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Mitigation Portfolio and Policy Instruments When Hedging Against Climate Policy and Technology Uncertainty

Abstract

In this paper, we use a stochastic integrated assessment model to evaluate the effects of uncertainty about future carbon taxes and the costs of low-carbon power technologies. We assess the implications of such ambiguity on the mitigation portfolio under a variety of assumptions and evaluate the role of emission performance standards and renewable portfolios in accompanying a market-based climate policy. Results suggest that climate policy and technology uncertainties are important with varying effects on all abatement options. The effect varies with the technology, the type of uncertainty, and the level of risk. We show that carbon price uncertainty does not substantially change the level of abatement, but it does have an influence on the mitigation portfolio, reducing in particular energy R&D investments in advanced technologies. When investment costs are uncertain, investments are discouraged, especially during the early stages, but the effect is mitigated for the technologies with technological learning prospects. Overall, these insights support some level of regulation to encourage investments in coal equipped with carbon capture and storage and clean energy R&D.

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Notes

  1. 1.

    http://www.westernclimateinitiative.org

  2. 2.

    Directive 2009/28/EC of the European parliament and of the council of 23 April 2009

  3. 3.

    www.ren21.net

  4. 4.

    See the model website, http://www.witchmodel.org/

  5. 5.

    It should be remarked that WITCH is a macroeconomic model, and thus cannot capture many barriers that prevent the full internalisation of innovation market failures at a finer scale, as it is often the case for small consumers and producers.

  6. 6.

    A logarithmic utility function implies that the relative risk aversion coefficient is equal to one. The pure rate of time preference is 3%, yielding interest rates consistent with historical saving rates [30].

  7. 7.

    Whereas zero or very low carbon taxes are as probable as very high taxes, the degree of uncertainty surrounding technology costs is more restricted, at least for mature technologies such as nuclear and renewable. This motivates the assumption of a 30% spread in the case of technology uncertainty as opposed to the larger variation considered in the case of uncertain carbon tax.

  8. 8.

    www.iea.org/papers/2009/CCS_Roadmap.pdf

  9. 9.

    Assuming 100% capture rate would avoid the decrease of CCS in the second half of the century, though CCS would nonetheless stabilise because of the increasing cost of injecting CO2 underground, which are endogenous in the model. Introducing biomass plus CCS (BECS) would further foster the role of CCS since it would allow to absorb CO2 from atmosphere, but we do not include it here because of concerns over the scalability of this technology due to competition with land use.

  10. 10.

    See Renewables Global Status Report, 2009 Update available at http://www.ren21.net/pdf/RE_GSR_2009_Update.pdf

  11. 11.

    We focus on this technology because in the case of nuclear and renewables there are no major changes and the year of maximum hedging remains always right before the date of learning.

  12. 12.

    In this case we show hedging capacity in 2030 rather than in 2035 because in 2035 investment costs are already different across scenarios.

  13. 13.

    As discussed in the previous section, the effect of technology risk on renewables and nuclear is monotonic for spreads above 50%. The effect of policy riskiness is always monotonic.

  14. 14.

    www.ren21.org

  15. 15.

    The target 20% has been chosen for illustrative purposes. In most countries RPS for 2020 are around 20% (China 21% from wind by 2020, India 15% from renewables by 2015, U.S. 20% from wind by 2030, EU 20% by 2020). Some countries in Middle East and North Africa have also similar targets. Regional policies are available at www.ren21.org. Regional and global renewable targets considered here are in line with those proposed by other studies such as the ETP Blue scenario by the International Energy Agency www.iea.org

  16. 16.

    A 20% RPS, either in 2020 or 2030, has a much larger impact than the EPS considered above and therefore the two instruments cannot be compared.

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Correspondence to Enrica De Cian.

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De Cian, E., Massimo, T. Mitigation Portfolio and Policy Instruments When Hedging Against Climate Policy and Technology Uncertainty. Environ Model Assess 17, 123–136 (2012). https://doi.org/10.1007/s10666-011-9279-x

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Keywords

  • Climate change
  • Information and uncertainty
  • Mitigation

JEL

  • C73
  • H23
  • Q54
  • Q