Climate Engineering and Abatement: A ‘flat’ Relationship Under Uncertainty

Abstract

The potential of climate engineering to substitute or complement abatement of greenhouse gas emissions has been increasingly debated over the last years. The scientific assessment is driven to a large extent by assumptions regarding its effectiveness, costs, and impacts, all of which are profoundly uncertain. We investigate how this uncertainty about climate engineering affects the optimal abatement policy in the near term. Using a two period model of optimal climate policy under uncertainty, we show that although abatement decreases in the probability of success of climate engineering, this relationship is concave implying a rather ‘flat’ level of abatement as the probability of climate engineering becomes a viable policy option. Using a stochastic version of an integrated assessment model, the results are found to be robust to a wide range of specifications. Moreover, we numerically evaluate different correlation structures between climate engineering and the equilibrium climate sensitivity.

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Notes

  1. 1.

    The broader term geoengineering in fact encompasses all engineering approaches to alter geophysical processes. While this term has been used extensively in recent years, “climate engineering” has been proposed to refer to methods altering the climate per se, notably the removal of carbon dioxide from the atmosphere and Solar Radiation Management (SRM). Throughout this article, we use the term climate engineering referring to SRM measures, as they have been frequently used interchangeably in the literature.

  2. 2.

    The recent simulations in MacMartin et al. (2014) suggest that possibly even smaller scale implementations and experiments might be feasible, but on a theoretical level.

  3. 3.

    The cancellation of the Stratospheric Particle Injection for Climate Engineering (SPICE) project in 2012 provides an example of the difficulties that research faces in this field due to public opinion or the governance of such projects (Pidgeon et al. 2013).

  4. 4.

    We applied a similar model in a cost benefit analysis (CBA) framework. Overall, the results are qualitatively very similar. The results are available from the authors upon request.

  5. 5.

    While the relationship between carbon concentration and temperature is concave, the authors find a linear response of temperature to cumulative emissions in trillion tons of carbon emitted of 1.0–2.1 \(^{\circ }\hbox {C/TtC}\).

  6. 6.

    A way to interpret this binary random variable is on the one hand the effectiveness of SRM to tackle global warming, but could also be the social acceptability or political feasibility to implement such a strategy.

  7. 7.

    In this simple relation we abstract from a non-linear forcing potential from climate engineering (Lenton and Vaughan 2009) and moreover the decay of the atmospheric carbon. While the former feature would limit the potential of climate engineering and thus strengthen our main result, the latter effect is included in the numerical application even though its role is minor, see Matthews et al. (2009).

  8. 8.

    See also Lemoine and Rudik (2014) who discuss the reasons for specifying temperature targets for climate policy.

  9. 9.

    Note that this would hold even in the case in which both climate engineering and abatement are used in the second period.

  10. 10.

    Note that a sufficient condition for the Assumption 1 to hold is a unambiguous ordering of the higher order derivatives between climate engineering and abatement up to order three (\(C_{G}^{\prime \prime }(x)\le C_{A}^{\prime \prime }(x)\) and \(C_{G}^{\prime \prime \prime }(x)\le C_{A}^{\prime \prime \prime }(x)\) ) and moreover that \(C_{A}^{\prime \prime \prime \prime }\le 0\).

  11. 11.

    Note that in general we don’t restrict the level of abatement, even though one could consider the case where, in particular for a high value of the climate sensitivity, traditional abatement measures can be not sufficient to meet a given climate target, see also Neubersch et al. (2014).

  12. 12.

    The reason that \({\widetilde{\varphi }}\) enters as a squared term here as well as in Eq. (7) can be explained by the fact that an increased effectiveness of climate engineering has both a marginal and inframarginal effect. It lowers marginal costs of climate engineering compared to abatement but at the same time increases the effectiveness of the SRM already applied thus lowering the needed amount to reach the same result in terms of radiative forcing.

  13. 13.

    Concordance describes the degree of association between two random variables in a more generalized way than correlation.

  14. 14.

    This effect is due to the fact that the target in terms of emission reduction depends on the reciprocal of the climate sensitivity. Since \(E[{\widetilde{x}}]=1\), the convexity around this point is comparably small as the hyperbola in this region can be approximated by a linear function and thus the effect of uncertainty of \({\widetilde{x}}\) alone is expected to be rather low.

  15. 15.

    As stated in Proposition 2, the situation where only the climate sensitivity is uncertain (depicted in light blue) implies a lower level of initial abatement. However, as argued before, this effect is much smaller than the effect of uncertain climate engineering.

  16. 16.

    We thank an anonymous referee for pointing out this interesting point to us.

  17. 17.

    In fact, if the rank correlation is positive, it might even be optimal to have zero abatement in the first period if the conditional expected value of climate sensitivity in the case where \({\widetilde{\varphi }}=0\) is sufficiently low. Nevertheless, in numerical examples we considered this turned out to be the case only for a very extreme positive correlation structure, which are far beyond realistic values.

  18. 18.

    Instead of accounting explicitly for the non-anticipative constraints, non anticipativity is implicitly defined through the characterization of predecessor/successor relationships among nodes in the scenario tree.

  19. 19.

    The stochastic programming formulation of WITCH increases computational time substantially, by 3-4 times for a two branch scenario tree, and by 20 for a four branch scenario tree. The four branch scenario tree cooperative solution (for which we cannot take advantage of parallel computing) takes 180 hours to solve on a 2.6 GHz Intel Xeon processor.

  20. 20.

    The target is an ‘overshoot’ one, i.e., the 2100 target level can be exceeded prior to 2100. It refers to the aggregate radiative forcing from Kyoto gases, Non-Kyoto gases, and aerosols. Direct forcing from nitrate aerosols, mineral dust and land surface albedo changes are not included in the list.

  21. 21.

    This version of the WITCH model features as carbon dioxide removal options biomass burning and CCS, which allows negative emission and which plays a major role in the results of the integrated assessment models (Tavoni and Tol 2010).

  22. 22.

    We performed a similar analysis using a CBA approach with a damage function rather than a fixed climate target. These results are available upon request from the authors.

  23. 23.

    In our model simulations, no abatement was the optimal strategy for values of \(\varphi \) as small as \(10^{-4}\).

  24. 24.

    In order to capture the effect of different climate sensitivity values, we have to define the stabilization target now in terms of temperature increase. We have chosen a value in line with previous runs.

  25. 25.

    Primary energy is measured in exajoules, energy intensity in MJ per US-$, carbon intensity in kgC/MJ and investment in bln. US-$.

  26. 26.

    Uncertainty can also be interpreted in terms of public acceptance or prohibitively high costs or side-effects.

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Correspondence to Johannes Emmerling.

Additional information

The authors would like to thank Reyer Gerlagh, Martin Weitzman, Scott Barrett, participants of a Workshop on Coupled Climate-Economics Modelling and Data Analysis at ENS, Paris, the FEEM-CMCC convention 2012 in Venice, and the AERE 2013 conference in Banff, and two anonymous reviewers for very helpful comments. The research leading to these results has received funding from European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 308329 (ADVANCE). The usual caveat applies.

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Emmerling, J., Tavoni, M. Climate Engineering and Abatement: A ‘flat’ Relationship Under Uncertainty. Environ Resource Econ 69, 395–415 (2018). https://doi.org/10.1007/s10640-016-0104-5

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Keywords

  • Climate engineering
  • Mitigation
  • Climate change
  • Uncertainty
  • Solar radiation management
  • Geoengineering