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On Backstops and Boomerangs: Environmental R&D under Technological Uncertainty

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  • Timo Goeschl

    () (University of Heidelberg, Department of Economics)

  • Grischa Perino

    () (University of Heidelberg, Department of Economics)

Abstract

The literature on environmental R&D frequently studies innovation as a two-stage process, with a single R&D event leading from a conventional polluting technology to a perfectly clean backstop. We allow for uncertainty in innovation in that the new technology may turn out to generate a new pollution problem. R&D may therefore be optimally undertaken more than once. Using and externding recent results from multi-stage optimal control theory, we provide a full characterization of the optimal pollution and R&D policies. The optimal R&D program is strictly sequential and has an endogenous stopping point. Uncertainty drives total R&D effort and its timing.

Suggested Citation

  • Timo Goeschl & Grischa Perino, 2007. "On Backstops and Boomerangs: Environmental R&D under Technological Uncertainty," Working Papers 0437, University of Heidelberg, Department of Economics, revised Jan 2007.
  • Handle: RePEc:awi:wpaper:0437
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Baker, Erin & Shittu, Ekundayo, 2008. "Uncertainty and endogenous technical change in climate policy models," Energy Economics, Elsevier, vol. 30(6), pages 2817-2828, November.

    More about this item

    Keywords

    stock pollution; backstop technology; multi-stage optimal control; pollution thresholds; uncertainty;

    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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