Climatic Change

, Volume 88, Issue 3–4, pp 293–308 | Cite as

Uncertainty and learning: implications for the trade-off between short-lived and long-lived greenhouse gases

  • Daniel J. A. Johansson
  • U. Martin PerssonEmail author
  • Christian Azar


The economic benefits of a multi-gas approach to climate change mitigation are clear. However, there is still a debate on how to make the trade-off between different greenhouse gases (GHGs). The trade-off debate has mainly centered on the use of Global Warming Potentials (GWPs), governing the trade-off under the Kyoto Protocol, with results showing that the cost-effective valuation of short-lived GHGs, like methane (CH4), should be lower than its current GWP value if the ultimate aim is to stabilize the anthropogenic temperature change. However, contrary to this, there have also been proposals that early mitigation mainly should be targeted on short-lived GHGs. In this paper we analyze the cost-effective trade-off between a short-lived GHG, CH4, and a long-lived GHG, carbon dioxide (CO2), when a temperature target is to be met, taking into consideration the current uncertainty of the climate sensitivity as well as the likelihood that this will be reduced in the future. The analysis is carried out using an integrated climate and economic model (MiMiC) and the results from this model are explored and explained using a simplified analytical economic model. The main finding is that the introduction of uncertainty and learning about the climate sensitivity increases the near-term cost-effective valuation of CH4 relative to CO2. The larger the uncertainty span, the higher the valuation of the short-lived gas. For an uncertainty span of ±1°C around an expected climate sensitivity of 3°C, CH4 is cost-effectively valued 6.8 times as high as CO2 in year 2005. This is almost twice as high as the valuation in a deterministic case, but still significantly lower than its GWP100 value.


Global Warming Potential Climate Sensitivity Abatement Cost Shadow Price Marginal Abatement Cost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • Daniel J. A. Johansson
    • 1
  • U. Martin Persson
    • 1
    Email author
  • Christian Azar
    • 1
  1. 1.Department of Physical Resource TheoryChalmers University of TechnologyGothenburgSweden

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