research, Elsevier, vol. 213(2), pages 442-454, September.
    2. Mort Webster & Karen Fisher-Vanden & David Popp & Nidhi Santen, 2015. "Should We Give Up After Solyndra? Optimal Technology R&D Portfolios under Uncertainty," NBER Working Papers 21396, National Bureau of Economic Research, Inc.
    3. Mort D. Webster & Karen Fisher-Vanden & David Popp & Nidhi R. Santen, 2015. "Should We Give Up After Solyndra? Optimal Technology R&D Portfolios under Uncertainty," CESifo Working Paper Series 5448, CESifo Group Munich.
    4. Tsur, Yacov & Zemel, Amos, 2012. "Dynamic and stochastic analysis of environmental and natural resources," Discussion Papers 120017, Hebrew University of Jerusalem, Department of Agricultural Economics and Management.
    5. Pottier, Antonin & Hourcade, Jean-Charles & Espagne, Etienne, 2014. "Modelling the redirection of technical change: The pitfalls of incorporeal visions of the economy," Energy Economics, Elsevier, vol. 42(C), pages 213-218.
    6. Baker, Erin & Shittu, Ekundayo, 2008. "Uncertainty and endogenous technical change in climate policy models," Energy Economics, Elsevier, vol. 30(6), pages 2817-2828, November.
    7. Tiefenbeck, Verena & Staake, Thorsten & Roth, Kurt & Sachs, Olga, 2013. "For better or for worse? Empirical evidence of moral licensing in a behavioral energy conservation campaign," Energy Policy, Elsevier, vol. 57(C), pages 160-171.
    8. Dolan, Paul & Galizzi, Matteo M., 2015. "Like ripples on a pond: Behavioral spillovers and their implications for research and policy," Journal of Economic Psychology, Elsevier, vol. 47(C), pages 1-16.
    9. Mare Sarr & Joëlle Noailly, 2017. "Innovation, Diffusion, Growth and the Environment: Taking Stock and Charting New Directions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 66(3), pages 393-407, March.

    More about this item

    Keywords

    Climate change Technological change Uncertainty Backstop technology Multi-stage optimal control;

    JEL classification:

    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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