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Accounting for Uncertainty Affecting Technical Change in an Economic-Climate Model

Author

Listed:
  • Valentina Bosetti

    (Fondazione Eni Enrico Mattei)

  • Laurent Drouet

    (LOGILAB-HEC, Université de Genève)

Abstract

The key role of technological change in the decline of energy and carbon intensities of aggregate economic activities is widely recognized. This has focused attention on the issue of developing endogenous models for the evolution of technological change. With a few exceptions this is done using a deterministic framework, even though technological change is a dynamic process which is uncertain by nature. Indeed, the two main vectors through which technological change may be conceptualized, learning through R&D investments and learning-by-doing, both evolve and cumulate in a stochastic manner. How misleading are climate strategies designed without accounting for such uncertainty? The main idea underlying the present piece of research is to assess and discuss the effect of endogenizing this uncertainty on optimal R&D investment trajectories and carbon emission abatement strategies. In order to do so, we use an implicit stochastic programming version of the FEEM-RICE model, first described in Bosetti, Carraro and Galeotti, (2005). The comparative advantage of taking a stochastic programming approach is estimated using as benchmarks the expected-value approach and the worst-case scenario approach. It appears that, accounting for uncertainty and irreversibility would affect both the optimal level of investment in R&D –which should be higher– and emission reductions –which should be contained in the early periods. Indeed, waiting and investing in R&D appears to be the most cost-effective hedging strategy.

Suggested Citation

  • Valentina Bosetti & Laurent Drouet, 2005. "Accounting for Uncertainty Affecting Technical Change in an Economic-Climate Model," Working Papers 2005.147, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2005.147
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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. Baker, Erin & Shittu, Ekundayo, 2008. "Uncertainty and endogenous technical change in climate policy models," Energy Economics, Elsevier, vol. 30(6), pages 2817-2828, November.
    3. Baker, Erin & Chon, Haewon & Keisler, Jeffrey, 2009. "Advanced solar R&D: Combining economic analysis with expert elicitations to inform climate policy," Energy Economics, Elsevier, vol. 31(Supplemen), pages 37-49.
    4. 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.
    5. Takanobu Kosugi, 2010. "Assessments of ‘Greenhouse Insurance’: A Methodological Review," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(4), pages 345-363, December.

    More about this item

    Keywords

    Stochastic Programming; Uncertainty and Learning; Endogenous Technical Change;
    All these keywords.

    JEL classification:

    • D62 - Microeconomics - - Welfare Economics - - - Externalities
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • Q29 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Other

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